An improved log-cosine variation slime mold - simplified gated recurrent neural network for the high-precision state of charge estimation of lithium-ion batteries

Abstract

Accurate estimation of SOC is crucial for the safe and efficient utilization of electric vehicles, portable devices and renewable energy systems. Neural networks have become a powerful tool in the field of SOC estimation, but they have a complex structure, difficult parameter tuning, and high performance requirements. For this reason, this paper proposes a simplified gated recurrent neural network (SGRN) that greatly simplifies the unitary network structure and parameters. A Nadam gradient optimizer is introduced to update the network weight parameters, and the network hyperparameters are optimized using the log-cosine variant slime mold algorithm (LVSM). The algorithm is validated under HPPC, BBDST, and DST operating conditions under 25 °C, 10 °C, and − 5 °C. The results show that the LVSM-SGRN algorithm has higher accuracy, lower performance consumption, and better temperature adaptability than the conventional LSTM algorithm as well as the EKF algorithm. This contributes to the integration and large-scale industrialization of the algorithm, and comprehensively improves the safety of lithium-ion batteries and accuracy of SOC estimation.