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Identification and dynamic simulation of electric-vehicle charging demand drivers: a high-order hybrid method

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  • Liu, Jing
  • Gao, Wei
  • Zhang, Xuewen
  • Zhang, Jie

Abstract

Rapid expansion of electric vehicles has made charging demand highly variable in space and time, yet its coupled drivers are poorly understood. We develop a three-stage embedded hybrid method to close this gap. Sixteen spatial, facility, pricing, and weather variables are first identified. Their causal weights are quantified through a Pearson correlation–transfer entropy–DEMATEL sequence. Finally, the weighted relations are embedded in a complex-network framework to perform node-failure, centrality, edge-weight sensitivity, and threshold-based dynamic-pricing simulations. A Shenzhen charger dataset validates the model. Results reveal that charging demand is jointly governed by spatial layout, economic regulation, and environmental conditions. Traffic adjacency, charger count, and occupancy dominate demand, whereas real-time price and inter-station distance, despite weak causality, hold the highest centrality and sensitivity. Dynamic pricing cuts peaks by ∼20 % in stable periods but fails under extreme shocks. We recommend mixed stations and coordinated pricing, demand-response, and storage strategies to enhance network resilience.

Suggested Citation

  • Liu, Jing & Gao, Wei & Zhang, Xuewen & Zhang, Jie, 2026. "Identification and dynamic simulation of electric-vehicle charging demand drivers: a high-order hybrid method," Transport Policy, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:trapol:v:178:y:2026:i:c:s0967070x25005219
    DOI: 10.1016/j.tranpol.2025.103978
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