International Journal on Science and Technology

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Machine Learning-based Hybrid EV Assistive System for Fuel and Electric Switching

Author(s) Dr. Muthukumar K, Ms. Shreemathy R, Ms. Sreenidhi A, Mr. Silvester S, Mr. Kungumaraj M
Country India
Abstract Modern Hybrid Electric Vehicles (HEVs) incorporate an Internal Combustion Engine (ICE) and an electric motor to increase fuel efficiency and lower pollutants. These vehicles offer a practical transition toward electrification, balancing eco-friendliness with convenience. An AI-based HEV assistive system
utilizes machine learning to optimize the switching between fuel and electric power sources, enhancing efficiency, performance, and sustainability. By continuously analyzing real time factors such as driving conditions, battery charge levels, fuel availability, traffic patterns, and driver behavior, the system
intelligently determines the optimal power source for any given situation. Advanced predictive algorithms enable seamless transitions, minimizing energy waste, reducing emissions, and extending battery life while maintaining a smooth driving experience. Additionally, the AI-driven system adapts over time, learning from historical data to refine its switching strategies for improved performance. This intelligent energy management approach not only enhances vehicle efficiency but also contributes to reducing operational costs and environmental impact, making hybrid vehicles more practical and eco-friendly.
Keywords Hybrid Electric Vehicle (HEV), Machine Learning, Node MCU, Temperature Sensor, H-Bridge.
Field Engineering
Published In Volume 16, Issue 3, July-September 2025
Published On 2025-09-29
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