基于磷酸铁锂电池组实验数据辨识模型参数
Experimental Data Driven Model Parameter Identificationfor LiFePO₄ Battery Packs
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摘要: 电动汽车普及背景下,动力电池 SOC 估算精度影响续航与安全。因锂电池 SOC 无法直接测量且内部结构复杂,需建立数学模型。本文针对磷酸铁锂蓄电池模组,选用二阶 RC 等效电路模型,通过 0.25C - 0.75C 脉冲充放电实验,对 75 串电池模组四阶段测试,利用电压响应特性及最小二乘法辨识模型参数。在 MATLAB 中构建模型,拟合 SOC 与开路电压关系并引入电流分段控制。仿真显示模型与实验数据曲线最大误差 1.67%,平均误差 0.52%,为电动汽车磷酸铁锂电池 SOC 估算提供一种可行方案。Abstract: In the context of the popularization of electric vehicles, the estimation accuracy of power batteries affects battery life and safety. Because the SOC of lithium batteries cannot be measured directly and the internal structure is complex, a mathematical model needs to be established. This paper uses a second-order RC equivalent circuit model for lithium iron phosphate battery modules, and uses 0.25C - 0.75C pulse charge and discharge experiments to test the 75-string battery packs in four stages, and uses voltage response characteristics and least squares method to identify model parameters. Models are constructed in MATLAB, fit the SOC to open-circuit voltage relationship and introduce current segmentation control. Simulation results show that the model has a maximum error of 1.67% and an average error of 0.52% compared to experimental data, providing a feasible solution for estimating the SOC of lithium iron phosphate batteries in electric vehicles.
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