This study presents a real-time energy management system for hybrid electric two-wheelers, leveraging Controller Area Network (CAN) data to optimize power distribution between the internal combustion engine and electric motor based on dynamic load inputs. The proposed EMS improves fuel efficiency, reduces emissions, and enhances battery utilization through adaptive energy flow strategies. Additionally, predictive maintenance and intelligent control algorithms ensure optimal hybrid operation. The findings highlight the advantages of real-time load-based energy management over conventional drive cycle-based methods. Future research will explore the integration of vehicle-to-everything (V2X) communication for traffic-aware energy optimization and AI-driven predictive diagnostics. This study contributes to the advancement of sustainable and efficient hybrid two-wheeler technology, addressing critical gaps in adaptive energy management and real-world validation.
Balasaheb et al. (Wed,) studied this question.