A Deep Learning Model for Predicting the State of Energy in Lithium-Ion Batteries Based on Magnetic Field Effects

The state of energy (SOE) is one of the most critical state indicators in battery management systems. However, its nonlinear characteristics present significant challenges in obtaining accurate SOE. Especially when applying different magnetic field strengths to perform battery charging and discharging tests, the change in battery energy becomes more complex due to the influence of the magnetization effect. In this paper, a deep learning network, combining an improved Informer and long short-term memory network (LSTM), was developed to estimate the SOE of lithium-ion batteries under different magnetic fields. First, we improve the decoder structure by adding a convolutional module using residual connections with trainable weight parameters to capture hidden states with more details.The improved decoder does not require label history information for decoding, which improves the generalization ability of the model. Finally, the output of the Informer network is a higher-dimensional hidden feature that is input into the LSTM network layer to output the SOE prediction value, which improves the original Informer network's ability to integrate sequences. Experiments with magnetic field and public datasets show the improved Informer-LSTM network achieves 0.31 % MAE, 0.42 % RMSE, and 1.79 % maximum error in SOE estimation, outperforming others in short sequence predictions.