Research article

Research on charging behavior of electric vehicles based on multiple objectives


  • Received: 21 April 2023 Revised: 28 June 2023 Accepted: 07 July 2023 Published: 28 July 2023
  • This paper proposes a multi-objective queuing charging strategy for electric vehicles (EVs) based on metrics of public interest. It combines common charging modes, such as random charging mode, tariff-guided mode and stop-and-charge mode. It introduces the problem of queuing charging for EVs by considering the realistic imbalances of vehicle-pile ratios in these common modes. A travel model and a charging model were developed in this study. Experiments prove that the proposed strategy has the highest comprehensive evaluation index, achieves the aim of low charging cost and high travel rate and considers the queuing problem, which is unavoidable in reality. It improves the convenience of life and reduces the charging cost. The proposed strategy smoothens the EV charging load curve, largely reducing the burden of charging load fluctuations on the grid and achieving a win-win situation for both supply and demand.

    Citation: Tien-Wen Sung, Wei Li, Qiaoxin Liang, Chuanbo Hong, Qingjun Fang. Research on charging behavior of electric vehicles based on multiple objectives[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 15708-15736. doi: 10.3934/mbe.2023700

    Related Papers:

  • This paper proposes a multi-objective queuing charging strategy for electric vehicles (EVs) based on metrics of public interest. It combines common charging modes, such as random charging mode, tariff-guided mode and stop-and-charge mode. It introduces the problem of queuing charging for EVs by considering the realistic imbalances of vehicle-pile ratios in these common modes. A travel model and a charging model were developed in this study. Experiments prove that the proposed strategy has the highest comprehensive evaluation index, achieves the aim of low charging cost and high travel rate and considers the queuing problem, which is unavoidable in reality. It improves the convenience of life and reduces the charging cost. The proposed strategy smoothens the EV charging load curve, largely reducing the burden of charging load fluctuations on the grid and achieving a win-win situation for both supply and demand.



    加载中


    [1] H. S. Das, M. M. Rahman, S. Li, C. W. Tan, Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review, Renewable Sustainable Energy Rev., 120 (2020), 109618. https://doi.org/10.1016/j.rser.2019.109618 doi: 10.1016/j.rser.2019.109618
    [2] S. Habib, M. Kamran, U. Rashid, Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks-A review, J. Power Sources, 277 (2015), 205–214. https://doi.org/10.1016/j.jpowsour.2014.12.020 doi: 10.1016/j.jpowsour.2014.12.020
    [3] Y. Zhou, X. Li, The state-of-art of the EV charging control strategies, in 2015 34th IEEE Chinese Control Conference (CCC), (2015), 7916–7921. https://doi.org/10.1109/ChiCC.2015.7260898
    [4] G. Rajendran, C. A. Vaithilingam, N. Misron, K. Naidu, M. R. Ahmed, A comprehensive review on system architecture and international standards for electric vehicle charging stations, J. Energy Storage, 42 (2021), 103099. https://doi.org/10.1016/j.est.2021.103099 doi: 10.1016/j.est.2021.103099
    [5] J. Zhang, J. Yan, Y. Liu, H. Zhang, G. Lv, Daily electric vehicle charging load profiles considering demographics of vehicle users, Appl. Energy, 274 (2020), 115063. https://doi.org/10.1016/j.apenergy.2020.115063 doi: 10.1016/j.apenergy.2020.115063
    [6] Y. Xiang, S. Hu, Y. Liu, X. Zhang, J. Liu, Electric vehicles in smart grid: a survey on charging load modelling, IET Smart Grid, 2 (2019), 25–33. https://doi.org/10.1049/iet-stg.2018.0053 doi: 10.1049/iet-stg.2018.0053
    [7] H. B. Moon, S. Y. Park, C. Jeong, J. Lee, Forecasting electricity demand of electric vehicles by analyzing consumers' charging patterns, Transp. Res. Part D: Transp. Environ., 62 (2018), 64–79. https://doi.org/10.1016/j.trd.2018.02.009 doi: 10.1016/j.trd.2018.02.009
    [8] R. Tu, Y. Gai, B. Farooq, D. Posen, M. Hatzopoulou, Electric vehicle charging optimization to minimize marginal greenhouse gas emissions from power generation, Appl. Energy, 277 (2020), 115517. https://doi.org/10.1016/j.apenergy.2020.115517 doi: 10.1016/j.apenergy.2020.115517
    [9] O. Elma, A dynamic charging strategy with hybrid fast charging station for electric vehicles, Energy, 202 (2020), 117680. https://doi.org/10.1016/j.energy.2020.117680 doi: 10.1016/j.energy.2020.117680
    [10] Y. Kim, H. Kim, K. Suh, Environmental performance of electric vehicles on regional effective factors using system dynamics, J. Cleaner Prod., 320 (2021), 128892. https://doi.org/10.1016/j.jclepro.2021.128892 doi: 10.1016/j.jclepro.2021.128892
    [11] A. Poullikkas, Sustainable options for electric vehicle technologies, Renewable Sustainable Energy Rev., 41 (2015), 1277–1287. https://doi.org/10.1016/j.rser.2014.09.016 doi: 10.1016/j.rser.2014.09.016
    [12] C. Chen, F. Shang, M. Salameh, M. Krishnamurthy, Challenges and advancements in fast charging solutions for EVs: A technological review, in 2018 IEEE Transportation Electrification Conference and Expo (ITEC), (2018), 695–701. https://doi.org/10.1109/ITEC.2018.8450139
    [13] M. Bilal, M. Rizwan, Electric vehicles in a smart grid: a comprehensive survey on optimal location of charging station, IET Smart Grid, 3 (2020), 267–279. https://doi.org/10.1049/iet-stg.2019.0220 doi: 10.1049/iet-stg.2019.0220
    [14] G. Xu, B. Zhang, S. Zhang, Multi-energy Coordination and Schedule Considering large-scale electric vehicles penetration, in 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), (2018), 1–5. https://doi.org/10.1109/EI2.2018.8582136
    [15] Z. Xiao, H. Li, T. Zhu, H. Li, Day-ahead optimal scheduling strategy of microgrid with EVs charging station, in 2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), (2019), 774–780. https://doi.org/10.1109/PEDG.2019.8807656
    [16] X. He, C. Tu, L. Wang, J. Li, Z. Li, Double-layer charging strategy for electric vehicles considering users' driving patterns, Autom. Electr. Power Syst., 42 (2018), 64–69. http://doi.org/10.7500/AEPS20170731005 doi: 10.7500/AEPS20170731005
    [17] S. Sachan, N. Adnan, Stochastic charging of electric vehicles in smart power distribution grids, Sustainable Cities. Soc., 40 (2018), 91–100. https://doi.org/10.1016/j.scs.2018.03.031 doi: 10.1016/j.scs.2018.03.031
    [18] L. Yan, X. Chen, Y. Chen, J. Wen, A cooperative charging control strategy for electric vehicles based on multiagent deep reinforcement learning, IEEE Trans. Ind. Inf., 18 (2022), 8765–8775. https://doi.org/10.1109/TⅡ.2022.3152218 doi: 10.1109/TⅡ.2022.3152218
    [19] J. Ping, Z. Yan, S. Chen, A two-stage autonomous EV charging coordination method enabled by blockchain, J. Mod. Power Syst. Clean Energy, 9 (2020), 104–113. https://doi.org/10.35833/MPCE.2019.000139 doi: 10.35833/MPCE.2019.000139
    [20] S. Iqbal, A. Xin, M. U. Jan, S. Salman, A. U. M. Zaki, H. U. Rehman, et al., V2G strategy for primary frequency control of an industrial microgrid considering the charging station operator, Electronics, 9 (2020), 549. https://doi.org/10.3390/electronics9040549 doi: 10.3390/electronics9040549
    [21] J. S. Pan, B. Sun, S. C. Chu, M. Zhu, C. S. Shieh, A parallel compact gannet optimization algorithm for solving engineering optimization problems, Mathematics, 11 (2023), 439. https://doi.org/10.3390/math11020439 doi: 10.3390/math11020439
    [22] U. Akram, M. Khalid, S. Shafiq, A strategy for residential demand response management in modern electricity markets, in 2018 IEEE International Conference on Industrial Technology (ICIT), (2018), 1138–1142. https://doi.org/10.1109/ICIT.2018.8352338
    [23] W. Chen, L. Zheng, H. Li, X. Pei, An Assessment Method for the Impact of Electric Vehicle Participation in V2G on the Voltage Quality of the Distribution Network, Energies, 15 (2022), 4170. https://doi.org/10.3390/en15114170 doi: 10.3390/en15114170
    [24] M. Han, A V2G scheduling strategy based on the fruit fly optimization algorithm, J. Phys.: Conf. Ser., 1952 (2021), 042063. https://doi.org/10.1088/1742-6596/1952/4/042063 doi: 10.1088/1742-6596/1952/4/042063
    [25] T. W. Sung, P. W. Tsai, T. Gaber, C. Y. Lee, Artificial Intelligence of Things (AIoT) technologies and applications, Wireless Commun. Mobile Comput., 2021 (2021), 9781271. https://doi.org/10.1155/2021/9781271 doi: 10.1155/2021/9781271
    [26] K. B. Lee, M. A. Ahmed, D. K. Kang, Y. C. Kim, Deep reinforcement learning based optimal route and charging station selection, Energies, 13 (2020), 6255. https://doi.org/10.3390/en13236255 doi: 10.3390/en13236255
    [27] Y. Zhou, Z. Li, X. Wu, The multiobjective based large-scale electric vehicle charging behaviours analysis, Complexity, 2018 (2018), 1968435. https://doi.org/10.1155/2018/1968435 doi: 10.1155/2018/1968435
    [28] V. C. Pedroso, C. A. Taconeli, S. R. Giolo, Estimation based on ranked set sampling for the two-parameter Birnbaum-Saunders distribution, J. Stat. Comput. Simul., 91 (2021), 316–333. https://doi.org/10.1080/00949655.2020.1814287 doi: 10.1080/00949655.2020.1814287
    [29] A. Rahayu, P. Purhadi, S. Sutikno, D. D. Prastyo, Multivariate gamma regression: Parameter estimation, hypothesis testing, and its application, Symmetry, 12 (2020), 813. https://doi.org/10.3390/sym12050813 doi: 10.3390/sym12050813
    [30] R. A. Verzijlbergh, Z. Lukszo, M. D. Ilić, Comparing different EV charging strategies in liberalized power system, in 2012 9th International Conference on the European Energy Market, (2012), 1–8. https://doi.org/10.1109/EEM.2012.6254807
    [31] H. Wang, X. Zhang, L. Wu, C. Hou, H. Gong, Q. Zhang, et al., Beijing passenger car travel survey: implications for alternative fuel vehicle deployment, Mitig. Adapt. Strateg. Glob. Change, 20 (2015), 817–835. https://doi.org/10.1007/s11027-014-9609-9 doi: 10.1007/s11027-014-9609-9
    [32] T. Yi, C. Zhang, T. Lin, J. Liu, Research on the spatial-temporal distribution of electric vehicle charging load demand: A case study in China, J. Cleaner Prod., 242 (2020), 118457. https://doi.org/10.1016/j.jclepro.2019.118457 doi: 10.1016/j.jclepro.2019.118457
    [33] S. Su, Method of location and capacity determination of intelligent charging pile based on recurrent neural network, World Electr. Veh. J., 13 (2022), 186. https://doi.org/10.3390/wevj13100186 doi: 10.3390/wevj13100186
    [34] X. Yang, D. Niu, L. Sun, Z. Ji, J. Zhou, K. Wang, et al., A bi-level optimization model for electric vehicle charging strategy based on regional grid load following, J. Cleaner Prod., 325 (2021), 129313. https://doi.org/10.1016/j.jclepro.2021.129313 doi: 10.1016/j.jclepro.2021.129313
    [35] F. Dandl, F. Fehn, K. Bogenberger, F. Busch, Pre-day scheduling of charging processes in mobility-on-demand systems considering electricity price and vehicle utilization forecasts, in 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), (2020), 127–134. https://doi.org/10.1109/FISTS46898.2020.9264862
    [36] T. W. Sung, B. Zhao, X. Zhang, An adaptive dimension differential evolution algorithm based on ranking scheme for global optimization, PeerJ Comput. Sci., 8 (2022), 1007. https://doi.org/10.7717/peerj-cs.1007 doi: 10.7717/peerj-cs.1007
    [37] S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software, 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008
    [38] J. Xue, B. Shen, A novel swarm intelligence optimization approach: sparrow search algorithm, Syst. Sci. Control Eng., 8 (2020), 22–34. https://doi.org/10.1080/21642583.2019.1708830 doi: 10.1080/21642583.2019.1708830
    [39] X. Yang, X. Bai, P. Li, H. Wei, Charging optimization of massive electric vehicles in distribution network, Electr. Power Autom. Equip., 35 (2015), 31–36.
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(917) PDF downloads(149) Cited by(0)

Article outline

Figures and Tables

Figures(24)  /  Tables(7)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog