eCite Digital Repository

Optimization of tremblay's battery model parameters for plug-in hybrid electric vehicle application

Citation

Zhang, Y and Lyden, S and Leon de la Barra, BA and Haque, ME, Optimization of tremblay's battery model parameters for plug-in hybrid electric vehicle application, Proceedings from the Australian Universities Power Engineering Conference, 19-22 November 2017, Melbourne, Australia, pp. 1-6. ISBN 9781538626481 (2017) [Refereed Conference Paper]


Preview
PDF
637Kb
  

Copyright Statement

Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

DOI: doi:10.1109/AUPEC.2017.8282405

Abstract

Accurate modeling of batteries for plug-in hybrid vehicle applications is of fundamental importance to optimize the operation strategy, extend battery life and improve vehicle performance. Tremblay’s battery model has been specifically designed and validated for electric vehicle applications. Tremblay’s parameter identification method is based on evaluating the three remarkable points manually picked from a manufacturer’s discharge curve. This method is error prone and the resultant discharge curve may deviate significantly from the experimental curve as reported in previous studies. This paper proposes to use a novel quantum-behaved particle swarm optimization (QPSO) parameter estimation technique to estimate the model parameters. The performance of QPSO is compared to that of genetic algorithm (GA) and particle swarm optimization (PSO) approaches. The QPSO technique needs less tuning effort than other techniques since it only uses one tuning parameter. Reducing the number of iterations should be a welcome development in most applications areas. Results show that the QPSO parameter estimation technique converges to acceptable solutions with fewer iterations than that obtained by the GA and the PSO approaches.

Item Details

Item Type:Refereed Conference Paper
Keywords:battery, modelling, Tremblay's battery model, parameter identification, genetic algorithm, particle swarm optimization, quantum-behaved particle swarm optimization
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Renewable Power and Energy Systems Engineering (excl. Solar Cells)
Objective Division:Energy
Objective Group:Energy Storage, Distribution and Supply
Objective Field:Energy Storage (excl. Hydrogen)
UTAS Author:Zhang, Y (Mr Ben Zhang)
UTAS Author:Lyden, S (Dr Sarah Lyden)
UTAS Author:Leon de la Barra, BA (Dr Bernardo Leon de la Barra)
ID Code:123948
Year Published:2017
Deposited By:Engineering
Deposited On:2018-02-02
Last Modified:2018-07-31
Downloads:36 View Download Statistics

Repository Staff Only: item control page