基于新型融合算法的锂电池荷电状态估计研究

Hao Zhu, Hanxin Shen, Siyu Deng, Jiaxiang Ye, Zeyu Xiao
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摘要

为了更好地估计锂电池的荷电状态(SOC),本文提出了一种将开路电压(OCV)方案、安培-小时(AH)积分策略和扩展卡尔曼滤波(EKF)方法相结合的新方法。基于混合脉冲功率下电池脉冲充放电的实验数据,建立了二级电阻-电容网络的等效电路模型。并拟合出SOC与开路电压的对应关系曲线,以识别锂电池等效电路模型参数。在MATLAB软件中对新型SOC融合算法进行了评估和仿真。结果表明,在动态测试条件下,该融合策略利用OCV获取SOC初始值,加快了EKF方法对预测值的收敛速度,避免了AH集成策略在SOC值90% ~ 100%范围内的累积误差。该方法实时估计电池荷电状态,并将荷电状态估计误差控制在2%以内。
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Research on SOC Estimation of Lithium Batteries Based on Novel Fusion Algorithm
In order to better estimate the State of Charge (SOC) of lithium batteries, this paper proposed a novel approach that combined the open circuit voltage (OCV) scheme, the ampere-hour (AH) integration strategy and the extended Kalman filter (EKF) method. Based on experimental data of battery pulse charging and discharging under hybrid pulse power, the equivalent circuit model of the second-order Resistor-Capacitance (SoRC) network was created. Besides, the curve of the corresponding relationship between SOC and open circuit voltage was fitted so as to identify the equivalent circuit model parameters of lithium batteries. Moreover, the novel SOC fusion algorithm was evaluated and simulated in MATLAB software. The obtained results demonstrated that under the dynamic test condition, the proposed fusion strategy accelerated the convergence of the EKF method for the predicted value and avoided the accumulative error of the AH integration strategy in the SOC value range of 90%-100% by utilizing the OCV to obtain the initial value of SOC. The proposed method estimates the SOC of the battery in real time and controls the SOC estimation error within 2%.
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