ZEBRA battery SOC estimation using PSO-optimized hybrid neural model considering aging effect

IEICE Electronics Express

Reza Pardis²  Davood Gharavian¹  Mansour Sheikhan² 

[1] EE Department, Shahid Abbaspour University of Technology;EE Department, Islamic Azad University, South Tehran Branch

关键词: hybrid neural networks;    state of charge;    estimation;    PSO algorithm;   

DOI  :  10.1587/elex.9.1115

学科分类：电子、光学、磁材料

来源: Denshi Jouhou Tsuushin Gakkai

【 摘 要 】

References(21)Cited-By(1)The state of charge (SOC) estimation for electric vehicles (EVs) is important and helps to optimize the utilization of the battery energy storage in EVs. In this way, aging is also a key parameter impacting the performance of batteries. In this paper, a hybrid neural model is proposed for the SOC estimation of ZEBRA (Zero Emission Battery Research Activities) battery considering the aging effect through the state of health (SOH) and the discharge efficiency (DE) parameters. The number of hidden nodes in neural modules is also optimized using particle swarm optimization (PSO) algorithm. The SOC estimation error of the proposed system is 1.7% when compared with the real SOC obtained from a discharge test.

【 授权许可】

Unknown