Physics-informed machine learning model for battery state of health prognostics using partial charging segments

Abstract

The accurate and efficient estimation of battery state-of-health (SoH) is an ever-significant issue for applications of lithium-ion batteries (LIBs). Physics-of-failure (PoF) and machine-learning (ML) approaches have shown success in the field of prognostics and health management. However, accurate prediction of these models depends on clear understandings of complicated underlying physics, or full access to massive historical usage data of the system, which are not always obtainable in the engineering applications. Studies on physics-informed ML frameworks have gained attention because they embed physics knowledge into ML algorithms; recently, this method has demonstrated the potential to enhance performance over traditional model-based techniques, particularly when handling highly non-linear complex systems, such as LIBs. In this study, an advanced physics-informed ML (PIML) framework is proposed for the LIB SoH estimation, which includes three steps. The physics-based finite element (FE) model is firstly used to calculate the influences of the dominating aging mode, i.e., the solide electrolyte interface (SEI) growth on anode particle surfaces, and on capacity loss of LIB under various operating conditions. Then, the FE results are fused with experimental data from NASA Ames Prognostics Center of Excellence to construct a multi-fidelity model. Lastly, the Gaussian Process Regression (GPR) model is trained to create the mapping between voltage curves and the corresponding SEI thickness. A case study with the partial charging voltage segment as input is introduced to validate the PIML framework. Overall, the framework demonstrates a favorable performance for SoH estimation, as well as providing a basis for future online estimation frameworks.