SEVMQVL: Sustainable and Economic Optimization of Electric Vehicle Powertrains: A Multifaceted Approach with Q-Learning and VARMA Models

Abstract

With the escalating climate crisis and depleting fossil fuel reserves, the impetus for more sustainable and efficient energy solutions, particularly in the automotive sector, has never been more acute. The inexorable drive towards greener and more efficient mobility solutions accentuates the necessity for advanced optimization models in electric vehicles (EVs) to ameliorate fuel efficiency, curtail energy consumption, mitigate emission levels, and augment battery life. Despite the advances in existing optimization models, they frequently manifest limitations in adaptive, generalizability, and real-time applicability, often yielding suboptimal performance under varied driving scenarios and conditions. Addressing these critical gaps, this paper presents a groundbreaking model that synergistically integrates Q-Learning, VARMA, and a novel Particle Swarm Grey Wolf Optimizer (PSGWO) to engineer a user-centric optimization solution for EVs. The model capitalizes on the strengths of each technique to achieve superior real-time impacts, rendering it an invaluable asset in diverse use cases. Our comprehensive analysis and real-world validations underscore the model’s profound ability to enhance fuel efficiency by 5.5%, diminish energy consumption by 8.5%, reduce emission levels by 4.5%, prolong battery life by 8.3%, and ameliorate regenerative braking efficiency by 5.9% compared to existing models. These significant improvements are instrumental in propelling the model’s utility across varied scenarios, depicting adaptability and high performance in each of the instance sets. The unprecedented advantages of the proposed work are illuminated through extensive simulations and evaluations, indicating its superior efficacy and robustness in optimizing powertrain systems. The model's adaptability and resilience in diverse conditions suggest promising potential in reshaping conventional powertrain optimization strategies, offering a beacon of hope in the quest for sustainable and efficient mobility solutions. This work proffers a paradigm shift in electric vehicle optimization, marrying state-of-the-art techniques to bridge existing gaps and deliver unparalleled performance improvements. Its application heralds a transformative era in sustainable transportation, pushing the boundaries of what is achievable in fuel efficiency, energy conservation, and environmental protection in the automotive sectors.