A novel RBFNN-UKF-based SOC estimator for automatic underwater vehicles considering a temperature compensation strategy

Accurate state of charge (SOC) estimation of batteries is a prerequisite for the reliable operation of automatic underwater vehicles. Currently, the accuracy of traditional SOC evaluation algorithms deteriorates significantly at low temperatures and low SOCs. Hence, a novel SOC estimator is proposed in this study, consisting of three efforts. Firstly, a new radial basis function neural network (RBFNN) battery model is built to replace the equivalent circuit model (ECM) for SOC estimation. Then, based on the relation between SOC and terminal voltage at a different temperature, a temperature compensation strategy is developed, which is an effortless operation and does not increase the computational burden. Finally, incorporating the new battery model, the temperature compensation strategy, and the unscented Kalman filter (UKF), a novel SOC estimation frame expressed as RBFNN-UKF is designed.