Machine Learning for Fuel Consumption Prediction and Driving Profile Classification Based On ECU Data
Keywords:
Machine Learning, Fuel Consumption, Driving Profile Classification, ECU Data, XGBoost, SVR, Ridge Regression, Random Forest, Logistic Regression, AdaboostAbstract
In recent years, real-time fuel consumption prediction and driving profile type have won prominence due to their effect on car efficiency and environmental sustainability. This assignment makes a speciality of leveraging machine getting to know algorithms to are expecting gas intake and classify riding profiles based totally on ECU (Engine Control Unit) facts. The existing system makes use of XGBoost, SVR (Support Vector Regression), and Ridge Regression. The proposed gadget ambitions to enhance predictive accuracy and profile class via incorporating Random Forest, Logistic Regression, and Adaboost algorithms. Driving profiles are categorised into five distinct instructions: Sporty, Eco, Calm, Normal, and Aggressive, based on fuel consumption styles. This approach not best offers insights into riding conduct but additionally helps the improvement of adaptive riding strategies and fuel-saving measures. By integrating superior system learning techniques, the project seeks to enhance each vehicle overall performance and environmental effect.
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