Optimization model of electric vehicle charging induction based on comprehensive satisfaction of users
BI Jun1,2, DU Yujia1, WANG Yongxing1, ZUO Xiaolong1
1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; 2. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China
Abstract：[Objective］ With the increasing prevalence of electric vehicles (EVs) in urban transportation systems, charging guidance service has become an effective means to solve the charging problem in the context of insufficient charging infrastructure. However, for optimizing the charging station selection decision-making plan of users, most existing research studies aim to minimize travel costs, which rarely considers the charging experience of users during travel and ignores the interaction of charging station selection decision-making between multiple users. To enhance the charging experience of users, based on the analysis of charging satisfaction of EV users, an EV charging guidance optimization model that integrates user satisfaction with detour distance, queuing time, and charging cost is proposed in this study. The model aims to maximize the average comprehensive satisfaction of multiple users. ［Methods］ To quantify the comprehensive satisfaction of users with charging stations during charging processes, evaluation indicators of detour distance, queuing time, and charging cost are constructed. To accurately deduce the queuing time of users at charging stations, this study fully considers the interaction influence of charging station selection decision-making between multiple EV users. Prediction models of the charging station operation state are established by considering several charging scenarios based on the arrival patterns of two successive users. According to the characteristics of the proposed model, an immune algorithm and the Floyd shortest path algorithm are applied to optimize the decision-making plan of charging station selections and the travel paths of multiple users, respectively. A numerical example with multiple charging requests is designed to confirm the feasibility and effectiveness of the proposed model and the algorithms. ［Results］ The experimental results indicated that optimal charging station selections and driving paths of multiple EVs to maximize average comprehensive satisfaction could be obtained by solving the optimization model. Compared with models with single optimization objectives, namely, minimum detour distance, shortest queuing time, and least charging cost, the average comprehensive satisfaction of EV users was increased by 15.0%, 17.8%, and 11.4%, respectively. The results also showed that average driving speed was a critical factor affecting optimal charging station selection and average comprehensive satisfaction of EV users. By analyzing the arrival patterns of two successive users at the same charging station under different charging scenarios, their queuing time after arriving at the charging station could be accurately obtained. Subsequently, optimal charging station selections by multiple users could be determined by considering the interaction that influences their selections. ［Conclusions］ The proposed optimization model provides multiple EV users with decision-making support for selecting charging stations by considering their interaction influences. Provided that the threshold values of each satisfaction indicator remain unchanged, the comprehensive average satisfaction obtained by the proposed model is considerably higher than that obtained by models with single objectives: minimum detour distance, shortest queuing time, and least charging cost. Thus, the proposed method can enhance the charging experience of EV users during travel.
毕军, 杜宇佳, 王永兴, 左小龙. 基于用户综合满意度的电动汽车充电诱导优化模型[J]. 清华大学学报（自然科学版）, 2023, 63(11): 1750-1759.
BI Jun, DU Yujia, WANG Yongxing, ZUO Xiaolong. Optimization model of electric vehicle charging induction based on comprehensive satisfaction of users. Journal of Tsinghua University(Science and Technology), 2023, 63(11): 1750-1759.
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2023, Vol. 63 
