With the further development of the electric vehicle (EV) industry, the reliability of prediction and health management (PHM) systems has received great attention. The original Li-ion battery life prediction technology developed by offline training data can no longer meet the needs of use under complex working conditions. The existing methods pay insufficient attention to the dispersive information of health indicators (HIs) under EV driving conditions, and can only calculate through standard configuration files. To solve the problem that it is difficult to directly measure the capacity loss in real time, this paper proposes a battery HI called excitation response level (ERL) to describe the voltage variation at different lifetimes, which could be easily calculated according to the current and voltage under the actual load curve. In addition, in order to further optimize the proposed HI, Box–Cox transformation was used to enhance the linear correlation between the initially extracted HI and the capacity. Several Li-ion batteries were discharged to the 50% state of health (SOH) through profiles with different depths of discharge (DODs) and mean states of charge (SOCs) to verify the accuracy and robustness of the proposed method. The average estimation error of the tested batteries was less than 3%, which shows a good performance for accuracy and robustness.
A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management