فهرست نشریات علمی دسترسی آزاد ایران

Advancements in technology have prioritized renewable energy (RE) and reduced the negative environmental impacts of transportation. This has stimulated the development of artificial intelligence (AI) techniques for efficient public transportation systems. The main challenge in developing electric vehicles (EVs) is minimizing energy consumption and charging time, and maximizing battery life compared to traditional transportation. An optimization of the battery management system (BMS) is required to improve the performance, efficiency, and reliability of EVs. This paper studies the comparative analysis of different optimization techniques for BMS in EVs. The comparative analysis of optimization approaches is explored based on the appropriateness of simulation, real-time implementation, computational complexity, and the capability of optimization. The combination of two techniques, such as machine learning and user-centric design, provides the best response for BMS in EVs. The hybrid technique accelerates the shift to sustainable mobility solutions and enables the full potential of EVs in order to minimize their ecological impact. The computational cost and uncertainty challenges for EVs can be minimized in further research.