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State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries
Complexity ( IF 2.3 ) Pub Date : 2021-02-18 , DOI: 10.1155/2021/8816250
Kai Wang 1 , Chunli Liu 1 , Jianrui Sun 2 , Kun Zhao 2 , Licheng Wang 3 , Jinyan Song 4 , Chongxiong Duan 5 , Liwei Li 6
Affiliation  

This paper studies the state of charge (SOC) estimation of supercapacitors and lithium batteries in the hybrid energy storage system of electric vehicles. According to the energy storage principle of the electric vehicle composite energy storage system, the circuit models of supercapacitors and lithium batteries were established, respectively, and the model parameters were identified online using the recursive least square (RLS) method and Kalman filtering (KF) algorithm. Then, the online estimation of SOC was completed based on the Kalman filtering algorithm and unscented Kalman filtering algorithm. Finally, the experimental platform for SOC estimation was built and Matlab was used for calculation and analysis. The experimental results showed that the SOC estimation results reached a high accuracy, and the variation range of estimation error was [−0.94%, 0.34%]. For lithium batteries, the recursive least square method is combined with the 2RC model to obtain the optimal result, and the estimation error is within the range of [−1.16%, 0.85%] in the case of comprehensive weighing accuracy and calculation amount. Moreover, the system has excellent robustness and high reliability.

中文翻译:

带有超级电容器和锂电池的复合储能系统的荷电状态估算

本文研究了电动汽车混合动力储能系统中超级电容器和锂电池的充电状态(SOC)估计。根据电动汽车复合储能系统的储能原理,分别建立了超级电容器和锂电池的电路模型,并采用递推最小二乘(RLS)方法和卡尔曼滤波(KF)在线识别模型参数。算法。然后,基于卡尔曼滤波算法和无味卡尔曼滤波算法完成了SOC的在线估计。最后,建立了SOC估计的实验平台,并利用Matlab进行了计算和分析。实验结果表明,SOC估计结果具有较高的精度,估计误差的变化范围为[-0.94%,0.34%]。对于锂电池,将递推最小二乘法与2RC模型结合以获得最佳结果,并且在综合称量精度和计算量的情况下,估计误差在[-1.16%,0.85%]的范围内。此外,该系统具有出色的鲁棒性和高可靠性。
更新日期:2021-02-18
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