A Method for Capacity Estimation of Lithium-Ion Batteries based on AM-LSTM-WKMSE

Accurately estimating the capacity of lithium-ion batteries (LIBs) is a critical step in ensuring battery safety. At the moment, the data-driven capacity estimation method is a straightforward and successful strategy. Nevertheless, the issue of missing feature information continues to plague the feature selection process. Additionally, the capacity regeneration phenomena and the mean square error (MSE) loss function’s limitations restrict estimation accuracy. To address these drawbacks, this study suggests a novel weighted kernel MSE (WKMSE) loss function, namely AM-LSTM-WKMSE, which combines the attention mechanism (AM) and long short-term memory (LSTM) for LIBs capacity estimation. First, the health indicators (HIs) were extracted from the voltage, current, temperature, and charging/discharging time curves. Following the correlation analysis of HIs, the AM is utilized to generate feature weights, which are subsequently entered into the LSTM. WKMSE is employed as a backpropagation loss function in training. Furthermore, experiments are conducted on the NASA dataset and the CALCE dataset. Comparing the method to alternative approaches, the testing results demonstrate that it has a 0.1% improvement in average the root mean square error of the batteries and superior estimation accuracy. Moreover, the proposed model exhibits strong generalization.