Underwater vehicles are important mobile platforms used for ocean exploration. However, temperature changes along the ocean depth are rapid and complex, making it difficult to estimate the SOC (state of charge). Besides, the EKF method, which is used widely for SOC estimation, ignores the higher-order terms of Taylor expansion, which may produce large truncation errors. To address this problem, this paper proposed a SOC estimation method based on the extended Kalman filter and regularised extreme learning machine (EKF–RELM). First, the relationship between model parameters and temperature is explored. Then the EKF is applied to estimate the value of SOC and the RELM is used ultimately to revise the estimated value. Offline experiments were conducted to assess the performance of the EKF–RELM method compared with the EKF method under different conditions. The estimation error of EKF–RELM was less than that of EKF under variable temperature and load conditions. Finally, trials were performed in Qiandao Lake, and the maximum error (ME) in the SOC estimation was found to be less than 1.67%.

水下航行器是用于海洋探索的重要移动平台。然而，沿海洋深度的温度变化迅速而复杂，因此很难估计 SOC（荷电状态）。此外，广泛用于 SOC 估计的 EKF 方法忽略了泰勒展开的高阶项，这可能会产生较大的截断误差。针对这一问题，本文提出了一种基于扩展卡尔曼滤波器和正则化极限学习机（EKF-RELM）的SOC估计方法。首先，探讨了模型参数与温度之间的关系。然后应用EKF估计SOC的值，最终用RELM修正估计值。进行离线实验以评估 EKF-RELM 方法与 EKF 方法在不同条件下的性能。在可变温度和负载条件下，EKF-RELM 的估计误差小于 EKF。最后在千岛湖进行试验，发现SOC估算的最大误差（ME）小于1.67%。