State of Charge Estimation of Lithium-Ion Battery for Underwater Vehicles Using MM-UKF Under Hierarchical Temperature Compensation

Underwater vehicles are crucial for advancing underwater geological exploration, military training, and the marine industry. However, the accuracy of lithium-ion battery SOC estimation algorithms for these vehicles deteriorates with depth changes due to variations in seawater temperature, leading to limited generalization. This paper proposes a SOC estimation framework that combines the UKF with the MLP-Mixer neural network for lithium-ion batteries in underwater vehicles. The proposed framework employs the MLP-Mixer neural network to predict battery terminal voltage, incorporating hierarchical temperature compensation based on temperature data from the BMS. This straightforward and efficient compensation method enhances prediction accuracy. Subsequently, the framework combines the MLP-Mixer neural network with the UKF to form a new estimator, MM-UKF. The MM-UKF estimator’s prediction accuracy, temperature adaptability, and generalization ability underwent comprehensive examination and evaluation using a public dataset under varying temperatures and complex operating conditions. Results indicate that: 1) The superior ability of the MLP-Mixer neural network to observe battery terminal voltage plays a significant role in the precise SOC estimation of MM-UKF; 2) The proposed method shows robustness and performs well under different driving cycles. The RMSE of SOC estimation does not exceed 0.01 under intense driving cycles, showcasing superior generalization ability; 3) While the estimator’s prediction accuracy deviates slightly from the reference value at low temperatures and SOC levels, it outperforms prediction methods based on MLP and LSTM neural networks in various temperatures and low SOC scenarios. Overall, the proposed SOC estimation method based on MM-UKF holds significant practical value for underwater vehicles operating under complex conditions.