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Technical Briefs

Using the Pareto Set Pursuing Multiobjective Optimization Approach for Hybridization of a Plug-In Hybrid Electric Vehicle

[+] Author and Article Information
Shashi K. Shahi

Product Design and Optimization Laboratory,  Simon Fraser University, Surrey, BC, Canadashashi_k_shahi@yahoo.ca

G. Gary Wang1

Product Design and Optimization Laboratory,  Simon Fraser University, Surrey, BC, Canadagary_wang@sfu.ca

Liqiang An

Mechanical Engineering Department,  North China Electric Power University, Baoding, Chinaanliqiang@gmail.com

Eric Bibeau

Department of Mechanical and Manufacturing Engineering,  University of Manitoba, Winnipeg, MB, Canadabibeauel@cc.umanitoba.ca

Zhila Pirmoradi

Product Design and Optimization Laboratory,  Simon Fraser University, Surrey, BC, Canadazpirmora@sfu.ca

1

Corresponding author.

J. Mech. Des 134(9), 094503 (Aug 07, 2012) (6 pages) doi:10.1115/1.4007149 History: Received November 14, 2011; Revised May 24, 2012; Published August 07, 2012; Online August 07, 2012

A plug-in hybrid electric vehicle (PHEV) can improve fuel economy and emission reduction significantly compared to hybrid electric vehicles and conventional internal combustion engine (ICE) vehicles. Currently there lacks an efficient and effective approach to identify the optimal combination of the battery pack size, electric motor, and engine for PHEVs in the presence of multiple design objectives such as fuel economy, operating cost, and emission. This work proposes a design approach for optimal PHEV hybridization. Through integrating the Pareto set pursuing (PSP) multiobjective optimization algorithm and powertrain system analysis toolkit (PSAT) simulator on a Toyota Prius PHEV platform, 4480 possible combinations of design parameters (20 batteries, 14 motors, and 16 engines) were explored for PHEV20 and PHEV40 powertrain configurations. The proposed approach yielded the optimal solution in a small fraction of computational time, as compared to an exhaustive search. This confirms the efficiency and applicability of PSP to problems with discrete variables. In the design context we have found that battery, motor, and engine collectively define the optimal hybridization scheme, which also varies with the drive cycle and all electric range (AER). The proposed method and software platform could be applied to optimize other powertrain designs.

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Figures

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Figure 2

Flow chart of the Pareto set pursuing approach

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Figure 4

Flow chart of the program structure for the automated optimization process

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Figure 5

Fuel efficiency, operation cost, and GHG emissions for PHEV20 using UDDS

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Figure 3

PSP multiobjective optimization algorithm with PSAT as a black box

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Figure 1

Optimal battery sizing

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