SOH estimation of lithium-ion batteries based on multi-feature deep fusion and XGBoost

Abstract

State of Health (SOH) is a crucial metric for battery management systems, and accurate estimation of battery SOH is essential for the underlying management and maintenance of batteries. Traditional data-driven approaches lack in-depth analysis of health features and fail to exploit the deep-level health information among these features. Therefore, this paper extracts six health features from both measurement and calculation perspectives based on actual measurable charging curve data, and conducts aging analysis of health features from a mechanistic perspective and Spearman correlation analysis from a quantitative perspective. Additionally, a strategy combining deep learning with ensemble learning is proposed. This strategy utilizes one-dimensional convolutional neural networks to deeply integrate multiple original features, extracting fusion features with deep-level health information. Subsequently, XGBoost is employed to learn the fusion features for estimating battery SOH. Finally, the effectiveness and superiority of the proposed method are verified on battery aging datasets from the University of Maryland and our laboratory. Experimental results demonstrate that the RMSE and MAE of the proposed method are both below 1 % and outperform other methods.