State-of-Health Estimation of Li-Ion Batteries Using Semiparametric Adaptive Transfer Learning

Abstract

For lithium-ion batteries (LIBs), precise state of health (SoH) estimation can provide direction for reasonable use, minimize battery failure rates, and extend battery life. Data-driven methods are promising for SoH estimation since they work effectively without requiring human interaction and have great nonlinear prediction capabilities. The majority of studies also found that the training data was sufficient. However, in real-world contexts, data collection is frequently time-consuming and expensive. This paper suggests using transfer learning to make the model less dependent on data for tracking battery state of health (SoH). Many approaches assume training and testing data have the same distribution. Due to distribution mismatch, a model that works for one data set may not for another. We propose an adaptive transfer learning technique relying on Gaussian Processes regression (AT-GPR) in this research. Automatically evaluating source-to-target task similarity can construct learning systems. Bayesian semi-parametric transfer kernel proposed to learn target task model. Battery cyclic aging data from two publicly available data sets in various working environments are considered to validate the suggested method. The experiments show that AT-GPR generates reliable prediction results; however, only 20% of the overall data set is made up of training data.