Electric vehicle charging and discharging scheduling strategy under dynamic traffic network considering battery health
School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China.
Research Article
Global Journal of Engineering and Technology Advances, 2025, 22(03), 061-070.
Article DOI: 10.30574/gjeta.2025.22.3.0050
Publication history:
Received on 26 January 2025; revised on 01 March 2025; accepted on 04 March 2025
Abstract:
In order to enhance user engagement in power grid scheduling, this paper proposes a battery health assessment method based on electric vehicle charging curves, combined with a traffic network model to dynamically determine EVs’ state-of-charge distribution. First, by analyzing EV charging curves, an algorithm is introduced that accurately evaluates battery health, relying on characteristic changes observed during the charging and discharging processes. Second, a dynamic traffic network model is designed to monitor and predict the state-of-charge distribution at various charging stations in real time, thereby enabling more rational allocation of power resources and improving energy efficiency. Finally, the Kepler optimization algorithm is employed to solve the charging strategy, aiming to balance battery health and grid load. Simulation results show that the proposed method effectively predicts EV battery health status while optimizing the state-of-charge distribution among charging stations, thus reducing grid load fluctuations and enhancing both the stability and operational efficiency of the power grid.
Keywords:
Charge-discharge optimization; Battery degradation; Dynamic Traffic network; KOA
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0