State of charge estimation of Li-ion battery for underwater vehicles based on EKF–RELM under temperature-varying conditions

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%.