Article Contents
ARTICLE   Open Access     Cite

Charging Guidance Strategy for Electric Ride-Hailing Vehicles Oriented Towards Building Energy Sharing

    Fund Project: This research was supported by the Shenzhen Science and Technology Program (KJZD20241122161901002) and the National Natural Science Foundation of China (Grant No. U22B20112 and No. 52207092).
More Information
  • Corresponding authors: leigan@hhu.edu.cn(L.G.);  cforgetit@163.com (G.F.) 
  • DownLoad: Full size image
  • With the characteristics of spatial-temporal flexibility, the charging load of electric vehicles can provide excellent flexibility for the low-carbon and economical operation of buildings, via implementing rational charging guidance strategies. Focusing on the electric ride-hailing vehicle (ERV) group, a charging guidance strategy for ERVs is established oriented towards building energy sharing, accounting for the influence on spatial distribution of charging loads caused by online car-hailing order matching. First, combining ERV mobility sharing with building energy sharing demand together, an urban mobility-energy sharing service framework is constructed and illustrated with the integration of information, energy, and value flows. Then, considering the spatial effect of mobility orders on the following charging behavior, a two-stage guidance strategy of ERVs is proposed. The first stage deals with a pre-charging guidance problem with final car-hailing order matching for ERVs triggered range alerts considering the region of building energy sharing requirement. Subsequently, in the second stage, an optimal charging station recommendation strategy is proposed to meet the refined energy sharing demand of the buildings nearby, considering ERVs’ charging decision-making behavior. Simulation results verify that the proposed method successfully creates a synergistic interaction between ERV charging loads and building energy sharing, with seamlessly improving the charging experience for ERV drivers.
  • 加载中
  • [1] IEA (2023). Tracking Clean Energy Progress 2023, IEA, Paris. https://www.iea.org/reports/tracking-clean-energy-progress-2023

    View in Article Google Scholar

    [2] Chen X. (2024). Green and low-carbon energy-use. Innov. Energy. 1:100003. DOI:10.59717/j.xinn-energy.2024.100003

    View in Article CrossRef Google Scholar

    [3] Chen X., Liu J., Lin B., et al. (2025). Promote the Revolution in Energy Use to Enhance the Impetus for Green Transformation and Development. Energy Use. 1:100001. DOI:10.59717/ipj.energy-use.2025.100001

    View in Article CrossRef Google Scholar

    [4] Chen X. (2025). Energy-use Net: Path to Green and Low-carbon Energy-use. Proceedings of the CSEE, 1-18. https://link.cnki.net/urlid/11.2107.tm.20250909.1416. 003.(in Chinese).

    View in Article Google Scholar

    [5] Wang Y., Ma M., Zhou N., et al. (2025). Paving the way to carbon neutrality: Evaluating the decarbonization of residential building electrification worldwide. Sustain. Cities Soc. 130:106549. DOI:10.1016/j.scs.2025.106549

    View in Article CrossRef Google Scholar

    [6] Liang Y., Yu C., and Pan W. (2025). Energy efficiency, renewables, and electrification contribute to decarbonising the operation of residential building stock in Hong Kong. Energy Build. 349:116520. DOI:10.1016/j.enbuild.2025.116520

    View in Article CrossRef Google Scholar

    [7] Yu J., Wang Q., Zhang X., et al. (2026). Optimizing photovoltaic energy sharing: A novel framework based on Stackelberg games in distribution networks. Renew. Energy. 256:123870. DOI:10.1016/j.renene.2025.123870

    View in Article Google Scholar

    [8] Ivona Š., Mirna G., and Tomislav C. (2025). Energy sharing: Encouraging citizen participation in energy communities and collective self-consumption. Energy. 338:138911. DOI:10.1016/j.energy.2025.138911

    View in Article CrossRef Google Scholar

    [9] Kang J., Wang J., Liu C., et al. (2024). Coordinated optimization of configuration and operation of a photovoltaic integrated building cooling system with electricity and ice storages under source-load uncertainties. Energy Build. 320:114600. DOI:10.1016/j.enbuild.2024.114600

    View in Article CrossRef Google Scholar

    [10] Li G., Xu X., Cheng X., et al. (2025). Robust configuration planning for net zero-energy buildings considering source-load dual uncertainty and hybrid energy storage system. Build. Environ. 282:113239. DOI:10.1016/j.buildenv.2025.113239

    View in Article CrossRef Google Scholar

    [11] Wu Y., Liu Z., Li B., et al. (2025). Optimal storage capacity for building photovoltaic-energy storage systems considering energy flexibility management. Energy Build. 338:115757. DOI:10.1016/j.enbuild.2025.115757

    View in Article CrossRef Google Scholar

    [12] Li S., Chen X., Bu L., et al. (2024). Two-stage optimization for the air conditioning system in public buildings with flexible control of indoor load. Energy Build. 312:114162. DOI:10.1016/j.enbuild.2024.114162

    View in Article CrossRef Google Scholar

    [13] Wang M., Wang M., Wang R., et al. (2024). Optimization scheduling for low-carbon operation of building integrated energy systems considering source-load uncertainty and user comfort. Energy Build. 318:114423. DOI:10.1016/j.enbuild.2024.114423

    View in Article CrossRef Google Scholar

    [14] Yao Z., Ran L., Wang Z., et al. (2024). Integrated management of electric vehicle sharing system operations and Internet of Vehicles energy scheduling. Energy. 309:132498. DOI:10.1016/j.energy.2024.132498

    View in Article CrossRef Google Scholar

    [15] IEA (2025). Global EV Outlook 2025, IEA, Paris. https://www.iea.org/reports/global-ev-outlook-2025

    View in Article Google Scholar

    [16] María V., Jose C., Juan G., et al. (2025). Integrating shared mobility into multimodal habits: A comparative analysis across shared micro- and 'macro'-mobility services. Transp. Policy. 174:103840. DOI:10.1016/j.tranpol.2025.103840

    View in Article CrossRef Google Scholar

    [17] Gao F., Sylvia Y., Han C., et al. (2024). The impact of shared mobility on metro ridership: The non-linear effects of bike-sharing and ride-hailing services. Travel Behav. Soc. 37:100842. DOI:10.1016/j.tbs.2024.100842

    View in Article CrossRef Google Scholar

    [18] Morteza T., Samuel S., and Xu M. (2022). Widespread range suitability and cost competitiveness of electric vehicles for ride-hailing drivers. Appl. Energy. 319:119246. DOI:10.1016/j.apenergy.2022.119246

    View in Article CrossRef Google Scholar

    [19] Tang W., Chen X. (Michael), and Lee D-H. (2025). Toward mobility incentive: Integrating green service and carbon inclusion scheme into the ride-hailing market. Transp. Res. B Methodol. 201:103320. DOI:10.1016/j.trb.2025.103320

    View in Article CrossRef Google Scholar

    [20] Zhang Z., Yu Q., Gao K., et al. (2025). Carbon emission reduction benefits of ride-hailing vehicle electrification considering energy structure. Appl. Energy. 377:124548. DOI:10.1016/j.apenergy.2024.124548

    View in Article Google Scholar

    [21] Li J., Xu X., Hamid R., et al. (2025). Optimization of electric vehicle charging strategies in residential integrated energy systems: A SARIMA model approach for dynamic electricity prices. Energy. 330:136600. DOI:10.1016/j.energy.2025.136600

    View in Article CrossRef Google Scholar

    [22] Wu H., Lan X., He Y., et al. (2025). Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation. Appl. Energy. 378:124800. DOI:10.1016/j.apenergy.2024.124800

    View in Article Google Scholar

    [23] Liu G., Wang B., Li T., et al. (2025). Multi-objective electric-carbon synergy optimisation for electric vehicle charging: Integrating uncertainty and bounded rational behaviour models. Appl. Energy. 389:125790. DOI:10.1016/j.apenergy.2025.125790

    View in Article CrossRef Google Scholar

    [24] Chen X. (2025). Urban Energy Use and Its Friendly Interaction with Power Grid. Energy Use. 1:100011. DOI:10.59717/ipj.energy-use.2025.100011

    View in Article CrossRef Google Scholar

    [25] Abdalla M. A. A., Min W., Bing W., et al. (2023). Double-layer home energy management strategy for increasing PV self-consumption and cost reduction through appliances scheduling, EV, and storage. Energy Rep. 10:3494-3518. DOI:10.1016/j.egyr.2023.10.019

    View in Article Google Scholar

    [26] Michele D., Tohid H., Anna R., et al. (2025). Enhancing voltage optimization in distribution networks through flexible operation of EV parking lots. Sustain. Energy Grids Netw. 41:101601. DOI:10.1016/j.segan.2024.101601

    View in Article CrossRef Google Scholar

    [27] Yan Q., Wang J., Lin T., et al. (2025). Peak-Valley difference based pricing strategy and optimization for PV-storage electric vehicle charging stations through ggregators. Int. J. Electr. Power Energy Syst. 169:110812. DOI:10.1016/j.ijepes.2025.110812

    View in Article CrossRef Google Scholar

    [28] Xiang Y., Yang J., Li X., et al. (2022). Routing optimization of electric vehicles for charging with event-driven pricing strategy. IEEE Trans. Autom. Sci. Eng. 19:7−20. DOI:10.1109/tase.2021.3102997

    View in Article CrossRef Google Scholar

    [29] Su S., Li Y., Yamashita K., et al. (2024). Electric vehicle charging guidance strategy considering “traffic network-charging station-driver” modeling: a multiagent deep reinforcement learning-based approach. IEEE Trans. Transp. Electrific. 10:4653−4666. DOI:10.1109/tte.2023.3322685

    View in Article CrossRef Google Scholar

    [30] Tao Y., Qiu J., Lai S., et al. (2024). Distributed electric vehicle assignment and charging navigation in cyber-physical systems. IEEE Trans. Smart Grid. 15:1861−1875. DOI:10.1109/tsg.2023.3293251

    View in Article CrossRef Google Scholar

    [31] Sun X., and Qiu J. (2021). Hierarchical voltage control strategy in distribution networks considering customized charging navigation of electric vehicles. IEEE Trans. Smart Grid. 12:4752−4764. DOI:10.1109/tsg.2021.3094891

    View in Article CrossRef Google Scholar

    [32] Habbal A., and Alrifaie M. F. (2024). A user-preference-based charging station recommendation for electric vehicles. IEEE Trans. Intell. Transp. Syst. 25:11617−11634. DOI:10.1109/tits.2024.3379469

    View in Article CrossRef Google Scholar

    [33] Yi Z., and Smart J. (2021). A framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet. Transp. Res., Part D: Transp. Environ. 95:102822. DOI:10.1016/j.trd.2021.102822

    View in Article CrossRef Google Scholar

    [34] Wang J., Cai H., Sun L., et al. (2025). MERCI: multi-agent reinforcement learning for enhancing on-demand electric taxi operation in terms of rebalancing, charging, and informing orders. Comput. Ind. Eng. 200:110711. DOI:10.1016/j.cie.2024.110711

    View in Article CrossRef Google Scholar

    [35] Li D., Hu C., Yang Q., et al. (2025). Charge or pick up. Optimizing E-taxi management: a dual-stage heuristic coordinated reinforcement learning approach. IEEE Trans. Autom. Sci. Eng. 22:8533−8553. DOI:10.1109/tase.2024.3486342

    View in Article CrossRef Google Scholar

    [36] Li X., Normandin-Taillon H., Wang C., et al. (2024). BM-RCWTSG: an integrated matching framework for electric vehicle ride-hailing services under stochastic guidance. Sustain. Cities Soc. 108:105485. DOI:10.1016/j.scs.2024.105485

    View in Article CrossRef Google Scholar

    [37] Zhong J., Liu J., and Zhang X. (2023). Charging navigation strategy for electric vehicles considering empty-loading ratio and dynamic electricity price. Sustain. Energy Grids Netw. 34:100987. DOI:10.1016/j.segan.2022.100987

    View in Article CrossRef Google Scholar

    [38] Ni W., Cheng P., Chen L., et al. (2024). Task assignment framework for online car-hailing systems with electric vehicles. IEEE Trans. Knowl. Data Eng. 36:9361−9373. DOI:10.1109/tkde.2024.3434567

    View in Article CrossRef Google Scholar

    [39] Patankar P. P., Hussain Rather Z., Liebman A., et al. (2025). Machine Learning-Based EV Charging Management System for Vehicle-to-Home and Vehicle-to-Building. IEEE Trans. Transp. Electrific. 11:11100−11111. DOI:10.1109/tte.2025.3571793

    View in Article CrossRef Google Scholar

    [40] Dai Y., Liu X., Li H., et al. (2025). Building-related electric vehicle charging behaviors and energy consumption patterns: An urban-scale analysis. Transp. Res., Part D: Transp. Environ. 141:104663. DOI:10.1016/j.trd.2025.104663

    View in Article CrossRef Google Scholar

  • Cite this article:

    Gan L., Jia Z., Bu L., et al. (2025). Charging Guidance Strategy for Electric Ride-Hailing Vehicles Oriented Towards Building Energy Sharing. Energy Use 1:100027. https://doi.org/10.59717/ipj.energy-use.2025.100027
    Gan L., Jia Z., Bu L., et al. (2025). Charging Guidance Strategy for Electric Ride-Hailing Vehicles Oriented Towards Building Energy Sharing. Energy Use 1:100027. https://doi.org/10.59717/ipj.energy-use.2025.100027

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

Figures(5)     Tables(1)

Share

  • Share the QR code with wechat scanning code to friends and circle of friends.

Article Metrics

Article views(860) PDF downloads(297)

Relative Articles

Cited by

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint