TY - JOUR
T1 - Degradation Pattern Recognition and Features Extrapolation for Battery Capacity Trajectory Prediction
AU - Li, Jinwen
AU - Deng, Zhongwei
AU - Che, Yunhong
AU - Xie, Yi
AU - Hu, Xiaosong
AU - Teodorescu, Remus
N1 - Publisher Copyright:
IEEE
PY - 2024
Y1 - 2024
N2 - 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 LiNi0.86Co0.11Al0.03O2 (NCA) cells and LiNi0.83Co0.11Mn0.07O2 (NCM) cells across all aging stages below 2.59% and 1.15%, respectively.
AB - 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 LiNi0.86Co0.11Al0.03O2 (NCA) cells and LiNi0.83Co0.11Mn0.07O2 (NCM) cells across all aging stages below 2.59% and 1.15%, respectively.
KW - Aging
KW - Batteries
KW - Battery health diagnosis
KW - Data mining
KW - Degradation
KW - Feature extraction
KW - multi-task learning
KW - Predictive models
KW - probabilistic neural network
KW - Trajectory
KW - trajectory prediction
KW - unsupervised clustering
UR - http://www.scopus.com/inward/record.url?scp=85179078391&partnerID=8YFLogxK
U2 - 10.1109/TTE.2023.3336618
DO - 10.1109/TTE.2023.3336618
M3 - Journal article
AN - SCOPUS:85179078391
SN - 2332-7782
SP - 1
JO - IEEE Transactions on Transportation Electrification
JF - IEEE Transactions on Transportation Electrification
ER -