High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on Adaptive Wavelet Neural Network

Feng-wu Zhou, Lujun Wang, Huipin Lin, Zhengyu Lv
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引用次数: 16

Abstract

The state of charge online estimation of EV/HEV lithium battery with high accuracy is very important, Since it can be used to prolong the battery lifetime and improve its performances. Traditional SOC estimation algorithms have show their drawbacks apparently, so the Adaptive Wavelet Neural Network(AWNN) based SOC estimation model is presented. By using adaptive algorithm to train the model, the accurate online SOC estimation is implemented. The simulation and experiment results are given and show that the proposed algorithm is an effective and feasible method to estimate the SOC of the lithium battery with fastest convergence speed and most high accuracy.
基于自适应小波神经网络的EV/HEV锂电池荷电状态在线高精度估计
高精度的EV/HEV锂电池充电状态在线估计对于延长电池寿命和提高电池性能具有重要意义。针对传统的荷电状态估计算法存在的明显缺陷,提出了一种基于自适应小波神经网络的荷电状态估计模型。采用自适应算法对模型进行训练,实现了准确的在线SOC估计。仿真和实验结果表明,该算法收敛速度快、精度高,是一种有效可行的锂电池荷电状态估计方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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