A Polynomial Regression Model with Bayesian Inference for State-of-Health Prediction of Li-ion Batteries

State-of-health (SOH) prediction is one of the key tasks of the Battery Management System (BMS) to ensure improved efficiency and safe operations of Lithium-ion (Li-ion) Batteries (LiBs). However, most of the existing SOH methods are either constrained by high model complexity or insufficient information about the historical degradation patterns of the battery cell. This paper proposes a Polynomial Regression Model with Bayesian Inference (PRMBI) for a robust SOH prediction of Li-ion batteries. The proposed PRMBI architecture leverages the strength of the semi-empirical modeling and data-driven methods for robust SOH prediction. The experimental results show that the proposed PRMBI significantly outperforms deep learning benchmark models when evaluated on battery cells that are still at early stages of degradation.