[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120646-en":3,"doc-seo-120646-105":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":20,"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},120646,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries","Mathematical modeling of lithium-ion batteries remains a central challenge for advanced battery management due to the need for both accuracy and computational feasibility. This paper presents two new hybrid frameworks that deeply integrate physics-based models with machine learning by feeding state information from physical models into learning architectures. Hybrid models combine electrochemical and equivalent circuit models with feedforward neural networks, yielding parsimonious structures and strong voltage prediction accuracy across a wide C-rate range. The approach extends to aging-aware, state-of-health conscious hybrid modeling, with experiments showing high voltage predictive accuracy over the battery’s cycle life.","Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries  \nHao Tua , Scott Mourab , Yebin Wangc , Huazhen Fanga,􀀃  \na Department of Mechanical Engineering, University of Kansas, Lawrence, KS 66045, USA b Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USAc Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, USA  \n7 Nov 2022  \nAbstract  \nMathematical modeling of lithium-ion batteries (LiBs) is a primary challenge in advanced battery management. This paper proposes two new frameworks to integrate physics-based models with machine learning to achieve high-precision modeling for LiBs. The frameworks are characterized by informing the machine learning model of the state information of the physical model, enabling a deep integration between physics and machine learning. Based on the frameworks, a series of hybrid models are constructed, through combining an electrochemical model and an equivalent circuit model, respectively, with a feedforward neural network. The hybrid models are relatively parsimonious in structure and can provide considerable voltage predictive accuracy under a broad range of C-rates, as shown by extensive simulations and experiments. The study further expands to conduct aging-aware hybrid modeling, leading to the design of a hybrid model conscious of the state-of-health to make prediction. The experiments show that the model has high voltage predictive accuracy throughout a LiB's cycle life.  \nKeywords: Hybrid modeling, Physics, Machine learning, Lithium-ion batteries  \n􀀃 Corresponding author  \nEmail address: [fang@ku.edu](fang@ku.edu) (Huazhen Fang)  \nmodel, which is broadly considered reliable and precise enough for almost all LiB management scenarios [3, 4] . Its accuracy yet comes with enormous computational complexity. This hence has motivated an incessant search for streamlined electrochemical models to balance between accuracy and computational costs. The single particle model (SPM) is one of the most parsimonious, which represents each electrode as a spherical particle and delineates lithium-ion intercalation and di􀀋usionin the particles [5] . With its simplified structure, it is computationally fast but accurate only for low to medium C-rates (below 1 C-rate) . Based on the SPM, there is a wide range of improved versions for higher accuracy under di􀀋erent conditions. They usually supplement the SPM with characterizations of thermal behavior [6, 7], electrolyte dynamics [8–11], degradation physics [12], and stress buildup [11] . Another important line of research lies in applying model order reduction methods to the DFN, SPM or other electrochemical models, with the aim of accelerating numerical computation [13–19] .  \nDi􀀋erently, ECMs leverage electrical circuits, usually based on resistors, capacitors, and voltage sources, to capture LiBs'current/voltage dynamics in a physically interpretable way. Compared to electrochemical models, ECMs have greatly more parsimonious structures and simpler governing equations, thus advantageous for computation and conducive to real-time control, prediction, and simulation. Some widely used ECMs include the Rint model, the Thevenin model, and the Dual Polarization model [20–22] . Recent literature has expanded the development of ECMs toward better prediction accuracy. Some studies seek to account for the e􀀋ects of hysteresis and temperature on a LiB's electrical dynamics [23–27] . Others design new ECMs to approximate certain electrochemical models [28–32] .  \nPreprint submitted to Applied Energy November 8, 2022  \nWhile ECMs have found increasing popularity, their structural simplicity restricts their accuracy, making them useful only for low to medium C-rates.  \nFor all the aforementioned models, their e􀀋ectiveness and fidelity will decrease as a LiB ages, since many parameters of a model can change drastically with the LiB's state-of-health (SoH). This hence has stimulated research ","cbCaicfxEsgF4dkM","https://ap.wps.com/l/cbCaicfxEsgF4dkM","pdf",1695059,1,15,"English","en",105,"# Abstract\n# Physics-based and electrochemical modeling background\n## Single particle model (SPM) and its limits\n## Degradation-aware extensions and model order reduction\n# Equivalent circuit model (ECM) background\n## Common ECMs and recent accuracy improvements\n# Aging-aware modeling\n# Machine learning battery modeling\n## Benefits and limitations of black-box ML\n# Proposed hybrid physics-ML frameworks","[{\"question\":\"What problem does the paper target in lithium-ion battery modeling?\",\"answer\":\"Achieving high-precision battery models while avoiding excessive computational complexity, and addressing performance degradation as batteries age.\"},{\"question\":\"How do the proposed frameworks integrate physics-based models with machine learning?\",\"answer\":\"They inform the machine learning model of state information from the physical model, enabling deep integration between physics and learning.\"},{\"question\":\"How is aging-aware prediction achieved in the hybrid modeling approach?\",\"answer\":\"The paper expands hybrid modeling to be state-of-health (SoH) conscious, constructing aging-aware hybrid models and validating voltage prediction accuracy across cycle life.\"}]","Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries | 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problem does the paper target in lithium-ion battery modeling?","Question",{"text":75,"@type":76},"Achieving high-precision battery models while avoiding excessive computational complexity, and addressing performance degradation as batteries age.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the proposed frameworks integrate physics-based models with machine learning?",{"text":80,"@type":76},"They inform the machine learning model of state information from the physical model, enabling deep integration between physics and learning.",{"name":82,"@type":73,"acceptedAnswer":83},"How is aging-aware prediction achieved in the hybrid modeling approach?",{"text":84,"@type":76},"The paper expands hybrid modeling to be state-of-health (SoH) conscious, constructing aging-aware hybrid models and validating voltage prediction accuracy across cycle 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