基于 FRLS-UKF 的储能电池荷电状态在线评估

Online Assessment for State of Charge of Energy Storage Battery Based on FRLS-UKF

摘要:

对储能锂离子电池荷电状态(state of charge,SOC)的准确估计，能保障储能锂电池系统安全稳定运行。针对储能锂电池等效电路模型参数不准确以及储能复杂环境噪声不确定的问题，基于戴维南等效电路模型，在实际测得的间隔恒流放电数据基础上利用带遗忘因子的递推最小二乘法进行等效电路参数辨识，将辨识后参数计算得到的电压与试验电压进行比较分析，验证所辨识模型的优劣并获取模型最优参数，在此基础上，提出一种基于无迹卡尔曼滤波算法对电池SOC进行准确估计。将所提算法与扩展卡尔曼滤波算法和安培小时积分法进行比较，并分析不同初始SOC对估计结果的影响。结果表明，所提方法在不同初始SOC条件下均可快速收敛并准确估计SOC，在初始SOC与模型参数无误差的条件下，估计精度高达99.2%。

Abstract:

The accurate estimation of state of charge (SOC) can make the battery management system more stable and reliable, which is of great significance to ensure the safe and stable operation of lithium ion battery system for energy storage. For the problems of inaccurate parameters of equivalent circuit model of energy storage lithium battery and uncertain complex environmental noise of energy storage, first of all, the interval constant current discharge data is measured, and recursive least square method with forgotting factor is used for parameter recognition of Thevenin equivalent circuit. Comparing the calculated voltage after recognition with the test voltage, the advantages and disadvantages of the recognition model are verified and the best parameters of the model are obtained. The algorithm based on unscented Karman filtering is proposed for accurate estimation of battery SOC. The proposed method is compared with the extended Karman filter algorithm and the Ampere hour integration method, and the effects of different initial SOC on the estimation results are analyzed. The experimental results show that the proposed method can quickly converge at different initial SOC, and under the conditions without errors of initial SOC and model parameters, the estimated accuracy is as high as 99.2%.