[Paper Review] Optimal Routing and Scheduling of Charge for Electric Vehicles: Case Study

John Barco, Andres Guerra+2 more|arXiv (Cornell University)|Oct 1, 2013

Electric Vehicles and Infrastructure19 citations

TL;DR

This paper proposes a coordinated optimization framework for routing and charging scheduling of battery electric vehicle (BEV) fleets, integrating energy consumption, charging costs, and battery degradation. Using a case study of an airport shuttle service in Colombia, it demonstrates that optimal scheduling significantly extends battery lifetime and reduces operating costs, though vehicle-to-grid (V2G) operations are only profitable under unrealistic conditions.

ABSTRACT

In Colombia, there is an increasing interest about improving public transportation. One of the proposed strategies in that way is the use battery electric vehicles (BEVs). One of the new challenges is the BEVs routing problem, which is subjected to the traditional issues of the routing problems, and must also consider the particularities of autonomy, charge and battery degradation of the BEVs. In this work, a scheme that coordinates the routing, scheduling of charge and operating costs of BEVs is proposed. The simplified operating costs have been modeled considering both charging fees and battery degradation. A case study is presented, in order to illustrate the proposed methodology. The given case considers an airport shuttle service scenario, in which energy consumption of the BEVs is estimated based on experimentally measured driving patterns.

Motivation & Objective

• To address the growing need for efficient public transportation using battery electric vehicles (BEVs) in Colombia.
• To develop a coordinated routing and charging scheduling system that minimizes operating costs while accounting for BEV-specific constraints.
• To model and analyze the impact of charging schedules on battery degradation over time.
• To evaluate the feasibility of vehicle-to-grid (V2G) operations in a real-world BEV fleet scenario.

Proposed method

• A mixed-integer linear programming (MILP) model is formulated to optimize BEV routing and charging schedules simultaneously.
• Energy consumption is estimated based on experimentally measured driving patterns from real shuttle operations.
• Operating costs include both direct charging fees and indirect costs from battery degradation, modeled as a function of charge cycles and depth of discharge.
• The model incorporates constraints on vehicle autonomy, charging station availability, and battery state-of-charge dynamics.
• A case study is conducted using an airport shuttle service scenario to validate the framework under realistic operational conditions.
• Sensitivity analysis is performed to assess the profitability of vehicle-to-grid (V2G) operations under varying electricity price and incentive scenarios.

Experimental results

Research questions

• RQ1 How can routing and charging schedules be jointly optimized to minimize total operating costs for a BEV fleet?
• RQ2 What is the impact of charging scheduling on battery degradation over the vehicle’s lifetime?
• RQ3 How do charging fees and battery degradation jointly influence the economic viability of BEV operations?
• RQ4 Under what conditions is vehicle-to-grid (V2G) operation profitable for a BEV fleet in a real-world setting?
• RQ5 How does the proposed framework perform in a real-world airport shuttle scenario with measured driving patterns?

Key findings

• Optimal charging scheduling significantly reduces battery degradation compared to uncoordinated charging, extending battery lifetime.
• The total operating cost of the BEV fleet is minimized when routing and charging are jointly optimized, with cost savings attributed to reduced energy and degradation expenses.
• Vehicle-to-grid (V2G) operations are only profitable under nonrealistic scenarios, such as extremely high electricity price differentials or subsidies.
• The model accurately predicts energy consumption based on real-world driving patterns, validating its practical applicability.
• Battery degradation is highly sensitive to charging frequency and depth, highlighting the importance of scheduling optimization.
• The case study demonstrates that coordinated scheduling can reduce total fleet costs by up to 15% compared to heuristic-based approaches.