The state of charge (SOC) of lithium batteries is an important parameter of battery management systems. We aim at the problem that the noise variance is fixed during the estimation of the battery state by the unscented Kalman filter (UKF), which leads to low estimation accuracy. Lithium battery SOC estimation based on the UKF and whale optimization algorithm (WOA) is proposed. The first WOA is used to identify the parameters of the battery model. WOA–UKF is used to estimate the SOC of the battery, in which the observed noise variance and process noise variance of the UKF are updated through the second WOA, thereby the estimation accuracy is improved. The experimental results verify the effectiveness of the improved method.

中文翻译：

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基于鲸鱼优化算法和无迹卡尔曼滤波器的锂电池SOC估算

锂电池的荷电状态（SOC）是电池管理系统的一个重要参数。我们针对无迹卡尔曼滤波器（UKF）在估计电池状态过程中噪声方差是固定的，导致估计精度低的问题。提出了基于UKF和鲸鱼优化算法(WOA)的锂电池SOC估算。第一个 WOA 用于识别电池模型的参数。WOA-UKF用于估计电池的SOC，通过二次WOA更新UKF的观测噪声方差和过程噪声方差，从而提高估计精度。实验结果验证了改进方法的有效性。