Battery capacity trajectory prediction by capturing the correlation between different vehicles

Abstract

Advances in battery technology and dwindling oil resources have greatly boosted the popularity of electric vehicles (EVs). Accurate prediction of battery capacity trajectory is critical to ensure safety and timely maintenance of EVs. However, present studies only based on laboratory data. To bridge this gap, this paper proposes a data-driven capacity prediction framework using the vehicle field data, which can automatically match the aging pattern (AP) of the vehicle and fully utilizes the correlation between vehicles and does not need to extract features. The real capacity of battery pack in vehicle is calculated by ampere-hour integration method combined with open circuit voltage correction. To assess the overall effectiveness of this diagnostic methodology, the target vehicle's accessible data is divided into three stages: early, middle, and late. It is demonstrated that after automatic matching of AP, the average mean absolute percentage error (MAPE) of capacity prediction based on the multioutput spectral mixture Gaussian process (MOSMGP) are reduced by 9.82%, 21.25%, and 26.92% respectively in three different aging stages. Compared with the other six machine learning methods, the MOSMGP has the highest accuracy in early prediction. Its average MAPE and average root mean squared error (RMSE) are only 1.39% and 1.92Ah, respectively.