AI-Based Hybrid EV Assistive System for Fuel and Electric Switching

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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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-02-10
DOI
https://doi.org/10.32628/cseit251112280
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
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article

AI-Based Hybrid EV Assistive System for Fuel and Electric Switching

R Savitha, Alluri Venkata Satwika, Gandavaram Lakshmi Poojitha, Gundavaram Abhinaya et al.
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Advanced Battery Technologies Research
article

AI-Based Hybrid EV Assistive System for Fuel and Electric Switching

R Savitha, Alluri Venkata Satwika, Gandavaram Lakshmi Poojitha, Gundavaram Abhinaya, P Jeevitha
article en

Abstract

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.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(1)
KPR Institute of Engineering and Technology (IN)
Openalex Percentile: Top 98%
Advanced Battery Technologies Research
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AI-Based Hybrid EV Assistive System for Fuel and Electric Switching — R Savitha, Alluri Venkata Satwika, et al. · International Journal of Scientific Research in Computer Science Engineering and Information Technology (2025) | TGRS Research Map | TGRS