Regression based battery state of health estimation for multiple electric vehicle fast charging protocols

In this work, a data-driven estimation method is developed to estimate the battery state of health (SOH), exploiting SOH features that can be obtained during fast-charging events. A newly expanded experimental dataset with six cells, cycled 1200 to 1800 times until 70% SOH is reached, is used and made available. Our investigation focuses on the variability that can be encountered in charging events due to different charging protocols (particularly for fast charging) and partial charging events. In particular, we investigated nine different SOH features, introducing novel formulations to increase their flexibility with respect to different charging events. Then, we assessed the practical implementability of these features and employed correlation and feature importance analyses to identify the most effective. Finally, we developed a linear regression model for SOH estimation using the selected features as inputs. The model shows an RMS prediction error as low as 1.09% over the battery lifetime and a maximum error no greater than 3.5% until SOH falls below 80%, corresponding to the end-of-life for automotive applications. The estimator is also shown to be robust against significant errors of the state of charge (SOC) input value (as high as 5%), ensuring it will perform well even when SOC is not accurately known. • Data-Driven SOH Estimation exploiting charging events. • Robust across different fast charging protocols. • Robust with respect to SOC estimation errors. • Validation on extended dataset. • Low-computational-effort suitable for online BMS.