Optimization of Electric Vehicle Discharge Strategy Based on Genetic Algorithm and Battery Loss - Hu, Yinquan; Liu, Heping (Distributed Generation & Alternative Energy Journal, 2026)
This study endeavors to address several critical challenges. Traditional evolutionary algorithms frequently encounter the issue of being trapped in local optima and grapple with insufficient population diversity during the optimization of electric vehicle (EV) discharge strategies. Moreover, the disorderly discharge of EVs can precipitate instability in power grids and precipitate excessive battery degradation. To surmount these obstacles, this study introduces an optimized approach for formulating EV discharge strategies. This approach leverages an enhanced genetic algorithm that explicitly accounts for battery degradation. The Monte Carlo simulation technique is employed to construct a discharge load model for EVs that incorporates battery degradation. This model enables precise simulation of the erratic discharge behavior of EVs and facilitates the calculation of the aggregate discharge load. The experimental outcomes reveal that the refined algorithm exhibits accelerated convergence. After roughly 100 iterations, the accuracy stabilizes near 1.0, achieving the minimum loss function value. From an economic standpoint, the total cost associated with the ordered discharge strategy that considers battery degradation amounts to 1,340 yuan, markedly lower than the 1,565 yuan incurred by the ordered discharge strategy that neglects battery degradation. This research has effectively curtailed charging expenses and battery wear, bolstered power grid stability, and furnished pragmatic optimization methodologies to foster the sustainable advancement of the EV industry.