Optimal Energy Management and Control Strategies for Electric Vehicles Considering Driving Conditions and Battery Degradation

¹ Assistant Professor, Department of EEE, GMR Institute of Technology, Rajam, AP, India - 532127
² Associate professor, Dept of Electrical, Telecommunication and Computer Engineering, School of Engineering and Applied Sciences, Kampala International University, Isakha, Uganda
³ Professor, Department of EEE, GMR Institute of Technology, Rajam, AP, India - 532127
⁴ Assistant Professor, Department of Mechanical Engineering, Vignan's Institute of Information Technology, Visakhapatnam, India
^5,6 B.Tech Scholar, Department of EEE, GMR Institute of Technology, Rajam, AP, India - 532127

^* Corresponding author: ramana.pilla@gmrit.edu.in

Abstract

Electric vehicles (EVs) are crucial for reducing greenhouse gas emissions and promoting sustainable transportation. However, optimizing energy management in EVs is challenging due to the variability in driving conditions and the impact of battery degradation. This paper proposes an advanced energy management and control strategy that accounts for these factors, aiming to enhance both vehicle performance and battery longevity. We integrate real-time data on driving conditions with detailed battery degradation models to develop a comprehensive control framework. Our methodology employs a combination of rule-based and optimization-based algorithms to dynamically adjust energy usage, ensuring optimal performance under diverse driving scenarios. Our strategy significantly improves energy efficiency and mitigates battery degradation compared to conventional approaches. Specifically, findings show an increase in overall driving range and a reduction in battery wear. Additionally, a sensitivity analysis underscores the robustness of our approach across different driving conditions and battery states. This research offers critical insights for the development of next-generation EV energy management systems, promoting longer-lasting and more efficient electric vehicles. Future work will focus on real-world testing and further refinement of the control algorithms to ensure practical applicability and enhanced performance in varied driving environments.