Electric Vehicle Charging Load Modeling Based on Influence Factor Analysis

doi:10.1007/s12204-023-2663-2

Abstract

Abstract: The spatial-temporal distribution of charging loads for electric vehicles is influenced by multiple factors, Nowadays, the accuracy of the forecasts needs to be improved and the completeness of the modeling is relatively lacking. Therefore, this paper proposes a method for modeling the charging load of electric vehicles based on the influence of multiple factors. First, an in-depth analysis of the factors affecting the charging load of electric vehicles was conducted. Then, a model of electric vehicle electricity consumption per unit kilometer was constructed based on the influencing factors. Next, the electric vehicle, the charging station, the traffic network and the grid are modeled separately. In addition, a unified model of vehicle-station-road-network was constructed through the interaction and coupling of information between the models. Finally, the spatial-temporal distribution of electric vehicle charging loads was simulated using real data from a region. The study shows that the model is able to simulate the charging load of electric vehicles more accurately. Different traffic flows and areas have a significant impact on the charging load distribution.

Key words: electric vehicle, influencing factors, vehicle-station-road-network, load forecasting

摘要： 电动汽车的充电负荷时空分布受到多种因素影响，预测的准确性还有待提高，建模的完备性比较缺乏。为此，提出了基于多种因素影响的电动汽车充电负荷建模方法。首先深入地分析了影响电动汽车充电负荷的因素，基于影响因素构建了电动汽车单位里程耗电量模型，然后分别建立了电动汽车、充电站、交通网、配电网模型，利用各模型之间的信息交互和耦合关系构建了车-站-路-网统一模型，最后以采集的某区域真实数据为例模拟了电动汽车充电负荷时空分布。结果表明：所构建的模型能够较为准确的模拟电动汽车不同时空分布的充电需求；不同车流量以及不同区域对充电负荷分布具有明显的影响。

关键词: 电动汽车，影响因素，车-站-路-网，负荷预测

CLC Number:

TM73
U49

WANG Guojun, WANG Liye, WANG Lifang, LIAO Chenglin. Electric Vehicle Charging Load Modeling Based on Influence Factor Analysis[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(6): 1232-1241.

References

[1] EHSANI M, SINGH K V, BANSAL H O, et al. State of the art and trends in electric and hybrid electric vehicles [J]. Proceedings of the IEEE, 2021, 109(6): 967-984.

[2] SUN F C. Green Energy and Intelligent Transportation—Promoting green and intelligent mobility [J]. Green Energy and Intelligent Transportation, 2022, 1(1): 100017.

[3] XIONG R, KIM J, SHEN W X, et al. Key technologies for electric vehicles [J]. Green Energy and Intelligent Transportation, 2022, 1(2): 100041.

[4] LIAO F, XU C Y, YAO J G, et al. Load characteristics of Changde region and analysis on its influencing factors [J]. Power System Technology, 2012, 36(7): 117-125 (in Chinese).

[5] ARIAS M B, KIM M, BAE S. Prediction of electric vehicle charging-power demand in realistic urban traffic networks [J]. Applied Energy, 2017, 195: 738-753.

[6] SHEPERO M, MUNKHAMMAR J. Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data [J]. Applied Energy, 2018, 231: 1089-1099.

[7] ZHANG Q, YANG J W, XIANG Y P, et al. Regional electric vehicle charging load modeling method considering meteorological factors [J]. Power System Protection and Control, 2022, 50(6): 14-22 (in Chinese).

[8] XIONG X, LIN G, HAO S, et al. Electric vehicle charging load forecasting considering temperature and traffic impact [J]. Electric Engineering, 2021(14): 73-76 (in Chinese).

[9] YI T, ZHANG C, LIN T Y, et al. Research on the spatial-temporal distribution of electric vehicle charging load demand: A case study in China [J]. Journal of Cleaner Production, 2020, 242: 118457.

[10] LIU K, WANG J B, YAMAMOTO T, et al. Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption [J]. Applied Energy, 2018, 227: 324-331.

[11] KURUKURU V S B, ALI KHAN M, SINGH R. Electric vehicle charging/discharging models for estimation of load profile in grid environments [J]. Electric Power Components and Systems, 2023, 51(3): 279-295.

[12] CHENG S, ZHAO Z, CHEN N, et al. Prediction of temporal and spatial distribution of electric vehicle charging load considering coupling factors [J]. Electric Power Engineering Technology, 2022, 41(3): 194-201 (in Chinese).

[13] QI C, ZHANG Z, LÜ G, et al. Spatial-temporal modeling of EV load considering user behavior decision [J/OL]. Southern Power System Technology, 2023. https://kns.cnki.net/kcms/detail//44.1643.TK.20230202.1703.006.html (in Chinese).

[14] DU X, LONG B, GUO Y, et al. Prediction of electric vehicles charging load based on user travel habits [J]. Intelligent Computers and Applications, 2022, 12(11): 54-63 (in Chinese).

[15] CHEN Y, JIANG Y, XU G, et al. Charging load forecasting for large-scale electric vehicle [J]. Power Demand Side Management, 2022, 24(5):71-77 (in Chinese).

[16] KE S, CHEN L, YANG J, et al. Electric vehicles travel guidance strategy based on semi-dynamic traffic flow state model [J]. Power System Technology, 2023, 47(8): 3362-3375 (in Chinese).

[17] SHAO Y C, MU Y F, YU X D, et al. A spatial-temporal charging load forecast and impact analysis method for distribution network using EVs-traffic-distribution model [J]. Proceedings of the CSEE, 2017, 37(18): 5207-5219, 5519 (in Chinese).

[18] YANG X R, LV L, XIANG Y, et al. Degradation charging scenarios and impacts on voltage stability of urban distribution network under “EV-road-grid” coupling [J]. Electric Power Automation Equipment, 2019, 39(10): 102-108, 122 (in Chinese).

[19] ZHANG C, PENG K, XIAO C. EV charging guiding strategy based on coordination of “EVs-road-network” [J]. Electric Power Automation Equipment, 2022, 42(10):125-133 (in Chinese).

[20] ZHENG Y S, LI F, DONG J L, et al. Optimal dispatch strategy of spatio-temporal flexibility for electric vehicle charging and discharging in vehicle-road-grid mode [J]. Automation of Electric Power Systems, 2022, 46(12): 88-97 (in Chinese).

[21] LIU Z, ZHANG Q, ZHU Y, et al. Spatial-temporal distribution prediction of charging loads for electric vehicles considering vehicle-road-station-grid integration[J]. Automation of Electric Power Systems, 2022 46(12): 36-45 (in Chinese).

[22] CHEN J, ZHOU Z D, ZHOU Z W, et al. Impact of battery cell imbalance on electric vehicle range [J]. Green Energy and Intelligent Transportation, 2022, 1(3): 100025.

[23] SHEN Z, XIAO L, SHEN F, et al. The experimental study on charge and discharge performance of NCM power battery system based on ambient temperature[C]// 2020 China-ASA Conference and Exhibition. Shanghai: China SAE, 2020: 548-555 (in Chinese).

[24] YU Y, YANG Y. Research on energy behavior testing on office building occupants in summer [J]. Environmental Engineering, 2015, 33(5): 153-156 (in Chinese).

[25] ZHU M L, YAN J Y, LIU X S. Analysis of road traffic congestion in Beijing and research on its mitigation countermeasures[J]. Journal of Municipal Technology, 2020, 38(3): 28-32 (in Chinese).