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An Improved Test Method of LiFePO4/Graphene Hybrid Cathode Lithium-Ion Battery and the State of Charge Estimation

机译:LiFePO4 /石墨烯混合阴极锂离子电池的改进试验方法及估计状态

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摘要

The state of charge (SoC) of the battery is a typical characterization of the operating state of the battery and criterion for the battery management system (BMS) control strategy, which must be evaluated precisely. The establishment of an accurate algorithm of SoC estimation is of great significance for BMS, which can help the driver judge the endurance mileage of electric vehicle (EV) correctly. In this paper, a second-order resistor-capacity (RC) equivalent circuit model is selected to characterize the electrical characteristics based on the electrochemical model of the LiFePO4/graphene (LFP/G) hybrid cathode lithium-ion battery. Moreover, seven open circuit voltage (OCV) models are compared and the best one of them is used to simulate the dynamic characteristics of the battery. It is worth mentioning that an improved test method is proposed, which is combined with least square for parameters identification. In addition, the extended Kalman filter (EKF) algorithm is selected to estimate the SoC during the charging and discharging processes. The simulation results show that the EKF algorithm has the higher accuracy and rapidity than the KF algorithm.
机译:电池的荷电状态(SoC)是电池工作状态的典型表征,也是电池管理系统(BMS)控制策略的标准,必须对其进行精确评估。建立准确的SoC估计算法对BMS系统具有重要意义,可以帮助驾驶员正确判断电动汽车的续航里程。本文在LiFePO4/石墨烯(LFP/G)混合阴极锂离子电池电化学模型的基础上,选择二阶电阻电容(RC)等效电路模型来表征电池的电特性。此外,还比较了七种开路电压(OCV)模型,并用其中最好的一种模型来模拟电池的动态特性。值得一提的是,提出了一种改进的测试方法,该方法与最小二乘法相结合用于参数识别。此外,采用扩展卡尔曼滤波(EKF)算法来估计充电和放电过程中的SoC。仿真结果表明,EKF算法比KF算法具有更高的精度和速度。

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