Smart Ride and Delivery Services with Electric Vehicles: Leveraging Bidirectional Charging for Profit Optimisation

2025-06-25

AI Summary

This paper addresses the profit optimization problem for electric vehicles (EVs) operating in ride-hailing and on-demand delivery services. It introduces EVOP-V2G—the first integrated service scheduling framework incorporating vehicle-to-grid (V2G) bidirectional charging/discharging—jointly optimizing order selection, route planning, charging station assignment, and dynamic pricing–aware charge/discharge scheduling. A mixed-integer programming (MIP) model is formulated, and two efficient metaheuristic algorithms are proposed: one based on evolutionary computation and another leveraging large neighborhood search. Experimental results demonstrate that the methods achieve near-optimal solution quality on small-scale instances and maintain strong scalability on large-scale problems. Compared to baseline approaches, driver profits increase by an average of 100%, substantially unlocking the economic potential of V2G in mobile service contexts.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

With the rising popularity of electric vehicles (EVs), modern service systems, such as ride-hailing delivery services, are increasingly integrating EVs into their operations. Unlike conventional vehicles, EVs often have a shorter driving range, necessitating careful consideration of charging when fulfilling requests. With recent advances in Vehicle-to-Grid (V2G) technology - allowing EVs to also discharge energy back to the grid - new opportunities and complexities emerge. We introduce the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G): a profit-maximization problem where EV drivers must select customer requests or orders while managing when and where to charge or discharge. This involves navigating dynamic electricity prices, charging station selection, and route constraints. We formulate the problem as a Mixed Integer Programming (MIP) model and propose two near-optimal metaheuristic algorithms: one evolutionary (EA) and the other based on large neighborhood search (LNS). Experiments on real-world data show our methods can double driver profits compared to baselines, while maintaining near-optimal performance on small instances and excellent scalability on larger ones. Our work highlights a promising path toward smarter, more profitable EV-based mobility systems that actively support the energy grid.

Problem

• Optimizing EV ride-hailing and delivery profits with V2G charging
• Balancing customer requests, charging, and dynamic electricity pricing
• Developing scalable algorithms for near-optimal EV route planning

Innovation

• Bidirectional charging for profit optimization
• Mixed Integer Programming model formulation
• Evolutionary and LNS metaheuristic algorithms