Electric vehicle path optimization research based on charging and switching methods under V2G

This study presents a novel framework for advancing sustainable urban logistics and distribution systems, with a pivotal focus on fast charging and power exchange modalities as the cornerstone of our research endeavors. Our central contribution encompasses the formulation of an innovative electric vehicle path optimization model, whose paramount objective is to minimize overall operational costs. Integrating V2G technology, we facilitate sophisticated slow charging and discharging management of EVs upon their return to distribution centers, enhancing resource utilization. Moreover, we introduce a robust algorithmic approach for estimating battery degradation costs, meticulously accounting for ambient temperature fluctuations and discharge depth. This methodology, combined with the V2G framework encompassing both charging modes, is effectively solved using a genetic algorithm, ensuring the logistics distribution model's optimal performance. Simulation outcomes underscore the remarkable capacity of our V2G model to augment operational flexibility in EV logistics distribution, culminating in substantial cost reductions. Simultaneously, it adeptly equilibrates peak and off-peak loads within the distribution grid, fostering a more resilient and efficient energy ecosystem. Through rigorous experimental comparisons, we delve into the intricacies of the charging and swapping mode model, offering profound insights that can inform strategic decision-making within the logistics sector regarding optimal charging and swapping strategies. Furthermore, we explore the ramifications of slow charging and discharging management on the distribution system's performance, illuminating their potential benefits. A comprehensive sensitivity analysis is conducted to unravel the factors that influence battery loss in EVs, revealing a pronounced positive correlation between elevated temperatures, deeper discharge depths, and accelerated battery degradation. This revelation underscores the importance of considering environmental conditions in EV operation and maintenance strategies.