Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Prabu, P.; Ganeshkumar, P.; Krishnan, Prabhakar; Dhanasekaran, S.; Jiang, Weiwei; Logeshwaran, J. Title A name given to the resource An improved LSTM based thermal prediction and control algorithm for battery management system in hybrid electric vehicles Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Communications in Heat and Mass Transfer;Volume;169;Issue;;Article No.;109930; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.icheatmasstransfer.2025.109930" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.icheatmasstransfer.2025.109930</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020670519?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020670519?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Prabu P., Department of Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11442, Saudi Arabia; Ganeshkumar P., Department of Computer Science, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Krishnan P., Center for Cyber Security and Networks, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam, India; Dhanasekaran S., Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, 641202, India; Jiang W., School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Logeshwaran J., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India Description An account of the resource Effective thermal management of lithium-ion batteries is critical for ensuring safety, longevity, and optimal performance in Hybrid Electric Vehicles (HEV). This research proposes an improved Long Short-Term Memory (LSTM) based thermal prediction and control algorithm for Battery Management Systems (BMS) to enhance temperature regulation accuracy and computational efficiency. The proposed model integrates an optimized LSTM network with attention mechanisms to capture long-term dependencies in thermal dynamics while reducing prediction latency. A multi-physics-based thermal model is employed to generate high-fidelity training data, accounting for electrochemical-thermal coupling effects. The algorithm incorporates adaptive learning rates and dropout regularization to mitigate overfitting and improve generalization under varying load conditions. A model predictive control framework is designed to leverage real-time LSTM predictions for proactive cooling strategy optimization, minimizing energy consumption while maintaining safe operating temperatures. The proposed model reached RMSE of Heat generation rate of 1.08 W/mA3, Entropy coefficient Error of 0.024 mV/K, Thermal conductivity of 0.626 w/mK, Latency of 28 ms, Cooling energy Consumption of 314.61 kWh and Temperature deviation of 3.34 AC. The proposed solution offers a computationally efficient, scalable framework for next-generation BMS, enhancing battery reliability and vehicle efficiency. 2025 Elsevier Ltd Subject The topic of the resource Adaptive learning; Battery; Electric vehicles; LSTM; Optimization; Reliability; Thermal management Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 7351933; CODEN: IHMTD Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online