AI TECHNIQUES IN ELECTRIC VEHICLE BMS.

Abstract

Electric vehicles (EVs) have gained significant attention in the automotive industry as a sustainable solution to reduce carbon emissions and address global environmental concerns. However, the efficiency of EVs can decline over time due to the degradation of battery health and performance. In this context, artificial intelligence (AI) techniques have emerged as promising tools to enhance EV safety, reliability, and efficiency by enabling accurate battery health assessment, fault detection, and thermal management. This research explores the impact of AI-based approaches on Battery Management Systems (BMS) in electric vehicles and evaluates their effectiveness. A comprehensive statistical analysis of relevant BMS-related literature is conducted using multiple evaluation methods. Key aspects such as research trends, authorship patterns, collaboration networks, publication sources, keyword distribution, and research categorization are analyzed in detail. Furthermore, the study examines the objectives, contributions, advantages, and limitations of various advanced AI techniques applied in BMS. It also highlights critical challenges and open issues in the field, along with practical recommendations and future research directions. The findings of this analysis provide valuable insights and serve as a guiding framework for researchers aiming to develop innovative, efficient, and sustainable battery management technologies for electric vehicles.