Chapter 5 · Hybridization 341
Figure 5.57
Implementation of an input split type transmission coupled with a rear drive.
Source: Toyota
Figure 5.58
Coupling diagram of an input split type transmission coupled with a rear drive.
342 Hybrid vehicles
Figure 5.59
Example of distributed drive with a machine on the rear axle on an originally
front-wheel drive vehicle.
Source: [Imberdis, 2010]
independent drive of each rear wheel. The drive can be installed on an architecture
based on a front wheel drive vehicle with a machine built into each rear wheel, as
on the Citroën C-Métisse concept car. This type of drive can also be considered on a
four-wheel drive vehicle, as a complement to a mechanical drive, with 2 machines on
the rear axle (Figure 5.60). In addition to four-wheel drive, this configuration allows
trajectory control, especially on bends, by suitable adjustment of the torques on the
two rear wheels;
Figure 5.60
Example of distributed drive with two machines on the rear axle on an origi-
nally four-wheel drive vehicle with mechanical transmission.
Source: [Imberdis, 2010]
Chapter 5 · Hybridization 343
- independent four-wheel drive. This configuration is obviously the most flexible
since the torque during traction and braking can be adjusted on each wheel accord-
ing to requirements. The machines can be installed in tandem on each axle or even
as unsprung mass in the wheels, as proposed in particular by Heuliez and Michelin in
their electric concept car Will presented in 2008. For a hybrid vehicle, this configura-
tion is compatible with series architecture.
These additional features will increase the vehicle performance in terms of safety and
driving comfort, while helping reduce the energy consumption by optimizing energy recov-
ery during braking on all four wheels.
In addition, positioning the electric drive on the rear axle of an originally front-wheel
drive vehicle reduces the R&D and industrialization costs since few modifications are
required in the engine compartment.
5.4 SUMMARY
5.4.1 Features and Gains
Table 5.8 provides a summary of the various features and characteristics of the components
which can currently be found on a hybrid passenger car.
5.4.2 Implementation
5.4.2.1 Drive
Car manufacturers and suppliers have studied numerous solutions to install the components
of their hybrid drives in their vehicles. The various configurations that have been discussed
in this chapter can be classified according to the position of the electric machine(s). The main
following cases can be identified:
- electric machine connected to the engine, placed longitudinally or transversally: the
machine can be located on the front, auxiliary side (la in Figure 5.61) (Stop-Start with
starter-alternator) or laterally in the traditional starter position (lb) for Stop-Start type
applications with reinforced starter. The machine can also be located on the back coax-
ially for mild hybrids (lc, ex-Honda IMA, Mercedes S400) or even for series hybrids,
the engine in this case no longer being connected to the wheels (Id);
- electric machine connected to the transmission (longitudinal or transverse): for
cost reasons, it may be worthwhile making only minor modifications to the transmis-
sion (manual, robotized, automatic gearbox or continuous transmission). The electric
machine is therefore installed at the gearbox input, connected to the primary (pre-trans-
mission), isolated from the engine by a clutch or fluid coupling, in single shaft configu-
ration (P2) (2a on Figure 5.62, e.g. Nissan Infiniti, PSA Hybrid HDi) or dual shaft
344 Hybrid vehicles
Table 5.8. Summary of the characteristics of various types of hybrid private cars according
to CCFA and FEV, from [Douaud, 2007]
Hybrid types Discrete Functional
Standard name
Features Micro Soft or Mild Full Plug-in, PHEV,
Stop-Start E-REV
All-electric micro-mild Stop-Start
range (km) 0 Good Stop-Start
Engine Conventional Stop-Start Stop-Start regeneration Good
Low Average Engine assist regeneration
regeneration regeneration Limited Engine assist
Engine assist electric mode "Driver"
("supervisor") electric mode
00 Electric range
Conventional Downsized 0to2 Non-interrupted
depending on the torque
type of driving Grid charging
Downsized Distributed
Intermittent drive
operation Supply of
energy to the
grid
20 to more than
100
Downsized to
very downsized
Highly
intermittent
operation
Peak power 2 to 4 4 to 6 10 to 20 20 to 60 40 to 110
of the electric
machine (kW) Integrated
200 to 370
Electric machine Belt, gears Belt, gears or Belt Integrated 5 to 20
connection Lithium
integrated or integrated
40 to 60
Storage voltage 14 42 100 to 150 200 to 300 depending
(V) on conditions
Total storage < 1 <1 <1 lto2
energy (kWh) NiMH NiMH1
Lead/
Electrochemistry Lead/Acid Acid and 15 to 25 25 to 40
supercaps
Consumption 5 to 7 7 to 12
savings on
European Test
Procedure (%)
Cost price 200 to 500 500 to 900 900 to 2,200 2,500 to 5,000 5,000 to 20,000
increase (€)
1. Note a recent trend to adopt lithium batteries even with these architectures.
Chapter 5 · Hybridization 345
configuration (2b, e.g. the IFP Energies nouvelles demonstrator Flex Hybrid). The
machine can also be connected to the secondary (post-transmission) by using for
example the power transfer unit (PTU) fitted on four-wheel drive vehicles in order to
minimize mechanical modifications (2c);
electric machine integrated to the transmission (longitudinal or transverse): in this
case, the outer volume of the transmission is retained to simplify integration in the engine
compartment (Figure 5.63), the transmission with the hybrid features being achieved in
most cases by two electric machines associated with different planetary gears and/or
clutch(es) (e.g. Toyota, Lexus, GM, BMW, Mercedes, TM4, GM Volt, etc.);
Figure 5.61
Examples of configurations with machines connected to the engine (transverse
configuration).
Figure 5.62
Examples of configurations with machines connected to the transmission
(transverse configuration).
Figure 5.63
Examples of configurations with machines integrated to the transmission
(transverse configuration).
346 Hybrid vehicles
electric machine(s) connected to the wheels: this configuration is found in hybridiza-
tions with through-the-road coupling or series hybrids. The machine can be associated
with a mechanical transmission, in single shaft position (4a on Figure 5.64) or not (4b,
4c). The transmission may have one or more ratios and a decoupling function at high
vehicle speeds, in order to limit the electric machine operating range. If 2 machines
are used, they can be installed as sprung mass, connected to a reducing gear (4d) or
as unsprung mass and built into the wheel, using a reducing gear (4e, e.g. Michelin
Active Wheel) or not (4f, e.g. TM4 Wheel motors). For further details, readers can refer
to Chapter 3 on electric machines.
Figure 5.64
Examples of configurations with machines connected to the wheels.
5.4.2.2 Vehicles
While the first vehicles sold retained the traditional configuration, front wheel drive, corre-
sponding to their original range, numerous alternative solutions have since appeared, includ-
ing several being commercialized by Toyota (5.5.2.3).
Figure 5.65 shows some examples of vehicles which have been proposed or which will be
commercialized shortly. We observe the wide variety of possible combinations already vis-
ible on these 4 examples and which illustrates the wealth of the hybridization concept, which
car manufacturers and suppliers will most certainly implement to create vehicles best adapted
to the various markets and types of driving.
Figure 5.65
Examples of implementation on vehicle (commercialized and pro
Chapter 5 · Hybridization 347
ototypes).
348 Hybrid vehicles
If we try to anticipate the future vehicle architectures, we could imagine a solution with
four wheel motors, with then without reducing gear, powered by a rechargeable battery and
generator driven by an engine, then directly by a fuel cell. In the longer term, the progress
made in the field of onboard energy storage will offer the possibility of all-electric solutions
with no onboard generator.
5.5 EXAMPLES
In this chapter, we decided to detail two examples of hybrid drives radically different in
terms of complexity, features, energy consumption savings and greenhouse effect, as well as
in terms of cost, i.e.:
- a parallel hybrid in a fairly simple configuration including a single electric machine and
implementing the features of a full hybrid, then those of a plug-in hybrid (P2 configuration);
- a power-split hybrid, based on the Toyota THS system which, almost 15 years after
first being put on the market, represents the hybrid vehicle with the highest number of
sales worldwide.
5.5.1 Parallel Hybrid Transmission
We based this example on the same vehicle as that described in Chapter 1, identified by
manufacturers as belonging to the low-mid range (type Peugeot 308, Renault Mégane, etc.).
Vehicles from the lower range were not considered, since although their C 0 2 emissions
are lower, amortizing a hybrid drive would be more of a problem. Lastly, for information,
according to the ADEME, the vehicle segment chosen would represent almost 30% of sales
in France in 2010. On this vehicle, we studied the effect of a hybrid drive with dual shaft
parallel coupling, implementing the features of a full hybrid but with two different manage-
ments, i.e.:
- management without battery charge corresponding to charge sustaining mode, a solu-
tion which we qualified as discrete hybrid (5.3.1),
- management with battery charge on the grid, thereby operating in charge depleting
mode, for a vehicle qualified as plug-in hybrid.
As we will now see, the sizes of the drive components will vary depending on the solu-
tion. These evaluations are based on the results of simulation studies conducted internally
and jointly with the Argonne National Laboratory [Marc, 2010], [Da Costa, 2012]. The vehi-
cles are equipped with a gasoline engine; to simplify the comparisons, we kept the gearbox
and differential of the original vehicle. To highlight the effect of machine sizing, the sim-
plified vehicle specifications, identical for both versions, can be described according to the
following criteria:
- maintain the vehicle dynamic performance: as measured by the acceleration from
0 to 100 km/h. Note that other criteria can be considered, e.g. accelerations (80 to
120 km/h) or the time required to overtake a vehicle;
Chapter 5 · Hybridization 349
- maintain the fastest possible speed on a gradient: we chose the criterion of 110 km/h
on a 5% gradient. In view of the small amount of energy available in the battery pack,
this criterion must be respected even with a fully discharged battery, i.e. with the
engine only.
Considering the first criterion alone, it appears that numerous solutions are possible, pro-
vided that the sum of powers developed by the electric machine and engine can reproduce the
power profile of the original engine. This choice is illustrated on Figure 5.66 which shows a
graph of engine power against electric machine power, with the vehicle operating in charge
sustaining mode. We see that the engine power decreases fairly quickly, when the electric
machine power increases. This situation could create the appearance of highly restricting
downgraded modes in case of low battery SOC (as described in paragraph 5.3.1.4). It nev-
ertheless appears that taking the second criterion into account will significantly limit the
amplitude of the engine power reduction and also reduce the effect of downgraded modes.
Figure 5.66
Graph of engine power against electric machine power and chosen performance
criterion (discrete hybrid).
For the vehicle used in charge sustaining mode, the optimum engine/electric machine
torque is chosen by adopting a global approach based on the criterion of fuel consumption
for various types of driving: urban, rural roads and motorway, in reference to the ARTEMIS
European program (Chapter 1). For all these cycles, the engine is considered as being warm
when starting. We have also shown the consumption values obtained during average driving
based on the distance distribution proposed in [André, 2004], i.e. 28% urban, 44% rural roads
and 28% motorway. We used optimum energy management in our simulations, as described
in Chapter 6, to guarantee implementation of the most efficient solution for each case tested.
The simulation results demonstrate that an optimum choice of components giving the highest
possible consumption savings can be found for the three types of use. The savings offered by
350 Hybrid vehicles
the electric drive will increase with the machine power, go through a maximum then decrease,
due in particular to the increase in mass of the electric system (machine, power electronics
and battery) which is no longer offset by the savings obtained [Badin et al, 2006b]. For the
example proposed and the vehicle operating in charge sustaining mode, optimum sizing for
the three types of driving leads to a 25 kW peak power electric machine with a 59 kW engine.
For the case of the plug-in vehicle, we chose an electric drive allowing the vehicle to
run in all-electric mode over an urban type trip, which places our vehicle in the category of
Urban Capable PHEVs. For urban driving conditions, in our example the electric machine
has a peak power of 35 kW. For the energy capacity of the lithium battery installed on the
vehicle, we assumed an all-electric range (AER) of about 30 km on the urban cycle, which
gives 8 kWh nominal total energy with an SOC range assumption of 90% to 30%.
Table 5.9 lists the main characteristics of the 3 vehicles considered.
Table 5.9. Characteristics of the simulated vehicles
Vehicle and drive characteristics Reference Drive type Plug-in
Discrete hybrid
Total mass (kg) 1,200 hybrid 1,380
Maximum power of the engine (kW) 80
Peak power of the electric machine (kW) 1,280 62
Instantaneous maximum power of the new 59 35
battery pack (kW) 25
Nominal capacity of the new battery pack (Ah) 41
Nominal energy of the new battery pack (kWh) 30
Power/energy ratio of the battery elements (h_1) 40
6 8.0
1.3 5
23
The energy consumption results obtained for each of the 3 vehicles and according to our
various types of driving are described in Table 5.10.
The discrete hybrid vehicle can be easily compared with the reference vehicle by consider-
ing the fuel consumptions, the battery state of charge being reset at the end of each calculation to
its initial value. The consumption values obtained corroborate the very high potential of hybridi-
zation for urban driving, with savings of more than 40% for a simple solution with a single elec-
tric machine, a downsized engine of the same technology and a hybrid vehicle identical in terms
of rolling and aerodynamic losses. Rural roads driving offers less potential for savings, although
still over 20%, while with motorway driving, which includes very short stopping periods and
very low braking recovery potential, the savings obtained do not exceed 10% in our case.
Due to the transfer of energy consumption to electricity, the plug-in hybrid vehicle, as we
have already mentioned (5.3.2.4), is more difficult to compare with the reference vehicle and
several methods can be applied. In order to take into account the overall impact due to vehicle
use, we decided to consider both the emissions due to fuel and to the electricity consumed on
a standard trip. To define this trip, we considered that our plug-in hybrid vehicle traveled a
Chapter 5 · Hybridization 351
home to work round trip in urban and periurban environment with a battery charge at home
after each day. To show the effect of the driving conditions, we made two assumptions about
the daily distances traveled:
- a short trip (trip 1) corresponding to two 20 km journeys each made in slightly under
30 minutes. According to driving statistics, this distance would represent nearly 60%
of the daily trips made in Europe [Zgheib and Clodic, 2009];
- a longer trip (trip 2) of twice 37 km, each traveled in nearly 45 minutes, which would
represent nearly 80% of the daily trips according to the same statistics.
For the shorter trip, Table 5.10 shows that the fuel consumption of the plug-in hybrid is
extremely low, less than 0.5 L/100 km. Obviously, this consumption does not include the
electricity consumption and cannot be extrapolated beyond the daily distance traveled. For
this type of driving, the plug-in hybrid vehicle transfers up to 80% of its fuel consumption to
electricity (definition in paragraph 5.3.2.4).
Table 5.10. Energy consumption(s) of simulated vehicles
Vehicle consumption(s)
Driving types L/100 km Savings in % L/100 km
(gC02/km) and Wh/km
Reference Discrete Plug-in hybrid
hybrid
Urban use 9.0(210) 4.9(114) 45
Rural roads use 5.45 (128) 4.15(97) 24
Motorway use 7.1 (166) 6.4 (149)
Average use 6.9(161) 5.0(116) 10
Trip 1:40 km 5.9(137) 4.25 (99) 28
Trip 2: 75 km 5.7(133) 4.2 (98) 28 0.40 and 142 l
26 2.0 and 76 l
1. This vehicle is characterized by a fuel consumption (expressed in L/100 km) and an electricity consumption
(expressed in Wh/km) corresponding to charging up its battery.
For the longer trip, the fuel consumption will increase to 2.0 L/100 km, i.e. a transfer rate
not exceeding 30%. These results call for the following remarks:
- the rate of energy consumption transfer to electricity of the plug-in hybrid will there-
fore decrease rapidly with the distance traveled between charges, as illustrated on
Figure 5.56; in addition, it varies considerably depending on the drive, the energy
management and the types of vehicle use;
- compared with the conventional vehicle, the plug-in hybrid reduces fuel consumption
by more than 90% on a daily trip of 40 km and nearly 60% on a longer trip of 75 km;
- the environmental impact of the vehicle can no longer be assessed by considering the
fuel consumption and corresponding GHG emissions alone: emissions due to electric-
ity generation must also be taken into account in a global "well to wheel" approach,
which we will discuss in Chapter 7, considering the same vehicle.
352 Hybrid vehicles
Referring to the values listed in Table 5.10, we can make the following remarks:
- if we consider the absolute consumption values, simulating the vehicle on a real trip
increases the values. Consequently, on the European Test Procedure, a vehicle such as
that used for the reference will have C 0 2 emissions of about 145 g/km, i.e. a difference
of more than 10% compared with our average figure in use of 161 g/km;
- in terms of technology, we decided to adopt the same level of maturity for the engine
and the vehicle itself, to highlight exclusively the advantage of hybridization.
Under these conditions, the emissions from the discrete hybrid vehicle in average use
are 116 gC02/km. Based on the above remarks, we can make the link with the Prius vehicle
described in the next chapter. This vehicle, for which over a period of almost 15 years Toyota
has optimized all the components and their management, emits 92 gC02/km on European
Test Procedure and can even reduce emissions by a further 3% if fitted with special tires,
reaching 89 g/km with 15" wheels.
The balance of the plug-in hybrid vehicle is complex due to these two consumptions and,
as mentioned above, a well to wheel assessment will be conducted in Chapter 7.
5.5.2 Toyota Prius
5.5.2.1 History
The creation of the Toyota Prius has been discussed in detail in the highly exhaustive book
published by François Roby in 2006, Vers la voiture sans pétrole ? [Roby, 2006]. The main
steps will be summarized here, but readers can refer to the book for more information.
While the first version was commercialized in December 1997, it had taken Toyota engi-
neers and technicians more than 4 years to successfully complete this project. The program
was in fact launched in autumn 1993 by the Toyota president himself, the aim being no less
than to define and build the global car for the 21st century. The specifications assigned to the
technical teams were no less challenging those imposed by Citroën for the pre-war 2 CV. The
vehicle had to offer a spacious compartment, consume less than 5 L/100 km and be pleasant
to drive in large urban areas. The prototype was to be produced before the 21st century, hence
its Latin name prius.
Extensive use of simulation tools for the vehicle and its components enabled Toyota to
select an architecture and a configuration for the components from the large number of solu-
tions available. The technological ingenuity was to choose a planetary gear to connect the
various components instead of reusing the existing concepts for conventional vehicles such
as the discrete or continuous gearboxes. The advantage of this configuration is that no decou-
pling unit is required in the transmission, such as a clutch, which must be controlled very
precisely to avoid reducing passenger comfort throughout the vehicle lifetime. The chosen
configuration, input split with planetary gear (5.2.5.2), allows operation with power split, a
feature already used in hydrostatic transmissions, and is optimized with intermediate storage
by battery. Toyota also optimized its engine by using an Atkinson cycle, in which the expan-
sion ratio is greater than the compression ratio. An expansion ratio of 13 could therefore be
Chapter 5 · Hybridization 353
obtained on this gasoline engine, by controlling the intake valves (VVT-i system), thereby
increasing the efficiency (Chapter 2).
To move from concept to series vehicle, Toyota engineers still had to solve numerous
problems on the key components: the power electronics and the battery. In the first case, the
main problems were to evacuate heat and obtain reliability compatible with automobile con-
straints. For the battery, the challenge was to develop a pack adapted to hybrid vehicle operat-
ing conditions, i.e. mainly intended to supply and accept power. Toyota decided to keep the
NiMH chemistry already used on its electric vehicles, while increasing the power and reduc-
ing the onboard energy to the strict minimum thanks to very high P/E designed cells (nominal
1.8 kWh, against 15 to 20 kWh for an electric vehicle). Cells were therefore developed with a
power to energy ratio of more than 11 (Chapter 4), compared with those produced for electric
vehicles which hardly exceed 3 to 5. There was also considerable uncertainty regarding the
battery lifetime. To limit the risks of premature ageing, therefore, only a very small part of the
discharge range was to be used, initially about 7% then 15% in 2001 [Nagata, 2003; Trigui
et al, 2002]. Accelerated ageing tests were conducted in laboratory at the same time to check
that the battery could still be used after an equivalent of 300,000 km simulated under normal
conditions (state of charge variation of 15% at 35 °C) or 200,000 km under more severe con-
ditions (resp. 30% and 55 °C) [Taniguchi, 2001]. Toyota was so confident that they proposed
the vehicle with an eight-year guarantee or 160,000 km on electric components.
To manufacture its vehicle, Toyota was also to implement the notion of Keiretsu, produc-
ing most ofthe components for its hybrid transmission internally, as illustrated on Figure 5.67.
Figure 5.67
Sourcing of major components for the Toyota Prius in the group.
Source: Toyota
The battery was to be the only component partially outsourced, with the creation of the
Panasonic EV Energy Co joint venture with Matshushita Electric at the end of 1996.
The first version of the vehicle, sold in Japan only, met with mixed success when it was
released. However, due to the improvements made to subsequent versions, the performance
and reliability of the drive, nearly 15 years after its launch the Prius still remains the hybrid
vehicle with the largest number of sales worldwide.
354 Hybrid vehicles
5.5.2.2 Principle and Manufacture
As already mentioned (5.2.5), the Prius implements a power split drive with input split con-
figuration. The schematic diagram and the layout of the various components are shown on
Figure 5.68.
Figure 5.68
Schematic diagram and THS transmission cross-section of the Prius.
Source: Toyota
5.5.2.3 Evolutions of the Toyota Hybrid System Concept
A. Evolutions of the Prius
Since the first version launched in October 1997 in Japan, Toyota has constantly optimized
the vehicle and its components to improve performance. The first version produced was a
compact car, but from the second version, Toyota moved to the mid-size platform of the
Avensis (Figure 5.69) (see Appendix 6).
What is remarkable about the results achieved by Toyota is that, over the years, they
continued to improve the vehicle efficiency, e.g. power of the components and dynamic per-
formance, with C 0 2 emissions on the European Test Procedure dropping with the release of
each new model, as shown on Figure 5.70.
Chapter 5 · Hybridization 355
Figure 5.69
Evolution of the various Prius models.
Source: Toyota
Constant improvements have been made to the drivetrain and the vehicle on each new
version, the main ones being indicated below. For further details, readers can refer to the
chapters in this book on electric machines and storage systems, or the various other publica-
tions [Takasaki et al, 2009], [Yaguchi et al, 2009], [Okamura et al, 2003]:
- electric machines, optimization of magnets in terms of size and position (3.2.2.3),
windings, cooling, extension of the operating range with addition of a reducing gear
(2009) and higher DC voltage with the installation of a 500 V (2003) then 650 V
(2009) booster;
- NiMH battery with change to prismatic and reduction of the internal resistance (2003),
constant improvement of the power and energy density of the batteries, extension of
the state of charge range used (about 7% on the 1997 version with nominal 1.8 kWh,
then about 15% on the 2003 version with 1.3 kWh), cooling optimization;
- engine with increased operating range and maximum power (from 43 kW in 1997 to
73 kW in 2009), then upsizing to 1.8 L with cooled EGR, electric water pump and
lower friction on the 2009 version. These improvements led in particular to a very
wide engine operating range with specific consumptions of less than 230 g/kWh (effi-
ciency greater than 34%), which is quite outstanding for a spark-ignition engine of this
power;
- collaborative braking with the use of an electro-hydraulic system based on that devel-
oped for the Estima in 2001 allowing optimized energy recovery during braking on the
2003 version (Chapter 1);
356 Hybrid vehicles
1. Note that this 1997 model was not optimized for the European Test Procedure
with cold start, being intended for Japan only.
Figure 5.70
Evolution of characteristics for the various Prius models.
Source: from Toyota
- vehicle with a lower Cx, use of tires with low rolling resistance and light materials in
order to keep the mass as low as possible;
- electrified auxiliaries (air-conditioner, engine water pump) with optimized control;
- plug-in version with lithium ion batteries and extended all-electric capabilities
(5.3.2.2.C).
For completeness, we will also mention the Prius plug-in versions obtained by adding
battery kits sold by certain professionals. These Prius cars "converted" into plug-in hybrids
are described in more detail in Chapter 5.3.2.2.C.
Chapter 5 · Hybridization 357
B. Applications to Other Vehicles
Toyota was very quick to announce its intention to generalize the concept of hybrid drives
to its entire range and its luxury model, the Lexus [Adachi et al, 2006; Mizuno et al, 2009].
Other models have therefore been proposed with drives more or less similar to the THS
installed on the Prius, implementing different variants in the architecture, the components or
their configuration. The main models are listed below:
- Toyota Prius (THS) in 1997 then THSII in 2003 (Toyota reference PI 12) and P410
from the 2009 model;
- Toyota Estima in 2001: commercialized in 2001, this minivan includes an original
hybrid drive (THS-C) with a 13 kW machine connected to the engine and the two
wheels via a CVT and an 18 kW machine on the rear axle. This vehicle also innovated
through the use of an electronically-controlled braking (ECB) system to optimize the
distribution between hydraulic and electric in order to recover as much energy as pos-
sible during braking [Nakamura et al, 2002] (Chapter 1);
- Toyota Crown also in 2001: only a few thousand of this high-end saloon were sold,
with a Stop&Go system featuring a belt-driven machine (5.3.1.3). Toyota called this
a Mild Hybrid system (THS-M), even though power assist to the engine is not men-
tioned in the description [Itagaki et al, 2002];
- Lexus RX400h in 2005 with the THSII 4WD (P310) then RX450h in 2008: the concept
is optimized with in particular a second electric drive on the rear axle (50 kW for the
RX400K) to give four-wheel drive capability and a vehicle dynamic integrated man-
agement system [Abe and Killmann, 2006]. Hybridization of the drivetrain limits the
normalized C 0 2 emissions from the vehicle, equipped with a 3.5 L V6 engine devel-
oping nearly 180 kW, to 150 g/km;
- Lexus GS450h in 2006 (LI 10): the transmission is used longitudinally on a rear-wheel
drive vehicle, with a two-stage final reducing gear to optimize dynamic performance
[Yamanaka, 2007];
- Lexus LS600h in 2007: in this V8-engined saloon, the longitudinal transmission is
used in an integral drive architecture with mechanical coupling;
- Lexus LS250h in 2009: launched on the Japanese and American markets and based
on the Prius platform, it is the most fuel-efficient vehicle in the hybrid Lexus range
(35/34 mpg).
- Toyota Auris, Auris II and Yaris released respectively in 2010, 2013 and 2012 with
drivetrain technologies derived from the Prius THS and adapted to the Low-mid and
Eco-low ranges.
Other manufacturers have also implemented systems more or less inspired by the Toyota
transmission, such as Nissan with the Altima, produced in 2002 with drive components sup-
plied by Toyota, and Ford with the Escape launched in 2003 on the basis of patent cross-
licensing with Toyota. The hybrid drive, improved even further by Ford, is fitted in particular
on the hybrid Fusion commercialized in 2009 and voted car of the year in the United States
in 2010.
358 Hybrid vehicles
5.5.2.4 Operating Features
The Prius is equipped with a full hybrid THS transmission implementing the features
described previously (5.3.1). Its operation is illustrated on Figures 5.71 and 5.72 which show
the changes in variables internal to the transmission supplied by the IFP Energies nouvelles
AMESim simulation platform, validated on a 2003 Prius during the French EVALVH pro-
ject conducted by ADEME-INRETS-IFP [Trigui et al, 2002].
The following remarks can be made on urban type driving (Figure 5.71):
- the vehicle takes off in all-electric mode, with the engine stopped (1);
- when the driver's demand is sustained, at end of acceleration, the engine starts and the
car is now moving in hybrid mode with power split by the generator (2); in our exam-
ple, the split ratio reaches 70%;
- when the demand becomes low, on the steady speed stage, the engine is switched off
and the vehicle is driven by the electric machine in electric mode (3);
- when the vehicle decelerates, the electric machine charges up the battery, engine off (4).
Figure 5.71
Example of urban type evolution.
The following remarks can be made on extra-urban type driving (Figure 5.72):
- during high steady speed stages with low power demand (70 km/h and more), we
observe that the transmission operates in recirculation with a generator supplying
power to the engine and an electric machine which operates as generator (5). In our
example, the split ratio reaches - 40%,
Chapter 5 · Hybridization 359
during accelerations, with a higher power demand, the system returns to split mode (6),
for steady speed stages up to 50 km/h, the vehicle runs in all-electric mode (7).
Figure 5.72
Example of extra-urban type evolution.
The balance of powers in the transmission components also reveals the two most fre-
quent operation situations of the drive. We see that during operation at high load (accelera-
tion), the power supplied by the engine is well split by the electric machines (Figure 5.73),
whereas for operation at low load (steady speed) and high speed, the power is well recircu-
lated (Figure 5.74).
Figure 5.73
Example of power balance with split flow (kW).
360 Hybrid vehicles
Figure 5.74
Example of power balance with recirculation (kW).
This example calls for several remarks:
- in the two situations described, the battery is slightly recharged but other situations
may be encountered depending on the initial battery state of charge and the tempera-
ture of the engine, its catalyzer or the battery. Appendix 7 described three cases cor-
responding to different initial battery states of charge on the European Test Procedure.
As can be seen, the battery state of charge has a direct impact on the use of all-electric
mode. While the distance traveled in electric mode represents nearly 40% of the total
cycle distance for a battery with an initial state of charge close to 50%, it will drop to
34% if the battery is discharged at the start and will, on the contrary, rise up to 4 3 % if
the battery is initially well charged;
- in the two situations described, we see that energy flows between the two electric
machines, with in each case one driving and the other generating, but there are situa-
tions where both machines act simultaneously as motor or generator.
The operating range of the engine is determined by the transmission characteristics
(reducing gear and planetary gear) and by the maximum generator speed. Figure 5.75 shows
the speed range that can be assigned to the engine depending on the vehicle speed and all
the transmission constraints 5. We can see that for each vehicle speed, the speed range of the
engine is determined by its own mechanical characteristics (max. and min. speeds), but also
by those of the generator (maximum speed in generator mode and in motor mode 6).
5. We have assumed in this diagram that no power is exchanged with the battery.
6. Constraints on other transmission components such as the maximum speed of rotation of the planet
gears have been considered as less restricting and have not been represented.
Chapter 5 · Hybridization 361
Figure 5.75
Illustration of the engine operating range depending on vehicle speed for a
power-split drive.
The figure also shows the following additional details:
- positions of the operating points with zero split ratio,
- power-split operating area, just above,
- recirculation operating area, just below,
- possible place of use of the engine for various vehicle steady speeds, if positioned on
its optimum efficiency region.
This diagram calls for the following remarks:
- in the operating area possible in all-electric mode, the engine can be used if necessary
to provide vehicle dynamic performance, charge the battery or for after-treatment sys-
tem thermal requirements (area 1);
- the limit of the operating area in all-electric mode is set at 60 km/h for driving comfort
considerations. If the driver wants to accelerate above this speed, the time to start the
engine could substantially reduce performance due to the large quantity of power to be
delivered. Considering only the mechanical capabilities of the drive components, the
vehicle could in fact drive at up to nearly 100 km/h in electric mode;
- in the hybrid operating area, the engine must be used to provide the vehicle dynamics
or for the transmission mechanical requirements (area 2);
362 Hybrid vehicles
- for the various steady speeds in the hybrid area, we can see that the system operates
with a very low recirculation rate, since the power demands are relatively low. To
maintain a steady speed on a slope, where there is a significant increase in the power
to be supplied by the engine, we see that the engine speed will rise and that the system
will switch into the power split area (arrow).
Driving in all-electric mode at low speed and being able, in hybrid mode, to decorrelate
the operating point of engine from the demand at the wheels will significantly improve its use
conditions, especially in urban driving. As can be seen on Figure 5.76, the engine operating
range is located in much more favorable areas than for a conventional drive, as illustrated in
Chapter 1.
The engine use conditions have been deduced from chassis dynamometer measurements
on a 2009 Prius. The specific consumption map is obtained from Toyota data [Kawamoto
e* a/., 2009].
Figure 5.76
Operating range of the engine 2ZR-FXE of a 2009 Prius according to various
vehicle conditions of use.
Source: IFP Energies nouvelles and from Toyota
Chapter 5 · Hybridization 363
REFERENCES
Abe S, Toyota Motor Corporation, Aichi, Japan and Killmann G, Toyota Motor Europe, Zaventem,
Belgium (2006) The Architecture of Hybrid Systems - from Toyota Prius to Lexus RX400h,
E-COSM - Rencontres Scientifiques O'IFP Energies Nouvelles - 2-4 October, pp 223-228.
Adachi M, Endo H, Mikami T, Yagi K, Toyota Motor Corp, Wakuta S and Uchida T Aisin AW Co
Ltd. (2006) Development of a New Hybrid Transmission for GS450h, EVS22 Yokohama, Japan,
October 23-28.
Ahn K and Cha SW (2008) University of Michigan, Ann Arbor, Seoul National University: Developing
Mode Shift Strategies for a Two-Mode Hybrid Powertrain with Fixed Gears, SAE 2008-01-0307.
André M, INRETS, LTE, Bron, France (2004) The ARTEMIS European Driving Cycles for Measuring
Car Pollutant Emissions: Science of the Total Environment 334-335, pp 73-84, Elsevier.
Axsen J, Kurani K and Burke A (2010) Institute of Transportation Studies, University of California
Davis, Davis CA, USA: Are Batteries Ready for Plug-in Hybrid Buyers?, Transport Policy 17,
pp 173-182, Elsevier.
Back M, Mercedes-Benz Cars Development (2008) Implementing Supervisory Control Strategies
for Mercedes-Benz Hybrid Vehicles, Les Rencontres Scientifiques D'IFP Energies Nouvelles
- Advances in Hybrid Powertrains - Rueil Malmaison, 25-26 November.
Badin F, INRETS, LEN, Bron France (1986) Véhicule à propulsion hybride, étude bibliographique,
application au mode thermique électrique, INRETS report NNV8603, October.
Badin F, Jeanneret B, INRET, LEN, Carminot P and Caillard B, Renault DR (1994) Gas Turbine
Hybrid Vehicle (VERT) Simulation Programme, 27th ISATA Congress, Aachen, October.
Badin F, Trigui R, Boubakri A, INRETS LEN, Combes E and Delhom M, Peugeot SA. (1996) Hybrid
Drive Trains Evaluation in Actual Use, 13th Electric Vehicle Symposium, Osaka Japan, October.
Badin F, INRETS, LTE, Bron, France (1997) Contribution à la modélisation des transmissions hybrides
thermiques électriques séries: réalisation d'un bilan énergie-pollution en usage réel: Doctorat en
Génie de l'Environnement de l'Université de Savoie, Juillet, Chambéry, France
Badin F and Jeanneret B, INRETS, LTE, Bron, France (2006a) Plug in Hybrids Environmental Pros-
pect, Plug-in Hybrid Workshop, EVS22 Yokohama, Japan, October 25.
Badin F, Scordia J, Trigui R, Vinot E and Jeanneret B, INRETS, LTE, Bron France (2006b) Hybrid
Electric Vehicles Energy Consumption Decrease According to Drive Train Architecture, Energy
Management and Vehicle Use: Hybrid Vehicle Conference 2006, The University of Warwick,
Coventry, December 12th & 13th.
Beretta J, PSA Peugeot Citroën, Alleau T, CEA, Badin F, INRETS, Bleijs C, EDF and Bonnal J, Ecrin
(2005) Le génie électrique automobile, la traction électrique, Lavoisier, ISBN 2-7462-1094-0.
Beretta J and Basso V, PSA Peugeot Citroen (2009) PSA Hybrid HDi Technologies, EVS24, Stavan-
ger, Norway, May 13-16.
Brejaud P, Charlet A, Chamaillard Y, Ivanco A and Higelin P, Université D'Orléans - PRISME/EPM,
Orléans, France (2008) Pneumatic-Combustion Hybrid Engine: a Study of the Effect of the
Valve Train Sophistication on Pneumatic Modes: Les Rencontres Scientifiques D'IFP Energies
Nouvelles - Advances in Hybrid Powertrains - Rueil-Malmaison, France, 25-26 November.
Broussely M (2007) Traction Batteries, EV and HEV. In: Industrial Applications of Batteries. From
Cars to Aerospace and Energy Storage, Elsevier.
Burke A, Zhao H, and Van Gelder E, University of California-Davis, Institute of Transportation Stud-
ies, USA (2009) Simulated Performance of Alternative Hybrid-Electric Powertrains in Vehicles
on Various Driving Cycles, EVS24 Stavanger, Norway, May 13-16.
Carlson R, Duoba M, Bohn T, Rousseau A, Sharer P, Pagerit S, Bocci D and Keller G, Argonne
National Laboratory, USA (2008) Plug-In Hybrid Vehicles: What Have We Learned?, Meeting
with French Auto Industry Delegation, ANL, November 18.
364 Hybrid vehicles
www.cartech.fr (2010) Chevrolet Volt: Électrique, Hybride? L'Explication De General Motors,
Octobre.
Chammas R, Toyota Motor Europe, France, Itagaki K, Takaoka T, Toyota Motor Corporation, Japan
and Biebuyck B, Toyota Motor Europe, Belgium (2011) Newly Developed Toyota Plug-in
Hybrid System and its Vehicle Performance under Real Life Operation, SAE 2011-37-0033.
Chang Y and Szumanowski A, Warsaw University of Technology, Warsaw Poland (2009) Hybrid
Drive Design for Minibus by Simulation, EVS24 Stavanger, Norway, May 13-16.
Chasse A, Hafidi G, Pognant-Gros P and Sciarretta A, IFP Energies Nouvelles (2009) Supervisory
Control of Hybrid Powertrains: an Experimental Benchmark of Offline Optimization and Online
Energy Management, E-CoSM'09, France, November 30 - December 2, 2009.
Conlon B, General Motors (2005) Comparative Analysis of Single and Combined Hybrid Electrically
Variable Transmission Operating Modes, SAE 2005-01-1162.
Da Costa A, IFP Energies Nouvelles, France, Kim N, ANL, États-Unis, Le Berr F, IFP Energies Nou-
velles, France, Marc N, Badin F, IFP Energies Nouvelles, France, Rousseau A, ANL, États-Unis
(2012) Fuel Consumption Potential of Different Plug-in Hybrid Vehicle Architectures in the
European and American Contexts: EVS26, Los Angeles, California, May 6-9, 2012.
Del Mastro A, Chasse A, Pognant-Gros P, Corde G, IFP Energies Nouvelles and Perez F, Gallo F, Hen-
nequet G, Renault (2009) Advanced Hybrid Vehicle Simulation: from « Virtual » to « HyHil »
Test Bench, ICE2009, Italy, September 2009, SAE 2009-24-0068.
Douaud A, Comité des Constructeurs Français d'Automobiles, Paris, France (2007) L'Automobile et la
Planète, Communication du CCFA, Janvier.
Ehsani M, Gao Y and Gay S, Texas A&M University and Emadi A, Illinois Institute of Technology
(2004) Modern Electric, Hybrid Electric and Fuel Cell Vehicles, CRC Press, New York, NY,
USA, ISBN: 0-8493-3154-4.
Electric Fuel (2004) Zinc-Air Battery. All Electric Bus Demonstration Project. Executive Summary
and Conclusions from Phase IV. Final Report.
Francisco V, Toyota Motor Engineering and Manufacturing North America, Inc (2011) Overview of
the Toyota Plug-In Hybrid (PHV), SAE 2011 Hybrid Technologies Symposium, Anaheim, CA,
USA, February 9-10.
Fuhs A (2009) Hybrid Vehicles and the Future of Personal Transportation, CRC Press, New York, NY,
USA, ISBN-13978-1-4200-7534-2.
Fukino M, Nissan Motor Co, Ltd (2009) R&D of Electric Drive Train Technologies, SAE 2009 Hybrid
Vehicle Technologies Symposium, San Diego, CA, USA, February 11-12.
Gauducheau JL, ADEME, France (2001) Expérimentation d'autobus Diesel/électriques et hybrides:
rapport intermédiaire d'évaluation, August.
Global Insight (2007) Global Insight Automotive 2007, Transmission Market Developments, Update.
Grewe T, Conlon B and Holmes A, General Motors (2007) Defining the General Motors 2-Mode
Hybrid Transmission, SAE 2007-01-0273.
Guldner J, BMW Manufacturing Co (2009) The BMW Concept X6 ActiveHybrid Within the BMW
Efficient Dynamics Strategy: SAE Hybrid Vehicle Technologies Symposium, San Diego, CA,
USA, February 11-12.
Guyonvarch G, Delaforge L, Haller R, Lepetit L, Loup D and Thuez JL, Valeo (2003) Climate Con-
trol, France: Air Conditioning Systems Adapted to Vehicle Electrification Range, EVS20, Long
Beach, CA, USA, November.
Guzzella L, ETH Zurich, Switzerland and Sciarretta A, IFP Energies Nouvelles, France (2007) Vehicle
Propulsion Systems, Introduction to Modeling and Optimization, Springer, Berlin, Germany,
ISBN 978-3-540-74691-1.
Hanssen G, Energy CS (2005) A Bridge to the Hydrogen Highway, EVS21 Monaco, April 2-6.
Hanssen G, Energy CS (2005) A Bridge to the Hydrogen Highway/The EnergyCS/Valence PHEV
Prius: Presentation at the PHEV Workshop, April 3rd, Monaco.
Chapter 5 · Hybridization 365
Harding G, Phillips B and Hammond J, Lucas Chloride EV Systems Ltd, Royaume-Uni (1983) The
Lucas Hybrid Electric Car, SAE 830113.
Hendrickson J, Holmes A and Freiman D, GM (2009) Powertrain New Transmission Products Group:
General Motors Front Wheel Drive Two-Mode Hybrid Transmission, SAE 2009-01-0508.
Howell D Office of Freedom Car & Vehicle Technologies (2007) Plug-In Hybrid Electric Vehicle Bat-
tery Research and Development Activities, US DOE PHEV Stakeholders Meeting, Washington,
June.
Howell D Office of Freedom Car & Vehicle Technologies (2009) Annual Merit Review, Energy Stor-
age R&D Overview, Presentation at the Office of Vehicle Technologies, May 19.
Iijima T, Honda R&D Co, Ltd (2006) Development of Hybrid System for 2006 Compact Sedan, SAE
2006-01-1503.
Imberdis O, IAV France, Freimann R, IAV GmbH, Germany and Lueck T, IAV Inc., USA (2010)
Distributed Simulation of Hybrid Powertrain with Focus on Vehicle Stability and Performance,
Revue Ingénieurs De L'Automobile, 804, Janvier-Février 2010 (ISSN 0020-1200).
Itagaki K, Teratani T, Kuramochi K, Nakamura S, Tachibana T, Nakao H and Kamijo Y, Toyota Motor
Corp. (2002) Development of the Toyota Mild-Hybrid System (THS-M), SAE 2002-01-0990.
Jeanneret B and Badin F, INRETS, LEN, Bron, France (1986) Étude de la gestion de l'énergie du véhi-
cule VERT, INRETS report, LEN 9626, December.
Jeanneret B, Badin F, INRETS, LTE, France, N'Guyen T and Periot R, Alstom France (1998) Drive
Train Evaluation Using Simulation, Application to the V2G/VEG Vehicle Developed by Alstom
and Renault VI: EVS15 Brussels Belgium, October.
Jeanneret B, Harel F, Trigui R and Badin F, INRETS, LTE, Bron, France (2002) Évaluation du véhicule
Nissan Tino Hybrid: C-VELEC02 Véhicules électriques et hybrides, enjeux technologiques et
perspectives, INPG, Grenoble France, 25-26 June.
Kawamoto N, Naiki K, Kawai T, Shikida T and Tomatsuri M, Toyota Motor Corporation (2009) Devel-
opment of New 1.8-Liter Engine for Hybrid Vehicles, SAE 2009-01-1061.
Kimura A, Abe T and Sasaki S, Toyota Motor Corp, Aichi, Japan (1999) Drive Force Control of a
Parallel-Series Hybrid System: JSAE Review 20, 337-341, Elsevier Science B.V.
Lee CY, Zhao H and Ma T, School of Engineering and Design, Brunei University, Uxbridge, UK
(2008) A Low Cost Air Hybrid Concept: Les Rencontres Scientifiques D'IFP Energies Nou-
velles - Advances in Hybrid Powertrains - Rueil-Malmaison, France, 25-26 November.
Marakby S, Ford (2010) Hybrid Core Engineering: Ford'S Plug in HEH, SAE 2010 Hybrid Vehicle
Technologies Symposium, San Diego, CA, USA, February 11-12.
Marc N, Prada E, Sciarretta A, Anwer S, Vangraefschèpe F and Badin F, IFP Energies Nouvelles,
France, Charlet A and Higelin P, PRISME, France (2010) Sizing and Fuel Consumption Evalu-
ation Methodology for Hybrid Light Duty Vehicles : EVS-25 Shenzhen, China, Nov. 5-9, 2010.
Meisel J, Georgia Institute of Technology, Atlanta, Georgia, USA (2009) An Analytic Foundation for
the Two-Mode Hybrid-Electric Powertrain with a Comparison to the Single-Mode Toyota Prius
THS-II Powertrain, SAE 2009-01-1321.
Miller JM and Miller JNJ, PLC Design Services (2005) Comparative Assessment of Hybrid Vehicle
Power Split Transmissions, 4th VI Winter Workshop Series, January.
Miller M, Holmes A, Conlon B and Savagian P, General Motors Company (2011) The GM "Voltec"
4ET50 Multi-Mode Electric Transaxle, SAE 2011-01-0887.
Mizuno Y, Ibaraki R, Kondo K, Odaka K, Watanabe H, Mizutani T, Kaneshige K and Kitada D, Toyota
Motor Corp. (2009) Development of New Hybrid Transmission for Compact-Class Vehicles,
SAE 2009-01-0726.
Nagata S, Umeyama H, Kikushi Y and Yamashita H (2003) Development of a New Battery System
for Hybrid Vehicles, Toyota Motor Corporation. Presented at the EVS 20 International Battery,
Hybrid and Fuel Cell Electric Vehicle Symposium, 11/2003, Long Beach, California, USA.
366 Hybrid vehicles
Nakamura E, Soga M and Akira Sakai, Toyota Motor Corporation (2002) Development of Electroni-
cally Controlled Brake System for Hybrid Vehicle, SAE 2002-01-0300.
Ogawa H, Matsuki M and Eguchi T, Honda R&D Co, Ltd (2003) Development of a Power Train for
the Hybrid Automobile - the Civic Hybrid, SAE 2003-01-0083.
Okamura M, Sato E and Sasaki S, Toyota Motor Corp, Aichi, Japan (2003) Development of Hybrid
Electric Drive System Using a Boost Converter, EVS20, 2003 Long Beach, California, USA,
November 15-19.
Plasse C, Chemin M, Lacamoire G et Von Westerholt E, Valeo Systèmes Électriques (2001) L'alterno-
démarreur, du Stop & Go au groupe motopropulseur hybride, Société des Ingénieurs Automo-
biles, Palais des Congrès de Versailles, 13 and 14 November.
Pognant-Gros P, Wasselin T and Badin F, IFP Energies Nouvelles, Solaize, France (2010) Power-Split
Hybrid Transmission Energetic and Dynamic Evaluation Program, IEEE-VPPC'2010, 13 Sep-
tembre, Lille, France.
Ravello V, Centro De Ricerche, FIAT, Orbassano, Italy (2006) HI-CEPS - Highly Integrated Combus-
tion Electric Propulsion System, FP6 Program Description (Confidential).
Revel R (2011) Batteries Métal - Air : Rapport Bibliographique, IFP Energies Nouvelles, Réf. 61690.
Reyss O, Due G, Pognant-Gros P and Sandou G (2008) Robust Torque Tracking Control for E-IVT
Hybrid Powertrain, IFAC Seoul World Congress.
Roby F (2006) Vers La Voiture Sans Pétrole, EDP Sciences, ISBN 2-86883-874-X.
Savagian P, General Motors (2011) Hybrid Systems and New Assist Powertrain, SAE 2011 Hybrid
Technologies Symposium, Anaheim, CA, USA, February 9-10.
Schroder H and Hofmann L, Volkswagen AG (2008) Conceptual Design of Power-Split Transmissions
for HEVs, Corporate Publication, February.
Smart J, Francfort J, Idaho National Laboratory, ID, USA and Karner D, Electric Transportation Engi-
neering Corporation, Phoenix, AZ, USA, Kirkpatrick M, White S, Idaho National Laboratory,
ID, USA, US Department of Energy (2009) Advanced Vehicle Testing Activity: Plug-in Hybrid
Electric Vehicle Testing and Demonstration Activities, EVS24, Stavanger, Norway, May 13-16.
Stridsberg Powertrain AB (2006) Strigear - a More Efficient Hybrid, Publication Stridsberg Powertrain
AB, November 2006.
Takasaki A, Mizutani T, Kitagawa K, Yamahana T, Odaka K, Kuzuya T, Mizuno Y and Nishikawa
Y, Toyota Motor Corporation (2009) Development of New Hybrid Transmission for 2009 Prius,
EVS24 Stavanger, Norway, May 13-16.
Takemoto H, Honda R&D Co, Ltd (2009) Insight, SAE 2009 Hybrid Vehicle Technologies Sympo-
sium, San Diego, USA, February 11-12.
Taniguchi A, Panasonic EV, Energy Co, Ltd, Shizuoka, Japan (2001) Development of Nickel/Metal-
Hydride Batteries for EVs and HEVs, Journal of Power Sources, 100, pp 117-124, Elsevier.
Täte E, Harpster M and Savagian P, General Motors Corporation (2008) The Electrification of the
Automobile: From Conventional Hybrid, to Plug-in Hybrids, to Extended-Range Electric Vehi-
cles, SAE 2008-01-0458.
Trigui R, Badin F, Jeanneret B, Harel F, Coquery G, Lallemand R, Ousten JP, INRETS, Castagne M,
Debest M, Gittard E, Vangraefschèpe F, Morel V - IFP Energies Nouvelles, Labbé J, ARMINES,
Baghli L, Rezzoug A, GREEN and Biscaglia S, ADEME (2002) Hybrid Light Duty Vehicles
Evaluation Program, EVS 19, Busan, Korea, 19-24 October.
Trummel M, Freeman R and Mazor S, US DOE (1984) DOE Hybrid Test Vehicle Phase III, Perfor-
mances Testing and System Characterization, DOE Internal Report.
Ulrich L, Chevy Volt (2011) GM Unveils the Highest Tech of Today's Hybrids: IEEE Spectrum, 48,
4, April, pp 34-35.
Chapter 5 · Hybridization 367
Venturi S, Tilagone R, Soleri D and Tona P, IFP Energies Nouvelles, France (2008) VEHGAN, "VEhi-
cule Hybride Au GAz Naturel", the Benefits of Using a Downsized CNG Engine in a Hybrid,
AEA, 4th European Conference on Alternative Energies for the Automotive Industry, Poitiers,
France, April 2- 3.
Venturi S (2007) Brevet INPI 0707118, 10/10/07.
Villeneuve A and Mendes P, Renault, France (2004) Dual Mode Electric Infinitely Variable Transmis-
sion: Aachener Kolloquium Fahrzeug Und Motorentechnik, pp 895-922.
Wall E, US DOE (2007) Plug-In Hybrid Electric Vehicle R&D Activity, Freedom Car and Vehicle
Technologies Program, US DOE PHEV Stakeholders Meeting, Washington, June.
Yaegashi T, Cordia Corporation Ltd (2009) The Future Challenges to Sustainable Mobility by Hybridi-
zation, IEAIA-HEV Annex XV Meeting, 18 February.
Yaguchi H, Uehara T, Watanabe K and Tokieda J, Toyota Motor Corporation (2009) Development of
New Hybrid System for Compact Class Vehicles, EVS24 Stavanger, Norway, May 13-16.
Yamanaka A, Toyota Motor Corporation (2007) Lexus Hybrids, SAE 2007 Hybrid Vehicle Technolo-
gies Symposium, San Diego, USA, February 7-8.
Yamasaki M, Konno H, Kuroiwa H and Ozaki N, Hitachi Ltd (2005) Automated Manual Transmission
with Torque Assist Mechanism for Reducing Shift Shock, SAE 2005-01-1783.
Yamashita H, Toyota Motor Corp., Japan (2003) Development of a New Battery System for Hybrid
Vehicles, EVS20, 2003 Long Beach, California, USA, November 15-19.
Zanasi R and Grossi F, Information Engineering Department, University of Modena E Reggio Emilia
Modena, Italie (2010) Modeling and Control of Power-Split Hybrid Electric Vehicles, IEEE-
VPPC'2010, September 1-3, Lille, France, 978-1-4244-8218-4/10.
Zgheib E and Clodic D, Center for Energy and Processes, École des Mines de Paris, France (2009) C02
Emission and Energy Reduction Evaluations of Plug-in Hybrid Vehicles, SAE 2009P011324.
Control of Hybrid
Vehicles
Antonio Sciarretta \
6.1 THE NEED FOR ENERGY MONITORING AND MANAGEMENT
6.1.1 Hybrid Architectures and Degrees of Freedom
In conventional propulsion systems, requests by the driver, who activates the accelerator,
brake, and, in some cases, clutch pedals, are monitored by a control structure intended to
determine the torque requirements for various actuators. For example, pressing the accelera-
tor pedal is interpreted as a torque request at the wheels and, therefore, as a power request,
based on the vehicle's speed. Depending on the computation path (Figure 6.1), which is
characterized by various constraints (speed regulator/limiter, vehicle stability, protection of
the transmission, etc.) and the torque requirements of auxiliary components, the propulsion
control system calculates the actual torque required from the combustion engine at every
moment, which will be translated into signals to the various actuators (throttle, injectors,
etc.).
Figure 6.1
Torque control structure in a conventional propulsion system.
370 Hybrid vehicles
Figure 6.2
Torque control structure in a hybrid propulsion system.
In hybrid propulsion systems (Figure 6.2), the first part of the path {interpretation of the
driver's intent) remains unchanged until a torque request, T t, is made. This is not assigned
uniquely to the combustion engine but to the powertrain as a whole, distributed between the
combustion engine and the electric machine(s). Once the brake pedal is pressed, the torque
request may become negative (resistant torque). In this case, the distribution will take place
between the hydraulic brakes and the so-called "regenerative" braking system (Chapter 1).
Therefore, the power of each machine is not determined directly by the driver's actions. Degrees
of freedom exist in the management of energy and the use of different machines. The definition
and control of those degrees of freedom are the responsibility of the energy management system.
To analyze the typical degrees of freedom of each hybrid architecture, we can use the con-
cept of constraint flow, like those shown in Figures 6.3 to 6.5. The graphs answer two questions:
- which power factors are determined if we know the torque and speed at the wheels?
- which must be determined by the energy manager?
In a parallel hybrid with a manual transmission, the speed at the wheels determines the
speed of the two machines, for a given transmission ratio and reduction ratio between the
electric machine and the axle, which is normally fixed. One degree of freedom has to be
chosen between the combustion engine torque (T ) and the electric machine torque (Tem)
to satisfy the requirements of T t, while accounting for the torque requirements of auxiliary
components, transmission losses and gearing losses, as well as other limitations, described
below. Fuel consumption of the combustion engine will then be determined from the torque
and engine rpm. Similarly, the torque and rpm of the electric machine are used to determine
its electrical power, calculate losses, and evaluate its "electrical consumption," which can be
compared to the consumption of the combustion engine. To do this, we define an "electro-
chemical power," Pech, as the variation in stored energy l. The diagram shown in Figure 6.3
is applicable to all types of parallel hybrids - with the electric machine positioned ahead
1. This electrochemical power is defined as the product of the current and the open-circuit voltage.
Therefore, as a first approximation, it differs from the electrical power at the terminals of the storage
system (battery or supercapacitor) by the losses (in a first approximation, ohmic losses).
Chapter 6 · Control of hybrid vehicles 371
of the transmission, after the transmission, or coupled "through-the-road" (Chapter 5). The
overall degree of freedom is, in all these cases, one of the two torques, T for example. Of
course, a second degree of freedom could be represented by the choice of transmission gear
ratio in situations where this is not imposed by a driving cycle.
Figure 6.3
Constraint flows for a parallel hybrid.
We can extend these considerations to other hybrid architectures. In the case of a series
hybrid (Figure 6.4), the speed of the wheels and the torque request determine the operating
point of the traction electric machine and, therefore, its electrical power. The electrical power
of the generator is determined by adjusting the operating point of the combustion engine and,
consequently, its speed and torque. As a result, the total electrical power at the battery termi-
nals remains fixed, as does the power, Pech.
Figure 6.4
Constraint flow for a series hybrid.
372 Hybrid vehicles
Now consider the case of a series-parallel hybrid whose mechanical connection occurs
through a system with one or more planetary gear sets (Figure 6.5). This connection is
described by the relationship between the four torque levels and four speeds of the connected
components (combustion engine, the two electric machines, and the final drive train). It turns
out that, even for highly complex systems with several planetary gear sets, we can write two
linear equations involving the speeds and two others involving the torques. Since torque and
speed at the wheels are fixed, we need only determine a speed and a torque as a degree of
freedom. The most intuitive choice is to use the torque and the engine's rpm, that is, to deter-
mine its operating point - and, therefore, its consumption and emissions - independently of
the conditions at the wheels. This choice determines the torque and speed of the two electric
machines and, therefore, their electrical power. By going back up the electrical chain, we find
that this also amounts to determining the power Pech.
Figure 6.5
Constraint flow for a series-parallel hybrid.
In all cases, the choice of the degrees of freedom is the role of the energy manager, as
can be seen in Figure 6.2. However, this choice is subject to several constraints. It must first
guarantee that the physical limits of the actuators are respected. When controlling the torque
in a purely combustion powertrain (Figure 6.1), this is very easily obtained by modulating the
driver's request between the maximum and minimum torque of the combustion engine at its
actual rpm. In a hybrid powertrain, however, we must ensure (i) that the choice of the degrees
of freedom is physically possible and (ii) that all the quantities in the drive chain dependent
on those degrees of freedom are physically possible.
In a parallel hybrid, not only must the choice of T to satisfy a T t setting be compatible
with the characteristics of the combustion engine, but the corresponding value Tem = T t
- T must also be compatible with the working range of the electric machine, just as the
resulting power, P must be compatible with the battery characteristics.
Chapter 6 · Control of hybrid vehicles 373
In a series hybrid, the choice of T and ω is made within the operating limits of the
motor and generator, as well as the limits imposed by the battery on P
Finally, in a series-parallel hybrid, T and ω must be chosen within the limits
imposed by the characteristics of the electric machines and battery, and the operation of the
planetary gear sets. In particular, the speed range of one of the electric machines may impose
speed limitations on the combustion engine, limits that vary with the speed of the vehicle.
In addition to the physical constraints, energy management is also sensitive to a number
of operating constraints, normally determined by the requirements of driving comfort. Typi
cally, this will involve preventing frequent stopping and starting of the combustion engine,
avoiding sudden changes in its power point, and, especially, in limiting torque setting changes
and the number of transmission mode changes, especially gear changes.
Outputs from the energy manager consist of setpoints for the actuators, among which we
should include the transmission chain in the broad sense. In effect, depending on the type of
transmission, we may need to control one or more clutches, a automated gearbox, and so on.
These setpoints are normally determined along with those for the torque of the combustion
engine and electric machines, and the state of the combustion engine. The latter is a Boolean
variable (start/stop) whose definition is clearly associated with the torque setpoint (which is
itself subject to several physical and comfort constraints, as described above). Additionally,
any request for a change in the engine's state triggers a start or stop procedure that inevitably
entails several chronological steps that require the coordination of all the actuators. This
dynamic coordination, which occurs during transitional states, is normally subject to a con
trol task positioned downstream of the energy manager, as shown in Figure 6.6.
Figure 6.6
Three-level torque control structure.
374 Hybrid vehicles
6.1.2 Energy Management Laws
Energy management laws fall into two categories that differ depending on how they are syn-
thetised: heuristic laws and optimal laws (in the mathematical sense). The first are currently
used in industry, while optimal management is still perceived as something of an innovation.
6.2 HEURISTIC ENERGY MANAGEMENT
In this chapter, we discuss heuristic laws. As an illustration, we examine a parallel hybrid
architecture capable of all-electric operation.
A heuristic energy manager is typically organized according to the illustration shown in
Figure 6.7 [Jeanneret, 1999a; Jeanneret and Harel, 1999b]. The torque request, T t, from
the block that interprets the driver's intentions - transformed into power, based on the speed
of forward motion - is combined with a request for power calculated on the basis of the bat
tery's state of charge (SOC), to obtain a global request, Ρ^6ΐη, that is virtually attributed to
the combustion engine (see below). This global virtual request is then sent to the "system
state" block, responsible for determining whether the combustion engine should be started or
stopped. Based on this decision, the subsequent block calculates the setpoint torque for the
electric machine and, therefore, the torque of the combustion engine. If we analyze the "sys
tem state" block (Figure 6.8), we typically observe the structure of & finite-state automaton,
characterized by at least three states: "stopped," "starting," and "running." The transitions
between states are determined by three signals: "stop," "start," and "end start." While the last
transition is associated with the speed of the combustion engine, the first two are determined
on the basis of a number of transition conditions.
Figure 6.7
General structure of a heuristic energy manager.
Chapter 6 · Control of hybrid vehicles 375
General structure of the "system state" block.
For example, the "stop" transition (to the all-electric or combustion engine "stop" state)
can be controlled by the simultaneous combination of the following conditions:
- the battery has sufficient charge
- the combustion engine is sufficiently hot
- the battery is sufficiently cold
- the vehicle speed is relatively low
- the overall power demand is relatively low
in which case the dual "start" transition will be determined by the failure to satisfy at least
one of these conditions. Additionally, the transitions - at least in the case of the "stop" tran-
sition - are normally delayed. To exit the "off state, the system must have remained in the
"on" state for at least a certain amount of time, and vice versa. Constraints arising from an
external infrastructure can also determine the operating mode (protected areas where all-
electric driving is required, for example).
Both Boolean logic and fuzzy logic are used to generate signals corresponding to the
truth condition of the transition states. In Boolean logic, to determine whether the conditions
have been respected, we compare the corresponding variables measured or estimated (for
example, the battery's state of charge, temperature, wheel speed, calculated power requests)
with numerical thresholds calibrated offline for each specific application. In fuzzy logic, we
determine the degree of truth of certain conditions based on functions, as shown in Figure 6.9
(for the SOC).
It is worth noting that some transition conditions can be directly represented on the char-
acteristic map of the combustion engine. For example, Figure 6.10 shows that the power
threshold when starting, Pstart (line S), separates two regions: one where the system oper-
ates in all-electric mode and the other where operation is hybrid. Consequently, the power
points that, for all-thermal operation, would fall below curve S and, therefore, at relatively
low efficiency, are eliminated and replaced by all-electric operation. Conversely, the points
that, for all-combustion operation, would fall above curve S, are shifted toward optimal
376 Hybrid vehicles
efficiency values (curve O) by making use of electrical storage. The surplus power is then
used to recharge the battery. Naturally, when the power demand is close to or greater than the
maximum power of the combustion engine (curve M), the two actuators are required to work
together (electrically assist).
Figure 6.9
Example of fuzzy logic applied to the state of charge of a battery.
Figure 6.10
Effect of heuristic laws on combustion engine power points.
Chapter 6 · Control of hybrid vehicles ΊΊΊ
Once the state of the combustion engine has been determined, its power setpoint is given
by:
(6.1)
where Ploss is an estimate of the sum of the losses in the electrical path connecting the com
bustion engine to the battery. At a given power, in a parallel architecture, the torque of the
combustion engine is also determined by the value of Penyco ; the mechanical power of the
electric motor, Ppwt P , and calculation of the torque setpoint follow from this.
Therefore, determination of Ρ^εηι is the key factor for this type of heuristic strategy. In
general, this value is the result of the following calculation, Pdem = Ppwt - P^em bat' w n e r e t n e
term Pdem bat represents the electrical power value at the battery terminals, which is positive
when discharging, negative when charging. This term only accounts for recharging by the
combustion engine, therefore, it is zero when braking or when the engine is starting. Oth
erwise, P^embat *s maPPed as a function of the battery's state of charge, as shown in Figure
6.11. Additional conditions can modulate the maximum recharge power based on tempera
ture, so that, if the battery is too hot, recharging can be prevented entirely. Conversely, when
the combustion engine is cold, recharge power will be saturated to an appropriate value. Note
that the power threshold for starting the combustion engine, Pstart, is logically a function of
SOC. When SOC is too low, this threshold tends toward zero (no electrical operation); when
the SOC increases, this threshold will increase as well (see example given in Appendix 7).
Figure 6.11
Possible change in battery power as a function of SOC in a heuristic law.
An alternative to the repeated coding of heuristic rules involves the use of control maps
that supply the results directly in the form of T , Tem, and so on, as a function of influence
variables (power demand, SOC, temperature, vehicle speed). A functional diagram is shown
378 Hybrid vehicles
in Figure 6.12 and the particular case of the GM Volt is illustrated in Figure 5.28. When the
number of variables is too high for an effective map representation, a neural network can be
used.
Figure 6.12
Example of control maps as a function of influence variables.
The heuristic strategy used for a parallel architecture can also be applied to series-parallel
hybrids, up to the point where we calculate the system state and power setting, P , using
equation (6.1). Beyond that, the nature of the system, which has several degrees of freedom
not found in parallel hybrids (6.1.1), requires that we independently choose the engine torque
(and, therefore, its rpm) to satisfy the power setting. Normally, this is chosen by maximizing
the efficiency of the combustion engine for a given power value. This is done off line for
all power values. An optimal speed curve is generated as a function of power (Figure 6.13).
We find the concept of the optimal operating line (curve O), for example, when operating a
combustion engine with a continuously variable transmission (CVT). Once curve O is stored
in the controller, it can be used to calculate ω and T as a function of P .
In series-parallel hybrids, knowing the torque and speed of the combustion engine,
together with the torque and speed at the wheels, determines, the torque and speed of both
electric machines, as shown in Figure 6.5. This is how the majority of the outputs of the "set-
points" block in Figure 6.7 are calculated.
If the system also comprises a DC/DC chopper/booster converter between the battery and
the electric machines, the settings vector also includes the ratio of the voltage increase. This
setting is calculated as a function of the measured battery voltage and the desired voltages at
the terminals of the two machines.
Chapter 6 · Control of hybrid vehicles 379
Figure 6.13
Optimal operating line (curve O) for controlling series and series-parallel
hybrids.
Although heuristic laws can be highly effective for a given system under specific driv-
ing conditions (cycle), their greatest shortcoming is their flexibility. In fact, these laws are
strongly dependent on calibration of the numerical thresholds, which is costly in terms of the
time spent on prototyping and does not guarantee optimal operation for driving conditions
unlike those used. Moreover, the focus on local optimization of the combustion engine, with
the determination of the O curve, etc., may not be the best choice in terms of overall energy
management. To overcome these drawbacks, the concept of optimal energy management has
been developed.
6.3 OPTIMAL ENERGY MANAGEMENT
6.3.1 Basic Concepts of Optimal Control Applied to Hybrids
Energy management, rather than being based on heuristic laws, can rely on optimization
of a mathematically defined evaluation criterion, in which case we refer to optimal energy
management.
Here, we discuss the derivation of the laws for optimal energy management for a typical
formulation of the problem [Sciarretta and Guzzella, 2007; Guzzella and Sciarretta, 2007;
Sciarretta et al, 2004]. Important extensions to this approach will be presented in section 6.5.
380 Hybrid vehicles
In this context, the principal criterion for optimization is fuel consumption or C 0 2 emis
sions during a normalized driving cycle. Here, the criterion is global, the goal being not to
minimize the fuel consumed by the combustion engine (Df^) at every moment but, rather,
overall consumption for the entire driving cycle (in general, the vehicle's "mission").
Of course, minimizing consumption alone would ordinarily lead to purely electric opera
tion (for example, a hybrid capable of operating in electric-only mode). Optimization must
incorporate at least one constraint on battery charge or the electrical energy storage system.
Note that this constraint is not applied at every instant but to the overall change in the charge.
Typically, energy management is required to reach a target charge state, xtarget, at the end
of the mission. This target can express the desire to ensure that the use of electrical energy
is consistent, with a final state of charge equal to the initial state. It can also be independent
of the charge at the start of the mission and determined, rather, by the desire to operate the
battery under conditions that ensure its extended life. In rechargeable hybrids (5.3.2.2), the
battery target may simply be a minimum charge value, which allows it to be recharged from
an external device (such as a charger).
Mathematically, we can formulate the problem of optimization as the search for a settings
vector, u(t), that minimizes the following criterion:
(6.2)
in such a way that the state of charge, x, at the end of the cycle is: (6.3)
Criterion (6.2) is, therefore, subject to the dynamics of the state of charge:
(6.4)
where L is the battery current and Qb its capacity 2. Note than in (6.2) the state of charge
has no direct influence on consumption. According to the theory of optimal control, the
optimal setpoint vector u, is found at each instant by minimizing the Hamiltonian, H(u, x, t):
(6.5)
This function combines consumption and the variation of state of charge through a new
quantity, λ, known as the Lagrange multiplier, which is a state variable associated with x and
subject to its own dynamics:
(6.6)
2. The dynamic model (6.4) is greatly simplified as it doesn't take into account losses inside the bat
tery. Electrochemical modeling provides a more effective description of the phenomena that associate
the current at the battery terminals with concentrations of electrochemical species responsible for the
charge.
Chapter 6 · Control of hybrid vehicles 381
In the practical case of electrochemical batteries, the change in f(u, x, t) and, therefore, in
H(u, x, t) with the state of charge is often negligible 3, which leads to a constant λ throughout
the cycle. Given these same assumptions, the definition of the Hamiltonian (6.5) assumes an
equivalent formulation:
(6.7)
which has the advantage of being physically more intuitive and of presenting the Lagrangian
multiplier in the form of a dimensionless coefficient s = - λ.Η / (Qb t . U A which plays the
role of an equivalencefactor between fuel power R. , = Dfo ,.Η, and electrochemical power
pech = T batt.Un0.
Simultaneous resolution of the above equations is made impossible by the fact that the
value of s is initially unknown. The condition that determines the value of s is condition (6.3),
which, unfortunately, is determined only at the end of the mission. In fact, this is a math
ematical problem with mixed boundary conditions, which requires considerable effort for its
solution. The next section illustrates how to overcome this difficulty.
6.3.2 Optimal Offline Energy Management
In some applications, the driving cycle is known in advance. For example, a vehicle homolo
gation test on a chassis dynamometer is conducted using a pre-determined speed profile,
based on which it is possible to determine the torque needed at the wheels. With this informa
tion, the off-line optimal energy management law can be calculated. Several methods can be
used for this calculation, including dynamic programming (DP) and Pontryagin's minimum
principle (PMP).
6.3.2.1 Dynamic Programming
Dynamic programming is a numerical algorithm based on the discretization of equations
(6.2) to (6.7) and their solution by working backwards over time, in Richard Bellman's for
mulation [Bryson and Ho, 1975; Bertsekas, 2000]. This involves calculating a cost function,
J(x,t), for each point in the discretized grid. Therefore, initial knowledge of the driving cycle
is necessary (offline optimization) [Pu and Yin, 2008; Gong et al, 2008; Liu and Peng,
2008]. The J function represents the minimum consumption needed to reach the "target"
point (xtarget, T) from point (x, t). Consequently, the value of this function for the starting
point J(x(0), 0) will yield the optimal consumption for the driving profile analyzed. Calcu
lating J requires the parallel calculation of the optimal setpoints vector, which will also be
configured as a function of (x, t).
Dynamic programming (DP) provides increasingly precise results based on the resolution
of the grid and, therefore, proportionally to the complexity of the calculation. The complexity
3. In practice, this is the change in internal resistance in the battery and its open circuit voltage with the
state of charge. This variation becomes less important as the range of variation of the SOC diminishes.
Consequently, this approximation is much more important for rechargeable hybrids, in which the SOC
can vary between a completely empty battery and a completely full battery.
382 Hybrid vehicles
is proportional to the number of points in the matrix and varies exponentially with the num-
ber of dimensions in the grid, which limits trying to solve optimization problems with several
dynamic variables in addition to x (see the examples in Section 6.5). Another critical aspect
of DP results from the interpolation algorithms between points in the grid, which must be
highly sophisticated if they are to handle the strong discontinuities in J near the borders
between acceptable and unacceptable points [Sundström et al, 2010]. However, DP is able
to very naturally accommodate the constraints imposed on x. For example, the minimum and
maximum limits of the SOC can easily be imposed by limiting the calculation grid to these
values, and do not require any special treatment.
Another interesting aspect of DP is that it combines particularly well with algorithms, as
suggested by some researchers, designed to estimate future driving profiles using stochas-
tic methods [Johannesson et al, 2007] or environmental sensors (GPS, for example) [van
Keulen et al, 2010]. Additionally, the intrinsic retroactive nature of DP, due to the implicit
dependence of u(t) on x, can be used on line in the form of maps, where the results of DP
calculated offline can be stored ahead of time or in the form of Boolean or fuzzy rules based
on those results [Peng, 2008].
6.3.2.2 Pontryagin's Minimum Principle (PMP)
Pontryagin's minimum principle (PMP) is based on the formulation of equations (6.2) to
(6.7) above, and is proposed as a more suitable alternative to DP, even for offline optimiza-
tion. While the state constraints are less easily manageable than they are in DP (although
techniques exist for managing them [Rousseau et al, 2007]), less time is needed to solve the
equations, because it is not necessary to use discretized values of the dynamic variable x.
In fact, the block diagram of the PMP algorithm is shown in Figure 6.14, with three itera-
tive loops. The outermost loop represents an iteration over an unknown value of the equiva-
lence factor s. With each iteration, a different estimate s is used. The driving cycle runs from
t = 0 to t = T (time loop). At each instant, wheel torque and rpm are known. By using a model
of the system, as indicated in Section 6.4, we can calculate the variables that define the Ham-
iltonian for a certain number of admissible settings vectors (loop over u).
Once the cycle is completely traversed, by calculating the optimal setpoints at each
moment and, therefore, the changes in the variables, we reach a state of charge that, in gen-
eral, will be different from the target value. As a result, we will have to modify our estimate
of s and run a new iteration with this new value. The dependence between x(T) and s is quali-
tatively represented in Figure 6.15. We see that overestimating s compared to the "true"
value of s gives a final SOC that is greater than the target value. This is due to the fact that
we assign too much weight to the consumption of electrochemical energy in the Hamiltonian
(6.7), which means that the energy manager will favor use of the combustion engine to
recharge the battery. If, on the other hand, s is underestimated, the value of the electrochemi-
cal energy will be too low and the control will favor discharging the battery.
Chapter 6 · Control of hybrid vehicles 383
Figure 6.14
Flow diagram for the PMP algorithm for offline optimization.
Figure 6.15
Dependence between the equivalence factor and the final SOC.
The PMP technique presented above is still used as an offline optimization technique
because the entire driving cycle must be known in advance. Nonetheless - and this is its
advantage compared to the other optimization methods described above - PMP has a very
similar online counterpart, with which it shares the essential elements of the calculation.
384 Hybrid vehicles
6.3.3 Optimal Online Energy Management
The online PMP strategy is shown in Figure 6.16. Note that, unlike Figure 6.14, there is a sin-
gle loop for setpoints. The driving cycle has been replaced by requests made directly by the
driver. Based on these, the energy manager can estimate the setpoints for torque and wheel
speed, as described in Section 6.1. Similarly, the loop over s has no reason to exist because,
on line, we can't reach the end of the driving cycle to refine estimates of this parameter. On
the contrary, estimates must be made repeatedly, at each instant, based on available measure-
ments, as shown in Figure 6.16 by the "s-estimate" block.
Figure 6.16
Flow diagram of the PMP algorithm for online optimization.
Based on the current estimate, s(t), of the equivalence factor, the online algorithm cal-
culates the degrees of freedom that minimize the Hamiltonian corresponding to the driver's
torque request. Because the Hamiltonian function given by (6.7) has the same units as fuel
consumption (power), these strategies are often known as equivalent consumption minimiza-
tion strategies (ECMS) [Sciarretta et al, 2004; Chasse and Sciarretta, 2011; Kessels et al,
2008; Serrao etal, 2011].
An ECMS strategy block diagram is shown in Figure 6.17 [Chasse and Sciarretta, 2011].
Note the estimate of the s block, which is modified as a function of changes in the SOC,
which are themselves estimated from the BMS (Chapter 4), because the SOC is not directly
measurable. A simple modification algorithm can be defined by reversing the dependence of
Figure 6.15. This is a linear (for the sake of simplicity) law between the equivalence factor
and SOC:
(6.8)
Chapter 6 · Control of hybrid vehicles 385
where k > 0 must be positive. The target value of the equivalence factor, sc, corresponds
to the target value of the state of charge, x . If the SOC is greater than the target value, a
correction is made to the equivalence factor, which decreases; it increases if the SOC is less
than the target value. Other possible corrections, not dependent on the SOC, will be briefly
described in Section 6.5.
Figure 6.17
Three-level structure of the ECMS energy manager.
The objective of the ECMS block in Figure 6.17 is to calculate the optimal Hamiltonian
by using the current estimate for s and the driver's torque request. In practice, it is useful to
retain two possible values: for the combustion engine when in operation (Hh b) and when
stopped (Hel). The first value is already the result of minimizing all the possible torque dis-
tributions between the combustion engine and the electric motor, while the second is the
Hamiltonian for all-electric operation. Before providing details of how these two values are
used to determine the state of the combustion engine ("engine state" block in Figure 6.17),
and possibly sending a stop or start command, we see that several techniques can be used
to calculate Hh b (calculating Hel is very simple because we can use equation (6.7) with
Pfùel = 0)· Here, we wish to point out the vector method and the map method (Figure 6.18).
In the first, we establish a certain number of acceptable setpoints u 4, for each of which we
calculate the respective Hamiltonian. The minimum of these calculated values is the value of
Hh b we are looking for. In the map implementation, this calculation is not performed online
but offline, as a function of the driver's request, the wheel speed, SOC, and the equivalence
factor. The results are stored in a map queried online as a function of the instantaneous values
of the input variables. Note that map implementation in the leading development software
is limited in practice to three input variables. Therefore, this approach requires approxima-
tions, which can involve neglecting the direct impact of SOC on the Hamiltonian (the same
approximation that led to (6.7)).
4. Recall that u can be a scalar or vector quantity, depending on the number of degrees of freedom of
the system.
386 Hybrid vehicles
Figure 6.18
Vector and mapped implementation of ECMS.
A simplified, but complete, calculation flow diagram, based on a vector implementation
of ECMS, is shown in the inset "Online ECMS calculation."
Finally, the "engine state" box in Figure 6.17 compares the two possibilities retained -
optimized hybrid and all-electric - and selects the optimal settings. As each transition between
the two cases involves a change of state of the combustion engine and, therefore, a perceptible
maneuver for the system, the energy manager must be very careful in its choice to avoid too
frequent or unwanted stop-and-start phases. This requirement can be realized within ECMS
by introducing penalty terms that are added to the Hamiltonian defined by equation (6.7). One
possible implementation, shown in Figure 6.19, consists in comparing Hh b and Hel indirectly
by means of a safety threshold expressed by a percentage, xon or xoff, in order to create a hys-
teresis effect. If the engine is stopped, Hh b must be less than xonHel before the transition to
hybrid mode; if, on the contrary, the combustion engine is running, the transition to all-electric
operation is allowed only when Hel is less than xoffHh b. Time thresholds can also be defined
to ensure that the engine state transition is activated only when the activating condition is true
for a sufficiently long period of time (t and t ff in Figure 6.19). Note that the two conditions
described ultimately play the same role as the heuristic conditions indicated above - the need
for energy supervision and management to make the transition from the state B = 1 to B
= 0 and vice versa. Nonetheless, unlike heuristic thresholds, the use of Hamiltonian functions
is based on an objective and quantitative criterion, as well as the optimization ofthat criterion.
Chapter 6 · Control of hybrid vehicles 387
Figure 6.19
Implementation of optimization constraints when starting a combustion engine.
IFP Energies nouvelles "HOT" software
(A. Sciarretta, IFP Energies nouvelles)
IFP Energies nouvelles is developing a tool for offline optimisation of hybrid pow-
ertrain supervisory control strategies, known as HOT (Hybrid Optimisation Tool). HOT
is based on the formulation of the optimum control problem described in the Optimum
energy management paragraph and in particular on its PMP resolution method (6.3.2.2).
To adapt the algorithm to each hybrid architecture (series, parallel, series-parallel
or power split) and to each configuration (pre- or post-transmission, parallel, input-split
or multimode power split, parallel on two axles, etc.), HOT uses a «universal» structure,
illustrated on Figure E6.1. The structure includes an engine E, two electric machines G
and M, five transmission ratios Re, Rg, Re, Rm and Rd, a connector U, a torque node
N2 and another element N1 which may be a second torque node or its dual, a speed
node. On the ports of a torque node (or "coupler"), the speeds are equal to each other
and the torques are added, whereas the opposite situation is observed in a speed node
(or "splitter"). The various solutions are obtained by choosing four indices: presence/
absence of G (ig = 1/0), presence/absence of M (im = 1/0), connector U closed/open
(iu = 1/0), N1 splitter/coupler (in = 1/0). An optional index is used to choose whether the
node N2 provides coupling on the same axle or on two different axles. The HOT algo
rithm interprets these indices, using them in a universal formulation of relations between
speeds and torques (kinematic matrix) at the various structure levels. The number and
type of degrees of freedom can therefore be evaluated automatically by algebraic opera
tions on the kinematic matrix. The software uses the equations provided in this chapter.
388 Hybrid vehicles
Figure E6.1
HOT "universal" hybrid structure and graphic interface.
HOT is supplied with a user interface under MS-Excel. The HOT graphic interface
allows users to enter the structure via the indices, as well as the components via the
data and the maps which define their static models described in paragraph 6.4. The
results are displayed graphically: during execution, users can track the iterations during
the search for the optimum equivalence factor, then display the profiles of all the vari
ables used together with global values such as consumption, the charge balance and the
energy balances at various points of the powertrain.
6.4 MODELING HYBRID DRIVE SYSTEMS FOR OPTIMIZATION
6.4.1 Forward and Backward Models
During offline optimization, as well as for online energy management, mathematical models
of the powertrain are needed. Figures 6.14 and 6.16 show that these models must calculate
system performance in terms of instantaneous consumption, change in SOC, and so on, as
a function of setpoints applied to components, and wheel speed and torque requests. Such
models must also satisfy the following criteria:
- processing time must be short
- they must allow for simple and, possibly, scalar configuration of components
- the interfaces of the various submodels for each component must be capable of inter-
changeable connections with one another
There is a class of models that satisfies these characteristics, namely, backward quasi-
static models (BQM) [Guzzella and Amstutz, 1999; Rizzoni et al, 1999]. To get a better
understanding of the associated terminology, we distinguish between dynamic and quasistatic
Chapter 6 · Control of hybrid vehicles Next Page
389
models. In quasistatic models, all the dynamics of the system are ignored; they are, however,
described in dynamic models.
For example, combustion engines can be described by dynamic models with high tempo-
ral (on the order of a degree of crankshaft rotation) and, possibly, spatial (mono- or multidi-
mensional) resolution. Such models have been developed at IFP Energies Nouvelles as part
of their ENGINE library (see inset p. 103). These dynamics can also be simplified. In this
case, the models are still dynamic but the values are averages (time resolution on the order of
an engine cycle). If we simplify the dynamics to the point of ignoring them, the engine will
then be represented as a quasistatic system, with instantaneous responses to changes in driver
requests (time resolution on the order of a second). The IFP-Drive library (see inset p. 396)
and PSAT [ANL, online], Advisor [EERE, online] software are based on this concept.
We can also distinguish between/brwarci and backward models in terms of their causality:
- forward models follow physical causality: for example, in a combustion engine, an
input variable would be the throttle opening or the injection time, and the output vari-
ables will include the torque produced on the crankshaft;
- backward models, however, reverse physical causality: they answer the question "what
is the value of the throttle opening needed to reach a given torque on the crankshaft"
or "what is the corresponding fuel consumption?"
Once these definitions have been introduced, it becomes clear, as shown in Figure 6.20,
that forward models are better suited to simulation, while backward models are typically
used for control applications. Additionally, as we move from component to system, the time
resolution will be less fine because the frequency of the associated control will be lower.
That is why, in energy optimization and management applications, we use backward models.
Figure 6.20
Multilevel models and their causality.
Previous Page Hybrid vehicles
390
6.4.2 Backward Models of Hybrid Components
As an example, a block diagram of a backward model (BQM) for a parallel hybrid with an
electric machine downstream of the transmission is shown in Figure 6.21 together with its
forward counterpart (FDM), which retains the physical causality of the real system. Note the
different role played by the "cycle" block in both causalities: in the BQM, the driving cycle
determines the vehicle's speed, V*veh; in the FDM, the speed setting is compared to the
actual speed of the vehicle by the "driver" block, which passes settings to the energy manager
to correct the difference.
Figure 6.21
Illustration of backward (top) and forward (bottom) causalities for a parallel
hybrid.
These models, applied to different components, are described below.
6.4.2.1 Vehicle
A BQM model of the vehicle is needed for offline optimization (Figure 6.14) while, for
online use, the values of T . and ω . are estimated and measured (Section 6.1). To calcu-
3 v7
rwxn nwt
The words you are searching are inside this book. To get more targeted content, please make full-text search by clicking here.
Heavy Duty Truck Systems by Sean Bennett
Discover the best professional documents and content resources in AnyFlip Document Base.
Search
Heavy Duty Truck Systems by Sean Bennett
- 1 - 50
- 51 - 100
- 101 - 150
- 151 - 200
- 201 - 250
- 251 - 300
- 301 - 350
- 351 - 400
- 401 - 450
- 451 - 500
- 501 - 514
Pages: