Data-driven and feature-sifting based state-of-health estimation for li-ion batteries using KPCA and NRBO-optimized transformer-LSTM hybrid model

Electrochemical energy storage technologies, such as lithium-ion batteries, are widely used in various scenarios, but their capacity, lifespan, and state-of-health (SOH) are susceptible to numerous influencing factors. To improve the accuracy of state-of-health (SOH) prediction for lithium-ion battery cells, this paper builds up a feature-based analysis framework, and proposes an enhanced predicting model, which integrates the principles of incremental capacity analysis (ICA) and differential voltage analysis (DVA), and the ideas of kernel principal component analysis (KPCA), and Pearson correlation analysis. The former ones are used to extract a set of health related features (HFs) from the filtered incremental capacity (IC) and differential voltage (DV) curves of batteries, while the latter ones are employed to eliminate redundant features. Furthermore, this work bridges the gap between the charging data of the cells and their SOH by building up a Newton-Raphson-based optimization Transformer-LSTM (NRBO-Transformer-LSTM) model, where NRBO algorithm is introduced to find out the optimized number of attention heads, learning rates and the regularization coefficient. Experimental validation on the NASA and CALCE battery datasets shows that, with 70% of the data used as the training set, the proposed method achieves maximum mean absolute errors (MAE) of 0.0084 and 0.0071, and root mean square errors (RMSE) of 0.0091 and 0.0089, respectively, significantly outperforming multiple benchmark algorithms in both prediction accuracy and stability.