Electrochamical Model-based SOC Estimations by Using Different Algorithms for Lithium-ion Batteries

Chao Lyu, Lulu Zhang, Junfu Li, Yanben Zhao, W. Luo, Lixin Wang
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Abstract

In order to compare the performance of different state estimation algorithms in electrochamical model-based SOC(state of charge) estimation for lithium-ion battery, this paper proposed a series of SOC estimation approaches which use different algorithms including extended Kalman filter(EKF), adaptive extended Kalman filter(AEKF), particle filter(PF) and dichotomy. Their accuracy, convergence and computation efficiency was examined at the end of the paper.
基于电化学模型的锂离子电池荷电状态估计
为了比较不同状态估计算法在基于电化学模型的锂离子电池荷电状态估计中的性能,本文提出了一系列使用扩展卡尔曼滤波(EKF)、自适应扩展卡尔曼滤波(AEKF)、粒子滤波(PF)和二分法等不同算法的荷电状态估计方法。最后对它们的精度、收敛性和计算效率进行了检验。
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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