project+report mohit+suri

FINAL PROJECT ME 597

PHEV

Component Sizing of

Plugin Hybrid Electric

Vehicle for Sub-Optimal

Fuel Efficiency and

emissions

Project Report Submitted By:

Mohit Suri


PHEV

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.


PHEV

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.

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