Enhanced electric vehicle battery management system employing bat algorithm with chaotic diversification strategies

Abstract

As the demand for electric vehicles (EV) continues to increase, the need for effective charging and switching of battery systems becomes more important. This article presents a method using the Bat Algorithm (BA) improved by chaotic diversification as well as social education to optimize the power source replacement and the electric vehicle charging procedure. The plan is intended to solve the issues of payment delay and battery management failure. The algorithm searches for better positions by combining chaotic diversity, while social learning supports the coordination of battery stations. Thanks to extensive simulation and real-world testing, our approach shows significant improvements in optimization and a reduced payback period. The results show that the suggested approach outperforms the current algorithms in terms of rotation speed and good solution. This research supports the development of efficient transportation by providing practical solutions to increase the efficiency of electric vehicle transfer and payment and ultimately encourage greater effort. © 2024 The Author(s). IET Power Electronics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.