A Novel Method for Battery SOC Estimation Based on Slime Mould Algorithm Optimizing Neural Network under the Condition of Low Battery SOC Value

Abstract

The State of Charge (SOC) is a crucial parameter in battery management systems, making accurate estimation of SOC essential for adjusting control strategies in automotive energy management and ensuring the performance of electric vehicles. In order to solve the problem that the estimation error of the traditional BP neural network increases sharply under complex conditions and low battery SOC values, a recurrent neural network estimation method based on slime mould algorithm optimization is proposed. Firstly, the data are serialized to include multiple discharge data. Secondly, the data are input into a recurrent neural network for SOC estimation, with a self-attention mechanism added to the network. Furthermore, it is found in the experiment that parameters have an impact on the estimation accuracy of the neural network, so the slime mould algorithm is introduced to optimize the parameters of the neural network. The experiment results show that the maximum error of the novel method is limited to within 5% under two conditions. It is worth noting that the SOC estimation error at low SOC value decreases instead of increasing, which shows the advantages of the novel method.