Data-Model Hybrid-Driven Simulation Method for Electric Vehicle Fleet Behavior

doi:10.16183/j.cnki.jsjtu.2024.082

Abstract

Abstract:

To address the issues of lack of actual electric vehicle (EV) charging load data, significant regional differences, and complex simulation methods, a behavior simulation method for EVs is proposed, which consists of a trip chain, an energy consumption chain, and a charging chain. First, to tackle the scarcity of charging data, a data-driven approach is adopted to derive the construction method and process of vehicle trip chain based on Gaussian mixture model (GMM). The travel patterns of EVs are obtained based on the similarities between EV and conventional vehicle trips. Then, energy consumption models are summarized, creating energy consumption chains based on trip chains. Finally, considering factors like charging anxiety, queueing, and charging time, the EV charging chain is developed. Simulations are conducted on common charging strategies to verify the impacts on user charging costs and the power grid.

Key words: electric vehicle (EV), trip chain, energy consumption chain, charging chain, Gaussian mixture model (GMM)

CLC Number:

TM732

LIU Lin, YANG Siyu, HUANG Xianan, CHEN Yantao, XU Huashuai, WANG Lingling, JIANG Chuanwen. Data-Model Hybrid-Driven Simulation Method for Electric Vehicle Fleet Behavior[J]. Journal of Shanghai Jiao Tong University, 2026, 60(1): 51-60.

Figures/Tables 12

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车型	整车质量/ kg	电池容量/ (kW·h)	CLTC续航里程/km
比亚迪海豹2023	1885	61.4	550
蔚来ET5	2165	75	560
小鹏P7i	1980	86.2	702
特斯拉Model S	2089	100	715
上汽大众ID.6 X	2150	63.2	460

参数	原始数据均值	拟合数据均值	误差/%
出行次数	2.77	2.60	-6.14
起始时间/min	566.05	555.61	-1.84
终止时间/min	1009.49	1004.54	-0.49
行驶时间/min	22.63	22.94	1.37
停车时间/min	147.52	153.68	4.18
行程距离/km	17.44	17.73	1.66
起始地点	2.67	2.66	-0.37
终止地点	2.71	2.77	2.21

行程类型	文献[9]预测误差/%	朴素贝叶斯模型预测误差/%	本文预测误差/%
HO&OH	-13.92	-14.23	-5.07
WO&OW	14.10	12.00	-5.74
HW&WH	8.98	9.53	5.54
OO	14.60	12.21	11.47

充电方式	充电发生次数比例/%	充电平均成本/元	充电总成本/元
无序充电	19.9	15.23	11410.46
避峰充电	15.9	12.71	7594.31