| This paper presents a novel AI model designed to address two critical challenges in electric vehicle (EV), range prediction and charging optimization. The primary objective of our work is to autonomously predict if EV charging is needed and to provide precise estimates of EV range through machine learning algorithms. Our key contribution is the integration of a binary decision-making element into the charging optimization process, which predicts the need for charging and enhances the efficiency of energy management to mitigate range anxiety. This contribution stands in stark contrast to existing systems that focus solely on range prediction. Furthermore, when implemented within a vehicle-to-vehicle (V2V) framework, our model lays the groundwork for autonomous energy governance and facilitates efficient energy exchange among EVs. |
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