Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life

The machine learning (ML) based methods show great promise for the online battery capacity prediction. However, the feasibility and simplicity of indispensable feature extraction are both challenging to existing ML based methods, which restrict the implementation in electric vehicle usages. To reduce the computational burden and simultaneously enhance the accuracy of prediction results, this paper proposes a ML based approach for swift capacity prediction leveraging fractional charging voltage segments. A total number of 21 voltage feature segments (VFSs) are intercepted with different voltage ranges to analyze the influence of voltage intervals on the capacity prediction. Meanwhile, three advanced ML models including random forest regression (RFR), relevance vector machine (RVM) and Gaussian process regression (GPR) are meticulously designed to build a mapping function between the intercepted VFSs and battery capacity. The battery capacity prediction can be attained subsequently based on the established matched relationship using the VFS captured in real-time. The experimental and contrastive results show that the best model can accurately predict battery capacity through whole cycle life with the maximum average relative error of only 1.95%. This work emphasizes the application potential of combining straightforward and reliable feature with ML algorithms for online battery capacity prediction.