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Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods

机译:基于分数阶和积分阶法的锂离子电池建模与荷电状态估计的比较

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In order to properly manage lithium-ion batteries of electric vehicles (EVs), it is essential to build the battery model and estimate the state of charge (SOC). In this paper, the fractional order forms of Thevenin and partnership for a new generation of vehicles (PNGV) models are built, of which the model parameters including the fractional orders and the corresponding resistance and capacitance values are simultaneously identified based on genetic algorithm (GA). The relationships between different model parameters and SOC are established and analyzed. The calculation precisions of the fractional order model (FOM) and integral order model (IOM) are validated and compared under hybrid test cycles. Finally, extended Kalman filter (EKF) is employed to estimate the SOC based on different models. The results prove that the FOMs can simulate the output voltage more accurately and the fractional order EKF (FOEKF) can estimate the SOC more precisely under dynamic conditions.
机译:为了正确管理电动汽车(EV)的锂离子电池,必须建立电池模型并估算充电状态(SOC)。本文建立了Thevenin分数阶形式和伙伴关系的新一代车辆(PNGV)模型,其中基于遗传算法(GA)同时识别包括分数阶的模型参数以及相应的电阻和电容值)。建立并分析了不同模型参数与SOC之间的关系。在混合测试周期下,对分数阶模型(FOM)和积分阶模型(IOM)的计算精度进行了验证和比较。最后,基于不同模型,采用扩展卡尔曼滤波器(EKF)估计SOC。结果证明,在动态条件下,FOM可以更精确地模拟输出电压,分数阶EKF(FOEKF)可以更精确地估计SOC。

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