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Coulomb counting state-of-charge algorithm for electric vehicles with a physics-based temperature dependent battery model

机译:基于物理的温度相关电池模型的电动汽车库仑计数荷电状态算法

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This paper proposes a new Coulomb counting state-of-charge (SOC) algorithm for a lithium iron phosphate battery that achieves accurate results (3.8% standard deviation) for several drive cycles and temperatures between -10°C and 20°C. The basis for this method is a physics-based battery model that compensates for the influences of drive dynamics and temperature on the SOC estimates, instead of the data-driven, empirical adjustments that have been reported in the literature to date. The method also provides compensation for battery aging effects by recursively updating the battery parameters using a Kalman filter algorithm. This method has been evaluated using both lab equipment and a converted Ford F150 electric truck. The results confirm the robustness of the proposed algorithm for suppressing the influences of drive dynamics and temperature on SOC estimates.
机译:本文提出了一种新的库仑计数荷电状态(SOC)算法,用于磷酸铁锂电池,可在-10°C至20°C的多个驱动周期内获得准确的结果(标准偏差为3.8%)。该方法的基础是基于物理的电池模型,该模型可以补偿驱动动力学和温度对SOC估计值的影响,而不是迄今为止文献中报道的数据驱动的经验调整。该方法还通过使用卡尔曼滤波器算法递归更新电池参数来提供对电池老化影响的补偿。已使用实验室设备和改装的福特F150电动卡车对该方法进行了评估。结果证实了所提出算法的鲁棒性,该算法可抑制驱动器动力学和温度对SOC估计的影响。

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