摘 要：为了提高混合动力汽车的充电效率，联合汽车车队和电力系统等多方，提出了协同调度优化策略。提出了多目标双层优化问题，其中上层优化以降低运营商成本和排放为目标，下层优化以最大化社会福利为目标。提出了基于 K-means 的车队聚类算法将车辆划分为车队，并提出了基于半整数的线性电池退化模型以刻画电池退化。结合真实的数据，文章使用实验验证提出优化策略的有效性。结果表明，提出的策略实现了成本和排放的均衡，提高了充电效率。
关键词：混合动力汽车；协同调度；多目标优化
DOI:10.19850/j.cnki.2096-4706.2022.16.036
中图分类号：TP393 文献标识码：A 文章编号：2096-4706（2022）16-0139-03
Research on Cooperative Scheduling Optimization for Hybrid Electric Vehicles
ZHENG Zhen1, TANG Fei 2
(1.Wuhan Vocational College of Software and Engineering, Wuhan 430205, China; 2.Wuhan Institute of Shipbuilding Technology, Wuhan 430050, China)
Abstract: In order to improve the charging efficiency of hybrid electric vehicles, a collaborative scheduling optimization strategy is proposed by combining the vehicle fleet with the power system. This paper proposes a multi-objective two-layer optimization problem, in which the upper-level optimization aims at reducing operator costs and emissions, and the lower-level optimization aims at maximizing social welfare. In this paper, a fleet clustering algorithm based on K-means is proposed to divide vehicles into fleets, and a half-integerbased linear battery degradation model is proposed to characterize the battery degradation. Combined with real data, this paper uses experiments to verify the effectiveness of the proposed optimization strategy. The results show that the proposed strategy achieves a balance of cost and emissions and improves charging efficiency.
Keywords: hybrid electric vehicle; collaborative scheduling; multi-objective optimization
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作者简介：郑振（1986—），男，汉族，湖北十堰人，讲师，硕士，研究方向：新能源；唐菲（1985-）女，汉族，湖北十堰人，工程师、硕士，研究方向：汽车设计。