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@article{201517,
        author = {Dr. Vanitha K and Arun Shivanish R and Sujin S U and Samvel B},
        title = {AI Based Battery Temperature Prediction System for Electric Vehicles},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {160-164},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201517},
        abstract = {Accurate thermal management of battery systems is critical for the safety, performance, and longevity of electric vehicles (EVs). Excessive battery temperature can lead to efficiency degradation and, in extreme cases, thermal runaway, posing serious safety risks. Traditional battery temperature monitoring methods rely heavily on physical sensors and simplified thermal models, which often fail to provide precise real-time predictions under dynamic operating conditions. This paper proposes an AI based battery temperature prediction system that leverages machine learning techniques to forecast battery temperature using key parameters such as voltage, current, and state of charge. A data-driven model, based on advanced algorithms such as Long Short-Term Memory (LSTM) networks, is developed to capture the temporal dependencies in battery behaviour. The proposed system is trained and validated using collected and pre-processed datasets to ensure robustness and accuracy. Experimental results demonstrate that the AI-based model significantly outperforms conventional methods in terms of prediction accuracy and response time. The proposed approach enhances battery management systems by enabling proactive thermal control, thereby improving safety, optimizing performance, and extending battery life in electric vehicles.},
        keywords = {.},
        month = {May},
        }