A Systematic Review on the Integration of Artificial Intelligence into Energy Management Systems for Electric Vehicles: Recent Advances and Future Perspectives
Paúl Arévalo, Danny Ochoa-Correa, Edisson Villa‐Ávila
Abstract
This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in artificial intelligence, including machine learning, deep learning, and genetic algorithms, have been analyzed for their impact on improving electric vehicle performance, energy efficiency, and range. This study highlights significant advancements in energy management optimization, route planning, energy demand forecasting, and real-time adaptation to driving conditions through advanced control algorithms. Additionally, this paper explores artificial intelligence’s role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors. Furthermore, the integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles’ energy management systems is discussed. The findings underscore artificial intelligence’s potential to foster innovation and efficiency in sustainable mobility, emphasizing the need for further research to overcome current challenges and optimize practical applications.