Study of Lithium-Ion Battery Charge State Estimation Based on BP Neural Network Fusion Optimized Sparrow Algorithm

Due to the complex linear relationships within a battery, the prediction of state of charge (SOC) has become a significant challenge. This paper designs a three-layer backpropagation (BP) neural network to predict the battery's SOC. To enhance the prediction accuracy, based on the charge-discharge mechanism of lithium batteries, the battery's voltage, current, and temperature are selected as input variables, with the state of charge as the output variable. Under three different operating conditions, the BP neural network can achieve certain predictive effects. However, due to the instability and large error fluctuations of the BP neural network during the SOC prediction process, the BP neural network is optimized. The Sparrow Search Algorithm (SSA) can enhance the model's ability to search for optimal values. By improving the Sparrow Search Algorithm, the uniform distribution of the initial population can be enhanced. The prediction results after optimizing the BP network show that, compared to the unimproved version, the stability of the prediction results can be improved, achieving more accurate predictions of SOC values, which has strong application value.