Design & Development of Energy management strategies for the improvement of fuel efficiency for 4WD hybrid electric vehicle

DESIGN & DEVELOPMENT OF ENERGY MANAGEMENT
STRATEGIES FOR IMPROVEMENT OF FUEL EFFICIENCY
FOR 4WD HYBRID ELECTRIC VEHICLE
Prepared By
SAIIFI HAIDER
Department of Mechanical Engineering
TECHNOLOGY GROUP, ARAI

CONTENT
• Introduction
• Problem Overview & Objectives
• Energy Management system
• Literature survey
• HEV Architecture & CAD Models
• Theoretical Models
• Co-simulation Control Strategy
• Plant Model Architecture
• Fuel Economy & Performance Calculation
• Simulation & results
• Conclusion & Future Scope
• Reference 2
College of Engineering, Pune

INTRODUCTION
• Air pollution in big cities has been a critical problem
for many years. Technical research reveals that the
main cause of city pollution is vehicles with an
Internal combustion engine. Conventional vehicle has
many disadvantages such as depending to a certain
type of energy (oil), producing toxic gases like CO,
CO2, and NO2, greenhouse gases like CO2, noise
pollution, and low efficiency and as a result loss of
energy.
• By improvement of technology and the use of more
advance batteries and the production of more
efficient electric motors and ICEs, vehicles were
introduced to markets to overcome some of the
problems involved. By creating multi-direction
electric drives, charge and charge depletion of such
vehicles were possible through the electric grid that
finally leads to generation of Plug-in Hybrid Electric
Vehicles. These vehicles were more efficient and
effective than conventional vehicles and as a result
gained much interest throughout the world.
• Nowadays, experience proved that pure electric
vehicles are faced with many limitations in spite of
many advances in this field and can be used in
limited driving distance and just for special
applications.
09-02-2021 3

Problem Overview
• Due to the complex structure of HEVs/PHEVs, the design of control strategies is a challenging task.
• The presence of two power sources focuses on the need of designing an energy management
strategy to split power between them. Which require lot of calculation to fulfil the needs and
maintain the driver’s comfort.
• The strategy should be able to minimize the fuel consumption and maximize the power utilization.
• In HEVs, the battery is a supporting power source which gets charged when ICE powers the vehicle
and also through regenerative braking.
• In HEVs the state of charge (SOC) of the battery is the same at the start and end of the trip; that is, it
works in charge sustaining mode.
• In PHEVs, the batteries are charged through mains; therefore, it can be depleted to the permissible
minimum level at the end of the trip; that is, it works in a charge depletion mode. PHEVs may call
upon to work in charge sustaining, charge depletion, or combination of both based on the
requirement.
09-02-2021 4

Challenges with HEV design
• Architecture /topology selection
• Selection and sizing of components
• Complexities in modelling
• Optimize performance
• Implement real-time control algorithms
09-02-2021 5

Biggest challenges
• Developing HEV control
algorithm through energy
management by maintaining
good drivability of the vehicle
09-02-2021 6

Objectives
To develop and Optimize a control strategy model for the energy
management of 4WD hybrid electric vehicle to improve fuel
efficiency.
Sub-objectives
09-02-2021 7

ENERGY MANAGEMENT
• Instantaneous Power Control.
• Improve Powertrain efficiency.
• Operating mode selection.
• Subject to Constraints.
• Attempt to minimize energy
consumption, maintain drivability.
09-02-2021 8

BENEFIT OF ENERGY MANAGEMENT
• It involves the distribution of energy(implying power as well depending on load
demand.
• It also implies protection of the system in case of some threshold id exceeded. For
Example- Voltage, Current SOC and so on.
• Since the resources is limited, it involves the best allocation of resources, with the
objectives of minimizing fuel consumption while observing various constraints.
• Good Energy management leads to better fuel economy and/or lower emissions, and it
also leads to enhanced life of devices.
• To be more précised, power/energy management has the goal to take a holistic view of
the system, not just from the point of view of fuel economy, or rather, it is to achieve a
minimum life cycle cost of the system, from the point of view of operations,
maintenance, and longevity all taken together.
09-02-2021 9

Bharat stage emission Standards
• Bharat stage Emission Standards (BSES) are emission standards instituted by the Government of India
to regulate the output of air pollutants from internal combustion engines and Spark-ignition engines
equipment, including motor vehicles. The standards and the timeline for implementation are set by the
Central Pollution Control Board under the Ministry of Environment, Forest and Climate Change.
• The standards, based on European regulations were first introduced in 2000.
• On October 2010, Bharat Stage (BS) III norms have been enforced across the country.
• In 13 major cities, Bharat Stage IV emission norms have been in place since April 2010 and it has been
enforced for entire country since April 2017.
• In 2016, the Indian government announced that the country would skip the BS-V norms altogether and
adopt BS-VI norms by 2020.
• In 2018, BS-VI at Delhi then in 2019 at NCR & in 2020 it was implemented through out Nationwide.
09-02-2021 10

Diesel
Serial No. Characteristics Unit Category Reference mass
Bharat
Stage
III
Bharat
Stage
IV
Bharat
Stage
V
Bharat
Stage
VI
Bharat
Stage
VI [OBD-I] 2020
Bharat
Stage
VI [OBD-II] 2023
Fuel Specification
1 Density at 15 °C kg/m3 820–845 820–845
2 Sulphur Content mg/kg 350 50 10 10 10 10
3
Cetane Number minimum and /
or
Cetane Index
51
and
46
51
and
46
4
Polycyclic Aromatic
Hydrocarbon
11 11
Emission Standards for Diesel Vehicles (GVW ≤ 3,500 kg) on or after 1st April 2020
1 co g/km
M(GVW≤2500 OR
upto 6 seaters)
0.64 0.5 0.5 1.75 1.75
N1 7M(GVW>2500 OR
above 6 seaters)
RM≤1305
1305<RM≤1760
1760<RM
0.64
0.80
0.95
0.50
0.63
0.74
0.5
0.63
0.74
1.75
2.2
2.5
1.75
2.2
2.5
2 HC g/km
M(GVW≤2500 OR
upto 6 seaters)
N1 7M(GVW>2500 OR
above 6 seaters)
RM≤1305
1305<RM≤1760
1760<RM
3 Nox g/km
M(GVW≤2500 OR
upto 6 seaters)
0.5 0.25 0.08 0.18 0.14
N1 7M(GVW>2500 OR
above 6 seaters)
RM≤1305
1305<RM≤1760
1760<RM
0.50
0.65
0.78
0.25
0.33
0.39
0.08
0.105
0.125
0.18
0.22
0.28
0.14
0.18
0.22
4 PM g/km
M(GVW≤2500 OR
upto 6 seaters)
0.05 0.025 0.0045 0.025 0.012
N1 7M(GVW>2500 OR
above 6 seaters)
RM≤1305
1305<RM≤1760
1760<RM
0.05
0.07
0.10
0.025
0.04
0.06
0.0045
0.0045
0.0045
0.025
0.025
0.03
0.012
0.012
0.012
5 Evap. Emission g/test
M(GVW≤2500 OR
upto 6 seaters)
N1 7M(GVW>2500 OR
above 6 seaters)
RM≤1305
1305<RM≤1760
1760<RM
6 HC+Nox g/km
M(GVW≤2500 OR
upto 6 seaters)
0.56 0.3 0.17
N1 7M(GVW>2500 OR
above 6 seaters)
RM≤1305
1305<RM≤1760
1760<RM
0.56
0.72
0.86
0.30
0.39
0.46
0.17
0.195
0.215
7 NMHC g/km
M(GVW≤2500 OR
upto 6 seaters)
0.29 0.29
N1 7M(GVW>2500 OR
above 6 seaters)
RM≤1305
1305<RM≤1760
1760<RM
0.29
0.32
0.35
0.29
0.32
0.35
8 PN*
Numbers/k
m
M(GVW≤2500 OR
upto 6 seaters)
6 × 10^8
N1 7M(GVW>2500 OR
RM≤1305 6 × 10^8
09-02-2021 11

Project plan
July August September October November December January February March April May June
Activities
Problem Identification & Define requirement
Literature review
Conventional Plant model development of
vehicle
Hybrid Electric vehicle Plant Model design &
Development in Amesim
Controller System -level and their subsystem
Design & Development
Base conventional vehicle plant model
validation taking chassis dynamometer tested
data.
Hybrid electric vehicle plant model design
,development & Comparison.
Subsystem integration and simulation
09-02-2021 12

Method statement
Problem Identification
Problem Definition
Literature Survey
Design of Energy management Control Strategy
Plant Model Design Controller development
Co-simulation
Interfacing
Fuel consumption Results & Comparison
Design modification Modification if required
09-02-2021 13

Literature Survey
09-02-2021 14

No. Research Papers Description
1
Mustafa, R., Schulze, M., Eilts, P., & Küçükay, F.
(2014). Intelligent energy management strategy
for a parallel hybrid vehicle. SAE Technical
Papers, 1. https://doi.org/10.4271/2014-01-1909
Developed an advanced control strategy for a parallel hybrid vehicle. where they had presented Four main steps,
particularly to achieve a reduction in fuel consumption.
• The first step is the development of a highly complex HEV model, including dynamic and thermal behaviour.
• Second, a heuristically control strategy is developed to determine the HEV modes.
• Third, a State of Charge (SOC) levelling is developed with the interaction of a fuzzy logic controller. It is proposed to
calculate the load point shifting of the Internal Combustion Engine (ICE) and the desired battery SOC.
• Fourth, novel multi-objective optimization techniques, such as a genetic algorithm, are used for the optimization of the
fuzzy logic controller and the heuristically control strategy. The simulation results show a potential fuel reduction
relative to the baseline control strategy
2
Rizoulis, D., Burl, J., & Beard, J. (2001). Control
strategies for a series-parallel hybrid electric
vehicle. SAE Technical Papers, 724.
https://doi.org/10.4271/2001-01-1354
Analysed the series-parallel power-split configuration Future Truck. Mathematical equations that describe the hybrid
power-split transmission are derived. The vehicle's differential equations of motion are developed and the system's need
for a controller is shown. The engine's brake power and brake specific fuel consumption, as a function of its speed and
throttle position, are experimentally determined. A control strategy is proposed to achieve fuel efficient engine operation.
The developed control strategy has been implemented in a vehicle simulation and in the test vehicle. Simulation and
experimental results are presented and discussed. The control strategy leads to a series hybrid vehicle behaviour at low
speeds and parallel hybrid vehicle behaviour at highway speeds. Furthermore, the strategy ensures charge sustaining
vehicle operation.[
3
Kondo, K., Sekiguchi, S., & Tsuchida, M. (2002).
Development of an electrical 4WD system for
hybrid vehicles. SAE Technical Papers, 2002(724).
https://doi.org/10.4271/2002-01-1043
During the development of this electrical 4WD system, they have found that it is necessary to determine the required rear
motor torque to allow practical 4WD performance while maintaining excellent fuel economy. Initially, the factors affecting
4WD performance was quantitatively analysed and then the rear wheel drive unit torque was be optimized. This results in
a new hybrid vehicle with practical 4WD performance and high efficiency.[
4
Van Keulen, T., De Jager, B., Kessels, J., &
Steinbuch, M. (2010). Energy management in
hybrid electric vehicles: Benefit of prediction.
IFAC Proceedings Volumes (IFAC-PapersOnline),
43(7), 264–269.
https://doi.org/10.3182/20100712-3-DE-
2013.00027
Suggested that prediction of the future power and velocity trajectories, based upon input from a geographical information system
with GPS, could improve the result of the real-time strategies. The simulation example shows that one can find examples where the
benefit of prediction ranges from marginal to substantial, depending upon the route and vehicle characteristics. It is argued that
prediction of the future power and velocity trajectories, for the EMS optimization has little or no fuel consumption benefit on roads
with mild grades, although it is possible to keep the state-of-charge closer to a preferred value of 50%. In practice benefits from
prediction can be expected, only when driving in a hilly terrain with long and steep descends. This also implies that the use of
stochastic or statistical properties of measured data from past routes, to optimize the future power split, offers limited benefits,
unless it is connected to a GPS with navigation that can locate the vehicle in a map. Future work will focus on an experimental
validation of the results presented here
09-02-2021 15

5
Wei, X. (2004). Modelling and Control of a
Hybrid Electric Drivetrain for optimum fuel
economy, performance and driveability. Fuel,
53(4), 1247–1256.
https://etd.ohiolink.edu/!etd.send_file?accessi
on=osu1095960915&disposition=attachment
They had designed control strategy based on models and tested in simulations, there research also includes building the
appropriate models and simulators for control design and defining the objective measures for the control criteria. The
control and the model here are called model-based control and control-oriented model. The model-based control design
applies optimal control theories and develops the strategy based on the control-oriented model.
The control strategy is found for the minimum fuel consumption first and then he takes the driveability into
consideration, two models, the quasi-static and the low frequency dynamic models which was established correspondingly.
6
Chen, M. Y., Yang, K., Sun, Y. Z., & Cheng, J. H.
(2019). An Energy Management Strategy for
Through-the-Road Type Plug-in Hybrid Electric
Vehicles. SAE International Journal of
Alternative Powertrains, 8(1), 61–74.
https://doi.org/10.4271/08-08-01-0004
• they had applied strategies on a plug-in hybrid vehicle with one traction motor and an engine with an ISG in the front
and another traction motor with a gearbox in the rear. The functional modules in the energy management strategy
simplify the structure complexity and facilitate the real-time implementation of the strategy algorithms. There are six
functional modules in the energy management strategy: CPC, TQD, FUN, GBC, TQS, and TQC. The vehicle state
information is entered to the strategy, and the overall torque is calculated by the TQD module. There are four power
modes for the vehicle: EV, RE, BOOST, and ICE modes. The battery-charging command and the gear-shifting command
are sent out to the respective control units from TQC module and GBC module. Three simulation test scenarios are
performed to investigate the energy management strategy
7
Caratozzolo, P., Serra, M., & Riera, J. (2003).
Energy management strategies for hybrid
electric vehicles. IEMDC 2003 - IEEE
International Electric Machines and Drives
Conference, 1(18435), 241–248.
https://doi.org/10.1109/IEMDC.2003.1211270
• In this project, he develops novel control strategies for the operation of a full parallel hybrid electric powertrain that
minimize the fuel consumption over the driving cycle. the global optimal solution was evaluated by means of dynamic
programming. Since the dynamic programming method requires a discrete-time model of the plant and a quantized
state-space, it is prone to numerical errors, particularly when a speciﬁed ﬁnal state is enforced
8
Kim, C., Namgoong, E., Lee, S., Kim, T., &
Kim, H. (1999). Fuel economy optimization for
parallel hybrid vehicles with CVT. SAE
Technical Papers, 724.
https://doi.org/10.4271/1999-01-1148
• They had proposed a new optimum strategy of fuel economy for parallel hybrid vehicles with CVT, which permits
analytic interpretation of the complex hybrid systems from a unified viewpoint. The proposed strategy generates the
control values giving the best fuel economy versus the current driving conditions. A series of simulation shows fuel
economy of about 20km/l in city driving modes
09-02-2021 16

9
Glenn, B., Washington, G., & Rizzoni, G. (2000). Operation
and control strategies for hybrid electric automobiles. SAE
Technical Papers, 724. https://doi.org/10.4271/2000-01-
1537
• They had designed control strategies to optimize the fuel efficiency of the ICE and not reduce the
vehicle's emissions. This is because Ohio State University's hybrid electric vehicle is ICE dominated,
meaning it has a small Degree of Hybridization. The selling point of these vehicles will mainly be
there ability to achieve good fuel economy. This has been dually accomplished in simulation by
optimizing the efficiency of the ICE and by forcing the ICE to operate at a point with a low fuel use
rate while the vehicle achieves low speeds.
10
RAHMAN, M. (2011). Internal Combustion Engine
Performance Characteristics. London South Bank University,
Thermoflui, 8–11.
• In this assignment he draws interest in internal combustion engine performance characteristics.
However, he understands the differences between air standard cycle efficiency and the brake
thermal efficiency and their relation with engine speed. On the other hand, this assignment also
helps him to think deepen in terms of heat losses in combustion chamber and so on.
11
Guoyuan Wu, Xuewei Qi, M. B. B. (2016).Advanced Energy
Management Strategy Development for Plug - in Hybrid
Electric Vehicles. A Research Report from the National
Center for Sus.
• In this study, they develop two different on-line energy management systems for plug-in hybrid
electric vehicles, i.e., Evolutionary Algorithm (EA) based EMS and Reinforcement Learning (RL) based
EMS.
12
Cacciato, M., Nobile, G., Pulvirenti, M., Raciti, A., Scarcella,
G., & Scelba, G. (2017). Energy management in parallel
hybrid electric vehicles exploiting an integrated multi-drives
topology. 2017 International Conference of Electrical and
Electronic Technologies for Automotive, 1–8.
https://doi.org/10.23919/EETA.2017.7993203
• They deal with an innovative energy management strategy for hybrid electric vehicle parallel
drivetrains by exploiting an integrated multi-drives system. The control strategy which was proposed
to improve the energy efficiency of the system while taking into consideration the technical
constraints related to the energy storage technologies in each possible operating scenario.
13
Nabavi, S. G. (2015). Design an Energy Management
Strategy for a Parallel Hybrid Electric Vehicle. Journal of
Asian Electric Vehicles, 13(1), 1705–1710.
https://doi.org/10.4130/jaev.13.1705
• They had applied a fuzzy compensator in order to reduce the costs in a parallel HEV. Also, a proportional
controller was used to better satisfy the driver’s demand. The fuzzy compensator optimizes the power
distribution between electric motor and combustion engine. which was shown that more favourable
performance was achieved by using both these controllers for a parallel HEV. The designed controller not
only helps to better provide the driver’s required power, but also plays a significant role in the reduction of
fuel consumption and pollution rate while improving the battery charge situation.
09-02-2021 17

14
Bashir, F., & Bakhsh, F. (2018). Energy Management
Strategies in Hybrid Electric Vehicles (HEVs).
4(February).
They simulated energy management in the real time hybrid electric vehicles (HEVs) by using different
strategies from which it gets improved. Thus, pollutant emissions and fuel consumption get reduced.
However, here only the off-line strategies concerning the electrical battery state of charge (SOC) is
considered.
15
Patil, K., Molla, S. K., & Schulze, T. (2012). Hybrid vehicle
model development using ASM-AMESim-Simscape co-
simulation for real-time HIL applications. SAE Technical
Papers. https://doi.org/10.4271/2012-01-0932
Discussed Automotive Simulation Models (ASM) for plant modelling and proper I/O interfaces and real-
time systems. Real time simulation of a system requires balance between complexity of model, solver
type, solver settings and real time hardware. He observed that with the use of ASM ready to use plant
models, developer can focus more on control systems development. ASM also allows integration of
models developed in different environment. Amesim and SimDriveline model has been integrated in
ASM and tested on real time system.
16
Lee, H., Jeong, J., Park, Y. il, & Cha, S. W. (2017). Energy
management strategy of hybrid electric vehicle using
battery state of charge trajectory information.
International Journal of Precision Engineering and
Manufacturing - Green Technology, 4(1), 79–86.
https://doi.org/10.1007/s40684-017-0011-4
They have proposed energy management strategy using future driving cycle information. The new
strategy includes DP and uses optimal control results from it, with predicted driving cycle information.
With extracted optimal battery SOC trajectory results along the vehicle travel distance, rule-based
control strategy controls the engine and the motor to follow the target SOC path, and it presents
improved fuel economy performance for vehicle simulation using various driving cycles as compared to
the previous rule-based control strategies
09-02-2021 18

HEV configuration
P0: The electric machine is connected with the internal combustion engine through a belt, on the front-end accessory drive.
P1: Crankshaft mounted electric machine.
P2: Input Shaft of Transmission
P3: Output Shaft of the Transmission
P4: On the rear or front differential
09-02-2021 19

Mode of Operations
09-02-2021 20

Model-Based Design (MBD)
 Evaluate an architecture
 Assess performance
 Early closed loop control
development
 Optimizing control and plant
simultaneously
 Model reuse – code
gen/HIL/verification & validation
09-02-2021 21

09-02-2021 College of Engineering, Pune 22
Vehicle Specification & Parameters
Type 4 Cylinder, Common Rail
GVW (Kg) 2520
Load (10% overload) kg 2772
Engine Displacement(cc) 1898 cm3
Max Power (KW/hp@rpm) 110KW (150 PS) @ 3600rpm
Max Torque (Nm @ rpm) 350Nm @ 1800-2600rpm
Overall Vehicle (LxWxH) (mm) 5295 mm x 1860 mm x 1855 mm
Coeff of drag(cd) 0.7
Coeff of rolling resistance(µ) 0.01
First Gear ratio(β1) 4.008
Second Gear ratio(β2) 2.301
Third Gear ratio(β3) 1.427
Fourth Gear ratio(β4) 1
Axle Ratio(α) 4.1
Diameter of the wheel(d) 0.7494
Frontal Area 3.4224
• The powertrain structure of the target vehicle
specification & parameters as shown below.
• Our hybrid powertrain includes one electric traction
motor (TM) connected in the front axle with 2 stage
reduction gearbox and an ICE in the rear axle through
Manual Gearbox connected in the line.
• Such powertrain configuration consider as a P4 hybrid
vehicle. The two clutches enable the switch of the
power sources, either from the ICE or from the TM.
• The gear ratios for the TM-side 2 stage reduction
gearbox is 3.143.
• The engine is a 110 kW 1.6-liter gasoline engine with a
turbocharger and coupled. On the other side, the TM
with the peak power of 135kW and are equipped to
propel the vehicle by pure electric power.
• In the vehicle chassis, the battery capacity is selected as
40.3 kWh and is monitored by the corresponding battery
management system unit.
The Hybrid Powertrain Structure

Electric motor specification
Motor type 3-Phase AC Induction
Power, Peak 135KW
Power, continuous 80KW
Torque, Peak (Duration
30 seconds minimum)
215N/m ,0 to 5000 RPM
Torque, continuous 110N/m ,0 to 5000 RPM
Speed Max 15000 RPM
Weight 34Kg
Volume 32*18*16
System Efficiency, peak 93%
09-02-2021 23
• The AC induction machine is selected in our hybrid drivetrain
due to its wide torque-speed range, high performance,
ruggedness, better failure mode and low cost.
• Advantage of electric machine is its reversible behaviour . This
means that can produce torque if it’s supplied with electrical
energy or it can produce electrical energy when it has an input
torque (due to vehicle inertia). When the electric machine is
producing torque it’s in motor mode, when it’s producing
electrical energy it’s in generator mode.
Electric motor
Torque & Power Performance

Electric
machine
Trans.
power
electronics
Bat.
EM
control
Fuel
ICE
Organization of HEV control
ICE
control
Trans
control
Energy Management Strategy
EMS
driver
requests
subsystem
controls
(subsystem
coordination)
BMS
Motor
gearbox
09-02-2021 25

CAD MODEL
09-02-2021 26

09-02-2021 27

Tractive Effort Calculation
• Aerodynamic drag
𝐹𝑎 =
1
2
𝜌𝑎𝑖𝑟𝐶𝑑𝐴𝑓𝑉2
• Rolling resistance
𝐹𝑟 = 𝛭𝑔𝐶𝑟 𝑐𝑜𝑠(𝑔𝑟𝑎𝑑𝑒)
• Gradient resistance
𝐹
𝑔 = 𝛭𝑔𝑠𝑖𝑛(𝑔𝑟𝑎𝑑𝑒)
• Wheel torque
𝑇𝑤 = 𝑇𝑖𝑐𝑒
𝑜𝑝𝑡
∗ 𝛽𝑖𝛽𝑑𝜂𝑔
• Tractive Effort transferred to the driving wheel
𝐹𝑥 =
𝑇𝑤
𝑟𝑑
• Tractive effort required for propulsion of vehicle
𝐹𝑡 = 𝐹𝑎 + 𝐹
𝑔 + 𝐹𝑟
𝐹𝑡=
1
2
𝜌𝑎𝑖𝑟𝐶𝑑𝐴𝑓𝑉2
+ 𝛭𝑔𝐶𝑟 𝑐𝑜𝑠(𝑔𝑟𝑎𝑑𝑒)+𝛭𝑔𝑠𝑖𝑛(𝑔𝑟𝑎𝑑𝑒)
• Total tractive effort
𝐹𝑡 = 𝐹𝑡𝑟 + 𝐹𝑡𝑓
𝛭 Vehicle mass in kg,
𝑉 Vehicle velocity in 𝑚/𝑠,
𝑔 Gravity acceleration in 𝑚/𝑠2
,
𝑔𝑟𝑎𝑑𝑒 Road grade,
𝐴𝑓 Vehicle frontal area in 𝑚2
,
𝐶𝑑 Drag coefficient,
𝐶𝑟 Rolling resistance coefficient,
𝐹
𝑎 Aerodynamic force in N,
𝐹
𝑔 Gravity force in N,
𝐹
𝑟 Rolling resistance force in N,
𝜌𝑎𝑖𝑟 Air density in kg/𝑚3
.
where,
In the longitudinal direction, the major
external forces acting on a two-axle
vehicle, include the rolling resistance
of front and rear tires 𝐹𝑟𝑓 and 𝐹𝑟𝑟 ,
which are represented by rolling
resistance moment 𝑇𝑟𝑓 and 𝑇𝑟𝑟 ,
aerodynamic drag 𝐹𝑎 , grading
resistance 𝐹
𝑔, and tractive effort of the
front and rear tires, 𝐹𝑡𝑓 and 𝐹𝑡𝑟. 𝐹𝑡𝑓 is
zero for a rear-wheel-driven vehicle,
whereas 𝐹𝑡𝑟 is zero for a front-wheel-
driven vehicle. The dynamic equation
of vehicle motion along the
longitudinal direction is expressed by
Dynamic Equation
𝑀𝑣
𝑑𝑉
𝑑𝑡
= 𝐹𝑡 − (𝐹𝑎 + 𝐹
𝑔 + 𝐹𝑟)
where
𝑑𝑉
𝑑𝑡
is the linear acceleration of
the vehicle along the longitudinal
direction and 𝑀𝑣 is the vehicle mass.
09-02-2021 28

Wheel
Wheel
β i : i=1,2,3
Tw
Tw
Te
Tg
Td
βd
Ie
Ig
Id/2
Id/2
Ѡe
Ѡg
Ѡw
Ѡw
RearWheel Drive
At rear Wheel Drive I’m using Engine as a source to fulfil the requirement
of power to drive the vehicle.
09-02-2021 29

In this model I have Assigned engine optimal torque with 𝑻𝒆 𝝎𝒆, 𝝒𝜽𝒆 to satisfy wheel torque
requirement.
𝐹𝑥 =
𝜂𝑔𝜂𝑑𝛽𝑖𝛽𝑑
𝑅
𝑇𝑒 𝜔𝑒, 𝜘𝜃𝑒 −
1
𝑅2
(𝜂𝑔𝜂𝑑𝛽𝑖
2
𝛽𝑑
2
Ι𝑒 + 𝜂𝑑𝛽𝑑
2
𝐼𝑔 + 𝐼𝑑 + 2𝐼𝑤)
𝑑𝑢
𝑑𝑡
Rear wheel Optimal torque calculation Simulink Model
09-02-2021 30

Wheel
Wheel
βg
Tw
Tw
Tm
Tg
Td
βd
Im
Ig
Id/2
Id/2
Ѡe
Ѡg
Ѡw
Ѡw
FrontWheel Drive
09-02-2021 31

In this model I have Assigned motor optimal torque with 𝑻𝒎 𝝎𝒎, 𝝒𝜽𝒎 .
𝐹𝑥 =
𝜂𝑔𝜂𝑑𝛽𝑔𝛽𝑑
𝑅
𝑇𝑚 𝜔𝑚, 𝜘𝜃𝑚 −
1
𝑅2
(𝜂𝑔𝜂𝑑𝛽𝑔
2
𝛽𝑑
2
Ι𝑒 + 𝜂𝑑𝛽𝑑
2
𝐼𝑔 + 𝐼𝑑 + 2𝐼𝑤)
𝑑𝑢
𝑑𝑡
Front wheel Optimal torque calculation Simulink Model
09-02-2021 32

• we need to calculate optimal torques depend on the SOC of the battery.
• if SOCmin ≤ SOC ≤ SOCmax,
we have to run the vehicle at its optimal efficiency range of engine and motor. we
can also find the optimal torque using ECMS algorithm.
• if SOC < SOCmin ,
then battery charging is only the priority, hence electric motor not used, as Tm = 0
and some part of engine torque can be used as regenerative torque,Treg charge the
battery. Hence Tice engine torque is used for both traction and to regeneration for
battery charging. Below Tice
opt
is optimal engine torque in terms of fuel consumption
where demanded torque can be less than or greater than Tice
opt
.
Torque Request Calculation
09-02-2021 33

Torque Request Calculation
Positive Wheel Torque (traction)
• If 𝑇𝑖𝑐𝑒
𝑜𝑝𝑡
>
𝑇𝑤
𝛽𝑔𝛽𝑑𝜂𝑔
, 𝑡ℎ𝑒𝑛 ∶
𝑇𝑖𝑐𝑒
𝑜𝑝𝑡
=
𝑇𝑤
𝛽𝑖𝛽𝑑𝜂𝑔
,
𝑇𝑟𝑒𝑔 = 𝑇𝑖𝑐𝑒
𝑜𝑝𝑡
− 𝑇𝑤
• If 𝑇𝑖𝑐𝑒
𝑜𝑝𝑡
≤
𝑇𝑤
, 𝑡ℎ𝑒𝑛 ∶
𝑇𝑖𝑐𝑒
𝑜𝑝𝑡
=
𝑇𝑤
𝛽𝑖𝛽𝑑𝜂𝑔
,
𝑇𝑟𝑒𝑔 = 0
• 𝐼𝑓 𝑆𝑂𝐶 > 𝑆𝑂𝐶𝑚𝑎𝑥 then only EM electric motor use as a primary
source. which means, 𝑇𝑖𝑐𝑒 = 0
𝑇𝑚
𝑜𝑝𝑡
=
𝑇𝑤
,
𝑇𝑟𝑒𝑔 = 0
Negative wheel torque (braking)
• Here we get optimal torque of motor and engine as,
𝑇𝑚
𝑜𝑝𝑡
= 0
𝑇𝑖𝑐𝑒
𝑜𝑝𝑡
= 0
𝑇𝑟𝑒𝑔 =
𝑇𝑤
.
where, ( 𝑇𝑟𝑒𝑔 + 𝑇𝑚
𝑜𝑝𝑡
) give us the final optimal torque
of electric motor EM and most important whenever
𝑇𝑖𝑐𝑒
𝑜𝑝𝑡
= 0,the engine speed run in an idle speed, which
is 700-900(rpm).
09-02-2021 34

Ref: google
09-02-2021 35

09-02-2021 36

09-02-2021 37

SOC % determination & Testing
• Minimal vehicle speed tracking error
• Actuator speeds/torque not noisy
• Power doesn’t exceeds limits for long periods
• SOC trends towards target
• SOC needs to be within a predefined operating range to avoid
any damage to the battery.
𝑆𝑂𝐶𝑚𝑖𝑛 ≤ 𝑆𝑂𝐶 ≤ 𝑆𝑂𝐶𝑚𝑎𝑥
09-02-2021 38

SOC and Power Algorithm
During traction:
𝑃𝑑𝑒𝑚𝑑 > 𝑃𝑖𝑐𝑒_𝑜𝑝𝑡𝑖𝑚𝑎𝑙
ICE runs at optimal region while
motor provide the remaining power.
During traction:
𝑃𝑑𝑒𝑚𝑑 < 𝑃𝑖𝑐𝑒_𝑜𝑝𝑡𝑖𝑚𝑎𝑙 ;
SOC≤ 𝑙𝑜𝑤𝑒𝑟 𝑙𝑖𝑚𝑖𝑡
ICE runs at optimal region and
motor use extra power to charge the
battery
During Braking:
𝑃𝑟𝑒𝑔𝑒𝑟_𝑑𝑒𝑚𝑑 < 𝑃𝑚𝑎𝑥
Motor regenerate, all of the braking
power charge the batteries.
During Braking:
𝑃𝑟𝑒𝑔𝑒𝑟_𝑑𝑒𝑚𝑑 > 𝑃𝑚𝑎𝑥
Motor regenerate, the friction brake
produces the remaining power
• The supervisory control rules for all commanded
speed and torque cases at the engine shaft. The vehicle
needs to meet the power request whenever possible
and optimality is sacrificed if the power request is not
met. The supervisory control strategy optimizes the
power split between two energy sources to achieve
minimum fuel consumption.
09-02-2021 39
SOC & Battery Power Model
𝑆𝑂𝐶𝐼𝑅,𝑇𝑅
𝑡 = 𝑆𝑂𝐶𝐼𝑅,𝑇𝑅
𝑡 − 1 + η𝐼𝑅,𝑇𝑅
)
𝐼(𝑡
𝐶𝐼𝑅,𝑇𝑅
Δ𝑡
Battery State of charge at time t [%]
𝑡 − 1 Battery initial state of charge [%]
𝐶𝐼𝑅,𝑇𝑅
or 𝑄𝑛 Battery rated capacity [Ah]
𝑡 Time [h]
η𝐼𝑅,𝑇𝑅
coulombic efficiency
𝐼(𝑡) charging/discharging current [A]
Δ𝑡 operating period
where,
With a measured charging/discharging current I and the corresponding
coulombic efficiency,[17] the rated SOC in an operating period Δt can be
calculated using Equation

Battery charge Depleting vs Charge sustaining
09-02-2021 40

09-02-2021 41

The torque request calculated earlier is multiplied by the gear ratio to find the torque request
at transmission output. This is needed because this is a p4 Hybrid Configuration .Therefore,
torque will be required to be calculated after the transmission for both the engine.
Torque request at Gear Box output
09-02-2021 42

Power Management
Bound battery power within dynamic
power limits of battery
Convert mechanical power request to
electrical power using efficiency map
Check if electric power request is
within limits
oOK allow original mechanical power
request
oNOT OK use limit for electrical power
,and convert to an allowable
mechanical power request
09-02-2021 43

Mech to Elec Power Estimate
09-02-2021 44
• Calculate the electrical Power request using efficiency map.
• It checks if electric power request is within the dynamic BMS power limit. If it is within the limit it will allow the regional
mechanical power request to go through it.
• If the limit is exceeded it uses upper limit for the Elec power request and converts the allowable mech power and torque
request using the efficiency map.

Torque limit model
Motor Speed coming from input signal is in radian is converted into Rpm which is
then assigned to 1-D lookup table to give the torque for both positive traction and
regeneration value. which is defined as upper limit and lower limit .then, whatever
the torque command will come gives output under this limits.
09-02-2021 45

Power Management algorithm:
• Estimate Elec power based on mech power using efficiency map
• Check if Elec power is within battery power limits
• If power within limit , use motor torque command
• If power limit exceeded, use limited motor torque
• For low Mot Speed, Motor Power Requirement will be a small number. Pass through torque command until Mot Speed > 2
09-02-2021 46

Fuel Economy & Performance Calculation
• There are various methods and approaches to predict and calculate fuel economy.
• For hybrid vehicle system design and analysis, fuel economy are normally calculated based on
numerical models or look-up tables extracted from engine dyno data.
• When driveability becomes one of the control criteria, the quasi-static model is obviously not
sufficient to evaluate it.
• Dynamics of driveability are in the frequency of a few hertz in a real vehicle and thus a low-
frequency dynamic model is built since it has the proper time scale.
• Most of the powertrain components are modelled as actuators with first or second order dynamics
plus saturations and some nonlinearity.
• The models for the battery, the final drive, the vehicle, the controller and the driver remain the
same as those used in the fuel economy optimization problem except for one of the controller
outputs is changed from engine torque request to commanded air mass flow rate.
• We have taken 37.95 kWh has 100% of the energy of one gallon of Diesel & one gallon is
3.7854 litres.
09-02-2021 47

Characteristics Unit
Bharat Stage
II
Bharat Stage
III
Bharat Stage IV
Bharat Stage
VI†
2001
(selected
cities), 2005
(nationwide)
2005
(selected
cities), 2010
(nationwide)
2010
(selected
cities), 2017
(nationwide)
2020↑(natio
nwide)
Ash, max % mass 0.01 0.01 0.01 0.01
Carbon Residue
(Ramsbottom) on 10%
residue, max †
% mass 0.3 0.3 0.3 0.3
Cetane Number (CN), min – 48* 51 51 51
Cetane Index (CI), min – 46* 46 46 46
Distillation 95% vol. Recovery
at °C, max
°C – 360 360 370
Flash point Abel, min °C 35 35 35 35
Kinematic Viscosity @ 40 °C cst 2.0-5.0 2.0-5.0 2.0-4.5 2-4.5
Density @ 15 °C Kg/m3
820-860 (820-
870)*
820-845 820-845 820-860
Total Sulfur, max mg/kg 500 350 50 10
Water content, max mg/kg 0.05% vol 200 200 200
Cold filter plugging point
(CFPP)
°C 18 18 18 18
a) Summer, max °C 6 6 6 6
b) Winter, max
Total contaminations, max mg/kg – 24 24 24
Oxidation stability, max g/mg3 – 25 25 25
Polycylic Aromatic
Hydrocarbon (PAH), max
% mass – 11 11 11
Lubricity, corrected wear scar
diameter (wsd 1,4) @ 60 °C,
max
μm (microns) 460 460 460 460
Copper Strip corrosion for 3
hrs @ 50 °C
Rating
Not worse
than No. 1
Class I Class I Class I
Implementation date
Indian Diesel Specification required meeting Bharat Stage II, III, & IV Emission Norms3
Fuel
Characteristics
09-02-2021 48

Annually Estimated Diesel Electricity Total
Costs ₹25973.15 ₹13206.69 ₹39253.21
Kilometres
7150.3154 10472.001 10,950
41% 59% 100%
Fuel Used 382.327 Litres 1,897 kWh
Driving
On a typical day, I drive... 48 to 49 kilometres
Annual kilometres 17520to 17885 kilometres
Charging
My home charger... 220V
My charger at work... 220V
Days per week that I drive to work... 5
Distance to work (one-way)... 16 Kilometres
Prices
Regular DIESEL cost ₹67.75 per litre
Electricity
Home ₹6.95 per KWH
Work ₹7-8 per KWH
09-02-2021 49

09-02-2021 50

Co-simulation Control Strategy for 4WD hybrid electric vehicle using
MATLAB Simulink and Amesim
Interface Icon Creation dialog box.
The completed interface block
• Designed to be used with the Simulink interface and
other interfaces but in our case it will be Simulink
• Block is used to define the variables which are provided
and received by the companion software.
• Connect the block inputs and output to the other
components of the model.
• Because the system contains Simulink interface blocks,
an S-Function is created.
• When you change from Parameter mode to Run mode,
special data files containing the parameters are written.
When you run the S-Function within Simulink, these files
will be read.
• If you do this you must ensure that the MATLAB Current
Directory is set to the directory where the Amesim model
is stored.
• Alternatively, you can use the Tools Start MATLAB menu
from Amesim
09-02-2021 51

Importing the model into Simulink
• The Amesim model at this stage exists as an S-
Function.
• Imported into Simulink.
• It is important to remember that when we quit
Amesim, the files defining your system are
compressed into a single file. This means that
Simulink would not have access to the S-
Function.
• For this reason, it is normal to have Amesim
and Simulink running simultaneously when
using the interface. This way, we can change
the parameters in the Amesim model and
restart the simulation very rapidly.
• Can also examine the results in Amesim.
The S-Function in Simulink.
The S-Function parameters.
09-02-2021 52

HEV Control architecture
System-level layer
Component-level layer
Physical layer
Hybrid Control Module (Supervisory Control)
ECM TCM MCU BMS
ENGINE TRANSMISSION MOTOR BATTERY
CAN 1 CAN 2 PHYSICAL
09-02-2021 53

INPUT
LAYER
APPLICATION
LAYER
OUTPUT
LAYER
09-02-2021 54

Major functions
• Supervisory control (State flow)
• Accelerator pedal Torque
• Regenerative Brake Blending
• Battery Management System
• Power Management
09-02-2021 55

Engine Speed
determination
Gear and Drive
selector
determination
Torque Request
Calculation
Operating Mode
Selector
Hybrid Mode
Intelligent Vehicle
Controller
Electric Mode
Conventional mode
Total Fuel
consumption
calculation
BATTERY SOC
VS VEHICLE
SPEED
BATTERY Structural
health & Thermal
condition Calculation
EMISSION
CALCULATIONS
ACCELERATOR
PEDAL
COMPARISON
09-02-2021 56

Standard VCU from Amesim
Port No. Title unit
Port 1 Electric motor torque command Nm
Port 2 Engine load
Port 3 Firing : engine on/off
Port 4 Clutch control (0 : disengaged, 1 : engaged)
Port 5 Braking command
Port 6 Engine rotary velocity rev/min
Port 7 Maximum & Minimum engine torque Nm
Port 8 Gearbox primary shaft rotary velocity rev/min
Port 9 vehicle speed m/s
Port 10 Acceleration command from drive
Port 11 Braking command from driver
Port 12 Gearbox ratio control
Port 13 Powertrain maximum & Minimum power W
Port 14 Battery state of charge %
Port 15 Maximum & Minimum motor torque Nm
Port 16 Electric motor rotary velocity rev/min
09-02-2021 57
 The control unit receives information from the driver
(acceleration, braking commands and gearbox ratio), the
electric motor (rotary velocity and maximum/minimum
torque), the engine (rotary velocity and maximum torque),
the battery (state of charge), the vehicle speed and the
gearbox (primary shaft speed).
 It analyses them in order to minimize the consumption of
the battery.
 The electric motor could be used as a generator to charge
the battery when the driver brakes. In this approach, the
electric motor and the engine are connected to the manual
gearbox. The control unit manages the power requested to
the engine and the electric motor.
 If the battery needs to be regenerated, the engine is used
to drive the vehicle, and if the power requested is lower
than its optimum power (according to its rotary velocity),
the difference is send to the electric motor to charge the
battery.

09-02-2021 58
Base Vehicle plant model architecture

HEV plant model architecture
09-02-2021 59

INPUT Requirements
• Driving cycle
• Gear Ratios
• Motor RPM Vs Brake Pedal Input
• Motor Operating Speed High cut OFF
• Motor Operating Speed LOW cut OFF
• Battery SOC Level 1 – 10
• Min & Max Motor Torque
• Ideal Engine Torque and RPM
• Motor Gear Ratio
• Max Engine torque
09-02-2021 60

SYSTEM OUTPUT
• Engine Torque request
• Total e-motor torque request
• Engine start and stop
• Regeneration start and stop
• Starter motor start and stop
09-02-2021 61

Drive cycle
• PCC
Pune city cycle
• MIDC
Modified Indian
Driving Cycle
• IDC
Indian Driving
Cycle
A driving cycle is a series of data
points representing the speed of a
vehicle versus time. Driving
cycles are produced by different
countries and organizations to
assess the performance of vehicles
in various ways, as for example fuel
consumption and polluting
emissions.
Selection of driving cycle.
09-02-2021 62

The driving cycles are given as inputs to plant model by designing a personal drive
cycle or giving a standard drive cycle.
09-02-2021 63

• The driver parameters are crucial in plant
modelling of the vehicle. The behaviour of
vehicle velocity depends on the
acceleration and braking control PID gain
values. The gain values are decided on trial
and error basis and best possible values are
selected so the vehicle velocity follows the
commanded velocity.
• This driver could be used in the modelling
of any vehicle with a manual gearshift:
vehicle with internal combustion engine,
electric motor, hybrid vehicle...
• This sub model computes several controls:
I. port 4: acceleration control "acc" [fraction]
II. port 3: braking control "br" [fraction]
III. port 2: gear shifting control "gb" [null]
09-02-2021 64
Selection of Driver and driving parameters

Vehicle
09-02-2021 65
 This is a sub model of vehicle. Both
front and rear axles are modelled
(allowing 4x4 applications) and the
user can choose between two
vehicle configurations:
1. road: rolling friction, road slope
and aerodynamic drag taken into
account,
2. roller test bench: user defined
roller test bench friction
coefficients.

• The battery SOC for different operating conditions are changed by
changing the value of SOC at port 4. The starting battery condition
of Hybrid mode and EV mode is decided by the state of charge.
The number of cells in series in battery pack are decided by taking
into the consideration that battery provides necessary voltage.
• Voltage can be fixed or variable
• 3 abbreviations are used in this component:
1. SOC: State Of Charge
2. DOD: Depth Of Discharge
3. OCV: Open Circuit Voltage
Parameters Unit Value
Number of battery pack
(Parallel Connection)
- 1
Number of cells in the battery
pack (Series Connection)
- 100
Nominal Voltage of Each cell V 3.6
Capacity of each cell Ah 13.4
09-02-2021 66
Battery

09-02-2021 67
Engine parameters
• It is a sub model of internal combustion engine (ICE) with different
application cases.
a. torque
b. torque & fuel consumption
c. torque & fuel consumption & exhaust pollutant emissions
• This engine component should be used whenever an engine performance
over a cycle is required.
• The prediction of the fuel consumption or emissions over driving cycles is
accurate

Table data Editor
09-02-2021 68

Motor parameters
• It is a model of electric motor/generator with its converter.
• The output torque and power losses can be determined either by
using data files or characteristic parameters.
• This model is bidirectional (motor/generator).
• From the input torque request Τ𝑠𝑒𝑡 at port 4, the torque Τ𝑙𝑖𝑚 is limited
as follows:
Τ𝑚𝑖𝑛 ≤ Τ𝑙𝑖𝑚 ≤ Τ𝑚𝑎𝑥
• With Τ𝑚𝑖𝑛 and Τ𝑚𝑎𝑥 the negative and positive torque corresponding
either to user-defined parameters or values read in tables as a
function of the operating point.

Dashboard
Electric Motor Mode Engine Mode Regeneration mode
09-02-2021 70

Power Flow Chart
Engine mode Power flow
Electric motor Power flow
09-02-2021 71

Simulation & Results
Conventional vehicle Commanded velocity v/s actual velocity Hybrid Commanded velocity v/s actual velocity
The hybrid model is compared with Baseline model for various performance parameters. The brief comparison on
fuel economy, between conventional vehicle and hybrid vehicle with some percentages of starting SOC is carried out.
09-02-2021 72

Total Fuel consumption
The fuel consumption is lesser for hybrid vehicle as visible in graph. The fuel consumption for hybrid is always lower than
fuel consumption of baseline in all operating conditions. As the vehicle velocity goes on increasing, the difference in the fuel
consumed by hybrid and baseline for same speed goes on increasing i.e. at higher speed efficiency of hybrid is better.
09-02-2021 73

Conventional vehicle
Hybrid electric vehicle
09-02-2021 74

Battery SOC
• The behaviour of battery soc is plotted with respect to time. The
battery soc starts from 80%.
Battery SOC vs Vehicle Speed
09-02-2021 75

Exhaust gas Results Comparisons
Hybrid electric vehicle Exhaust Gas details
09-02-2021 76

Conventional vehicle Exhaust Gas details
09-02-2021 77

Comparisons of Conventional vehicle & Hybrid
vehicle CO emission results
Comparisons of Conventional vehicle & Hybrid
vehicle CO2 emission results
Comparisons of Conventional vehicle & Hybrid vehicle NOx
emission results
09-02-2021 78

Sensors
Sensors/Actuator
• Potentiometer(Throttle position sensor)
• Potentiometer(Brake position sensor)
• Potentiometer(Clutch position sensor)
• Reed Switch/Digital Switch
• Digital Switch
• Hall effect sensor(Vehicle Speed)
• Tachometric sensor(Engine RPM)
• Mechanical 3 point selector switch
Locations
• Accelerator Pedal
• Brake Pedal
• Clutch Pedal
• Transmission Gear Box
• Vehicle Ignition Key
• Final Drive Shaft
• Engine
• Vehicle Dashboard
09-02-2021 79

Software
MATLAB/ Simulink
Amesim
PTC Creo
09-02-2021 80

Define
Requirement
System-level
Design
Subsystem
Design
[EM]
Subsystem
integration and test
System-level
integration
and test
Complete
integration
and test
Subsystem
implementation
[plant model/C-code
Production]
Integration of controller on vehicle
Toolchain for deployment of controller on Vehicle
The SCU developed on Software is deployed on the hardware using the software of VeriStand, in which the model
will be built as. So, file, the inputs and outputs will be mapped with the pins of hardware. This model then
deployed on hardware.
09-02-2021 81

Conclusion & Future Scope
• Simulation Results for the Engine only model running NEDC cycle
drive resulted in giving fuel Consumption of 12kmpl and after
hybridization into 4WD model shows increase in mileage.
• SCU model developed in Simulink can be used as a base platform
simulations either using Simulink solver or co-simulation between
AMESIM & MATLAB Simulink to validate and optimize hybridization
parameters for different 4WD models.
• As we know Hybridization of vehicle is not popular projects among
OEMs, they focusing more on pure electric vehicle. This project can
be proof of concept to start focusing more on developing hybrid
electric vehicle models in this new HYBRID era.
09-02-2021 82

References
1. Mustafa, R., Schulze, M., Eilts, P., & Küçükay, F. (2014). Intelligent energy management strategy for a parallel hybrid vehicle.
SAE Technical Papers, 1. https://doi.org/10.4271/2014-01-1909
2. Rizoulis, D., Burl, J., & Beard, J. (2001). Control strategies for a series-parallel hybrid electric vehicle. SAE Technical Papers,
724. https://doi.org/10.4271/2001-01-1354
3. Kondo, K., Sekiguchi, S., & Tsuchida, M. (2002). Development of an electrical 4WD system for hybrid vehicles. SAE
Technical Papers, 2002(724). https://doi.org/10.4271/2002-01-1043
4. Van Keulen, T., De Jager, B., Kessels, J., & Steinbuch, M. (2010). Energy management in hybrid electric vehicles: Benefit of
prediction. IFAC Proceedings Volumes (IFAC-PapersOnline), 43(7), 264–269. https://doi.org/10.3182/20100712-3-DE-
2013.00027
5. Wei, X. (2004). Modeling and Control of a Hybrid Electric Drivetrain for optimum fuel economy, performance and
driveability. Fuel, 53(4), 1247–1256.
https://etd.ohiolink.edu/!etd.send_file?accession=osu1095960915&disposition=attachment
6. Chen, M. Y., Yang, K., Sun, Y. Z., & Cheng, J. H. (2019). An Energy Management Strategy for Through-the-Road Type Plug-
in Hybrid Electric Vehicles. SAE International Journal of Alternative Powertrains, 8(1), 61–74. https://doi.org/10.4271/08-08-
01-0004
7. Caratozzolo, P., Serra, M., & Riera, J. (2003). Energy management strategies for hybrid electric vehicles. IEMDC 2003 - IEEE
International Electric Machines and Drives Conference, 1(18435), 241–248. https://doi.org/10.1109/IEMDC.2003.1211270
8. Kim, C., Namgoong, E., Lee, S., Kim, T., & Kim, H. (1999). Fuel economy optimization for parallel hybrid vehicles with
CVT. SAE Technical Papers, 724. https://doi.org/10.4271/1999-01-1148
9. Glenn, B., Washington, G., & Rizzoni, G. (2000). Operation and control strategies for hybrid electric automobiles. SAE
Technical Papers, 724. https://doi.org/10.4271/2000-01-1537
09-02-2021 83

10. RAHMAN, M. (2011). Internal Combustion Engine Performance Characteristics. London South Bank University, Thermoflui,
8–11.
11. Guoyuan Wu, Xuewei Qi, M. B. B. (2016). Advanced Energy Management Strategy Development for Plug - in Hybrid
Electric Vehicles. A Research Report from the National Center for Sus.
12. Cacciato, M., Nobile, G., Pulvirenti, M., Raciti, A., Scarcella, G., & Scelba, G. (2017). Energy management in parallel hybrid
electric vehicles exploiting an integrated multi-drives topology. 2017 International Conference of Electrical and Electronic
Technologies for Automotive, 1–8.
13. Nabavi, S. G. (2015). Design an Energy Management Strategy for a Parallel Hybrid Electric Vehicle. Journal of Asian
Electric Vehicles, 13(1), 1705–1710. https://doi.org/10.4130/jaev.13.1705
14. Bashir, F., & Bakhsh, F. (2018). Energy Management Strategies in Hybrid Electric Vehicles (HEVs). 4(February).
15. Patil, K., Molla, S. K., & Schulze, T. (2012). Hybrid vehicle model development using ASM-AMESim-Simscape co-
simulation for real-time HIL applications. SAE Technical Papers. https://doi.org/10.4271/2012-01-0932
16. Lee, H., Jeong, J., Park, Y. il, & Cha, S. W. (2017). Energy management strategy of hybrid electric vehicle using battery state
of charge trajectory information. International Journal of Precision Engineering and Manufacturing - Green Technology,
4(1), 79–86. https://doi.org/10.1007/s40684-017-0011-4
17. Feng, F., Lu, R., & Zhu, C. (2014). A Combined State of Charge Estimation Method for Lithium-Ion Batteries Used in a Wide
Ambient Temperature Range. 3004–3032. https://doi.org/10.3390/en7053004
18. Hevp4reference application from MATLAB/Simulink.
19. https://en.wikipedia.org/wiki/Bharat_stage_emission_standards
20. Marano, V., Rizzoni, G., Tulpule, P., Gong, Q., & Khayyam, H. (2012). Gestion énergétique intelligente pour véhicules
électriques hybrides rechargeables: Rôle de l’infrastructure de systèmes de transport intelligents (STI) dans l’électrification
des véhicules. Oil and Gas Science and Technology, 67(4), 575–587. https://doi.org/10.2516/ogst/2012019
09-02-2021 84

21. Singh, K. V., Bansal, H. O., & Singh, D. (2019). A comprehensive review on hybrid electric vehicles: architectures and
components. Journal of Modern Transportation, 27(2), 77–107. https://doi.org/10.1007/s40534-019-0184-3
22. Amtex Electronics. (2017). Battery Charging Terminology. 14–22.
23. Sumit Sharma, Anju Goel, R Suresh, C Sita Lakshami, Richa Mhatta, S. S. (2013). Assessment of emission test driving
cycles in India : A case for improving compliance. October 2014, 23. https://doi.org/10.13140/RG.2.1.1589.4163
24. https://www.hta.com.np/index.php/2017-12-03-07-00-27/2017-12-03-11-21-49/sensors &
https://sing365.com/wiki/Sensors
25. Ali, A. M., & Söffker, D. (2018). Towards optimal power management of hybrid electric vehicles in real-time: A review
on methods, challenges, and state-of-the-art solutions. Energies, 11(3), 1–24. https://doi.org/10.3390/en11030476
09-02-2021 85

“Surely we have a
responsibility to leave for
future generations a planet
that is healthy and
habitable by all species”

Design & Development of Energy management strategies for the improvement of fuel efficiency for 4WD hybrid electric vehicle

Design & Development of Energy management strategies for the improvement of fuel efficiency for 4WD hybrid electric vehicle

Design & Development of Energy management strategies for the improvement of fuel efficiency for 4WD hybrid electric vehicle

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados(20)

Similar a Design & Development of Energy management strategies for the improvement of fuel efficiency for 4WD hybrid electric vehicle

Similar a Design & Development of Energy management strategies for the improvement of fuel efficiency for 4WD hybrid electric vehicle(20)

Último

Último(20)

Design & Development of Energy management strategies for the improvement of fuel efficiency for 4WD hybrid electric vehicle