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The successful integration of statistical machine learning techniques into battery health diagnosis has significantly advanced the development of transportation electrification. To achieve predictive maintenance of batteries, we propose a comprehensive data-driven approach for battery capacity trajectory prediction based on degradation pattern (DP) recognition and health indicators (HIs) extrapolation. First, two HIs ( Qmean/RVmean ) are extracted from 10-minute sequence data before and after a full charge. Second, an unsupervised learning approach is employed for the early-stage battery DP analysis and clustering. Finally, a long short-term memory (LSTM) network is utilized to construct the HIs extrapolation and capacity prediction models. Multi-task learning (MTL) is implemented to predict HI sequences, enabling simultaneous extrapolation of multiple HIs and the sharing of parameters between different HIs during training. A probabilistic neural network is incorporated into the capacity trajectory prediction model to assess the uncertainty of prediction results. The proposed approach is validated using two battery datasets, where predictions for three represented aging stages are presented and evaluated. The results demonstrate accurate and robust predictions, with the average mean absolute percentage error (MAPE) for LiNi 0.86 Co 0.11 Al 0.03 O 2 (NCA) cells and LiNi 0.83 Co 0.11 Mn 0.07 O 2 (NCM) cells across all aging stages below 2.59% and 1.15%, respectively.
Li et al. (Tue,) studied this question.