Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries - The Role of Data Fidelity

Aug 3, 2026

Monitoring the internal temperature of lithium-ion batteries is essential for safe operation because thermal gradients naturally emerge during use. Internal temperature is harder to access than surface temperature, creating an urgent need for accurate, real-time estimation methods that support thermal management and safety. The proposed framework blends physics-based modeling with machine learning by generating simulation data through parameter and input sweeps, then pre-training a learning model and refining it via transfer learning with unsupervised domain adaptation using limited operational data without internal temperature labels.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages28

Reading Time1 h 11 min

AccessFree to download

Abstract

1. Introduction

Aalborg Universitet Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries The Role of Data Fidelity Zheng, Yusheng; Liu, Wenxue; Che, Yunhong; Grimm, Ferdinand; Zhao, Jingyuan; Hu, Xiaosong; Onori, Simona; Teodorescu, Remus; Offer, Gregory J. DOI (link to publication from Publisher): 10.48550/arXiv.2509.10380 Creative Commons License CC BY 4.0 Publication date: 2025 Document Version Publisher's PDF, also known as Version of record Link to publication from Aalborg University Citation for published version (APA): Zheng, Y. , Liu, W. , Che, Y. , Grimm, F. , Zhao, J. , Hu, X. , Onori, S. , Teodorescu, R. , & Offer, G. J. (2025) . Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity. arXiv. [https://doi.org/10.48550/arXiv.2509.10380](https://doi.org/10.48550/arXiv.2509.10380) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. -Users may download and print one copy of any publication from the public portal for the purpose of private study or research. -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from [vbn.aau.dk](vbn.aau.dk) on: August 02, 2026 Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity Yusheng Zheng 1, Wenxue Liu2,3, Yunhong Che 1,4*, Ferdinand Grimm5, Jingyuan Zhao6, Xiaosong Hu2**, Simona Onori7, Remus Teodorescu 1, Gregory J. Offer8 1Department of Energy, Aalborg University, Aalborg 9220, Denmark 2College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China 3Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA 4Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA 5Industrial Electrical Engineering & Automation, Lund University, Ole Römers väg 1, Lund, 221 00, Sweden 6Institute of Transportation Studies, University of California, Davis, CA 95616, USA. 7Department of Energy Science and Engineering, Stanford University, Stanford, CA, USA 8Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, United Kingdom Corresponding authors: Yunhong Che ([yunhche@mit.edu](yunhche@mit.edu)) and Xiaosong Hu ([xiaosonghu@ieee.org](xiaosonghu@ieee.org)) Abstract Monitoring the internal temperature of lithium-ion batteries is essential to their safe operation, as thermal gradients develop naturally within the cell during usage. Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for resource-efficient and scalable development of accurate, robust, and adaptive internal temperature estimation algorithms by blending physicsbased modeling with machine learning, in order to address the key challenges in data collection, model parameterization, and estimator design that traditionally hinder both approaches. In this framework, a physicsbased model is leveraged to generate simulation data that includes different operating scenarios by sweeping the model parameters and input profiles. Such a cheap simulation dataset can be used to pre-train

FAQs

Why is internal temperature monitoring critical for lithium-ion batteries?

Internal temperature directly affects safety, especially because thermal gradients develop during operation and can become much higher than surface temperatures at high rates. Accurate monitoring enables effective thermal management and detection of anomalies.

How does the proposed framework combine physics modeling with machine learning?

A physics-based model is used to generate simulation datasets by sweeping model parameters and input profiles, which supports pre-training. The approach then addresses simulation-to-reality mismatch through transfer learning with unsupervised domain adaptation.

What data is required to fine-tune the model on target batteries?

Fine-tuning uses limited operational data from target batteries without internal temperature measurements, relying on unsupervised domain adaptation to bridge the gap between simulation and real conditions.

Aug 3, 2026

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