Battery state of charge online estimation based on particle filter
Battery state of charge online estimation based on particle filter
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DOI：
10.1109/cisp.2011.6100603
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发表时间：
2011-12
期刊：
影响因子：
--
通讯作者：
Mingyu Gao;Yuanyuan Liu;Zhiwei He
中科院分区：
文献类型：
--
作者：
Mingyu Gao;Yuanyuan Liu;Zhiwei He
电池电量状态估计是电池管理系统的关键技术之一。准确估计电池的充电状态有助于提高电池的性能，增加电动汽车的安全性。提出了一种基于粒子滤波的电池电量状态估计方法。将电池视为一个非线性动态系统，并将电池的充电状态作为唯一的状态变量。系统的动态特性采用两种模型来描述，一种是状态传输模型，另一种是描述电荷状态与终端电压、放电电流等之间关系的测量模型。实验结果表明，该方法是有效的。
Battery state of charge estimation is one of the key techniques to battery manage system. An accurate estimation of the state of charge can help to improve the performance of the battery and increase the security of the electric vehicle. A particle filter based battery state of charge estimation method is proposed in this paper. The battery is looked on as a nonlinear dynamic system and the state of charge of the battery is used as the only state variable in it. Two models are used to describe the dynamics of the system, one is the state transmission model and the other is the measurement model which describes the relationship between the state of charge and the terminal voltage, the discharge current, etc. Experiment results show that the proposed method is effective and efficient.