A Data-Driven-Based Framework for Battery Remaining Useful Life Prediction

Electric vehicles are expected to dominate the vehicle fleet in the near future due to their zero emissions of pollutants, reduced fossil fuel reserves, comfort, and lightness. However, Battery Electric Vehicles (BEVs) suffer from gradual performance degradation caused by irreversible chemical and physical changes inside their batteries. Moreover, predicting the health and remaining useful life of BEVs is difficult due to various internal and external factors. In this paper, we propose an integrated data-driven framework for accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries used in BEVs. As part of this work, we conducted a comprehensive analysis and comparison of publicly available battery datasets, providing an up-to-date list of data sources for the community. Our framework relies on a novel feature extraction strategy that accurately characterizes the battery, leading to improved RUL predictions. Feature types can be divided into three groups: initial state features, current state features, and historic state features. Experimental results indicate that the proposed method performs very well on the NASA benchmark dataset with an average accuracy of 97% on batteries that are not used during the training phase, demonstrating the ability of our framework to operate with batteries being discharged in any conditions