HV Battery SOC Estimation Algorithm Based on PSO

Qiang Sun, Haiying Lv, Shasha Wang, Shixiang Jing, Jijun Chen, Rui Fang

Abstract

In view of the traditional algorithm to estimate the electric vehicle battery state of charge (SOC), lots of problems were caused by the inaccurate battery model parameter estimation error. Particle swarm optimization (PSO) algorithm is a global optimization algorithm based on swarm intelligence, it has intrinsic parallel search mechanism, and is suitable for the complex optimization field. PSO is simple in concept with few using of parameters, and easily in implementation. It is proved to be an efficient method to solve optimization problems, and has successfully been applied in area of function optimization. This article adopts the PSO optimization method to solve the problem of battery parameters. The paper make a new attempt on SOC estimation method, applying PSO to SOC parameters optimization. It can build optimization model to get a more accurate estimate SOC in the case of less parameters with faster convergence speed.

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Paper Citation


in Harvard Style

Sun Q., Lv H., Wang S., Jing S., Chen J. and Fang R. (2018). HV Battery SOC Estimation Algorithm Based on PSO.In 3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT, ISBN 978-989-758-312-4, pages 667-670. DOI: 10.5220/0006976606670670


in Bibtex Style

@conference{icectt18,
author={Qiang Sun and Haiying Lv and Shasha Wang and Shixiang Jing and Jijun Chen and Rui Fang},
title={HV Battery SOC Estimation Algorithm Based on PSO},
booktitle={3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT,},
year={2018},
pages={667-670},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006976606670670},
isbn={978-989-758-312-4},
}


in EndNote Style

TY - CONF

JO - 3rd International Conference on Electromechanical Control Technology and Transportation - Volume 1: ICECTT,
TI - HV Battery SOC Estimation Algorithm Based on PSO
SN - 978-989-758-312-4
AU - Sun Q.
AU - Lv H.
AU - Wang S.
AU - Jing S.
AU - Chen J.
AU - Fang R.
PY - 2018
SP - 667
EP - 670
DO - 10.5220/0006976606670670