原文信息：
Transfer-driven prognosis from battery cells to packs: An application with adaptive differential model decomposition
原文链接：
https://www.sciencedirect.com/science/article/pii/S0306261924016738
Highlights
(1) Degradation differences between battery cells and packs were shown experimentally.
(2) The interpretability was incorporated via a two-stage model decomposition.
(3) A cell-pack transfer pipeline was developed to mitigate domain discrepancies.
(4) Achieve reliable trajectory prediction using a probability-based approach.
(5) Achieve stable early and real-time prediction errors below 5% across cells and packs.
Abstract
The modelling of performance degradation and lifespan prediction in lithium-ion batteries is crucial for their efficient and stable operation. However, research on performance degradation and lifespan prediction under the setting of multiple cell groups has not yet received sufficient attention. To address this gap, we designed and conducted degradation experiments on battery cells and packs, highlighting and demonstrating the disparities between individual battery cells and packs. This realistic disparity ultimately motivated our investigation and development of a transfer-driven prognostic approach for lithium-ion battery packs. First, we utilized the Euclidean distance for normalizing cell-level trajectories and introduced a two-stage decomposition approach for feature stabilization and differential model construction. Subsequently, we developed a cell-pack transfer pipeline based on Euclidean distance to mitigate domain discrepancies. Finally, we achieved simultaneous trajectory distribution prediction using a probability-based approach incorporating the unscented transform. Our prediction approach achieved a stabilized prediction error below 5% at both the cell level and the pack level for both early and real-time lifetime predictions, offering a valuable contribution to the field of lithium-ion battery pack performance prognosis.
Keywords
Transfer-driven prognosis迁移驱动预测
Prognostics and health management预后健康管理
Differential model decomposition差分模型分解
Battery pack电池组
SOH健康状态
Graphics
Fig. 4. Comparison of the degradation trajectories of different battery cells and packs.
Fig. 5. Process diagram of ADMD.
Fig. 13. Real-time percentage prediction error throughout the full lifespan.
Fig. 14. Confidential remaining useful time prediction in real-time.
Fig. 16. Prediction performance of different approaches on the cell-level source domain.
Fig. 17. Prediction performance of different approaches on the pack-level target domain.
Fig. 18. Intuitive performance comparison of different approaches on different cells and packs.
团队简介
本研究由温州大学，南卡罗来纳大学等单位的研究人员共同完成。
通信作者简介：
向家伟，温州大学机电工程学院二级教授、IET Fellow，洪堡学者、浙江省“有突出贡献中青年专家”、“万人计划”科技创新领军、“钱江学者”特聘教授、日本学术振兴会学者、151人才重点层次、浙江省“杰青”、广西省十百千第二层次、广西省教育厅创新团队负责人，在高端装备预测性维护与健康管理、大数据与新一代人工智能诊断等领域进行了长期研究，积累了较多的实验设备、实验场地和项目研究经验。
第一作者简介：
吕东祯，男，1994年出生，河南洛阳人，博士。2017年本科毕业于哈尔滨工程大学能源与动力工程专业，保送至华中科技大学攻读硕士学位，2019年硕博连读攻读博士学位，2023年博士毕业。目前在温州大学机电工程学院任职，长期从事锂电池及其他高端装备的寿命预测和健康管理研究。
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