Performance Evaluation of Machine Learning Techniques for Intelligent Electric Vehicle Operations
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Abstract
Electric vehicles are widely used today because they are clean and environment friendly. However, the performance of an electric vehicle depends on many factors such as battery level, speed, distance, and driving conditions. Traditional systems used in electric vehicles are not smart enough to handle these changes properly. Machine Learning helps vehicles learn from data and make better decisions. In this paper, different Machine Learning techniques are used and compared to improve electric vehicle operation. Algorithms like Linear Regression, Decision Tree, Random Forest, Support Vector Machine, and Neural Network are tested using electric vehicle data. The performance of each model is evaluated based on accuracy, error, and prediction time. The results show that Machine Learning models can improve efficiency and give better predictions compared to traditional methods. This study helps in choosing the right Machine Learning technique for intelligent and efficient electric vehicle operation.
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References
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