Cloud-Integrated Adaptive Deep Learning Framework for Real-Time Battery Core Temperature Estimation and Enhanced Thermal Safety

Cloud-Integrated Adaptive Deep Learning Framework for Real-Time Battery Core Temperature Estimation and Enhanced Thermal Safety | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 March 2025 V1 Latest version Share on Cloud-Integrated Adaptive Deep Learning Framework for Real-Time Battery Core Temperature Estimation and Enhanced Thermal Safety Authors : Akash Samanta 0000-0002-1590-0840 [email protected] and Sheldon Williamson Authors Info & Affiliations https://doi.org/10.22541/au.174129484.41375174/v1 295 views 231 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper presents a novel cloud-integrated deep learning framework for the real-time core temperature estimation of automotive lithium-ion batteries (LIBs). Accurate core temperature estimation is crucial for rapid thermal management, thermal runaway prediction, and improving battery life and efficiency. The proposed framework integrates physical sensors, a cloud-based data acquisition system, and a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network deployed on both the cloud and a local machine for real-time data collection, core temperature estimation, and visualization. This solution addresses the limitations of traditional temperature monitoring methods, enabling real-time processing and predictive capabilities. The paper discusses the model's architecture, experimental validation, and deployment strategies. The Bi-LSTM network achieves a core temperature estimation accuracy of 0.16°C, even with unknown cell data at varied ambient temperatures and Crates. Unlike existing state-of-the-art studies, this work provides the first demonstration of real-time core temperature estimation with an estimation accuracy of 0.31°C alongside the measurement and visualization for an entire battery module, marking a significant advancement in the field. Additionally, the paper demonstrates real-time decision-making informed by core temperature to prevent overheating and thermal runaway. This is achieved by implementing an automotive-grade CAN communication-based feedback control loop for the charging and discharging of the LIB module. The study also emphasizes the benefits of considering core temperature in improving the response time of thermal management by at least 2 minutes compared to surface temperature-informed control, which is extremely crucial for preventing overheating and protection from thermal runaway. A detailed discussion of computational costs and latency is included, providing a practical reference for realworld applications. Supplementary Material File (realtime_core_temperature_compressed.pdf) Download 1.10 MB Information & Authors Information Version history V1 Version 1 06 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords battery management systems electric vehicles lithium-ion battery thermal management systems thermal safety transportation electrification Authors Affiliations Akash Samanta 0000-0002-1590-0840 [email protected] View all articles by this author Sheldon Williamson View all articles by this author Metrics & Citations Metrics Article Usage 295 views 231 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Akash Samanta, Sheldon Williamson. Cloud-Integrated Adaptive Deep Learning Framework for Real-Time Battery Core Temperature Estimation and Enhanced Thermal Safety. Authorea . 06 March 2025. DOI: https://doi.org/10.22541/au.174129484.41375174/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Dominic Karnehm, Akash Samanta, Christian Rosenmüller, Antje Neve, Sheldon Williamson, Core Temperature Estimation of Lithium-Ion Batteries Using Long Short-Term Memory (LSTM) Network and Kolmogorov–Arnold Network (KAN), IEEE Transactions on Transportation Electrification, 11 , 4, (10391-10401), (2025). https://doi.org/10.1109/TTE.2025.3559633 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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