A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current

Abstract

Accurate health status estimation and capacity prediction of lithium-ion batteries are important means to prevent a series of problems such as capacity loss, driving range and safety accidents caused by the aging of batteries. Research on battery capacity prediction based on constant discharge rate has become increasingly mature. However, as the main power source for electric vehicles, discharge current of lithium-ion battery is constantly changed by the influence of time-varying vehicle speed. Considering the effect of random variable current (RVC) discharge on battery capacity degradation, a novel predicting method for circulating capacity of lithium-ion battery is proposed. Firstly, features are extracted from the battery charging and discharging process. Secondly, the correlation between features and battery capacity is analyzed by using the grey relational analysis, and features with the higher correlation coefficient are selected as final health features. Thirdly, the online sequential extreme learning machine optimized by beetle antenna search is proposed and used to predict capacity of lithium-ion battery. Experimental results show that the minimum battery capacity RMSE predicted is 1.0294, and the cycle capacity error is mostly within the range of -3mAh∼3mAh, which proves that the method can more accurately estimate the capacity of lithium-ion batteries under RVC conditions.