This document describes simulations performed in Autonomie software to optimize the component sizing of a plug-in hybrid electric vehicle (PHEV) to improve fuel efficiency and reduce emissions. Various parameters of the engine, motors, battery and controls were varied in the Toyota Prius model and the results compared. The optimized configuration found was an engine power of 48kW, traction motor of 52kW, battery of 5.8kWh with optimized cell configuration, and motor 2 of 45kW. This optimized design improved the fuel economy from the default 52.6 to 59.56 MPG.

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Component Sizing of
Plugin Hybrid Electric
Vehicle for Sub-Optimal
Fuel Efficiency and
emissions
Project Report
Submitted By:
Mohit Suri

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Index
1. Abstract
2. Introduction
3. About Autonomie
4. Objective
5. Model Configuration
6. Open loop and Closed loop
7. Flow Chart
8. Developing Cost Function
9. Simulation
10. Discussions and Conclusion
11. References

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Abstract:
The Project involves simulating a Plug-in hybrid vehicle Toyota Pruis model in
Autonomie software for optimizing the fuel economy by sizing the components
involved in energy production in HEV. The software has been developed by Argonne
labs. The project is approached by creating a flowchart of the problem and following
the steps accordingly. It includes developing a cost function for optimization. Trial and
error method is used to determine the possible optimized result. The vehicle model
consists of driver block, environment block and vehicle block with individual
parameters which can be changed. Various simulations were done and optimized
values have been selected as best values for component sizes to get maximum fuel
economy from all the simulation runs. The Project report consists of Introduction,
flowchart, cost function, parametric simulations, results and discussions.

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Introduction:
The concept of plug in series hybrid electric drive train was developed from the
vehicle drive train. The Lohner-Porsche Mixte Hybrid, produced as early as 1899, was
the first hybrid electric car. However electric vehicles using present technologies have
some disadvantages: a limited drive range due to shortage of energy storage in the on-
board batteries, limited payload and volume capacity due to heavy and bulky batteries,
and a longer battery charging time.
The initial objective of developing a PHEV was aimed at extending the drive range
and decreasing the fuel consumption that ultimately gives less emissions.
There are two basic plug-in hybrid configurations:
 Series plug-in hybrids, also called Extended Range Electric Vehicles (EREVs).
Only the electric motor turns the wheels; the gasoline engine is only used to
generate electricity. Series plug-ins can run solely on electricity until the battery
needs to be recharged. The gasoline engine then generates electricity to power
the electric motor. For shorter trips, these vehicles might use no gasoline at all.
 Parallel or Blended Plug-in Hybrids. Both the engine and electric motor are
mechanically connected to the wheels, and both propel the vehicle under most
driving conditions. Electric-only operation usually occurs only at low speeds.

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Series Plug-in HEV
Parallel Plug–in HEV
Charge
Port

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About Autonomie
Autonomie is a Plug-and-Play Powertrain and Vehicle Model Architecture and
Development Environment to support the rapid evaluation of new
powertrain/propulsion technologies for improving fuel economy through virtual design
and analysis in a math-based simulation environment.
Autonomie is an open architecture to support the rapid integration and analysis of
powertrain/propulsion systems and technologies for rapid technology sorting and
evaluation of fuel economy improvement under dynamic/transient testing conditions.
The capability to sort technologies rapidly in a virtual design environment results in
faster improvements in real-world fuel consumption by reducing the time necessary to
develop and bring new technologies onto our roads.

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The various views in Autonomie GUI are:

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Objective:
The main objective of this Project is to improve the miles per equivalent gallon fuel
consumed accounting for electrical energy, gasoline usage and reduced emissions.
In this Project Trial and error method was used to optimize the component sizing of
Plugin Hybrid electric vehicle Powertrain for fuel efficiency and emissions while
meeting the critical performance requirements. Ordinary differential equation solver4
(Runga Kutta) method was used by Autonomie as default solver.
The main component that uses fuel and produce was engine. So sizing the
component of the engine for reduced emissions and increased Fuel economy was
prime motive or target. Then accordingly traction motor size was changed/altered to
get optimized values of fuel economy. Then battery pack configuration was changed.
Various simulations were done to get optimized sizing that would result in better fuel
economy and accordingly less emissions. It was observed that factors like and
(controller gains) has some effect on the overall fuel economy, so individual
parametric study was done and results were obtained.
USO6 was used as the cycle for the simulation run with runtime of 600 seconds. It a
highway driving cycle with an average speed of 50 Mph and maximum speed of 80.3
Mph.

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Model Configuration:
Power Split Design in Autonomie
The Above configuration shows the layout of Split-Power Plug-In Hybrid electric
vehicle. The Components in this Model include:
Engine
Traction Motor
Battery (ESS)
Chassis
Motor 2
Final Drive
Electric Accessories

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Open Loop for Battery:
Open Loop for Engine:

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Control Architecture(Closed Loop)

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Flowchart of the Simulation

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Developing a Cost Function
According to the paper the cost function was developed while meeting the critical
performance requirements and also improving fuel economy and reducing
emissions. The cost function is given by:
, X =
Where, Nbm = number of battery modules
Cf = fuel consumption
Pm = Motor Power
Pe = Engine Power
The Objective Function is given as:
F (Pm, Pe, Nbm,Cf) = w1 + w2 + w3 + w4
here, wi are the weighting factors of the objective function variables.

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Simulations:
Below is the result file that is generated after the different simulation runs.
The initial Power of the Engine was taken as 57 KW. The power of the Engine was
altered from 57 to 49 KW. Finally from 49 KW to 48 KW and simulation was run. The
results show that engine with power 48 KW gives maximum fuel efficiency.
The Various Comparison Plots are as follows:
State of Charge VS time

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Comparison of Plants SOC VS time with Changing Engine Power values
Comparison of Engine Power VS time with Changing Engine Power values
Comparison of Motor Power VS time with Changing Engine Power values

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Comparison of Battery(ESS) Power VS time with Changing Engine Power values
Best Result(Engine) Plots:
Engine,Traction Motor, Motor 2 Power VS Time
Engine,Traction Motor Power VS Time

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Battery(ESS) Power VS Time
Linear Acceleration VS Time
Changing Battery Parameters:
The battery Parameters were changed now. The cells in series were initially 60 and
cells in parallel were 1. Different configurations were used such as cells in series from
2 and then reduced to 25. Accordingly cells are parallel were changed from 1 to 10 to
12 to 15. Thus giving the battery power to be 5.8 KWh. Below are the results obtained
from simulation.

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Battery SOC VS Time (best result)
Sizing Of Traction Motor and Motor 2:
The traction motor size was varied from 45KW to 60KW and the results were
noted. It was noted that there is not much difference in the improvement of the
fuel economy though big motors would increase weight and cost. So the
optimized size was taken as 52KW. Similarly the Size of Motor 2 was varied
from 30KW to 45 KW and the results obtained by both the simulations are given
below:
Results for Traction motor size changes

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Results for Motor 2 size changes
The comparison between simulation values can be seen in plots below:
Battery, Motor Power VS Time
Battery, Engine Power VS Time

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Engine Power with Different Motor Power VS Time
Battery Power (with Optimum Traction Motor Size) VS Time
Battery Power (with Optimum Motor 2 Size) VS Time

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Linear Speed VS Time
Engine Power, Motor Power VS Time
Total Power VS Time

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Traction Motor, Motor 2 Power VS Time
SOC VS Time
and (Parametric Study):
The and values are controller gains. The parametric study was done changing
the values in every simulation. The results are shown below:
Study of

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Study of
Best Result:
Plugging in all the best values of the components obtained in the simulation runs and
changing them again to work best in one combination. The best mileage obtained was
59.56 MPG which was improvement from the default 52.6 MPG

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Discussion and Conclusion:
Autonomie is a good software/platform to verify our proposed designs in a cost
effective and easy way. Changing the engine size, Battery parameters and optimizing
the size of motor greatly increases the fuel economy which is accompanied by reduced
emissions. These designs can be used in real-time situations which will greatly
increase the drive range of Plug-in Hybrid Electric Vehicle one of the problems
prevailing in Hybrid Electric Vehicles.
References:
1. Autonomie help documents
2. Harpreet singh Banvait, Xiao Lin, Sohel Anwar, and Yaobin Chen, “Plug-in
Hybrid Electric Vehicle Energy Management System using Particle Swarm
Optimization”, Electric Vehicle Symposium (EVS – 24), Stavanger, Norway,
May 13 – 16, 2009.
3. Optimal Sizing of a Parallel PHEV Powertrain-Mitra Pourabdollah, Nikolce
Murgovski, Anders Grauers, and Bo Egardt, Fellow, IEEE.