Development of high‐fidelity mathematical models and state‐of‐charge (SOC) estimation of Li‐ion battery becomes a significant challenge when the temperature effects are considered. In this paper, we propose an enhanced temperature‐dependent equivalent circuit model for a Li‐ion battery and applied it for battery parameters estimation and model validation, as well as SOC estimation. First, the new battery model is elaborated, including a newly integrated resistance‐capacitor structure, a static hysteresis voltage and a temperature compensation voltage term. The forgetting factor least square approach is utilized to realize the parameter identification. Next, the proposed battery model is employed to estimate battery SOC by incorporating the extended Kalman filter algorithm. Finally, simulation results are provided to demonstrate the superior performance of the proposed battery model in comparison with the common first‐order Thevenin temperature model. Compared with Thevenin model, the maximal values of relative reconstruction error and root mean squared error with the proposed battery model are decreased by about 33.3% and 50.0%, respectively, for the battery terminal output voltage, 50.0% and 53.0%, respectively, for the SOC estimation, under three different test profiles.

中文翻译：

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使用扩展卡尔曼滤波器的锂离子电池的增强的温度相关模型和荷电状态估计

考虑温度影响时，锂离子电池的高保真数学模型和荷电状态（SOC）估计的发展成为一项重大挑战。在本文中，我们为锂离子电池提出了一种增强的温度相关等效电路模型，并将其用于电池参数估计和模型验证以及SOC估计。首先，阐述了新的电池模型，包括新集成的电阻-电容器结构，静态滞后电压和温度补偿电压项。遗忘因子最小二乘方法用于参数识别。接下来，通过结合扩展的卡尔曼滤波器算法，利用提出的电池模型来估算电池SOC。最后，提供的仿真结果证明了所提出的电池模型与常见的一阶戴维南温度模型相比具有优越的性能。与戴维南模型相比，该电池模型的相对重构误差和均方根误差最大值分别降低了约33.3％和50.0％，其中电池端子输出电压分别为50.0％和53.0％。在三种不同的测试配置文件下的SOC估算值。