Artificial Neural Networks Based Energy Management System for Electric Vehicles.

Abstract

Optimizing Electric Vehicle Energy Management for Enhanced Performance and Electric vehicles (EVs) have emerged as a promising solution to address environmental concerns and reduce dependence on fossil fuels. However, the widespread adoption of EVs faces several challenges, including limited battery capacity, range anxiety, and a lack of robust charging infrastructure. Addressing these challenges is crucial for the successful integration of EVs into the transportation ecosystem. This research paper presents a comprehensive study of the Energy Management System (EMS) for electric vehicles, a critical component that plays a pivotal role in optimizing energy usage and improving overall efficiency. The EMS is responsible for monitoring and controlling various systems within the EV, ensuring the reliable and efficient operation of the powertrain. The proposed EMS controller and energy management strategy (EMS) demonstrate improved time response to sudden and slowly varying load demands, resulting in reduced battery stress and an enhanced battery life span. The study also explores the development of systems capable of accurately predicting energy usage, minimizing losses, and enhancing the efficiency of the battery, while also improving the overall system cost. By effectively managing the power flow between different energy sources in the electrified powertrain, the EMS directly impacts the vehicle's performance, reliability, and user experience. This research provides valuable insights into the design and implementation of advanced EMS solutions, paving the way for the widespread adoption of electric vehicles and the realization of a sustainable transportation future.