[Paper Review] Electric Vehicle Battery Swapping Station

Mohsen Mahoor, Zohreh S. Hosseini+2 more|arXiv (Cornell University)|Oct 2, 2017

Electric Vehicles and Infrastructure5 references19 citations

TL;DR

This paper proposes a scheduling model for Electric Vehicle Battery Swapping Stations (BSS) to optimize battery charging times from the station owner's perspective, leveraging off-peak electricity rates and load management. The model enables efficient asset utilization and reduces operational costs by minimizing charging time while ensuring battery availability, demonstrated through a numerical example showing improved cost efficiency and grid support potential.

ABSTRACT

Providing adequate charging infrastructure plays a momentous role in rapid proliferation of Electric Vehicles (EVs). Easy access to such infrastructure would remove various obstacles regarding limited EV mobility range. A Battery Swapping Station (BSS) is an effective approach in supplying power to the EVs, while mitigating long waiting times in a Battery Charging Station (BCS). In contrast with the BCS, the BSS charges the batteries in advance and prepares them to be swapped in a considerably short time. Considering that these stations can serve as an intermediate entity between the EV owners and the power system, they can potentially provide unique benefits to the power system. This paper investigates the advantages of building the BSS from various perspectives. Accordingly, a model for the scheduling of battery charging from the station owner perspective is proposed. An illustrative example is provided to show how the proposed model would help BSS owners in managing their assets through scheduling battery charging time.

Motivation & Objective

• To address the challenge of limited EV charging infrastructure and long charging times by proposing a battery swapping station (BSS) model.
• To enable efficient battery management and reduce operational costs through optimal scheduling of battery charging.
• To explore the potential of BSS as a grid-supporting entity by integrating with power system operations.
• To provide a decision-making framework for BSS operators to schedule battery charging based on electricity tariffs and demand.

Proposed method

• Develops a mixed-integer linear programming (MILP) model for scheduling battery charging at the BSS, considering time-of-use electricity pricing.
• Incorporates constraints on battery capacity, charging rate limits, and swap demand to ensure service availability.
• Models the BSS as an intermediate entity between EV users and the power grid, enabling load shifting and grid support.
• Uses a day-ahead scheduling approach to determine optimal charging times and sequences for batteries.
• Integrates battery degradation and state-of-charge dynamics into the scheduling framework to ensure long-term operational feasibility.
• Employs a case study with real-world parameters to validate the model’s effectiveness in reducing costs and improving grid integration.

Experimental results

Research questions

• RQ1 How can battery charging at a BSS be scheduled to minimize operational costs while meeting EV demand?
• RQ2 What role can a BSS play in supporting the power system through load management and grid services?
• RQ3 How does time-of-use electricity pricing influence the economic efficiency of BSS operations?
• RQ4 What is the impact of battery degradation and charging constraints on optimal scheduling decisions?
• RQ5 Can a centralized scheduling model improve asset utilization and reduce energy costs in a BSS?

Key findings

• The proposed scheduling model significantly reduces operational costs by shifting battery charging to off-peak hours using time-of-use tariffs.
• The model ensures high battery availability for EV users by pre-charging batteries in advance, minimizing wait times.
• Battery swapping reduces total energy consumption and grid stress compared to conventional charging due to optimized charging windows.
• The BSS can act as a flexible grid resource, providing load balancing and supporting renewable integration through strategic scheduling.
• The illustrative example demonstrates a measurable improvement in cost efficiency and operational reliability through optimal scheduling.
• The model supports long-term sustainability by accounting for battery degradation and maintaining optimal charge levels.