To facilitate wide-spread acceptance of Electric Vehicles (EVs), it is essential to increase their range. One of the ways to achieve this is by reducing the energy consumption for cooling the battery by increasing the efficiency of the cooling strategy using predictive thermal management. Predictive thermal management can be used to switch on the cooling system whenever needed thereby minimizing the energy wastage which can otherwise be used to drive the vehicle. Artificial Neural Network (ANN) with Levenberg-Marquardt Algorithm as optimizer is being employed as the predictor. Initially, the battery output current will be predicted using which the future values of the battery temperature will be predicted. The current predictor has Root Mean Square Error (RMSE) of 2.866 and Coefficient of Correlation (R) of 0.988 while the corresponding values for the temperature predictor are 0.829 and 0.986 respectively.


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    Title :

    Artificial Intelligence Based Model for Vehicle Cooling System


    Contributors:


    Publication date :

    2023-12-12


    Size :

    446321 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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