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A State of Charge Estimation Method for Lithium-Ion Battery Using PID Compensator-Based Adaptive Extended Kalman Filter
Complexity ( IF 2.462 ) Pub Date : 2021-02-22 , DOI: 10.1155/2021/6665509
Zheng Liu; Yuan Qiu; Chunshan Yang; Jianbo Ji; Zhenhua Zhao

With the widespread application of electric vehicles, the study of the power lithium-ion battery (LIB) has broad prospects and great academic significance. The state of charge (SOC) is one of the key parts in battery management system (BMS), which is used to provide guarantee for the safe and efficient operation of LIB. To obtain the reliable SOC estimation result under the influence of simple model and measurement noise, a novel estimation method with adaptive feedback compensator is presented in this paper. The simplified dynamic external electrical characteristic of LIB is represented by the one-order Thevenin equivalent circuit model (ECM) and then the ECM parameters are identified by the forgetting factor recursive least squares method (FFRLS). Fully taking into account the feedback effect of terminal voltage innovation, the combination of adaptive extended Kalman filter (AEKF) and innovation vector-based proportional-integral-derivative (PID) feedback is proposed to estimate the LIB SOC. The common single proportional feedback of Kalman filter (KF) is replaced by the innovation vector-based PID feedback, which means that the multiple prior terminal voltage innovation is used in the measurement correction step of KF. The results reveal that the AEKF with PID feedback compensation strategy can improve the SOC estimation performance compared with the common AEKF, and it reveals good robust capability and rapid convergence speed for initial SOC errors. The maximum absolute error and average absolute error for SOC estimation are close to 4% and 2.6%, respectively.

中文翻译:

基于PID补偿器的自适应扩展卡尔曼滤波器的锂离子电池充电状态估计方法

随着电动汽车的广泛应用,动力锂离子电池(LIB)的研究具有广阔的前景和重大的学术意义。充电状态(SOC)是电池管理系统(BMS)的关键部分之一,用于为LIB的安全有效运行提供保证。为了在简单模型和测量噪声的影响下获得可靠的SOC估计结果,提出了一种带有自适应反馈补偿器的新型估计方法。LIB的简化动态外部电气特性由一阶戴维南等效电路模型(ECM)表示,然后通过遗忘因子递归最小二乘法(FFRLS)识别ECM参数。充分考虑了端子电压创新的反馈效应,提出将自适应扩展卡尔曼滤波器(AEKF)与基于创新矢量的比例积分微分(PID)反馈相结合来估计LIB SOC。卡尔曼滤波器(KF)的普通单比例反馈已被基于创新矢量的PID反馈所取代,这意味着在KF的测量校正步骤中使用了多个先验端电压创新。结果表明,与普通AEKF相比,具有PID反馈补偿策略的AEKF可以提高SOC估计性能,并且具有良好的鲁棒能力和针对初始SOC错误的快速收敛速度。SOC估计的最大绝对误差和平均绝对误差分别接近4%和2.6%。
更新日期:2021-02-22
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