International Journal of Science, Engineering and TechnologyAuthors: Abhendra Pratap Singh, Nandini Sharma, Rachit Sharma, Gauri Kathait, Daksh Bhatia
Abstract: The fast growing use of electric vehicles creates a larger need for intelligent systems that can adaptively optimize energy use in real time. This paper provides an overview of how AI driven predictive optimization frameworks for EV’s are changing the way energy management will be executed. It reviewed the impact of artificial intelligence, machine learning, and deep learning on the limitations of conventional methods for managing and optimizing energy. Advanced algorithms such as Long short term Memory networks, XGBoost, deep reinforcement learning, fuzzy logic, and genetic algorithms allow the predictive modelling of battery performance, power usage, and energy requirements based on an individual route. Utilizing real time data, such as traffic volume, road slope, weather information, and driver behaviour, these systems create a dynamic and context based approach to determining the most effective way to use energy, ultimately resulting in greater distance travelled between recharges and increased battery life. They also explore how Vehicle To Grid Communication (V2G) and model predictive control will work together to optimize energy usage in an operational environment that includes issues, such as sensor reliability and communication dropouts. Some conclusions from this literature review render AI driven energy management systems have excellent performance in controlled traditional environments, but challenges remain in developing standard testing protocols, ensuring data safety from cyberattacks, and the need for onboard real time systems to develop standardised protocols for evaluation and testing. Therefore, in order to use a holistic and multi technology AI structure for predictive analytics, a significant amount of work remains to be done to develop standard operating protocols for energy management of electric vehicles and their associated energy networks.
DOI: http://doi.org/10.5281/zenodo.21719385
