Accurate state of charge (SOC) is great significant for lithium-ion battery to maximize its performance and prevent it from overcharging or overdischarging. This paper presents an ensemble Kalman filter- (EnKF-) based SOC estimation algorithm for lithium-ion battery. Firstly, the lithium-ion battery is modeled by the first-order RC equivalent circuit, and the multi-innovation least square (MILS) algorithm is used to perform online parameter identification of the model parameters. Then, the ensemble Kalman filter (EnKF) is introduced to estimate the state of charge. Finally, two typical experiments including constant current discharge experiment and cycling dynamic stress test are applied to evaluate the performance of the joint algorithm of MILS and EnKF. The experimental results show that the joint algorithm-based ensemble Kalman filter can achieve fast tracking and higher estimation accuracy for lithium-ion battery SOC.

中文翻译：

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基于集成卡尔曼滤波器的MILS算法估算锂离子电池的充电状态

准确的充电状态（SOC）对于锂离子电池发挥最大性能并防止其过度充电或过度放电至关重要。本文提出了一种基于集成卡尔曼滤波器（EnKF-）的锂离子电池SOC估计算法。首先，利用一阶RC等效电路对锂离子电池进行建模，并使用多元创新最小二乘算法（MILS）对模型参数进行在线参数识别。然后，引入集合卡尔曼滤波器（EnKF）来估计充电状态。最后，通过恒流放电实验和循环动应力测试两个典型实验，对MILS和EnKF联合算法的性能进行了评估。