Meta-heuristics optimization in electric vehicles-an extensive review

Optimization through meta-heuristics in electric vehicular (EV) transport has emerged as the key to improve the existing technologies and pave way for their mass deployment and revolutionize the current transport system while lowering greenhouse emissions. Range and cost have been the main aspects that continue to hinder the development of EVs. This paper provides a comprehensive outlook of the five major areas of optimization (over the last two decades) in EVs i.e., design optimization, energy management, optimal control, optimized charging/ discharging and routing with both single and multi-objective methods examined and analyzed. The mathematical modelling, formulation of objective functions and constraints are studied followed by an in-depth survey of the state-of-the-art publications in each category. Furthermore, a classification of the various analytical, conventional and nature-inspired optimization algorithms (swarm-intelligent, evolutionary and modern meta heuristics) based on their popularity is made with an analysis of their merits and demerits followed by a study of the various constraint handling techniques. Additionally, a literature survey of the advanced and improved variants of these meta-heuristics is also made providing a systematic reference for EV optimization with intelligent algorithms. Finally, a classification of the various simulatory platforms and driving cycles is provided to help the upcoming researchers gain insightfulness and expertise on the ongoing trends in the EV optimization domain.

Automated summary

Optimization through meta-heuristics in electric vehicular transport is crucial for improving existing technologies, revolutionizing the current transport system, and reducing greenhouse emissions. This paper provides a comprehensive overview of the research in five major areas of EV optimization over the past two decades, categorizes and analyzes various optimization algorithms and constraint handling techniques, and serves as a systematic reference for intelligent algorithms in EV optimization.