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Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Journal of Energy Chemistry 2023年第10期85卷 534-546,I0013页

作者： Mingqiang Lin Yuqiang You Jinhao Meng Wei Wang Ji Wu Daniel-Ioan Stroe School of Mechanical and Electrical Engineering Fujian Agriculture and Forestry UniversityFuzhou 350100FujianChina Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesJinjiang 362200FujianChina School of Electrical Engineering Xi’an Jiaotong UniversityXi'an 710049ShaanxiChina School of Management Xi'an Jiaotong UniversityXi'an 710049ShaanxiChina School of Automotive and Transportation Engineering Hefei University of TechnologyHefei 230009AnhuiChina AAU Energy Aalborg UniversityAalborg 9220Denmark

Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

关键词： Lithium-ion battery degradation trajectory Long-term prediction Transferred convolutional neural network

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