A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Summary Lithium‐ion battery state‐of‐health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model‐based and data‐driven estimator is developed to achieve accurate and reliable state‐of‐health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state‐space representation is constructed based on the data‐driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method. An increase in internal resistance from the battery model is defined as the health indicator to quantify battery capacity degradation. A linear state‐space representation is developed based on the linear relationship between the defined health indicator and capacity degradation using data‐driven linear regression method. Kalman filter–based framework is constructed to trace the capacity degradation with the maximum error lower than 2.12% over all our experiments.