AI-Based Hybrid EV Assistive System for Fuel and Electric Switching
DOI:
https://doi.org/10.32628/CSEIT251112280Keywords:
Sensor Integration and Data Collection, ope Detection and Mode Switching, RF-Based Speed Control, Machine Learning for Predictive Energy Management, Centralized Control UnitAbstract
This project presents an innovative energy management system for hybrid electric vehicles (HEVs) that optimizes fuel efficiency and reduces emissions. The system integrates advanced slope detection techniques to automatically switch between electric and fuel modes based on the vehicle's terrain and driving requirements. Additionally, an RF-based speed control mechanism is implemented to adjust vehicle speed according to designated zones, such as hospitals, schools, and accident-prone areas. The system utilizes sensors and machine learning algorithms to detect and respond to various driving conditions, ensuring improved fuel efficiency, reduced emissions, and enhanced safety. By seamlessly integrating energy management and automated speed control, this project offers a comprehensive solution for next-generation HEVs, promoting sustainable transportation and intelligent mobility.
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