不同温度下的基于 BPNN-AUKF 的新型自动水下航行器 SOC 估计器

This study proposes a state of charge (SOC) estimation method based on backpropagation neural network (BPNN) and adaptive unscented Kalman filter (AUKF). Firstly, a series of temperature compensation strategies were studied and designed to improve the estimation accuracy under low temperature and low SOC conditions, focusing on the relationship between battery SOC and terminal voltage at different temperatures. Secondly, a battery model coupled with temperature compensation strategy was established using backpropagation neural network (BPNN). This model can better adapt to battery state changes under low temperature and low SOC conditions, improving the accuracy of SOC estimation. Finally, a SOC estimation framework for BPNN-AUKF was established based on the BPNN battery model. By utilizing the information and residual sequences between measured and predicted values, the system process and measurement noise covariance were estimated and corrected. Through experimental verification, it was found that this method has significant advantages in low-temperature environments. Compared with traditional methods, it can more accurately estimate the SOC of batteries and has good generalization ability. This SOC estimator based on BPNN-AUKF method is not only suitable for autonomous unmanned underwater vehicles (AUV), but also has broad application value for other vehicles working in complex environments.