Chapter 6 · Control of hybrid vehicles 391
late the torque at the wheels from the vehicle speed imposed by the cycle, we simply invert
Newton's law applied to the vehicle. Given the forces of aerodynamic resistance, rolling
resistance, and gravity, the model can be written as:
(6.9)
where mf , is the equivalent mass of the vehicle, including the inertia of rotating parts.
6.4.2.2 Transmission
If we consider a transmission with discrete gear ratios (manual or automated), its BQM rep
resentation can be written as:
(6.10)
where η is the transmission's efficiency, s the sign of the torque (positive for traction, nega
tive when braking), and l the transmission ratio of the gear being used (determined by the
cycle or selected by the energy manager).
6.4.2.3 "Coupling" Node
A mechanical node of this type represents the coupling of torque. Physically, it can involve
an assembly of two machines on the same shaft, a gear reducer or a belt with two different
shafts, or even through-the-road coupling through two drive axles (see the classification of
parallel hybrids in Chapter 5). In all these cases, at a torque node, torques are added and
speeds conserved. For the example in Figure 6.21:
(6.11)
6.4.2.4 Power Splitter
In series-parallel hybrid architectures (Figure 6.5), a key element is the power split device, a
component that virtually combines four rotating shafts (the two electric machines, the com
bustion engine, and the transmission shaft). Physically, this can involve a simple planetary
gear set combined with a torque node (as in the Toyota Hybrid System II in the Prius) or an
assembly of two planetary gear sets, with or without an attached torque node. In the case of
the Toyota Hybrid System, we can write:
(6.12)
where z is the characteristic ratio of the planetary gear set. For the general case, it is always
possible to write a general formulation that can be used for a BQM-type model, that is:
392 Hybrid vehicles
(6.13)
The N matrix has at least three non-zero elements. A special case is serial hybrid archi-
tecture, in which the generator (em2) is connected solely to the combustion engine, while the
traction machine is connected to the transmission shaft. We can still define a matrix, N, that,
in this case, is diagonal.
6.4.2.5 Internal Combustion Engine
The BQM of a combustion engine is constructed around its static map of fuel consumption
as a function of torque and rpm (Figure 6.22). This representation can be simplified by using
the so-called Willans approach, which consists in evaluating the rate of consumption as an
affine function of requested power:
(6.14)
where e is the indicated efficiency of the engine. This equation shows that, because of inter-
nal losses, consumption is cancelled only for negative engine torque, unless an injection
cut-off strategy is used. In this case, we have D ^ = 0 if T < 0 (Chapter 2). Consumption
at slow speeds is treated as a particular case of (6.14). Note that consumption can take into
account driving an alternator to power the auxiliary network.
Figure 6.22
Static model (map) of a combustion engine.
Chapter 6 · Control of hybrid vehicles 393
6.4.2.6 Electric Machine
Electric machines are described in a BQM in terms of their overall efficiency, normally
mapped as a function of the machine's mechanical operating point. Such maps often include
losses in the power converter, so that the calculated electrical power represents the power
drawn from the battery:
(6.15)
A typical map for an electric machine is shown in Figure 6.23. Here, the torque lim-
its vary with rpm in two regions of the map. In the speed range, up to the base speed, the
maximum continuous operating torque is practically constant. Beyond this speed, we enter
the flux-weakening range, which is characterized by a torque limit that decreases with the
machine's rpm (Chapter 3).
Figure 6.23
Static model (map) of an electric machine.
6.4.2.7 Battery
The BQM description of batteries implies that the input variable is electrical power, from
which the model must separately calculate current and voltage, as well as the variation in
state of charge. One class of simple models is that of the equivalent circuit (or U-R models),
in which a battery is represented by a continuous source of electricity, U0, in series with an
electrical resistance, Rbat, both of which vary with certain parameters, such as SOC, tempera-
ture, and so on. This model implies that the current extracted from (if positive) or charged (if
negative) in the battery can be calculated from the following equation:
(6.16)
394 Hybrid vehicles
We see that (6.16) involves a maximum theoretical value for battery power
Pbua+t, max = U£0//4RUba+t. In p*ractice this limit is never reached because the limits that constrain
battery operation are those imposed on current and voltage. The easiest way to calculate
state of charge relies on "coulomb counting" of the electrical charges, Q, exchanged by the
battery:
(6.17)
where η, is the faradaic efficiency of the battery and Q0 the battery's nominal capacity.
The faradaic efficiency ir can include electrical charge losses (parasitic reactions) that
appear primarily during the charge phase. More complex models of onboard energy storage
are described in Chapter 4.
Prototyping of control laws (SIL/MIL, H IL, etc.)
(G. Le Solliec, IFP Energies nouvelles)
The hybrid vehicle generates new design and validation issues. It consists of building
blocks involving a range of disciplines (mechanical, thermal, electrotechnical, automatic,
etc.) whose intrinsic operation, apart from its own control, is highly dependent on the
interactions with the other subsystems.
Widespread use of prototyping and simulation tools for system design generally
reveals two major limitations:
- monodisciplinary virtual design, which does not take into account all constraints
of the complete vehicle from the outset, leading to a risk of the design failing to offer the
overall performance required.
- use of real/virtual mixity in a HiL approach, exclusively to validate the control sys
tem and not earlier during the design phase.
The procedure is intended to reproduce as early as possible, when designing a
component, the operating conditions of the vehicle in real use, by considering all its com
ponents (the abstraction level and the real/virtual mixity depending on the stage reached
in the overall vehicle design).
Prior to this phase, it will be worthwhile having a virtual experimentation labora
tory, which will receive all developments inherent to the vehicle (all component models
and the associated control strategies). This virtual laboratory will allow an initial compari
son between firstly the vehicle specifications and objectives and, secondly, a complete
dynamic simulation representative of the final operation.
Using a real-time platform including all vehicle simulation models and controllers, it
will be possible to compare on dynamic test bench (Figure E6.2 illustrates the case of an
engine test bench but an electric or full powertrain bench would be similar) a real compo
nent, whose design phase is already well advanced, with an emulation of the complete
vehicle behaviour, and therefore validate this component in a global approach.
Chapter 6 · Control ofhybrid vehicles 395
When developing an energy supervisor, these tools will allow:
- upstream design of each component to meet the overall objectives,
- validation of a real system and the associated control and supervision strategies,
- off-line then on-line energy optimisation,
- other obstacles such as comfort, safety and dependability to be taken into account.
Figure E6.2
Control law development methodology.
6.4.3 Forward Models of Hybrid Components
Forward models are used to validate energy management laws determined by backward mod-
els. The FQM are based on the same equations (6.9) to (6.17). The major differences involve:
- equation (6.9), used to calculate the term dVveh/dt as a function of T t; this introduces
a dynamic into the calculation
- the voltage and current, which, in the electric machine model, play a separate role,
unlike their behavior in the BQM; voltage normally determines the maximum torque
curves
- the U-R model of the battery, which is directly solved without the use of equation
(6.16)
396 Hybrid vehicles
The IFP-Drive library
(J.-C. Dabadie, IFP Energies nouvelles)
The IFP-Drive library is dedicated to global simulation of complete vehicles. The
characteristic timescale is the engine cycle. It can be used in particular to evaluate
various drive architectures (conventional, including more or less advanced degrees of
hybridisation, possibly even all electric) by calculating the energy consumption, polluting
emissions, performance, etc. The vehicle simulator is built by assembling sub-models,
representative of the various components, which are then completed by characteristic
data or using experimental test results.
The AMESim IFP-Drive library contains in particular the components required for
simulation of hybrid vehicles. Figure E6.3 illustrates the Toyota Prius 2 vehicle simulator,
built under AMESim using, mainly, components of the IFP-Drive library.
The main components of IFP-Drive used to model the vehicle include:
- t h e engine; the engine model available in the IFP-Drive library is a «mean-value»
engine based on consumption and pollutant emission maps; the computation
times for this type of global simulation are generally faster than «real time», i.e. the
time required for the simulation computations is less than the actual duration of
the phenomenon itself.
- t h e electric motors; as with the IC engine, mapping models are used: losses are
mapped according to engine speed and torque, the maximum and minimum tor
ques depend on the engine speed,
- the battery, with a model Ubat = UO - Rbat.lbat, the open circuit voltage and the
internal resistance depending on the SOC,
- the driver, who controls acceleration and braking, so that the vehicle speed follows
the speed profile imposed.
In this simulator, the strategy is calculated in an ECU model under Simulink. The
component interfacing with Simulink is located at the top centre of the diagram.
Chapter 6 · Control of hybrid vehicles 397
Figure E6.3
Sketch of the Prius 2 vehicle simulator (under AME Sim).
398 Hybrid vehicles
Prédit HyHiL programme
(A. Chasse, IFP Energies nouvelles)
The Prédit HyHiL project was launched in partnership with IFP Energies nouvelles
and the companies Renault, D2T and the Grenoble Electrical Engineering Laboratory
(G2ELAB). Its objective was to demonstrate and acquire a suite of development and
validation tools for hybrid powertrains.
Experimental validation of a hybrid powertrain and its control/supervision logics is
usually carried out on a test bench where the entire propulsion chain is installed. This
method is naturally highly representative of the conditions which will exist on board the
vehicle, but it can only occur in the last powertrain design and tuning phase when the
architecture has been completely defined and the components chosen. The ultimate goal
of the HyHiL project is to enable quantitative comparisons between different architectures
or configurations and between different components before manufacturing the chosen
prototype. To reach this goal, the project aims to develop an innovating tool built around
a high-dynamic engine test bench. The engine test bench is coupled to hardware-in-the-
loop a systems including dynamic models of the mechanical and electrical behaviour of
the rest of the powertrain. This solution (HyHiL test bench) offers extensive flexibility when
modelling the electric propulsion chain (choice and position of the battery and the electric
motor with its power electronics, etc.) and the mechanical transmission. In addition, the
physical presence of the engine guarantees representative results, for example in terms
of polluting emissions, especially in case of hybrid developments seen as integration
around the conventional powertrain (Figure E6.7).
The project also aims to develop simulation and optimisation tools complementary
to the HyHiL test bench, from pure simulation and off-line static optimisation of supervi
sion strategies, up to implementation of hardware-in-the-loop 5 systems including the
HyHiL test bench, as well as real-time co-simulation of strategies. This objective of this
tool chain is to provide continuity between the various component modelling levels and
offer the possibility of performing as many validations as possible regarding the behaviour
of models and their associated controllers on table before experimentation.
More precisely, this tool chain consists of:
-Hybrid Optimisation Tool (HOT, Figure E6.1): tool to calculate the ideally reach
able optimum of a hybrid traction chain in terms of consumption, emissions, etc.,
according to the formulation of paragraph 6.3 Optimum energy management"
and using the quasi-static models of paragraph 6.4 "Modelling of hybrid drive
trains for optimisation"; in this context, this is a quasi-static simulation tool,
- AMESim/Matlab co-simulation platform (Figure E6.4): simulation platform including
dynamic models of the traction chain components (engine, electric machines, etc.)
as well as the supervision and control layer,
5. Hardware in the loop: simulation method characterised by the combination of real components con-
nected to a simulated real-time part.
Chapter 6 · Control of hybrid vehicles 399
Figure E6.4
Breakdown between the various components/units present in a hybrid vehi-
cle. Application to the case of the hybrid Scenic vehicle for the co-simulation
platform.
■xMOD integration platform: simulation platform identical to the previous one, but
integrating the models and control using different software, transparently for the
user,
■ hardware-in-the-loop platform (HIL, Figure E6.5): HIL simulation platform with
real-time execution of models and the control layer; exchanges between the com
ponents and the general organisation are identical to the engine test bench in
order to validate the assembly on bench,
■ Hy-HIL engine test bench (Figure E6.6): high-dynamic engine test bench inte
grating an engine mechanically connected to a low-inertia generator in order to
represent the vehicle dynamic behaviour seen by the internal combustion engine;
this behaviour is dictated by the traction chain models, executed in real time.
400 Hybrid vehicles
Figure E6.5
Breakdown between the various components/units in a hybrid vehicle.
Application to the case of the hybrid Scenic vehicle for the HIL platform.
Figure E6.6
Breakdown between the various components/units in a hybrid vehicle.
Application to the case of the hybrid Scenic vehicle for the HyHiL test bench.
Chapter 6 · Control of hybrid vehicles 401
Figure E6.7
Presentation of the hybrid Scenic vehicle proposed by Renault as test case.
Source: Renault
6.5 OUTLOOK FOR A FUTURE GENERATION OF HYBRID VEHICLES
6.5.1 Extension of Optimal Control
The optimization problem presented in the section on optimal energy management can be
extended to include additional criteria and conditions other than minimization of consump-
tion alone. One criterion that can be quite obviously added is that of pollution emissions in
overall vehicle mission. In general, we can express the optimization criterion as the integral
(to be minimized) of a costfunction, L, that can combine the rates of consumption and emis-
sion (for example, flow in g/s) as a weighted sum:
(6.18)
Clearly, the compromise reached between consumption and emissions is strongly depend-
ent on the coefficients, c^ These parameters must be calibrated based on the weight we intend
to give to the two types of results.
Other criteria, driving comfort or the driver's experience, for example, can be joined to
(6.18) using weighting factors that are suitably defined. Unlike the equivalence factor, s,
these factors are completely arbitrary and can also be used to compare different drive systems
(gasoline versus diesel) in general [Ao et al, 2009].
402 Hybrid vehicles
Consumption and emissions do not depend solely on the engine's power point, as implic-
itly stated by (6.14), but also on its temperature as well as the temperature of the catalytic
converter. In a hybrid vehicle, the combustion engine is designed to stop for relatively
lengthy periods of time during which the temperatures decrease, which leads to higher values
of L at the start of the following phase of the working engine. To correctly manage this new
compromise, optimization must take into account the thermal state of the engine and its cata-
lytic converter. This is done by adding a second state variable to the SOC (x then becomes a
vector). This variable can be the temperature of the lubricating oil (the main factor of cold-
start overconsumption) or that of the catalytic converter (characteristic of high emissions due
to an inactive converter). In all cases, this leads to a modification of the Hamiltonian (6.7)
to include the variation in thermal state, in other words, the variation in stored heat energy
(thermal power, Pth):
(6.19)
and the introduction of a second Lagrange multiplier, s2. To calculate or estimate this value
while online, techniques similar to those introduced in Section 6.3 can be used.
6.5.2 Thermal Management
Overall thermal management in a hybrid vehicle is generally more complex than what we have
presented above. The cooling system of a combustion engine - which can be treated as an accu-
mulator of thermal energy - can be used as a heat source, primarily to warm the passenger
compartment. In a comprehensive approach to energy management, the withdrawal of calories
functions as a disturbance, as does the extraction of mechanical or electrical power intended for
auxiliary devices activated by the driver (for example, air-conditioning compressors [Chapter 5]).
In some designs, the cooling system is equipped with a supplementary storage system (a
reservoir or phase-change unit): control valves are used to store hot water while the engine
is running. This can then serve to accelerate the increase in temperature of the engine and,
eventually, the catalytic converter when starting. Cold storage systems are also being studied
that can free the system from the need for mechanical power, which is essential for satisfying
air-conditioning demands when the combustion engine is stopped [Valeo, 2004].
Additionally, the exhaust system in a combustion engine can be equipped with a heat recov-
ery system to produce electricity, as in a steam cycle (the "Rankine" system), or a system of
thermopiles (thermoelectric modules). At present such systems are being studied by various
component makers and manufacturers. Overall energy management is affected by the presence
of these systems because of the "gratuitous" injections of electrical energy into the DC bus as
well as the possible restoration of heat energy (a cold source in the case of thermopiles).
The combustion engine, however, isn't the only system that can store heat energy. We
also need to consider electric machines and the battery, with their relatively slow thermal
dynamics, and especially power electronics, with a much shorter dynamic response. As ana-
lyzed in Chapters 3 and 4, the characteristics of these components are strongly dependent on
their temperature. Considerable research activity is currently being devoted to optimizing the
design of cooling systems in electric systems, as well as their integration.
Chapter 6 · Control of hybrid vehicles 403
Figure 6.24
Energy flow during heat management of a complex hybrid.
6.5.3 Brake Management
The optimized approach presented in the above sections can be applied to the traction phase,
when T t > 0, and to the braking phase, when T t < 0. However, during this last phase, a
few additional considerations must be taken into account. In particular, we need to consider
dissipative braking ("conventional" brakes actuated by a hydraulic circuit), which is used in
parallel with "regenerative" braking (torque absorption by the electric machine(s) operating
as generators). For safety reasons associated with vehicle dynamics, the torque absorbed by
mechanical brakes must be redistributed between the two axles, while regenerative braking
normally acts on the drive axle alone or on two axles in the case of hybrids with distributed
drive systems (Chapter 1). Management rules must then distribute T t between both axles
(T t = Tbr j + Tbr 2) and then to the drive axle between dissipative braking and regenerative
b r a k i n g ( T b r , l = T b r , l , d + Tem)·
404 Hybrid vehicles
The distribution between the axles is similar to that found in conventional vehicles, as
described in Chapter 1. The most efficient choice is an ideal distribution along the equal-
adhesion parabola (variable distribution rate).
When distributing torque between dissipative and regenerative braking, the best solution
in terms of energy optimization (which would be the result of applying the ECMS method)
is still one that favors regenerative braking. However, this is effective only up to the point
where the capacity of the electric drive system (machine, electronics, storage) becomes satu-
rated, which the system will translate into a maximum torque setpoint (in absolute values
since the value is negative) for the electric machine. Additionally, for reasons of safety,
dissipative braking may be preferred for high torque values. On the other hand, low brake
torque values normally occur only with regenerative braking. The resulting braking strategy
is illustrated in Figure 6.25. In the case where the electric machine is placed upstream of
the transmission, the transmission gear ratio, q(t), during the braking phase can always be
selected using the ECMS approach, similarly to the traction phase, with, of course, the addi-
tional constraints shown in Figure 6.25.
This strategy requires electronic control specifically for the brake system. A less efficient
strategy in terms of energy and driver feedback, but simpler to implement, consists in the use
of constant distribution between the two dissipative contributors, as in conventional braking.
In this case, the distribution between the axles moves away from the equal-adhesion parabola.
Figure 6.25
Example of curves of optimal brake distribution between dissipative and regen-
erative braking in an electric drive vehicle with a front axle ("1")·
6.5.4 Recharge Management for Plug-in Hybrids
The optimized approach to energy management also remains valid for plug-in hybrids (Chap-
ter 5). In this type of application, the goal in adjusting SOC is not to maintain its value within
an optimal range of operation but, rather, to reach the minimal acceptable value of SOC,
Chapter 6 · Control of hybrid vehicles 405
xmin' a t t n e en(* °f m e vehicle's mission - for example, at the end of each day. A mission is
normally defined between two recharges from the mains.
The simplest approach consists in reaching xmin by using the system as an all-electric
system (charge-depleting mode) and retaining the minimum value of SOC by implementing
a hybrid strategy (charge-sustaining mode). Published results show, however, that greater
savings (10% and more) can be obtained with a so-called blended-mode strategy, which can
be described as the combination of optimal management - for example, with (6.8) - and a
variable definition of the target SOC, such as:
(6.20)
where D(t) is a parameter that describes the state of progress of the mission (distance, for
example) and Dtot, the total duration of the mission [Tulpule et al., 2010]. In this way, the tar-
get SOC will tend toward the minimum acceptable value at the end of the mission. Naturally,
the critical element in this approach lies in the evaluation of Dtot, which can be extremely
difficult except for customary movements known in advance or in connection with a naviga-
tion system.
6.6 CONCLUSION
To conclude this chapter, it will be useful to compare the contributions from optimized
energy management with heuristic solutions, especially for problems associated with overall
management of the powertrain system.
A first example is shown in Figures 6.26 and 6.27, which illustrate the contribution in
terms of reduced consumption throughout the cycle from an optimized thermal management
strategy using the approach shown in equation (6.19). The results of this ECMS type strategy
are compared with an ECMS that does not take into account engine temperature during a
cold start, and with a heuristic strategy based on empirical rules calibrated for different tem-
peratures of the combustion engine. The estimated benefit (during simulation) of optimized
management is 4.5% compared to the heuristic strategy.
406 Hybrid vehicles
Figure 6.26
Contribution of a thermal management (TM) strategy optimized using ECMS
in terms of consumption reduction over the cycle.
Source: [Lescot et al, 2010]
Figure 6.27
Contribution of a thermal management (TM) strategy optimized using ECMS
compared to a heuristic strategy.
Source: [HICEPS, online]
A second example is given in Figure 6.28. It shows the benefits in terms of fuel economy
and reduction in ΝΟχ emissions, measured (with the equipment described in inset D) using
the approach given in (6.18). Including ΝΟχ in the minimization criteria and increasing the
corresponding weighting factor (from Cj = 0.2 to Cj = 0.5) are also beneficial for consumption
to a certain extent. The gain compared to a simple stop-start strategy is also visible. It is worth
nothing that the reduction in unburned hydrocarbon emissions (HC) decreases as cl increases.
Chapter 6 · Control of hybrid vehicles
Figure 6.28
Contribution of an ECMS strategy with simultaneous minimization of con
sumption and NOx emissions. The a factor coincides with cl in (6.18). Abso
lute results: FC (liters/100 km) = 4.4 (all combustion), 4.2 (with stop-start),
3.5 (with ECMS, a = 0.2), 3.4 (with ECMS, a = 0.5); ΝΟχ (mg/km) = 108 (all
combustion), 100 (with stop-start), 93 (with ECMS, a = 0.2), 62 (with ECMS,
a = 0.5); HC (mg/km) = 120 (all combustion), 111 (with stop-start), 20 (with
ECMS, a = 0.2), 24 (with ECMS, a = 0.5).
On-line calculations according to the ECMS
(A. Sciarretta, IFP Energies nouvelles)
Some examples of calculations made using the on-line energy manager are given
below. They refer to the parallel hybrid architecture of the HyHiL programme (see insert
p. 412), with a main electric machine downstream from the gearbox.
As shown on Figure 6.17, the main input signals at a given time t are:
- the torque requested at the wheels, T t(t),
- the estimated SOC, Q(t),
- the estimated equivalence factor, s(t), for example according to (6.8),
- and the vehicle speed Vveh(t).
The outputs sent to the "engine state" unit are the two Hamiltonians He!(t) and
Hhyb(t), as well as the optimum gearbox ratio q°(t) and an index corresponding to the
engine torque k°(t) (if engine running).
ÇÈ| is relatively easy to calculate: according to definition (6.7), He) is equal to the
product of s(t) and the electrochemical power, which corresponds to purely electrical
operation. The calculation is carried out in four steps:
-calculation of the electric motor torque and speed, using (6.10) and (6.11):
4 0 8 Hybrid vehicles
(E6.1
- calculation of the electric power required, using a static map according to (6.15):
(E6.2
- calculation of the battery electric power, adding the power of the electric auxiliaries
to that of the electric machine:
(E6.3
- calculation of the electrochemical power according to (6.16) and (6.17), i.e. using
the internal resistance and open circuit voltage data, which normally depend on
the SOC and the temperature:
(E6.4
Hh b is more difficult to calculate: a vector of possible setpoints must first be defined.
Due to the hybrid architecture considered here, there is only one degree of freedom
which can be represented by the engine torque. Furthermore, the gearbox ratio must be
chosen. We therefore build a matrix of possible torque values, organised according to the
various permissible gearbox ratios (q = 1, ... nq) and according to a discretisation with
nk torque values (k = 1, ... nk), equally distributed between the maximum torque and the
minimum torque.
(E6.5
(the argument t has been removed from the variables which depend on £and q).
The hybrid Hamiltonian is then calculated according to the following steps, for each
combination (k, q):
- calculation of the engine torque and speed truly available:
(E6.6
- calculation of the fuel consumption, according to (6.14):
(E6.7
Chapter 6 · Control of hybrid vehicles 409
- calculation of the electric machine torque and speed, using a power balance at
the mechanical node:
(E6.8
- calculation of the electric power required, using a static map according to relation (6.15):
(E6.9
- calculation of the battery electric power, adding the power required to start the
engine to that of the electric machine, if necessary:
(E6.10)
- calculation of the electrochemical power, according to relations (6.16) and (6.17):
(E6.11
- (optional) calculation of additional terms AH(k, q) which will penalise combinations
that are undesirable as regards driving comfort, even though they are efficient from
the strictly energy point of view; for example, to avoid over-sudden changes in the
engine operating point, any change in the Hamiltonian can be penalised:
(E6.12
- calculation of Hhyb as minimum of the possible Hamiltonians, using definition (6.7):
(E6.13
The "engine state" unit compares He!(t) and Hhyb(t) and chooses Beng(t) according
to the method illustrated on Figure 6.19. Finally, the engine and electric motor torque
setpoints are calculated as follows:
(E6.14)
410 Hybrid vehicles
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Rousseau G, Sinoquet D and Rouchon P (2007) Constrained Optimization of Energy Management for
a Mild-Hybrid Vehicle. E-COSM - Rencontres Scientifiques De L'IFP, OGST Rev. IFP, 62, 4,
pp 623-634.
Sciarretta A, Back M and Guzzella L (2004) Optimal Control of Parallel Hybrid Electric Vehicles,
IEEE Transactions on Control Systems Technology, 12, pp 352-363.
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Chapter 6 · Control of hybrid vehicles 411
Serrao L, Onori S and Rizzoni G (2011) A Comparative Analysis of Energy Management Strategies for
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Comparative Study
of Hybrid Vehicles:
Greenhouse Gas
Emissions, Energy
Consumption,
and Cost
Frédérique Bouvart, Lionel Thellier, Simon Vinot \
In this chapter we address the impact of vehicle electrification from two perspectives: their
environmental and energy footprint, and their cost, both of which are expressed over the
entire vehicle lifetime.
Hybrid vehicles are distinguished from conventionally powered vehicles by several fac-
tors that should be taken into account in comparative evaluations if we intend to be compre-
hensive. The principal factors considered here are:
- The manufacture of specific drive components for hybrid vehicles (discrete or plug-
in), especially batteries and electrical machines. These components require a specific
manufacturing process, which can have an effect on energy consumption (expressed in
MJ) and greenhouse gas emissions (expressed in C02eq l) during vehicle manufacture.
- Vehicle maintenance during use. This can be affected by the fact that some compo-
nents, such as the battery, must be replaced during the vehicle's lifetime.
- Recycling of the vehicle and its components. In the case of hybrids, this can be affected
by the same factors as vehicle manufacture.
- Energy pathways. These may differ from those of conventional internal combustion
engine vehicles (ICE vehicles) if we consider, for example, a plug-in hybrid that will
consume hydrocarbons and electricity, whose resulting emissions will have to be taken
into consideration as part of the analysis.
- Economic impact of the vehicle and its use. This is important when preparing vehicle
penetration scenarios and must be taken into consideration.
1. The unit used to define the amounts of greenhouse gases emitted is equivalent-C02, or C02eq. This
means that the global warming potential of an emission or gas is expressed in comparison with C02.
The values given here in C02 represent the average of the major greenhouse gases as a function of
their global warming potential in 100 years as defined by the IPCC: 25 kgC02 /kg for methane (CH4),
298 kgC02eq/kg for nitrous oxide (N20).
414 Hybrid vehicles
- Impact on sensitive materials. Hybrid vehicles require a number of materials for the
manufacture of their components, such as lithium for the battery or rare earth metals
for electrical machines containing magnets. Knowledge of the impact of manufactur-
ing these vehicles on reserves and material production channels should also be taken
into account when preparing penetration trips.
This chapter presents the state of the art of our knowledge in comparing green
house gas emissions and the energy and economic balances of hybrid vehicles
(discrete and plug-in) with a reference ICE vehicle. In doing so, we have explained
the differences observed in the corresponding bibliography (existing studies) in terms
of prevailing computational methods and data. We then provide examples of results.
These correspond to specific cases that can be used to illustrate the positioning of
innovative transmission mechanisms. Consequently, they cannot be generalized or
presented as reference values. These calculations involve simulated - not real - vehi
cles under specific conditions of use, which differ from average or standardized use1.
As a result, the calculated elements are intended solely to illustrate the evaluation
process and clarify the subsequent analysis (identification of the most important con
tributing factors to the balances, parameters that justify the variability of these results,
illustration of comparisons of one type of drivetrain with another, and so on).
1. Refer to chapter 1 in this book for a detailed description of the vehicles and uses studied.
7.1 GREENHOUSE GAS EMISSIONS AND ENERGY CONSUMPTION
7.1.1 Methodology and General Principles
The environmental and energy impact of hybrid vehicles is evaluated by assembling an inven-
tory of energy consumption and greenhouse gas (GHG) emissions. This inventory is used
to evaluate the overall impact of a pathway in a so-called "cradle-to-grave" approach. Our
methodology will be applied to the reference ICE vehicle presented in chapter 1 and to two
hybrid vehicles, discrete and plug-in (5.5.1). These analyses can assist us in distinguishing:
- The complete energy pathway(s), including production, transport, and use ofthe energy
vector(s). Here, we refer to fuel lifecycles or "well-to-wheels" (WTW) analysis;
- The manufacture, maintenance, and end of life of the vehicle and its components. In
this case we will refer to vehicle lifecycle.
These two principal components - vehicle and energy pathway - taken into account in
our approach are illustrated in Figure 7.1. The environmental balances of pathways (products
and services, such as transport) are based on the principles of Life Cycle Assessment defined
in ISO 14040-44 [ISO, 2006a; ISO, 2006b].
The environmental impacts of each of the pathways are then converted to a "service" unit:
"identical vehicle performance over a distance of one kilometer" (generally using a standard-
ized driving cycle) in order to compare pathways on a common baseline.
Figure 7.1
Scope of environmental and energy balances.
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 415
416 Hybrid vehicles
The scope of these studies (Figure 7.1) is intentionally exhaustive to avoid any pollution
transfer, that is, to make sure that no improvement made to a transformative step results in a
degradation that might have a negative impact on the upstream or downstream environmental
balance. This approach is the only one that is relevant when comparing fuel versus alternative
vehicle pathways, especially when they differ significantly in their structure (for example, a
comparison of biofuel pathways involving agricultural processes with reference petroleum
pathways).
The study presented below is broader than most WTW studies, in which vehicle lifecycle
is generally not taken into account, since the associated environmental effects are the same
for all systems compared and this does not play a role in differentiating pathways. This is
effectively true whenever we compare the same ICE vehicle using a fuel made from crude oil
and a biofuel. However, comparison of hybrid vehicles - and especially plug-in or electric
hybrid vehicles - with conventional ICE vehicles must take into consideration vehicle lifecy-
cle as well as fuel lifecycles. The few studies available today estimate that the manufacture
of components specific to hybrid vehicles (discrete [HEV] and plug-in [PHEV]) results in an
increase in the impact of vehicle lifecycle on the order of 2% to 20% in terms of energy con-
sumption and 5% to 23% in terms of GHG emissions compared with an ICE vehicle [Baptista
et al, 2010; Wang et al, 2007]. The discrepancies between these estimates are, for the most
part, associated with differences in vehicle model, materials, the size of specific components
of the HEV (batteries especially), the assumptions used for the end of life of these compo-
nents (battery recycling), and even the place of production (type of electricity used).
7.1.2 Example of a Discrete Hybrid Vehicle
This section presents the GHG emission and total primary energy consumption balances for a
HEV and an equivalent conventional ICE vehicle while taking into account fuel and vehicle
lifecycles. In this case, the calculation of fuel lifecycle is relatively simple because the hybrid
vehicle consumes only gasoline.
7.1.2.1 Assumptions and Data
The "well-to-tank" impacts associated with the supply of gasoline are taken from a European
study, JRC/EUCAR/CONCAWE [JRC/EUCAR/CONCAWE, 2008]. These represent 0.14
MJ of consumed energy and 12.5 gC02eq emitted per MJ of gasoline distributed.
The vehicle in question was introduced in Chapter 1. Its consumption was evaluated by
simulating different types of use (5.5.1); the results are shown in Table 7.1. The C 0 2 exhaust
emissions released by vehicles are derived from consumption values. Here, consumption
and emission values correspond to the well-to-wheel impact.
It is important to note that the consumption values used in our calculations are slightly
higher than those used in the majority of reference studies for two reasons. First, these con-
sumption values are for a vehicle equipped with current engine technology, unlike some stud-
ies, which include significant performance improvements to vehicles and engines. Obviously,
such assumptions result in a reduction of fuel consumption of the same order of magnitude
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 4 1 7
for a discrete hybrid vehicle and a comparable reference vehicle. Therefore, the inclusion of
future improvements to vehicles and engines is unlikely to alter the ranking of vehicles and
reference vehicles presented in this chapter.
Table 7.1. Fuel consumption and C02 exhaust emissions released from the reference vehicle
and the discrete hybrid vehicle
Type of use Vehicle consumption C 0 2 exhaust emissions
L/100 km gC02/km
City use
Rural roads use Reference Discrete Reference Discrete
Motorway use hybrid hybrid
Average use
9.0 4.9 210 114
5.45 4.15 128 97
7.1 6.4 166 149
6.9 5 161 116
Secondly, as with the majority of well-to-wheel studies, the consumption values used in
this same European study [JRC/EUCAR/CONCAWE, 2008] were measured over the stand-
ard European cycle (NEDC), which is not representative of actual usage conditions. Studies
have shown that the differences in fuel consumption between a standard cycle and actual
usage can exceed 25% [Francfort, 2004]. Moreover, fuel consumption can also vary con-
siderably due to the use of comfort features, such as air conditioning, or the type of driving
involved (aggressive driving can increase vehicle fuel consumption by 10% to more than
100% compared with more relaxed driving behavior [Lenaers, 2009]).
For our study, however, we have chosen to work with relative consumptions in different
actual usage trips (urban, rural roads, and motorway, as defined in Chapter 1) in order to
evaluate the gains from the use of hybrid vehicles in each of these uses. Average use cor-
responds to a mixture of these uses (breakdown of distances traveled: 28% urban, 44% rural
road, and 28% motorway), which is representative of the use of passenger cars in Europe
today [André, 2004]. Our results, presented in the following sections, are not directly com-
parable from this point of view with the majority of well-to-wheel studies. However, we can
point out that in the European reference study [JRC/EUCAR/CONCAWE, 2008], consump-
tion of 5.9 liters/100 km on NEDC cycle was retained for the gasoline reference ICE vehicle
(gasoline vehicle 2010+), which is consistent with the consumption value considered for our
vehicle during average use (6.9 liters/100 km). The difference between the two values, on the
order of 15%, is typically associated with actual usage conditions, which are more unfavora-
ble than the standard NEDC cycle.
Concerning C 0 2 exhaust emissions from the different vehicles studied, we can state that
hybrid vehicles allow for a reduction compared with the conventional gasoline vehicle that
is obviously proportional to the decrease in fuel consumption. This gain varies with use. It
is significant for urban driving (46%), lower but still significant for rural roads (24%) or
motorway driving (10%).
418 Hybrid vehicles
Additionally, energy consumption and greenhouse gas emissions associated with the
vehicle's lifecycle vary with the source, depending on the segment and model of vehicle,
the materials it is made of, and their respective ends of life. In the present case, the data
associated with the ICE vehicle are based on a study conducted by EMPA [Althaus and
Gauch, 2010] on ICE, hybrid, and plug-in hybrid vehicles using ECOINVENT lifecycle
assessment data [Spielmann et al, 2007]. The ICE vehicle in question is a Volkswagen Golf
VI, whose characteristics are very similar to those of the reference vehicle used in this study.
The result is 5.62 tC02eq and 100.5 GJ (approximately 2.4 TOE) per vehicle for all steps of
manufacture, maintenance, and vehicle end of life (Table 7.2). This study has the advantage
of providing data for GHG emissions and energy consumption for the three types of vehicle
examined here (ICE, hybrid, and plug-in hybrid), of being recent, and of considering a vehi-
cle produced and used in Europe.
The EMPA study provides greenhouse gas emissions data and primary energy consump-
tion data for the hybrid electric vehicle (HEV), distinguishing between the effects of manu-
facture, maintenance, and vehicle end of life (vehicle lifecycle) and the impact associated
with the battery lifecycle. However, the battery is a crucial item for HEV and plug-in hybrid
vehicles (PHEV). Table 7.2 presents results from available studies concerning the lifecycle
of lithium-ion batteries. The studies in question focus primarily on battery use in the transport
sector, but some involve mass-market electronics as well [Ecoinvent, 2011].
As seen from the above table, the available studies show considerable disparity in green-
house gas emissions for 1 kWh of battery (up to a factor of 5). In contrast, total primary
energy consumption, whenever available, shows less significant differences (up to a factor
of 2.3). Similarly, with respect to the distribution of greenhouse gas emissions and primary
energy consumption between materials in the battery and its manufacture, the studies diverge:
- [Syrota et al., 2011] indicates that 70% of the energy consumption is associated with
battery manufacture and 30% with the production of materials used in the battery;
- [Ishihara et al., 2002] indicates that 81 -90% of the energy consumption and 77-80% of
the greenhouse gas emissions are due to manufacturing materials;
- [Sullivan and Gaines, 2010] indicates that, in the studies it examined, 30-80% of the
energy consumption is due to the production of materials used in the battery.
These findings are confirmed by the battery manufacturer Saft [Siret, 2011], which
pointed to the existence of considerable uncertainty at present about the lifecycle assessment
data of active elements, that is, certain specific metals required for battery manufacture. This
uncertainty affects not only the environmental impact from the large-scale extraction of these
materials but their availability as well [JRC, 2011]. Finally, experts have noted that some
publications (e.g. [Majeau-Bettez et al., 2011]) overestimate the number of charge cycles for
a lithium-ion battery and, therefore, its lifespan.
For battery lifecycle data we have retained the values found in [Armand and Tarascon,
2008] using data from [Ishihara et al, 2002]. These same values are recommended in the
study by the Centre d'Analyse Stratégique [Syrota et al, 2011]. The values retained for the
lifecycles of conventional ICE vehicles and HEV are shown in Table 7.3. However, to evalu-
ate the impact of greenhouse gas emissions from battery manufacture on vehicle lifecycle a
sensitivity analysis of the battery will be conducted.
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 4 1 9
Table 7.2. Summary of results of available lifecycle assessment studies of lithium-ion batteries
Reference Stages in the Details on the lifecycle Greenhouse gas
battery lifecycle stages evaluated emissions and total
primary energy
consumption (per
battery kWh)
[Majeau- Production of raw Extraction of resources and 250 kgC02eq/kWh l
Bettez et al, materials production of raw materials 221kgC02eq/kWh1
2011]
Battery manufacture Consumption of materials, energy,
[Held, 2011] and emission of pollutants
End of life
Production of raw No
materials
Extraction of resources and
Battery manufacture production of raw materials
Consumption of materials, energy,
and emission of pollutants
[Wang et al, End of life No 84 kgC02eq/kWh
2006] and Production of raw 2.05GJ /kWh
[Sullivan and materials Extraction of resources and
Gaines, 2010] production of raw materials eq
Battery manufacture
[Ishihara Consumption of materials and 75 kgC02eq/kWh
et ai ,2002] End of life energy 1.25 GT /kWh
via [Armand Production of raw
and Tarascon, materials No eq
2008]
Battery manufacture Extraction of resources and 56-200 2 kg
[Centre production of raw materials C02eq/kWh
d'Analyse End of life 0.90 GT /kWh
Stratégique, Production of raw Consumption of materials, energy,
2011] materials and emission of pollutants eq
[Ecoinvent, Battery manufacture Yes, battery recycled 54 kgC02eq/kWh
2011] 1.09 GT /kWh
End of life Extraction of resources and
Production of raw production of raw materials cq
materials
Consumption of materials and
Battery manufacture energy
End of life No
Extraction of resources and
production of raw materials
Consumption of materials, energy,
and emission of pollutants
No
[Notiere al., Production of raw Extraction of resources and 53 kgC02eq/kWh
2010] materials production of raw materials 0.92 GT/kWh
Battery manufacture Consumption of materials, energy,
and emission of pollutants
End of life Yes, battery recycled
1. The study does not give any values for total primary energy consumption.
2. The range of values shown is due to the different mixes of electricity production for battery manufacture (mini-
mum: Switzerland, maximum: China).
420 Hybrid vehicles
Finally, the impacts associated with vehicle lifecycle must be converted to the distance
driven (the unit of comparison in well-to-wheel evaluations); the total distance considered
being an important datum that directly affects results. We find that this value varies signifi-
cantly in existing studies. For example, the effects of vehicle lifecycle are amortized over
a distance of 100,000 km in the calculations performed by Toyota [Toyota, 2009], while
Argonne National Laboratory uses a value of approximately 250,000 km [Moon et al, 2006].
We can also cite the highly controversial study conducted by the automobile marketing com-
pany CNW Marketing Research, which went so far as to use a value of nearly 600,000 km
for a Hummer, which it compares with a Prius, where the distance traveled is estimated to
be approximately 175,000 km, a figure 3.5 times smaller [CNWMR, 2007]. Naturally, such
discrepancies will lead to significant differences in the results. For example, the battery is one
of the sensitive components of the hybrid vehicle, about which doubts persist concerning its
lifespan. In our example we have assumed the same lifespan for the vehicles studied and their
components: 150,000 km. This distance represents an "average" value that is consistent with
the assumptions used for the economic evaluation of these vehicles, which is shown below
together with the fleet tests conducted by American laboratories (5.5.2). However, the impact
of a battery replacement has been estimated in order to test the sensitivity of greenhouse gas
and energy consumption balances against this assumption. If two batteries are needed to cover
the total distance of 150,000 km, the greenhouse gas and energy balances over the lifecycle of
a vehicle are respectively increased by 15.5% and 12.9% compared with the reference vehicle.
A summary of the data sources and results for vehicle lifecycles is shown in Table 7.3.
Table 7.3. Comparison of impacts associated with the lifecycle of reference and discrete hybrid vehicles
Reference Hybrid vehicle Hybrid vehicle Sources
vehicle (one 1.3 k W h 2 (two 1.3 k W h 3
battery) batteries)
Greenhouse kgC02eq/ 5625 Vehicle 4: Vehicle: Ref. Veh.:
gas emissions véh. Vehicle manufacture: 6,400 6,500 [Althaus and
(%p/rICE) Gauch, 2010]
78% including including Hybrid Vehicle:
Maintenance: 14% battery: batteries: [Althaus and
Gauch, 2010]
End of life: 8% 100 200 and [Ishihara
(+ 13.7%) (+15.5%) et al, 2002]
gœ2eq/ 37.5
km driven 1 42.6 43.3
Total primary TOE/veh. 2.41 Vehicle: Vehicle: Ref. Veh.:
energy (% p/r Vehicle manufacture: 2.69 2.72 [Althaus and
consumption ICE) Gauch, 2010]
78% including including Hybrid Vehicle:
Maintenance: 20%> battery: batteries: [Althaus and
Gauch, 2010]
End of life: 2% 0.04 0.07
(+11.3%) (+12.9%)
MJ/km 0.67 and [Ishihara
0.75 0.76 et al, 2002]
e4 1
driven 1
1. For 150,000 km.
2. We assume that the initial battery pack is not replaced over the lifespan of the vehicle (150,000 km).
3. We assume that the battery pack is replaced once during the lifespan of the vehicle (150,000 km).
4. The values for greenhouse gas emissions and total primary energy consumption are fixed values taken from the
bibliography for a hybrid vehicle, whether plug-in or not. Only the battery is not included and can be distinguished.
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 4 2 1
7.1.2.2 Results
The results of greenhouse gas emission and primary energy consumption balances are shown
in Table 7.4 and Figures 7.2 and 7.3 for three situations of actual use and one situation
reflecting the average use of a vehicle in Europe, including the respective contributions of
vehicle lifecycle and fuel.
Table 7.4. Comparison of greenhouse gas and energy balances of vehicles and energy pathways
(reference ICE vehicle and discrete hybrid vehicle)
Vehicle Fuel lifecycle
lifecycle
Total From well From tank Total
to tank to wheel
Greenhouse gas balance (in gC02eq/km)
Urban 38 246 36 210 283
Reference Rural roads 38 150 22 128 187
vehicle Motorway 38 194 28 166 232
Average use 38 189 28 161 226
Urban 43 133 19 114 176
Hybrid Rural roads 43 114 17 97 156
vehicle
(1 battery l) Motorway 43 174 25 149 217
Average use 43 136 20 116 179
Urban 43 133 19 114 177
Hybrid Rural roads 43 114 17 97 157
vehicle
(2 batteries 2) Motorway 43 174 25 149 218
Average use 43 136 20 116 179
Primary energy consumption balance (in MJea/km)
Urban 0.67 3.26 0.40 2.86 3.93
0.24 1.74 2.66
Reference Rural roads 0.67 1.99 0.32 2.26 3.25
vehicle Motorway 0.67 2.58 0.31 2.20 3.18
0.22 1.55 2.52
Average use 0.67 2.51 0.19 1.32 2.25
0.28 2.03 3.06
Hybrid Urban 0.75 1.77 0.22 1.59 2.55
vehicle Rural roads 0.75 1.51
(1 battery l) Motorway 0.75 2.31
Average use 0.75 1.81
Urban 0.76 1.77 0.22 1.55 2.53
Hybrid Rural roads 0.76 1.51 0.19 1.32 2.26
vehicle
(2 batteries 2) Motorway 0.76 2.31 0.28 2.03 3.07
Average use 0.76 1.81 0.22 1.59 2.56
1. We assume that the initial battery pack is not replaced during the lifespan of the vehicle (150,000 km).
2. We assume that the initial battery pack is replaced once during the lifespan of the vehicle (150,000 km).
422 Hybrid vehicles
Figure 7.2
Well-to-wheel greenhouse gas balances for reference ICE and discrete hybrid
vehicles.
In general, we find that hybridization results in a reduction of greenhouse gas and energy
balances over the entire vehicle footprint on the order of 36-38% for city use, 15-16% for
local driving, and 19-21% for average use, but that the use of a hybrid vehicle on the highway
provides no significant environmental gain (Figures 7.2 and 7.3). If we assume that the bat-
tery is changed once during the hybrid vehicle lifespan in question (150,000 km), the gains
in terms of greenhouse gas emissions and energy consumption are affected very little overall,
the battery of the discrete hybrid vehicle being limited to 1.3 kWh of energy.
The differences we observe in the well-to-wheel greenhouse gas and energy balances of
the hybrid vehicle compared with an ICE vehicle are the result of two antagonistic changes:
- An increase in the impact caused by specific components of the hybrid vehicle on
"vehicle lifecycle" (Table 7.3). However, this increase remains limited to 13.7%
(15.5% if two batteries are used) for greenhouse gas and 11.3% for energy (respec-
tively 12.9%);
- A significant reduction in the impact on "fuel lifecycle" proportional to the reduction
in fuel consumption (Table 7.1).
Overall, it appears that the small contribution of the "vehicle lifecycle" component in
well-to-wheel balances (17-33% of the energy balance and 13-27% of the greenhouse gas
balance for a hybrid vehicle) will lead to overall reductions that remain close to the gains in
fuel consumption (Figures 7.2 and 7.3).
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 4 2 3
Figure 7.3
Total well-to-wheel primary energy consumption balances for reference ICE
and discrete hybrid vehicles.
These conclusions corroborate the results of other studies of hybrid vehicles, which also
indicate that the increase in greenhouse gas emissions and energy consumption in manufac-
turing and hybrid vehicle end-of-life are largely compensated by the benefits associated with
a reduction in fuel consumption [Moon et al, 2006; Toyota 2009].
Table 7.2, showing different values of greenhouse gas emissions associated with a one kil-
owatt-hour lifecycle for a lithium-ion battery, reveals the considerable disparity among the
sources. If we consider the maximum value shown (250 kgC02eq/kWh of battery), we find that:
- The greenhouse gas emissions associated with vehicle lifecycle are increased by 5.2%
and 10.3% respectively, depending on whether or not the battery pack is replaced dur-
ing the life of the vehicle.
- The gains in greenhouse gas emissions of the hybrid vehicle compared with the ICE
vehicle during the various usage trips are hardly affected. In fact, a difference of only
1 to 2% is found.
However, even if the conclusions agree, it is important to remember that the greenhouse
gas and energy balance values obtained in this study are not directly comparable with those
found in the majority of well-to-wheel studies (especially [JRC/EUCAR/CONCAWE, 2008]
and [ADEME, 2011]), primarily for the following reasons:
- We have taken into account the impacts of vehicle lifecycle, which leads to an increase
in the balances.
424 Hybrid vehicles
We have expressed fuel consumption for actual conditions of use, whereas the major-
ity of studies use consumption values that are measured over the standard cycle, which
are lower (7.1.2.1). The same is true for C 0 2 exhaust emissions, which, consequently,
cannot be compared with the European objectives [Official Journal of the European
Union, 2009] for average C 0 2 emissions values for all vehicles sold (120 gC02/km in
2012 and 95 gC02/km in 2020 for new private cars).
We have considered the impact of hybrid systems purely for comparison purposes,
without taking into account possible improvements to the vehicle (aerodynamics, roll-
ing, etc.) or its internal-combustion engine. Consequently, some studies report greater
well-to-wheel gains than those presented here by comparing an ICE vehicle with a
specific hybrid vehicle with a number of innovations. This is the case with Lenaers, for
example, who compares a Peugeot 307 with a Toyota Prius [Lenaers, 2009].
7.1.3 Plug-in Hybrid Vehicle
This section presents the methodology (assumptions and data) used to establish greenhouse
gas emission and total primary energy consumption balances for a plug-in hybrid vehicle and
an equivalent ICE vehicle by considering the lifecycles of the energy pathways and that of
the vehicle. The calculation of "fuel" lifecycle is more complex. In this case the gasoline and
electricity energy pathways of the hybrid vehicle using different usage assumptions must be
taken into account. Examples of results are provided at the end of the section to illustrate the
process. The plug-in hybrid vehicle has the benefit of being able to transfer part of its fossil
fuel consumption to electricity. However, as we saw earlier (5.3.2.4), the energy transfer rate
is extremely variable. It depends on the distance traveled between two charging operations,
so the vehicle must be evaluated over a given trip, taking this distance into account. Below
we present the case of a plug-in hybrid vehicle, using the distance examples indicated (5.5.1).
It is important to bear in mind that the resulting balances concern the example used and can-
not be generalized or presented as reference values.
7.1.3.1 Assumptions and Data
When establishing greenhouse gas emission and energy consumption balances for a plug-in
hybrid vehicle compared with those for an ICE vehicle, considerable attention must be paid to:
- the vehicle use and the distance between two charging operations, fuel consumption
and electricity consumption for the plug-in hybrid vehicle being highly dependent on
this choice;
- the methodology and data used to calculate the environmental impacts associated with
the supply of electricity for future use in road transport;
- the data selected for the environmental balance of the lifecycle of the plug-in hybrid
vehicle (production of raw materials, components, vehicle manufacture, maintenance
and end-of-life).
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 4 2 5
Discrepancies among one or more of these three items almost always explain the variability
in the results of the greenhouse gas and energy balances of plug-in hybrid vehicles from one
study to another. In this section we focus on explaining all the assumptions and data needed to
prepare such balances, while supporting our argument with a review of the literature.
Concerning the impact of vehicle lifecycle, the discrepancies observed among the results
found in existing studies of plug-in hybrid vehicles can be explained using the same approach
used for the HEV (differences of segment and vehicle model, construction materials, and their
respective ends of life). However, the existing uncertainty in the environmental assessment of
batteries carries more weight in the assessment of the PHEV than it does for the HEV.
In our study, the approach used for the plug-in hybrid vehicle is identical to that for the
hybrid electric vehicle (7.1.2.1). The variations in greenhouse gas emissions and energy con-
sumption associated with the lifecycle of the plug-in hybrid vehicle are always determined
by using the EMPA study [Althaus and Gauch, 2010] and by taking into consideration the
characteristics of the more energetic battery of the plug-in hybrid vehicle. As with the hybrid
electric vehicle, the plug-in hybrid vehicle is equipped with a lithium-ion battery and its
lifecycle data will always be based on the values given in the Ishihara [2002] study used by
Armand and Tarascon [2008]. As seen in Table 7.5, the reference vehicle is retained.
Table 7.5. Comparison of the impacts associated with the lifecycle of ICE and plug-in hybrid vehicles
Plug-in hybrid Plug-in hybrid
vehicle1 vehicle1
Reference vehicle (one 8 kWh (two 8 kWh Sources
battery 3) batteries 4)
Greenhouse kgC02eq/ 5625 Vehicle5: Vehicle5: Ref. Veh.:
gas veh. Vehicle 6,900 7,500 [Althaus and
emissions (% p/r manufacture: 78% Gauch, 2010]
ICE) Maintenance: 14% including including Hybrid Vehicle:
End of life: 8% battery: batteries: [Althaus and
Gauch, 2010]
gC02eq/km 37.5 600 1,200 and [Ishihara
traveled 2 (+ 23%) (+ 33%) et al, 2002]
Total TOE/veh. 2.41 46 50 Ref. Veh.:
primary (% p/r Vehicle [Althaus and
energy ICE) manufacture: 78% Vehicle: Vehicle: Gauch, 2010]
consumption Maintenance: 20% 2.90 3.13 Hybrid Vehicle:
MJ /km End of life: 2% [Althaus and
traveledl including including Gauch, 2010]
0.67 battery: batteries: and [Ishihara
et al, 2002]
0.24 0.48
(+ 20%) (+ 30%)
0.80 0.87
1. Hybrid electric vehicles and plug-in hybrid vehicles are considered to be technically similar, only the battery differs.
2. Calculated on the basis of 150,000 km for comparison with Table 7.3.
3. We assume that the initial battery pack is not replaced over the vehicle lifespan (150,000 km).
4. We assume that the initial battery pack is replaced once over the vehicle lifespan (150,000 km).
5. The values for greenhouse gas emissions and total primary energy consumption are fixed values taken from the
bibliography for a hybrid vehicle, whether plug-in or not. Only the battery is not included and can be distinguished.
426 Hybrid vehicles
The well-to-tank impact associated with the supply of energy for the plug-in hybrid
vehicle includes:
- greenhouse gas emissions and energy consumption associated with supplying gasoline
(extraction, production and transport of crude oil, production in refinery and transport/
distribution of the gasoline produced);
- the greenhouse gas emissions and energy consumption associated with the supply of
electricity (extraction, production, and transport of energy resources necessary for the
operation of electrical power plants, and the production and transport/distribution of
electricity).
Data for greenhouse gas emissions and energy consumption associated with the sup-
ply of gasoline vary according to the sources but have no appreciable impact on the results
of the complete balances for a plug-in hybrid vehicle, which is much more sensitive to other
factors previously mentioned (values retained for vehicle lifecycle, electricity supply, vehicle
use, and associated gasoline and electricity use). The data used are identical to those con-
sidered for the discrete hybrid vehicle and the ICE vehicle: 0.14 MJ energy consumed and
12.5 gC02eq emitted per MJ of gasoline distributed [JRC/EUCAR/CONCAWE, 2008].
The contribution of greenhouse gas emissions and energy consumption associated with
the supply of electricity in the complete PHEV balances varies considerably from study to
study. This is clearly associated with the electricity production technologies considered and,
more specifically, the nature of the primary resource (enriched uranium, coal, natural gas,
biomass, etc.) and the efficiency of the electrical power plant. Greenhouse gas balances for
energy production from biomass or de-carbonated non-renewable resources (nuclear) are
obviously much lower than those for production from fossil fuel resources, such as natural
gas, fuel oil and, a fortiori, coal. Moreover, for the same fossil resource, an improvement in
the efficiency of the power plant will have the effect of reducing the energy and greenhouse
gas balances of the electrical pathway.
In the large majority of lifecycle assessment studies on electric vehicles, the electric-
ity consumed per vehicle is assumed to be produced from a mix of technologies and
electrical pathways. To define this mix, several approaches are possible.
The most conventional consists in considering a representative mix of the average elec-
tricity production at a given scale and date (generally, on the national scale for the present
situation, using recent statistical values). For France, in 2008, this results in a mix that con-
sists primarily of nuclear electricity (nearly 80%) and hydropower electricity (12%), with
production from fossil resources representing approximately only 9%. Naturally, the choice
of a highly de-carbonated mix leads to a relatively low greenhouse gas balance for electric-
ity supply (estimated to be between 80 gC02eq/kWh of electricity [Syrota et al, 2011] and
146 gC02eq/kWh [European Commission, 2011], depending on the source) compared with
other countries where the contribution of production from fossil resources is much larger.
Other approaches are also possible for defining the mix of electricity production tech-
nologies for a given use. RTE and ADEME [RTE and ADEME, 2007] have contributed
to the development of novel approaches, the most relevant of which for our purposes (new
use of electricity in the transportation sector) is the so-called "marginal" approach. This
approach consists in identifying - taking into consideration existing electricity production
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 4 2 7
equipment - the source required to satisfy new demand (the least expensive source able to
satisfy additional demand). The nature of the last kilowatt-hour used (or marginal kilowatt-
hour) depends, among other things, on instantaneous demand, available capacity, and net-
work constraints. The calculations made by ADEME and RTE show that 25% of the time
(essentially during night and weekend periods) an increase in consumption in France does
not lead to additional greenhouse gas emissions. However, for the remaining 75% of the
time, the compensation takes place with thermal means of production (coal, gas, fuel oil),
which leads to an increase in GHG emissions because the specific emissions associated with
these technologies are high (between 400 and 950 gC02eq/kWh). Based on these estimates,
the marginal greenhouse gas content of a kilowatt-hour can vary from 450 to 700 gC02eq/
kWh in France [RTE and ADEME, 2007], compared with a much lower value used for the
average current French mix (values given above). There are however a number of areas,
especially in Europe and the United States, where electricity production is primarily based on
fossil resources, where the discrepancy between the GHG emissions given by each of the two
approaches (marginal content and average mix) is much lower than it is in France.
Compared with the conventional "average mix" approach, the marginal approach has
the advantage of emphasizing the effect of the charging time on emissions and consump-
tion associated with electricity production. In France, peak electricity consumption generally
occurs between 6 and 8 p.m. [RTE, 2010], a period when the concentration of greenhouse
gas per marginal kilowatt-hour is at its highest because all sources are involved. Therefore,
the peak consumption period would be the worst period of time to charge a vehicle from the
point of view of GHG emissions.
Finally, regardless of the methodological approach used (average or marginal mix), the
most recent factors influencing the nature of the electrical mix are:
- The geographic context and scale (national versus regional): for example, the structure
of the French electricity production stock is very different from that of other European
countries. Similarly, electricity transport and distribution networks and, consequently,
line losses, are different.
- The temporal context, because the electricity supply network evolves over time. The
study of future contexts complicates evaluations because additional assumptions must
be made concerning future developments in the electricity supply network.
As a result, the variations in GHG and energy balances when supplying electricity
for electric vehicles can be explained by the following factors:
- the nature of the mix of electric technologies, which is itself associated with:
• the methodology used (average versus marginal mix);
• the geographic context;
• the temporal context;
- the assumptions made about vehicle charge schedule if we consider a marginal mix;
- the description of production technologies (primarily the efficiencies of power plants
and emissions).
For our study, we have chosen to base our calculations on average electricity produc-
tion mixes. The marginal approach is equally useful and would benefit from further develop-
ment in the future, especially because it can be used to evaluate the change in C 0 2 emissions
428 Hybrid vehicles
per kilowatt-hour as a function of the charge schedule. In our calculations of energy and
GHG balances, no specific assumption about the charge schedule was made, unlike the cost
evaluation described in this chapter, for which it was assumed that charging took place at
night. The use of the average mix approach may, in the case of France, appear conservative
to the extent that the contribution of nuclear power to the marginal nighttime mix is greater,
which would result in a lower GHG balance for a plug-in hybrid vehicle charged at night.
Yet, the conventional approach alone can illustrate the variability of plug-in hybrid vehicle
balances in electricity mixes, to the extent that several geographic areas are considered. In the
present case, France and the United States were chosen to represent strongly de-carbonated
and strongly carbonated mixes respectively. The European context has been studied exten-
sively as it represents an intermediate situation. China is an interesting "extreme" worth
studying because of its current electricity mix, which is more strongly carbonated than that
of the United States. However, the lack of prospective data for the area prevent evaluation
of GHG and energy balances out to 2030, which is the reason why we have not taken it into
consideration here.
Energy and GHG balances for electricity generation in these different countries take into
account the supply of fuel and power plant operation. For our study, the respective contri-
butions of the electricity production technologies in the average annual mix are taken from
Eurostat [European Commission, 2010] for France and Europe (EU 27) in 2008 and the
World Energy Outlook [IEA, 2010b] for the United States in 2008. The GHG and energy
balances of each production technology are extracted from the LCA ECOINVENT database
[Frischknecht et al, 2007]. The data for prospective mixes in France and Europe in 2030 are
taken from a study conducted by IFP Energies nouvelles [IFP Energies nouvelles, 2010],
based primarily on a report by the European Commission [European Commission Direc-
torate-General for Energy and Transport, 2008]. Additionally, the deployment of carbon
capture and storage (CCS) technologies using assumptions from the International Energy
Agency (IEA) [IEA 2009a; IEA 2009b], together with the lifespans of existing power plants
were taken into account when defining electricity mixes for 2030. Lastly, the mixes and the
GHG energy balances of electricity production in the United States by 2030 are extracted
from the LCA GEMIS database [Öko Institut, 2010].
Finally, losses from electricity transport over distribution networks are also included in
our evaluations. These losses are calculated from IEA statistics [IEA, 2010] for the year 2008
and are assumed to be equivalent in 2030. The values used are shown in Table 7.6.
Table 7.6. Percentage losses associated with the electrical distribution network in 2008
Geographic zone Losses associated with
the distribution network
Europe
France 6.7%
6.0%
United States 6.0%
Source: IFP Energies nouvelles, based on [IEA, 2010a]
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 4 2 9
Figure 7.4
The electrical mixes and emissions used for this study.
Figure 7.4 presents the energy and GHG balances from the various electrical mixes used.
It indicates the relative importance of the method of electricity production in the overall mix
and identifies the proportion of non-renewable energy in total primary energy consumption.
These balances take into account impacts at production sites (electrical power plants) as well
as the various upstream (extraction and transport of fuel to the power plant) and downstream
(transport and distribution of the electricity produced) steps involved.
430 Hybrid vehicles
It is worth noting that the GHG balance obtained for existing electricity supply in France
appears high (105 gC02eq/kWh) compared with other values commonly used (80 gC02eq/
kWh in [Syrota et al, 2011]). Nonetheless, we have retained this value to provide a degree of
uniformity in the sources used (creation of 2008 production mixes France and Europe from
the same statistics database Eurostat and data on the current production technologies taken
from the same Ecoinvent database). Additionally, the order of magnitude seems relevant
since other studies make use of higher figures (146 gC02eq/kWh in [European Commission,
2011]).
Finally, to evaluate well-to-wheel GHG and energy balances for a plug-in hybrid vehicle
compared with an ICE vehicle, assumptions must be made about vehicle use and data col-
lected about the corresponding fuel and electricity consumption. This information can be
used to calculate the tank-to-wheel impact, in other words, the impact associated with the use
of energy in the vehicle. When measuring energy consumption, this corresponds to fuel and/
or electricity consumptions in the vehicle; when measuring GHG emissions, this corresponds
to C 0 2 exhaust emissions calculated directly from fuel consumption.
As indicated earlier, the consumption balance for a plug-in hybrid vehicle must be deter-
mined for a specific distance traveled (the trip), between two consecutive battery charges.
To illustrate the impact of daily distance traveled on the environmental balances of a plug-
in hybrid vehicle, we have used two different trips (40 and 75 km) made daily for 15 years
(5.5.1). The vehicle consumption and emission data for the two trips are summarized in
Table 7.7. These tank-to-wheel consumption and emission values also include, for electric-
ity, the efficiency of the charger in the vehicle.
Table 7.7. Energy consumptions and C02 emissions of ICE and plug-in hybrid vehicles for two different trips
Vehicle consumption(s)
Reference PHEV Trip
Type of use Reference PHEV
Trip 1:40 km Gasoline Gasoline Electricity Gasoline Gasoline Electricity
Trip 2: 75 km (L/100 km) (L/100 km) (kWh/km) (L/trip) (L/trip) (kWh/
trip)
5.9 0.4 0.142 2.36 0.16 5.7
5.7 2 0.076 4.27 1.5 5.7
C 0 2 vehicle exhaust emissions
Type of use Reference PHEV Trip
(§C°2eq/km) (§C°2eq/km)
Trip 1:40 km Reference PHEV
Trip 2: 75 km 137 9 (§C°2eq/triP) (§C°2eq/triP)
133
5,480 378
47 9,975 3,542
Chapter 7 · Comparative study ofhybrid vehicles: greenhouse gas emissions, energy consumption, and cost 43
The difference in distance traveled in our two examples of daily travel lead to two com-
pletely different distance values over the lifetime of the vehicle: 120,000 k m 2 et 225,000 km3.
Note that these values bracket the 150,000 km considered for the hybrid electric vehicle.
Such an approach is unconventional for a well-to-wheel study; however, it allows us to pro-
vide an assessment of a plug-in hybrid vehicle and reveal the effect of a change in the daily
distance traveled. This approach will be taken up again in the economic evaluation developed
later in this book.
7.1.3.2 Results
Based on the assumptions and data described in the previous sections, the results of GHG and
primary energy consumption balances for the ICE vehicle and the plug-in hybrid vehicle are
given in Table 7.8 and in Figures 7.5 and 7.6. Once again, these results are for specific cases
and are intended merely to illustrate the environmental evaluation procedure and clarify any
subsequent analysis (identification of the most important contributing factors to environmen-
tal balances, parameters that justify the variability of these results, etc.).
2. Daily distance of 40 km traveled 200 days a year (number of days worked) for 15 years (vehicle
lifetime).
3. Daily distance of 75 km traveled 200 days a year (number of days worked) for 15 years (vehicle
lifetime).
lifecycle T
Greenhouse gas balance (in g C02ea/km) Well to Tank to Total/km Electricity Vehicle
tank wheel 161 supply consumption
Reference vehicle 48
24 137 156 0 0
Europe 58 29 55 78 0
29 55 15 0
2008 France 58 55
58 29 55 104 0
United 58 55
29 55 55 0
Trip 1 States 29 17 0
PHEV Europe
29 77 0
2030 France 58
58 23 133 0 0
United 8 47 41 0
States 8 47 0
8
Reference vehicle 25 8 47 0
55
Europe 31 8 47 0
8 47 29 0
2008 France 31 9
31 8 47 0
United 31 41
Trip 2 States
PHEV Europe
2030 France 31
31
United
States
Table 7.8a. Results of GHG balance for the vehicles considered (reference ICE and PHEV) for trips 1 a
Balance per kilometer
Energy lifecycle
Vehicle Gasoline Electricity
Table 7.8b. Results of energy balances for the vehicles co
Bala
En
Vehicle Gasoline
lifecycle Well to Tank to
tank wheel
Tota
Total primary energy consumption balance (MJeQ/km)
Reference vehicle 0.9 0.3 1.9 2
Europe 1.0 0.0 0.1 0
2008 France 1.0 0.0 0.1 0
1.0 0.0 0.1 0
United 1.0 0.0 0.1 0
Trip 1 States
PHEV Europe
2030 France 1.0 0.0 0.1 0
1.0 0.0 0.1 0
United
States
Reference vehicle 0.5 0.3 1.8 2
Europe 0.5 0.1 0.6 0
Trip 2 2008 France 0.5 0.1 0.6 0
0.5 0.1 0.6 0
United 0.5 0.1 0.6 0
PHEV States
Europe
2030 France 0.5 0.1 0.6 0
0.5 0.1 0.6 0
United
States
onsidered (reference ICE and PHEV) for trips 1 and 2 Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 433
ance per kilometer
nergy lifecycle For illustrative
purposes only:
Electricity Total/km
totalfor the
al/km Electricity Vehicle 3.0 trip used
supply consumption 2.9
2.9 MJ/trip
2.2 0.0 0.0 3.2 122
0.1 1.2 0.6 2.4 117
0.1 1.2 0.6 2.5 117
2.3
0.1 1.4 0.6 2.5 127
2.2
0.1 0.7 0.6 2.2 97
0.1 0.8 0.6 2.4 102
1.9
0.1 0.6 0.6 2.0 94
1.9
2.1 0.0 0.0 191
0.7 0.6 0.3 166
0.7 0.6 0.3 166
0.7 0.8 0.3 176
0.7 0.4 0.3 146
0.7 0.4 0.3 151
0.7 0.3 0.3 143
434 Hybrid vehicles
A. Greenhouse Gas Emission Balance
The evolution of GHG emissions per kilometer based on the distance traveled, the vehicle
type, and the energy mix is shown in Figure 7.5. We find that complete GHG balances for the
plug-in hybrid vehicle are better than those for the reference vehicle studied for all the elec-
tricity mixes used in this study. The GHG gains compared with the reference (figures located
at the top of the barsin Figure 7.5) range, for trips 1 and 2, from 17 to 60% and from 22 to
48% respectively. Note that the results shown in Figures 7.5 and 7.6 correspond to plug-in
hybrid vehicles equipped with a single battery over their lifetime. The analysis of the impact
of battery replacement shows low sensitivity to this parameter, GHG and energy balances
increasing by no more than 1 to 3.5% overall, depending on the geographic region examined.
1. GHG emissions due to vehicle electricity consumption are nil in all the cases.
Figure 7.5
Greenhouse gas balances for the reference vehicle (RefV.) and the plug-in
hybrid (PHEV) in trips 1 and 2.
For trip 1, the 2010 gains vary with the electrical mix used. The minimum gain is 17%
(United States) and the maximum gain is 60% (France), with an intermediate value (30%) for
Europe as a whole. Given the expected evolution of electrical mixes by 2030, we can predict
an increase in gains in GHG balances compared with an ICE vehicle due to the increased
use of renewable energy, improvements in efficiency, reduced use of fossil-fuel based power
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 4 3 5
plants, and the deployment of technologies for C 0 2 capture and storage. Additionally, future
GHG balances for plug-in hybrid vehicles compared with a current reference vehicle will
probably be lower than those shown in Figure 7.5. That is because the results shown for 2030
only reflect sensitivity to the evolution of the electricity mix, no assumption of improved
vehicle performance was taken into account. The reduction of vehicle energy consumption
by that time will certainly result in additional gains.
Finally, reduced gasoline consumption in plug-in hybrid vehicles compared with an ICE
vehicle has several consequences for overall GHG balances. Aside from reduced exhaust
emissions, the contribution of gasoline supply (well-to-tank) is obviously also lower. How-
ever, a new component (electricity) appears. Nonetheless, the GHG emissions from electric-
ity consumption and lower gasoline consumption contribute to lower well-to-wheel GHG
emissions than that of the reference vehicle in all cases. However, the gain varies with the
electricity mixes used.
For trip 2 (longer distance traveled), the gains in GHG emissions differ from those in
trip 1. The evolution of gains varies with geographic region but the ranking of balances
remains the same: the largest gain is obtained in France, the smallest in the United States.
This is explained by the fact that the distance for minimizing GHG emissions involved in
production of the ICE vehicle and the plug-in hybrid vehicle differs in trip 1 (120,000 km)
and trip 2 (225,000 km), which leads to a difference in GHG emissions per kilometer driven
over the lifecycle of the vehicle. From the point of view of GHG emissions associated with
energy consumption (gasoline and electricity) per kilometer traveled, trip 2 reduces the gains
of the plug-in hybrid vehicle compared with the ICE vehicle and relative to trip 1. This results
from the fact that, over this longer distance traveled, fuel consumption plays a much greater
role than electricity consumption. Additionally, the more the electricity mix is de-carbonated,
the greater the reduction. In terms of GHG emissions in the energy lifecycle, trip 1 is more
beneficial than trip 2, which highlights the value of adjusting the autonomy of the plug-in
hybrid vehicle for charge depletion with the daily distance traveled.
In conclusion, we find that the source of electricity (associated production technologies)
is the most sensitive parameter in evaluating the overall GHG balance for a given plug-in
hybrid vehicle compared with an ICE reference vehicle. For the cases considered so far
(France, Europe, and the United States), the complete GHG emissions balances are, in all
cases, significantly lower than those of the gasoline vehicle. However, this conclusion can-
not be generalized to all situations. For example, if electricity is produced exclusively from
coal (existing technologies without carbon capture and storage), the overall GHG balance
of the plug-in hybrid vehicle studied would then be approximately 26% greater than that of
the gasoline ICE vehicle. The overall effect of the plug-in hybrid vehicle on GHG emissions
would then be negative and its only benefit would be the possible elimination of local pollu-
tion (atmospheric and noise) in urban areas.
As with the discrete hybrid vehicle, we can incorporate the impact of the disparity in the
values of GHG emissions associated with the lifecycle of a kilowatt-hour of Li-ion battery
(Table 7.2). If we consider the maximum value shown (250 kg C02eq/kWh of battery), we
find that:
436 Hybrid vehicles
- the GHG emissions associated with the lifecycle of the plug-in hybrid vehicle com-
pared with the ICE vehicle are increased by 48 and 83% respectively, depending on
whether the battery pack is replaced during the life of the vehicle
- the reductions in GHG emissions of the plug-in hybrid vehicle compared with the ICE
vehicle for different use trips are somewhat affected. A deviation of 13 to 56% and 18
to 44% is found for trips 1 and 2, respectively, but an increase in GHG emissions is
always present in the countries studied.
B. Energy Consumption Balances
Figure 7.6 shows the total primary energy consumption (renewable and nonrenewable) asso-
ciated with the reference vehicle and the plug-in hybrid vehicle studied in trips 1 and 2,
while distinguishing well-to-tank and tank-to-wheel energy consumptions. The percentage
of nonrenewable energy for the electricity mixes considered (Figure 7.4) ranges from 94 to
98% and from 86 to 92%, respectively, for 2010 and 2030. As a result, although electricity
replaces gasoline in varying amounts, depending on the trip, the great majority of consumed
energy still comes from nonrenewable sources.
Figure 7.6
Total primary energy consumption balances for ICE (RefV.) and plug-in hybrid
(PHEV) vehicles in trips 1 and 2.
Chapter 7 · Comparative study of hybrid vehicles: greenhouse gas emissions, energy consumption, and cost 4 3 7
It appears that total primary energy consumption varies with the electricity mix consid-
ered. Changes compared with the reference vehicle range from - 23 to + 5% and from - 25
to - 8%, respectively, for trips 1 and 2.
In trip 1, the ranking of energy balances based on geographic region (and, therefore,
based on the mix considered) differs from that observed for GHG balances. Indeed, depend-
ing on the respective contributions of the various electricity production technologies, the
overall energy efficiency of the electricity mixes will vary (Figure 7.4). The efficiency of a
coal-fired power plant or a nuclear power plant today is approximately 30%, which is much
lower than that for a hydropower plant (95%). This explains why the energy balances of the
United States cases are slightly worse than those of Europe and France in 2008.
While the benefits of plug-in hybrid vehicles compared with ICE vehicles are currently
mitigated in terms of their energy balance (improvement or slight loss depending on the
geographic region), the expected change in the electricity mixes by 2030 should alone enable
us to achieve values that are systematically lower than those for the reference vehicle (gains
on the order of 16 to 25% in all cases). However, the results shown for 2030 reflect only
the electricity mix itself (renewal of the installed base with more efficient power plants).
In our study, no assumption of a change in vehicle performance has been taken into
consideration.
In trip 2, no degradation of the energy balance has been found between the plug-in hybrid
vehicle and the reference vehicle, contrary to trip 1. As we pointed out for the GHG bal-
ance for trip 2, which covers a longer distance, more gasoline than electricity is consumed.
However, this increase in gasoline consumption is not reflected in a systematic reduction of
gains (or an increase in degradation) for total energy consumption compared with the refer-
ence vehicle. For a given electricity mix, the evolution of the energy balances for the plug-in
hybrid vehicle between trips 1 and 2 depends on the difference between the energy efficiency
of the "gasoline supply and ICE" chain and that of the "electricity supply and electric drive"
chain, the sole variable between compared cases being the efficiency of power production.
7.1.4 Comparison of Results for Discrete Hybrid Vehicles and Plug-in
Vehicles: Conclusion and Perspectives
To conclude the comparison of the different vehicles studies, Figures 7.7 and 7.8 present
complete GHG emission and primary energy consumption balances for the ICE vehicle, the
hybrid electric vehicle, and the plug-in hybrid vehicle in trips 1 and 2, per kilometer, using
the consumption values shown in Table 7.9.
In general, we find that the gains in greenhouse gas emissions (Figure 7.7) and in total
primary energy consumption (Figure 7.8) associated with the hybrid electric vehicle com-
pared with the ICE vehicle are relatively constant and on the order of 18 to 22%. For the plug-
in hybrid vehicle, however, the gains or losses compared with the ICE vehicle vary with the
distance traveled - both in terms of GHG emissions and primary energy consumption - for
the reasons given above.
438 Hybrid vehicles
Table 7.9. Gasoline and electricity consumption in the studied vehicles
(reference, HEV, and PHEV) in trips 1 and 2
Vehicle consumption
Type of use Reference Discrete hybrid Plug-in hybrid
L/100 km
Trip 1:40 km L/100 km L/100 km Wh/km
Trip 2: 75 km 5.9
5.7 4.25 0.4 142
4.2 2 76
For GHG emissions, we find that the plug-in hybrid vehicle results in a gain that is greater
than or equal to the hybrid electric vehicle regardless of the electricity mix considered; this
holds true for both trips and corresponding uses. This finding has also been verified by Zgheib
and Clodic [2009]. However, the trend reverses for primary energy consumption. In effect,
the energy balances associated with the hybrid electric vehicle are systematically lower than
those for the plug-in hybrid vehicle, whatever the electricity mix.
1. GHG emissions due to vehicle electricity consumption are nil in all the cases.
Figure 7.7
Greenhouse gas balances for reference (RefV.), hybrid (HEV), and plug-in
hybrid (PHEV) vehicles in trips 1 and 2.
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