A multi-dimensional residual shrinking network combined with a long short-term memory network for state of charge estimation of Li-ion batteries

The multi-dimensional interaction and noise interference of the original data increase the difficulty of state-of-charge (SoC) estimation. Meanwhile the existing SoC estimators prefer to estimate the SoC of consecutive time steps, which may lead to cumulative errors and data leakage. To solve these problems, a fusion network combining a multi-dimensional residual shrinkage network (MRSN) with a long short-term memory network (LSTM) is proposed for SoC estimation. Meanwhile, a sequence-to-point processing method is utilized to avoid data leakage, which is based on historical input data for estimating SoC at a single time step. Specifically, MRSN uses small sub-networks to remove redundant noise and extracts multi-scale local features on different channels to obtain stronger correlation features. LSTM uses historical inputs to obtain multi-scale temporal correlation. After that the features extracted by both networks are fused in the channel dimension to enhance the accuracy of SoC estimation. Experiments on the public dataset show that the mean absolute error (MAE) of the results is kept within 0.5 % under four different temperature conditions. Furthermore, the MAE is 0.18 % at 25 degrees, which verifies that the proposed network could significantly improve the accuracy of the estimation. [Display omitted] •A sequence-to-point data processing method could avoid the phenomenon of data leakage;•A fusion network of MRSN and LSTM could extract both temporal and local spatial features;•MCNN could extract the local temporal features of each of the three input variables;•Deep residual shrinkage network could remove the redundant noise of original data;•Experiments under various conditions verified the estimation performance of the fusion network;