“…Data-driven models include classical machine learning models, deep learning models, and hybrid models. In early battery states estimation research, classical machine learning models are mainly used, and common models include artificial neural networks (ANN) [18,19], support vector machine (SVM) [20,21], and Gaussian process regression (GPR) [22,23], hidden Markov model (HMM) [24,25], random forest (RF) [26,27], fuzzy control [28,29], autoregressive(AR) [30,31], relevance vector machine (RVM) [32,33], etc. Although classic machine learning models can estimate battery states based on a small number of data samples, the estimation quality relies on expert experience to manually extract features, and the estimation accuracy is greatly affected by the selected features.…”