Data-Driven Transfer-Stacking-Based State of Health Estimation for Lithium-Ion Batteries

Abstract

State of health (SOH) of lithium-ion batteries plays a vital role in the safe and reliable operation of electric vehicles. However, most of the existing SOH estimation methods still require a large number of battery aging data, while the established model usually lacks generalization. Here, we build a data-driven transfer learning model to obtain more generality on the SOH estimation. Firstly, potential health features are extracted from battery charging data and then pruned via the importance function. Secondly, support vector regression (SVR) is employed to establish source models with different battery data, which takes selected features as the input and capacity as the output. Thirdly, the transfer-stacking (TS) method is utilized to combine all source models. A TS-SVR method for SOH estimation is then established only using the first 30 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> target battery data after solving the optimization problem of assigning weight to each source model. Finally, the proposed algorithm is verified by three different battery datasets and shows better estimation performance than the comparative algorithms. It is proved that the proposed method uses only a small amount of target battery data, while together with the source battery data, can achieve an accurate SOH estimation during its life cycle.