[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123602-en":3,"doc-seo-123602-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123602,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Knee-Point-Conscious Battery Aging Trajectory Prediction Based on Physics-Guided Machine Learning","Early prediction of aging trajectories of lithium-ion batteries is crucial for cycle life testing, quality control, and battery health management. Purely data-driven machine learning is often too time-consuming and resource-intensive, especially under accelerated aging with complex, time-varying degradation mechanisms. A physics-guided machine learning approach is proposed to incorporate electrode-level physical information, predict the trajectory knee point, and build an aging trajectory prediction algorithm. On a LiNiCoMnO2 dataset, only 14 cells enable lifetime prediction error of 2.02% using the first 50 cycles, compared with 100+ cells for non-physics models.","Knee-point-conscious battery aging trajectory prediction of lithium-ion based on physics-guided machine learning  \nDownloaded from: [https://research.chalmers.se](https://research.chalmers.se), 2023-04-21 14:41 UTC  \nCitation for the original published paper (version of record):  \nJia, X., Zhang, C., Li, Y. et al (2023). Knee-point-conscious battery aging trajectory prediction of lithium-ion based on physics-guided  \nmachine learning. IEEE Transactions on Transportation Electrification.  \n[http://dx.doi.org/10.1109/TTE.2023.3266386](http://dx.doi.org/10.1109/TTE.2023.3266386)  \nN. B. When citing this work, cite the original published paper.  \n©2023 IEEE. Personal use of this material is permitted.  \nHowever, permission to reprint/republish this material for advertising or promotional purposes  \nThis document was downloaded from [http://research.chalmers.se](http://research.chalmers.se), where it is available in accordance with the IEEE PSPB  \nOperations Manual, amended 19 Nov. 2010, Sec, 8.1.9. ([http://www.ieee.org/documents/opsmanual.pdf](http://www.ieee.org/documents/opsmanual.pdf)) .  \n(article starts on next page)  \nKnee-Point-Conscious Battery Aging Trajectory Prediction Based on Physics-Guided  \nMachine Learning  \nXinyu Jia, Graduate Student Member, IEEE, Caiping Zhang, Senior Member, IEEE, Yang Li, Member, IEEE, Changfu Zou, Senior Member, IEEE, Le Yi Wang, Life Fellow, IEEE, and Xue Cai  \nAbstract—Early prediction of aging trajectories of lithium-ion (Li-ion) batteries is critical for cycle life testing, quality control, and battery health management. Although data-driven machine learning (ML) approaches are well suited for this task, unfortunately, relying solely on data is exceedingly timeconsuming and resource-intensive, even in accelerated aging with complex aging mechanisms. This challenge is rooted in the highly complex and time-varying degradation mechanisms of Li-ion battery cells. We propose a novel method based on physics-guided machine learning (PGML) to overcome this issue. First, electrodelevel physical information is incorporated into the model training process to predict the aging trajectory’s knee point (KP). The relationship between the identified KP and the accelerated aging behavior is then explored, and an aging trajectory prediction algorithm is developed. The prior knowledge of aging mechanisms enables a transfer of valuable physical insights to yield accurate KP predictions with small data and weak correlation feature relationship. Based on a Li[NiCoMn]O2 cell dataset, we demonstrate that only 14 cells are needed to train a PGML model for achieving a lifetime prediction error of 2.02% using the data of the first 50 cycles. In contrast, at least 100 cells are needed to reach this level of accuracy without the physical insights.  \nIndex Terms—Accelerated aging, battery aging trajectory prediction, data-driven method, machine learning, knee point, physics-guided.  \nI. INTRODUCTION  \nW ITH their high energy density and decreasing costs, lithium-ion (Li-ion) batteries have emerged as a critical  \nform of energy storage for realizing electrified transportation and smart grids [1] . Li-ion battery cells, however, are prone to manufacturing defects and inconsistent aging behavior, leading to varying capacity fade rates and service life [2, 3] . Accurate early prediction of the aging trajectory and lifetime can be timeand cost-saving for design and quality control from a battery  \nThis work has not been presented at a conference or submitted elsewhere previously. This work was supported in part by the National Natural Science Foundation of China under Grant 52222708 (the Excellent Young Scientists Fund), in part by the National Natural Science Foundation of China under Grant 51977007, and in part by the National Natural Science Foundation of China under Grant 52007006. (Corresponding authors: Caiping Zhang and Yang Li).  \nXinyu Jia, Caiping Zhang, and Xue Cai are with the National Active Distribution Net","cbCaihAdf9EP9iPz","https://ap.wps.com/l/cbCaihAdf9EP9iPz","pdf",5563527,1,15,"English","en",105,"# Abstract\n# Introduction\n## Background and motivation\n## Data-driven methods and limitations\n## Key requirements for ML models","[{\"question\":\"Why is early prediction of lithium-ion battery aging trajectories important?\",\"answer\":\"It supports cycle life testing, quality control, and battery health management by enabling earlier estimation of aging behavior and lifetime.\"},{\"question\":\"What challenge limits purely data-driven machine learning for this task?\",\"answer\":\"Training solely on data can be time-consuming and resource-intensive, especially when accelerated aging involves complex, time-varying degradation mechanisms.\"},{\"question\":\"How does the proposed physics-guided method improve prediction performance?\",\"answer\":\"It integrates electrode-level physical information to train the model, identifies the aging trajectory knee point, and uses the resulting physics-informed prior knowledge to make accurate knee-point predictions with small data.\"}]","Knee-Point-Conscious Battery Aging Trajectory Prediction Based on Physics-Guided Machine Learning | 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is early prediction of lithium-ion battery aging trajectories important?","Question",{"text":75,"@type":76},"It supports cycle life testing, quality control, and battery health management by enabling earlier estimation of aging behavior and lifetime.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenge limits purely data-driven machine learning for this task?",{"text":80,"@type":76},"Training solely on data can be time-consuming and resource-intensive, especially when accelerated aging involves complex, time-varying degradation mechanisms.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed physics-guided method improve prediction performance?",{"text":84,"@type":76},"It integrates electrode-level physical information to train the model, identifies the aging trajectory knee point, and uses the resulting physics-informed prior knowledge to make accurate knee-point predictions with small 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