State of health prediction of lithium-ion batteries using accelerated degradation test data

Lithium-ion (Li-ion) batteries are 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 in order 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 (SoH) and of the Remaining Useful Life (RUL) of batteries is not trivial, since they cannot be directly measured. Predictive models should be therefore applied to accomplish this task, by linking SoH and/or RUL to variables measured during previous charge/discharge cycles (e.g., capacity, voltage, current, temperature). In this paper, a Multiple Linear Regression (MLR) model is developed to predict the SoH of batteries on the basis of historical data. The model exploits data collected for the battery under investigation and collected data of batteries characterized by Accelerated Degradation Tests (ADTs). Predictions of the RUL, given in terms of number of remaining charge/discharge cycles, can be further extracted from predictions of the SOH obtained through the proposal. Numerical experiments based on actual data are presented to validate the performance of the proposal