In a rapidly evolving world, characterized by a growing emphasis on sustainable transportation, the pursuit of enhanced Electric Vehicle (EV) efficiency and reliability has become paramount. This abstract presents an innovative and intelligent system poised to redefine the management of EV batteries, prioritizing optimal performance and user safety. Our notable framework uses the capacities of cutting-edge LSTM machine learning algorithm to precisely anticipate, screen, and control EV battery temperatures. Its core objectives are clear: maintain battery temperatures within the optimal range and pre-emptively identify critical anomalies before they escalate. At the heart of this system lies its machine learning capabilities, continuously forecasting battery temperature trends. When optimal temperature is not within the stipulated range, the system swiftly takes action with dual purposes: heating or cooling the battery to safeguard its health and extend its lifespan. Yet, this system goes beyond mere reactivity to temperature extremes; it excels in predictive prowess, identifying irregularities in battery temperature patterns before they become critical issues. This proactive approach equips the system to issue timely alerts and intervene to prevent potential hazards. Statistics, enabling them to stay well-informed, take preventive actions, and experience worry-free journeys.


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    Titel :

    Electric Vehicle Battery Temperature Prediction Using LSTM Algorithm


    Beteiligte:
    Sridhar, B (Autor:in) / Allirani, S (Autor:in) / Reshmin Shafi, R S (Autor:in) / Rithvik Abinav, K R (Autor:in) / Siddharth, A (Autor:in)


    Erscheinungsdatum :

    31.07.2024


    Format / Umfang :

    713720 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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