Machine Learning For Real _Time Prediction Consumption And Driving Classification Based On Ecu Data

The increasing demand for intelligent vehicular systems has led to the integration of machine learning for real-time prediction and driving behavior analysis.This paper explores a novel approach using machine learning techniques to predict fuel consumption and classify driving styles based on real-time Engine Control Unit (ECU) data.By utilizing high-frequency parameters such as throttle position, engine RPM, vehicle speed, and fuel flow, a dataset is generated that reflects actual driving behavior in various conditions.These parameters are then processed through a machine learning pipeline consisting of preprocessing, feature selection, and model training using algorithms like Random Forest, Gradient Boosting, and Support Vector Machines.The proposed framework achieves high prediction accuracy for fuel consumption and reliable classification of driving patterns such as aggressive, normal, and economical.Real-time data processing capabilities make the system suitable for deployment in modern vehicles, offering immediate feedback to drivers and aiding in fuel-efficient driving.The system's potential applications include fleet management, driver coaching, and environmental impact reduction.Experimental evaluations conducted using real-world datasets demonstrate the robustness and efficiency of the proposed approach, outperforming traditional rule-based models in both accuracy and response time.The findings support the feasibility of integrating machine learning with ECU systems to enhance vehicle intelligence and contribute to smarter transportation ecosystems.