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Towards a Multimodal Charging Network: Joint Planning of Charging Stations and Battery Swapping Stations for Electrified Ride-Hailing Fleets

Zhijie Lai, Sen Li

TL;DR

The paper tackles the challenge of electrifying ride-hailing fleets by proposing a multimodal charging network that jointly deploys charging and battery swapping stations. It develops a multi-stage, nonconvex optimization model that integrates elastic demand, spatial charging equilibrium, and queueing dynamics, and introduces relaxation-based methods to obtain a tight upper bound (approximately $3\%$ gap) on the optimal profit. Through a Manhattan case study, it demonstrates that joint planning outperforms single-facility deployments, achieving up to $17.5\%$ higher profit and substantial charging-cost reductions, while noting potential cross-zone traffic impacts. The work provides actionable insights on deployment sequencing and the complementary value of charging and swapping, and suggests extensions to handle demand rejection and differentiated pricing, as well as real-time dynamics and grid integration.

Abstract

This paper considers a multimodal charging network in which charging stations and battery swapping stations are jointly built to support an electric ride-hailing fleet synergistically. Our argument is based on the observation that charging an EV is a time-consuming burden, and battery swapping faces scaling issues due to its deployment costs. However, charging stations are cost-effective, making them ideal for scaling up EV fleets, while battery swapping stations offer quick turnaround and can be deployed in tandem with charging stations to improve fleet utilization and reduce operational costs. To fulfill this vision, we consider a ride-hailing platform that jointly builds charging and battery swapping stations to support an EV fleet. An optimization model is proposed to capture the platform's planning and operational decisions. In particular, the model incorporates essential components such as elastic passenger demand, spatial charging equilibrium, charging and swapping congestion, etc. The overall problem is formulated as a nonconcave program. Instead of pursuing the globally optimal solution, we establish a tight upper bound through relaxation and decomposition, allowing us to evaluate the solution optimality even in the absence of concavity. Through case studies for Manhattan, New York City, we find that joint planning of charging and battery swapping stations outperforms deploying only one of them, yielding a total profit that is 11.7% higher than swapping-only deployment under a limited budget, and 17.5% higher than charging-only deployment under a sufficient budget. These results underscore the complementary benefit between charging and battery swapping facilities.

Towards a Multimodal Charging Network: Joint Planning of Charging Stations and Battery Swapping Stations for Electrified Ride-Hailing Fleets

TL;DR

The paper tackles the challenge of electrifying ride-hailing fleets by proposing a multimodal charging network that jointly deploys charging and battery swapping stations. It develops a multi-stage, nonconvex optimization model that integrates elastic demand, spatial charging equilibrium, and queueing dynamics, and introduces relaxation-based methods to obtain a tight upper bound (approximately gap) on the optimal profit. Through a Manhattan case study, it demonstrates that joint planning outperforms single-facility deployments, achieving up to higher profit and substantial charging-cost reductions, while noting potential cross-zone traffic impacts. The work provides actionable insights on deployment sequencing and the complementary value of charging and swapping, and suggests extensions to handle demand rejection and differentiated pricing, as well as real-time dynamics and grid integration.

Abstract

This paper considers a multimodal charging network in which charging stations and battery swapping stations are jointly built to support an electric ride-hailing fleet synergistically. Our argument is based on the observation that charging an EV is a time-consuming burden, and battery swapping faces scaling issues due to its deployment costs. However, charging stations are cost-effective, making them ideal for scaling up EV fleets, while battery swapping stations offer quick turnaround and can be deployed in tandem with charging stations to improve fleet utilization and reduce operational costs. To fulfill this vision, we consider a ride-hailing platform that jointly builds charging and battery swapping stations to support an EV fleet. An optimization model is proposed to capture the platform's planning and operational decisions. In particular, the model incorporates essential components such as elastic passenger demand, spatial charging equilibrium, charging and swapping congestion, etc. The overall problem is formulated as a nonconcave program. Instead of pursuing the globally optimal solution, we establish a tight upper bound through relaxation and decomposition, allowing us to evaluate the solution optimality even in the absence of concavity. Through case studies for Manhattan, New York City, we find that joint planning of charging and battery swapping stations outperforms deploying only one of them, yielding a total profit that is 11.7% higher than swapping-only deployment under a limited budget, and 17.5% higher than charging-only deployment under a sufficient budget. These results underscore the complementary benefit between charging and battery swapping facilities.
Paper Structure (31 sections, 2 theorems, 49 equations, 16 figures, 3 tables, 1 algorithm)

This paper contains 31 sections, 2 theorems, 49 equations, 16 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Modeling Electric Ride-Hailing Markets
  4. Infrastructure Planning for EV fleets
  5. Mathematical Formulation
  6. Charging Network Expansion
  7. Passenger Demand Management
  8. Potential Charging Demand
  9. Spatial Charging Equilibrium
  10. Queueing Time at Stations
  11. Decision-Making Problem
  12. Solution Method
  13. Finding Stationary Points
  14. Derivation of Upper Bounds
  15. Model Relaxation
  16. ...and 16 more sections

Key Result

Proposition 1

For any $q_{ij,t} > 0,\forall i,j\in\mathcal{I}$ and the corresponding transition matrix $P_t = [P_{ij,t}]_{i,j\in\mathcal{I}}$, the stationary charging demand distribution $n_t$ exists and is uniquely given by

Figures (16)

  • Figure 1: The mixed queueing network for a battery swapping station.
  • Figure 2: Objective value under different values of relaxation parameter $\epsilon$.
  • Figure 3: Performance of the infrastructure network under different deployment schemes.
  • Figure 4: Distance between the potential charging demand and actual charging demand under different budget levels.
  • Figure 5: Infrastructure network expansion under different budget levels, with the red circles representing charging stations, the blue squares representing battery swapping stations, and their sizes representing the quantity of each type of facilities.
  • ...and 11 more figures

Theorems & Definitions (7)

  • Proposition 1
  • proof
  • Remark 1
  • Definition 3.1: Spatial Charging Equilibrium
  • Remark 2
  • Proposition 2
  • proof