An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and PSO-GRNN algorithm

Abstract

As a critical index in ensuring the safe and reliable deployment of lithium-ion batteries (LIBs), the estimation performance of state-of-health (SOH) seriously affects the popularization of electric vehicles (EVs). However, for the existing SOH estimation methods based on machine learning algorithms, the health features are manually extracted mainly based on personal experience, without algorithm-based automatic preferential selection. Thus, this study proposes a battery SOH estimation method based on the feature-importance ranking strategy and generalized regression neural network optimized by particle swarm optimization (PSO-GRNN). Firstly, 19 vital features were extracted from charging voltage, charging current, temperature, and incremental capacity (IC) curves. Secondly, the importance indexes for these features were calculated and optimally ranked by fusing the machine learning algorithm (random forest) and statistical analysis (Pearson correlation coefficient). Accordingly, different combinations of high-ranking features were compared to determine the best number of the final feature set. Thirdly, the SOH estimation model is built by the PSO-GRNN algorithm with optimal 7 high-ranking features. Based on the publicly available NASA datasets, the proposed SOH estimation model was validated on the data from a single-cell battery. Furthermore, the datasets from three cells with different cycling test conditions were utilized, where the data from two cells and the other cell were alternately selected as the training and test sets, respectively. The SOH estimation errors (squared absolute error and mean squared error) for single-cell and multi-cell experiments were less than 1 % and 3 %, respectively. Experimental results show that the proposed method is robust and can achieve accurate and lightweight SOH estimation.