PROCEEDINGS OF ECOS 2017 - THE 30
INTERNATIONAL CONFERENCE ON
EFFICIENCY, COST, OPTIMIZATION, SIMULATION AND ENVIRONMENTAL IMPACT OF ENERGY SYSTEMS
JULY 2-JULY 6, 2017, SAN DIEGO, CALIFORNIA, USA
Methodology for Fuel Saving Optimization of a
Serial Hybrid Electric Vehicle using Gas Turbine
as Energy Converter
Wissam Bou Nader
, Charbel Mansour
, Maroun Nemer
and Olivier Guezet
Ecole des Mines de Paris, Centre Efficacité Energétique des Systèmes CES, Palaiseau, France,
Lebanese American University, Industrial and Mechanical Engineering department, New York, United-
Ecole des Mines de Paris, Centre Efficacité Energétique des Systèmes CES, Palaiseau, France,
PSA Group, Centre technique de Vélizy, Vélizy, France,
Significant research efforts have been invested in the automotive industry on hybrid-electrified powertrains in
order to reduce the passenger cars’ dependence on oil. Powertrains electrification resulted in a wide range
of hybrid vehicle architectures. Fuel consumption of these powertrains strongly relies on the energy
converter performance, as well as on the energy management strategy deployed on-board. This paper
investigates the potential of fuel consumption savings of a serial hybrid electric vehicle (SHEV) using a gas
turbine (GT) as energy converter instead of the conventional internal combustion engine (ICE). An exergo-
techno explicit analysis is conducted to identify the best GT-system configuration. An intercooled
regenerative reheat cycle is prioritized, offering higher efficiency and power density compared to other
investigated GT-systems. A SHEV model is developed and powertrain components are sized considering
vehicle performance criteria. Energy consumption simulations are performed on WLTP cycle using dynamic
programing as global optimal energy management strategy. A sensitivity analysis is also carried out in order
to evaluate the effect of the battery size on the fuel consumption. Results show improved fuel consumption
with GT as auxiliary power unit (APU) compared to ICE. Moreover, GT offers other intrinsic advantages such
as reduced mass, suitable vehicle integration as well as a multi-fuel use capability. Consequently, the
studied GT-APU presents a potential for implementation on SHEVs.
Gas Turbine, Exergetic analysis, Serial hybrid, dynamic programming, global optimization.
Different manufacturers have investigated the integration of gas turbines (GT) in conventional
powertrains over years as the main energy converter instead of actual conventional internal
combustion engines (ICE). Early GT models in the 60’s and 70’s for conventional powertrain
vehicles showed poorer acceleration response and higher fuel consumption compared to internal
combustion engine vehicles (ICEV) [1,2]. These drawbacks where mainly due to operating the GT
at high speed even at idle conditions, in addition to mechanically coupling the turbine to the vehicle
driving load, which resulted in a low efficiency operating range of the GT-system. Despite the
many technological advancements and improvements made later on GTs such as variable turbine
geometry, water injection for improving the performance, and increase of turbine inlet temperature
(TIT), the acceleration lag and the poor fuel efficiency were still the main reasons hindering their
deployment in conventional powertrains.
A review of recent research and development programs of automotive manufacturers revealed new
interests in GT for automotive applications, demonstrated in several vehicle prototypes [3-7].
Moreover, the review of the recent literature showed interesting insights on GT consumption
reductions. A study on GT for automotive applications at Chalmers University of Technology,
showed a potential of GT compared to ICE when operating at optimal efficiency point . A
complementary study at the University of Rome showed that GT emissions at optimal efficiency
operation meet the Euro 6 emissions levels of CO, NOx and soot even without the use of after-
treatment systems . In addition, GT-systems offer other intrinsic benefits for vehicle powertrains
such the reduced number of moving parts, vibration-free operation, low maintenance cost, high
durability and the absence of cooling system .
Based on the aforementioned findings, GT-systems present a forthcoming potential for improving
modern vehicle efficiency and emissions, with the benefit of fuel-use flexibility when compared to
ICEVs; particularly, for serial hybrid electric vehicle technologies (SHEV). Serial hybrid
powertrains combine a thermal and an electric powertrain in a series energy-flow arrangement, as
illustrated in figure 1. The thermal powertrain is constituted of an energy converter and an electric
generator, and is referred to as Auxiliary Power Unit (APU). The APU is mainly used to recharge
the battery once depleted; however, the electric powertrain provides sufficient power to overcome
the driving load. Consequently, since the APU operation is kinematically decoupled from the
vehicle speed, the energy converter operating point is easily controllable to operate on the best
efficiency point .
Figure 1: Powertrain configuration of serial HEV (adapted from ).
On another hand, several GT-system options could be considered for integration in SHEVs,
combining a basic GT to heat recovery systems and single or multi-stage compressions and
expansions. There have been numerous studies published over the past decade in the academic
literature. These studies covered a multitude of GT-system configurations and performance analysis
in different applications, such as industrial [11-22] and aeronautics [23, 24]. However, there have
only been a few detailed papers on GT-systems suitable for automotive applications [9, 25, 26] due
to the lack of competitiveness of GT compared to ICE in conventional powertrains.
Hence, in order to better inform the benefits of GTs in serial HEVs, and based on the above insights
in the literature for re-adopting GT-systems in automotive applications, this study propose a
comprehensive methodology to identify the potential GT-system options and select the optimal
system configuration for SHEV application.
An assessment of the different GT-system options applicable to SHEV is carried out in section 2,
based on exergy analysis and automotive technological constraints. Observed results are then used
for the prioritization and the selection of the optimal GT-system configuration. The selection
criteria are (1) optimizing the system efficiency and (2) improving the power density compared to
ICE. Thereafter, the identified GT-system is integrated in an SHEV model in section 3, and a
comparison between two SHEV models with different APU technologies (GT-APU and ICE-APU)
is presented. SHEV models are developed with a backward approach, and the powertrain
components are sized according to automotive performance criteria such as the maximum vehicle
speed and acceleration. Finally, energy consumption simulations of both models are compared on
the WLTP driving cycle, and a sensitivity analysis on the battery size impact on energy
consumption is presented. Note that Dynamic Programing (DP) is adopted as Energy Management
Strategy (EMS) in order to provide the global optimal strategy to power ON and OFF the APU.
Consequently, the analysis considers only the impact of the GT-system on consumption and
exclude the influence of rule-based EMS .
2. Methodology for Optimal Gas Turbine System Selection
This section presents the methodology adopted to evaluate the potential of GT-systems in SHEVs.
It consists of two-steps assessment plan, as described in sections 2.1 and 2.2, and summarized in
figure 2. The first assessment step consists of an energetic and exergetic analysis applied to the
basic GT cycle, where the system efficiency, specific work, and exergy are calculated. Based on
resulting exergy destructions in the system, modifications of the basic GT cycle are presented, by
considering several measures such as heat recovery and multi-stage compressions among others, in
order to reduce exergy losses. Accordingly, the list of potential GT-system configurations is
The energetic and exergetic calculations are then carried out in the second assessment step on all
identified GT-system configurations. Components technological constraints and automotive design
constraints are considered, and the optimal and realistic GT-system configuration for the SHEV
application is selected, based on the overall exergetic and technological assessments. Components
technological constraints such as the maximum TIT, the maximum compression ratio per stage and
the maximum components’ efficiencies are based on state-of-the-art data of available technologies
for automotive applications. However, automotive design constraints such as the number of
compression and expansion stages, the power density and the number of heat exchangers are
applied in order to simplify the system-integration complexity in vehicles.
Fig. 2. Exergo-technological explicit selection method of the best-suited GT-system for serial HEV
2.1. Energy and exergy analysis of simple gas-turbine system
In this section, the modeling for the basic Brayton GT cycle illustrated in figure 3 are presented.
First law of thermodynamics is applied to each component in order to deduce the cycle thermal
efficiency and power density. Equations are well detailed in the literature, and can be consulted in
Fig. 3. Basic Brayton gas-turbine configuration.
Exergy analysis is then carried out in order to trace the work losses in the system, their types and
quantities. Hence, it informs better than the energy analysis on the possible options to reduce the
inefficiencies and make a better use of the fuel. Exergy model equations for each component are
presented in . The exergy destruction calculations for the combustion chamber requires the use
of Gibbs function value for fuel. However, it was substituted in this study by equation (3), where
the average temperature in the combustion chamber is estimated from (2) [29, 34-36].
: Exergy destruction in the combustion chamber (kJ/kg)
: Enthalpy at inlet or outlet of the component (kJ/kg)
: The exergy flow at inlet or outlet of the component (kJ/kg)
: Average temperature in the combustion chamber (K)
: Enthalpy difference in the combustion chamber (kJ/kg)
: Entropy difference in the combustion chamber (kJ/kg.K)
: Reference temperature (K)
Exergy destruction results of the investigated basic GT-system are illustrated in figure 4. The figure
points out the two highest shares of exergy losses, occurring in the combustion chamber and the
exhaust gas at the turbine outlet.
Fig. 4. Distribution of exergy destruction in the Brayton gas-turbine system with turbine inlet
temperature of 1250°C and maximum cycle pressure of 1.2 MPa.
It was demonstrated in several studies [34-37] that the exergy destruction in the combustion
chamber decreases as the average temperature increases. Accordingly, two ways can be considered
to decrease these exergy losses: (1) increasing the TIT while respecting metallurgic constraints, and
(2) increasing the average combustion temperature through a regenerator upstream of the
combustion chamber .
As for the second major source of exergy destruction, losses from the exhaust gases at the turbine
outlet to the ambient air can be recovered by adopting waste heat recovery systems. Two recovery
options are applicable: (1) an external heat recovery system through a steam Rankine bottoming
cycle, and (2) an internal heat recovery system, using a regenerator.
The exergy destruction shares of the compressor and turbine illustrated in figure 4 can be reduced
by improving the efficiency of these components.
Based on these findings, the list of the different GT-system options considered in this study is
presented below, based on the combination of the suggested techniques for exergy losses reduction
as illustrated in figure 5. The considered GT-system options are as follow:
1. Combined Cycle Gas Turbine, with GT coupled to a steam Rankine cycle (CCGT)
2. Regenerative GT (RGT)
3. Regenerative GT with Organic Rankine Cycle (RGT-ORC, with 1234yf working fluid)
4. Intercooled Regenerative GT (IRGT)
5. Intercooled Regenerative Reheat GT (IRRGT)
6. Isothermal Compression Regenerative GT (ICRGT)
7. Isothermal Compression Regenerative Reheat GT (ICRRGT)
8. Isothermal Compression Regenerative Isothermal Expansion GT (ICRIEGT)
Note that the efficiency and power density of the first five stated above GT-system options can be
further improved if isothermal compression and expansion are considered. In fact, the isothermal
compression maximizes the heat recovery process in the regenerator, and the isothermal expansion
maximizes the expansion work [23, 37]. To this end, isothermal compressions are considered in
ICRGT, ICRRGT and ICRIEGT and isothermal expansion in ICRIEGT (options 6 to 8). Although
isothermal processes are technically difficult to achieve and remain currently theoretical, they are
considered in this study for comparison purposes, and to emphasize their additional benefits.
Nevertheless, many studies in the literature investigated technical options for getting close to
isothermal processes such as intercooling compression , cooling compression with water or
liquid nitrogen , reheat cycles  and multi-combustion turbine .
Fig. 5. Exergy assessment methodology for the identification of the GT-system options with reduced
exergy losses in a basic GT-system.
2.2. Energy and exergy analysis of identified potential gas-turbine
The identified GT-system options of figure 5 are assessed based on energy and exergy, in order to
prioritize these options and select the most suitable configuration. Table 1 summarizes the
simulation parameters considered in the analysis, taking into account the components technology
limitations and automotive design constraints. Note that pressure losses across the different
components were considered in the calculations.
Table 1. Simulation parameters based on state-of-the-art component specifications and automotive
Parameter Unit Value
Max number of compression stages - 2
Compressor Maximum pressure ratio - 4
Compressors efficiency % 80
Compressor inlet pressure drop % 0.5
Maximum Cycle pressure MPa 1.2
Intercoolers Pressure Drop
Intercoolers Outlet Temperature
Regenerator Pressure Drop Cold side
Regenerator Pressure Drop Hot side
Combustion Chamber Pressure Drop
Turbine Inlet Temperature (TIT)
Turbines isentropic efficiency % 85
Turbine expansion ratio
Steam Rankine max pressure MPa 10
Steam condensing temperature °C 100
ORC / SRC pump and turbine efficiency % 80 and 85
ORC fluid - 1234yf
Organic Rankine max pressure MPa 3
Organic condensing temperature °C 45
Handle small mass flow , have short length and better resistance to foreign object damage .
The number of compressing stages defines the size of the machine [43, 44].
Achieved compression ratio in a single stage radial compressor .
Based on Chrysler GT compressor achievable efficiency .
Tests on Micro GT  / 0.4% for industrial applications such SGT5-4000F .
Limit the compression and expansion stage numbers.
Conservative value. Pressure drop is about 3 to 4% for turbocharged ICE intercooler [47, 48].
Conservative values. 45 to 55°C for turbocharged ICE Intercooler [47, 48].
Conservative value. 90% achieved by Chrysler GT  and higher for Rotary ceramic regenerators [40, 41].
Conservative value. Around 2.8% and 1.8% of pressure drop .
Pressure drop for an industrial SGT5-4000F GT are respectively 0.4% and 1.3% [13, 18].
2 to 3 per cent of static pressure for a large industrial unit and 6-8 per cent for an aero engine [41, 42].
200°C higher than Chrysler GT  due to improvement in material. 1600°C industrial GT with cooling techniques .
Based on Chrysler GT achievable efficiency (84%) .
Limited by rotation speed and by choking because discharging to the atmosphere at ambient pressure.
8 Mpa piston machine for waste heat recovery using steam Rankine cycle on automotive applications .
Limit the size of the condenser and avoid negative pressure which cause air infiltration.
Achievable value at constant operating point.
Non-toxic, with low ODP coefficient that replace the R-134a OF in vehicle applications.
Avoid supercritical phases which requires specific pump machines.
Close to air conditioning condensing temperature on automotive applications
Figures 6 and 7 illustrate the efficiency, net specific work and optimal pressure ratio simulation
results of the investigated GT-systems, compared to the ICE. ICRIEGT presents the highest
efficiency and net specific work; however, as discussed in the previous section, this cycle is not
realistic for implementation in SHEV since it relies on isothermal compression and expansion.
Consequently, IRRGT (figure 8) is the optimal GT-system considered for the rest of this study,
which emulates the isothermal compression and expansion of ICRIEGT through a dual stage
compression with an intercooler and a dual-expansion turbine with a reheater.
Fig. 6. Optimum efficiency comparison of ICE and the investigated GT-system options.
0% 20% 40% 60%
Overall Efficiency (%)
Fig. 7. Net specific work of ICE and the investigated GT-system options at optimal efficiency.
Fig. 8. Intercooled Regenerative Reheat Gas Turbine Cycle.
3. Vehicle Model
In order to evaluate the benefits of the IRRGT-system in terms of fuel savings compared to ICE, a
series hybrid powertrain consisting of IRRGT-APU and an electric traction system is modeled and
presented in this section. The vehicle corresponds to a front-wheel drive architecture that combines
a mono-directional APU to a bidirectional electric powertrain, as illustrated in figure 1. The APU
consists of an IRRGT-system and an electric generator, operating at the optimal efficiency when the
APU is ON. The APU and the electric traction motor are sized in order to ensure similar
performance to a medium class hybrid vehicle with maximum speed of 160 km/h and acceleration
from 0-100km/h in 9.6s.
Different battery capacities of 2, 5, 10 and 20 kWh are considered in the analysis in order to assess
the impact of the battery size on improving fuel consumption. The additional battery weight with
the increased capacity was taken into account. Values were retrieved from commercialized battery
Table 2 summarizes the vehicle parameters needed for the modeling, and equations (4) to (8)
present the powertrain backward model. Note that longitudinal dynamics of the chassis are only
considered and on flat roads.
392 357 418
SI-ICE GT CCGT RGT-ORC RGT IRGT IRRGT ICRGT ICRRGT ICRIEGT
Net Specific Work (kJ/kg)
Table 2: Vehicle and components specifications.
Vehicle specifications Symbol Unit
Vehicle Mass M kg 1470
Front area S m² 2.17
Drag coefficient C
Wheel friction coefficient f
Air density ρ kg/m3 1.205
Wheel radius R
Auxiliaries consumption P
Battery capacity C kWh 2, 5, 10, 20
Battery max power P
Battery weight M
kg 116, 188, 259, 356
IRRGT-system power P
Generator efficiency η
EM power P
electric motor efficiency
An engine on/off variable : is considered in equation (6) in order to control the APU start
operations. :2 takes the value of 0 for the APU-off and 1 for the APU-on. Dynamic
programming (DP) was considered in this study in order to provide the global optimal strategy to
control the APU operations [30, 31]. It decides on the optimal strategy @
for the scheduled route at each instant while minimizing the cost function F presented in equation
(9). Consequently, DP computes backward in time from the final desired battery state of charge
to the initial state G
the optimal fuel mass flow rate HI
&G3:' in the
discretized state time space as per equations (10) to (12).
Note that the resulting optimal APU on/off strategy @
must not cause the components to violate
their relevant physical boundary constraints in terms of speed, power or SOC, in order to ensure
their proper functioning within the normal operation range. These constraints are included in the DP
model and summarized in equations (13) to (20).
with discrete step time:
number of time instances:
(with n the time length of the driving cycle)
state variable equation:
Battery power constraint:
Motor torque constraint:
Motor speed constraint
Generator power constraint:
Generator speed constraint:
4. Results and discussion
Two different SHEV configurations are compared in this section: the suggested IRRGT-APU and a
reference ICE-APU. The IRRGT-APU is designed to operate at its optimal operating point and
delivers 40 kW of mechanical power. The ICE-APU uses a 1.2 liters spark ignition engine with
maximum efficiency of 37%. During APU operations, the ICE is allowed to operate at any point of
its torque-speed map. For both models, gasoline is the fuel used, and the simulations are performed
on a sequence of five WLTP driving cycles, covering around 115 km.
Two sets of simulations are conducted:
1. The first set emulates the behavior of self-sustaining hybrids with a zero use of electric
energy from the battery at the end of the cycle. Thus, the initial and final battery SOCs are
set at 60%. APU operation and battery SOC results are illustrated in figure (9).
According to the literature, the battery capacities in self-sustaining hybrids are relatively
small (less than 5 kWh) when compared to plug-in hybrids, since batteries are used as
energy buffers. This is confirmed in figure (11) which compares the observed fuel
consumption of the two considered models for the four battery capacities investigated.
Results show that 3.5% more fuel is consumed as battery capacity increased from 2 to 20
kWh. The additional consumption is explained by the unnecessary additional carried weight
of the 20 kWh battery, used only as energy buffer.
2. The second set of simulations emulates the behavior of plug-in hybrids and extended-range
electric vehicles, with the option of battery recharge from the grid. Simulations are
performed at an initial SOC of 80% and a final SOC by the end of the trip at 30%. APU
operation and battery SOC results are illustrated in figure (10). Figure (11) highlights the
potential of increasing the battery capacity on reducing the fuel consumption. More than
half of the fuel consumption can be saved if battery capacity is increased from 2 to 20 kWh.
Fig. 9. Results emulating SHEV with 2 kWh battery on 5 WLTP
(SOCi = SOC
Fig. 10. Results emulating SHEV with 20 kWh battery on 5 WLTP
(SOCi = 80%, SOC
SOC (ICE-APU model)
SOC (GT-APU model)
SOC (ICE-APU model)
SOC (GT-APU model)
Comparing the fuel consumption results between the IRRGT-APU and the ICE-APU (figure 11),
28% to 30% savings are observed under the two sets of simulations. These savings are explained by
the higher operating efficiency of the IRRGT since it was constrained to operate at its optimal
efficiency. Although the ICE was not constrained to operate at one operating point, results showed
that ICE operation was at the optimal operating line (OOL) where the efficiency remains between
36 and 37%.
Fig. 11. Comparison of fuel consumption between IRRGT-system and ICE on SHEV.
5. Conclusion and perspectives
An exergo-techno explicit method considering energy and exergy analysis, as well as automotive
technological constraints was applied in this study to identify the suitable GT-system for serial
hybrid vehicle applications. The Intercooled Regenerative Reheat Gas Turbine (IRRGT) was
selected. It offered the highest efficiency and power density compared to the investigated realistic
GT-systems and to conventional internal combustion engines. A series hybrid vehicle is modelled
and the IRRGT-APU and ICE-APU energy converters are simulated and compared in term of fuel
consumption using the DP optimal control as APU management strategy. A parametric study was
also conducted in order to evaluate the impact of battery capacity and weight on fuel consumption.
Simulation results showed that the IRRGT-system offers 30% fuel consumption savings compared
to ICE on serial hybrid configuration. Results also highlighted the interest of considering large
battery capacities for maximizing fuel savings in serial hybrids. 53% of fuel savings were observed
between self-sustaining ICE-APU serial hybrid with a 2 kWh battery capacity and an extended-
range IRRGT-APU serial vehicle with 20 kWh battery. However, this advantage came at the
expense of an increased vehicle cost and battery volume, which were not discussed in this study.
In addition to the fuel savings, the IRRGT-system offered other intrinsic automotive advantages
such as a reduced mass compared to ICE, a suitable vehicle integration as well as a multi-fuel use
capability, which makes it a potential energy converter option for implementation on serial hybrid
powertrains in the future.
The methodology presented in this study will be further elaborated in order to evaluate the fuel
consumption saving for GT-systems on different vehicle applications ranging from small to large
and SUV vehicles. Simulations will include Real Driving Cycles (RDE) and other vehicle energetic
criteria such as the cabin thermal needs. Moreover, the selected IRRGT cycle will be further
investigated with water injection at different locations in the machine such as downstream the
second compressor in order to maximize heat recovery through the regenerator as well as to reduce
pollutant emissions, mainly nitrogen oxide.
3,2 2,9 2,5
3,42 3,45 3,48 3,54
4,80 4,86 4,91 4,99
2 5 10 20
Battery Capacity (kWh)
GT - SOCi=0.8 - SOCf =0.3 ICE - SOCi=0.8 - SOCf =0.3
GT-SOCi = 0.6 - SOCf = 0.6 ICE - SOCi=0.6 - SOCf =0.6
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