具有电流偏差和噪声扰动的H_∞观测器在线估计电池SoC状态

Battery State-of-Charge Online Estimation Based on H_∞ Observer with Current Debasing and Noise Distributions

针对电池管理系统采集的电流信号和电压信号往往含有有色噪声,且该噪声会明显影响电池状态估计性能的问题,把噪声看成扰动,提出了具有偏置电流估计功能的H_∞观测器用于电池SoC的在线估计。首先,建立具有电流偏差和噪声扰动的电池状态模型,其次设计具有偏置电流估计功能的H_∞观测器,并且通过仿真深入分析该观测器对SoC估计效果、去偏功能对估计性能的影响以及观测器对模型偏差的鲁棒性和参数适配性,最后通过实验分析验证了该方法的有效性。

In battery management systems, there are always colored noises in the sampled battery current signals and voltage signals, which make it hard to achieve the accurate battery state of charge estimation. Regarding these noises as distributions, an Ho~ observer with current debasing for online batter state of charge （SoC） estimation is proposed in this paper. Firstly, the battery stated model with current debasing and noise distribution is built. Secondly, H∞ observer is designed with current debasing. The estimation accuracy, performance, robust to model errors and parameter adaptation of the observer are analyzed by simulation. At last, experiment results demonstrate its effectiveness.