基于扩展卡尔曼滤波的电动汽车动力电池SOC计算方法

Xin Liu, ZengMu Cheng, Feng-Yan Yi, T. Qiu
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引用次数: 8

摘要

电池SOC的估算是电池管理系统的核心,SOC直接影响电池管理系统(BMS)的决策和控制。在本设计中,考虑了卡尔曼滤波校正方法,并考虑了充放电速率、温度和充放电周期对SOC估计的影响。在此基础上,提出了卡尔曼滤波校正算法,并将其应用于纯电动汽车电池管理系统中。结果表明,卡尔曼滤波校正算法有效地修正了Ah法的误差,提高了估计精度,为电池管理系统提供了一种更准确的SOC估计方法。
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SOC calculation method based on extended Kalman filter of power battery for electric vehicle
Battery SOC estimation is the core of the battery management system, SOC directly affect the battery management system (BMS) decision-making and control. In this design, the Kalman filter correction method is taken into account and the influence of charge and discharge rate, temperature and charge/discharge cycles on the SOC estimation is considered. Based on this method, the Kalman filter correction algorithm is proposed, and its application in the pure electric vehicle battery management system. The results show that Kalman filter correction algorithm effectively corrects the error of Ah method, improves the estimation precision, and provides a more accurate SOC estimation method for battery management system.
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