State of Health Estimation for Smart Batteries using Transfer Learning with Data Cleaning

Smart battery with optimized pulsed current produced by the bypass is one prospective technology to prolong the service life of the batteries in electric vehicles. The accurate and reliable state of health (SOH) estimation is one significant process before the decision of the control. This paper proposes a proper solution for battery SOH estimation that can be applied to both constant and pulsed current charging scenarios. Specifically, a data cleaning process is proposed for preprocessing the fluctuated measurement, while retaining the main aging information. From the pre-processed data under different charging profiles, four SOH features are extracted, and the correlation coefficients prove their effectiveness with both constant current and pulsed currents. Later, a transfer learning-based model is developed which shows improved accuracy of the SOH estimations under pulsed current scenarios. Finally, experiments have been conducted to verify the proposed method. In the case of model retraining using only the first 10% of unseen data, satisfactory results can be obtained (the error is less than 2.626%). By increasing the data for model retraining to 20%, a fitted coefficient of larger than 0.994 between the estimations and real values is obtained, resulting in a low estimation error of less than 0.8%.