State of Health Prediction of Lithium-Ion Batteries Using Accelerated Degradation Test Data

Abstract

Lithium-ion batteries are the core equipment of electric vehicles (EVs) and they are expected to spread out through smart power systems [e.g., smart grids (SGs)]. Batteries are subject to degradation over time, which gradually reduces the supplied voltage and capacity until they fail to meet minimum requirements. Battery prognostic is mandatory in order to keep track of the degradation over time and to schedule maintenance and replacement before failures, which can cause severe damages and harmful effects on EVs and SGs. However, the estimation of the state of health and the remaining useful life of batteries is not trivial, since neither can be directly measured. In this article, multiple linear regression models are developed within deterministic and probabilistic frameworks to predict the SoH of batteries based on historical charge/discharge data. The models exploit informative data collected for the battery under investigation and stored data of batteries characterized by accelerated degradation tests. Numerical experiments based on actual data are presented to validate the performance of the proposal.