Non-myopic dynamic routing of electric taxis with battery swapping stations

https://doi.org/10.1016/j.scs.2020.102113Get rights and content

Highlights

  • We provide an effective way to develop sustainable green taxis for smart cities.

  • A non-myopic dynamic routing of electric taxis with a look-ahead policy using a Markov decision process (MDP) is proposed.

  • A dynamic dispatch system and detours to charging stations for a fleet of electric taxis is considered.

  • A TSPPD with battery capacity constraints is proposed to obtain potential tours.

  • Using New York City dataset, we exhibit a socially efficient level between operator cost and customer delay.

Abstract

Electric vehicles (EVs) have a key aspect in reducing the greenhouse gas effect, maintenance, and energy expenditures of drivers. A type of integration of the electric vehicle (EV) charging infrastructure is emerging based on the premise of battery swapping. Drivers can exchange their empty batteries quickly with full batteries from any battery swapping station. The limited battery capacities of electric taxis require visiting the swapping stations during pickup and drop-off tours, which entails choosing the route more effectively to avoid customer delay. We propose the dynamic routing of electric taxis with a look-ahead policy using a Markov decision process (MDP) for assigning an electric taxi fleet to customers with the assumption of elastic demand. This is the first model that focuses on the design of a non-myopic routing of electric taxis that considers the limited battery capacity through serving customers, detours of the taxi drivers to battery swapping stations (BSS), and integration of customers delay and the system cost into a dynamic non-myopic pricing policy under the objective of maximizing social benefit. Using battery recharging locations and taxicab trip data in New York City, we showed an improvement in the average of social welfare, due to use of clean and smart taxi routes based on the proposed dynamic non-myopic routing policy by up to 8% compared to the routing problem without a look-ahead policy.

Section snippets

Introduction and motivation

The effects of GHG on climate change encourage engineers and planners to consider a revolution in the transportation segment particularly in the public transit system and on-demand mobility service. For instance, 28 % of total U.S. GHG emissions in 2009 are caused by the transportation segment (U.S. Environmental Protection Agency, 2009). This is in the weighty part because 97 % of U.S. transportation energy is highly dependent on oil (U.S. Department of Transportation, 2010). Thus, it is

Literature review

The main difference between the traditional DARP and the EV-DARP is that electric vehicles have battery capacity restrictions, and thus their battery may need to be recharged during serving customers. The routing of EVs over the road where the battery capacity is limited and must be recharged at a recharging station or exchanged with a full battery at the BSS was first introduced by Ichimori et al. (1981). For this problem, there are static models or dynamic models without a look-ahead policy

Problem definition

Customers send their service requests to the dispatch center for any specific pickup locations to drop-off locations. Then, the dispatch system makes optimal decisions dynamically on taxi routes, prices, and schedules by containing battery management and detours of taxi drivers to battery swapping stations (BSS). The empty battery can be changed with a full battery quickly in one to two minutes by being swapped instead of recharged. The subgraph of vehicle routing and pricing problem is defined

Mathematical formulations

We propose a new dynamic dispatch of electric taxis that incorporates battery swapping stations. This system provides scheduling and sequencing of serving customers who have pickup and drop-off requests; uses a dynamic pricing policy, tour length, and customer delay; and improves social welfare. An energy consumption function is used to estimate the energy at each location, where it implements a TSPPD with battery capacity constraints to obtain potential tours. We run a dynamic dispatch policy

Numerical examples

Numerical calculations were performed to verify the efficiency of the proposed problem of routing of electric taxis in the real-world application. The experiments had two goals: (1) to determine whether the proposed methodology is applicable in terms of inputs and outputs of the model and (2) to prove whether the proposed non-myopic routing of electric taxis problem can improve the social welfare and fuel consumption compared to the myopic case. In order to show the outputs of our model such as

Conclusion

The first system for dynamic non-myopic routing of electric taxis with battery swapping stations under non-myopic pricing policy is proposed. We implemented a more realistic energy consumption function that considers depletions in the battery stemming from the transmission (gear) system and the motor. We also formulated and solved the TSPPD with battery capacity constraints in order to obtain potential tours, and used it in our dynamic dispatch policy. The tour length and social welfare could

Disclaimer

The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. The contents do not necessarily reflect the official views or policies of the Center for Transportation, Environment, and Community Health (CTECH) and other project sponsors or the Federal Highway Administration. This report does not constitute a standard, specification or regulation. This document is disseminated under the sponsorship of the

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported in part by National Science Foundation project CMMI-1462289 and the Lloyd’s Register Foundation, UK. The authors are grateful to the Editor in Chief of the journal, and two anonymous reviewers, for their valuable comments.

References (57)

  • G. Hiermann et al.

    The electric fleet size and mix vehicle routing problem with time windows and recharging stations

    European Journal of Information Systems

    (2016)
  • J. Hof et al.

    Solving the battery swap station location-routing problem with capacitated electric vehicles using an AVNS algorithm for vehicle-routing problems with intermediate stops

    Transport. Res. Part B: Methodol.

    (2017)
  • L. Hu et al.

    Analyzing battery electric vehicle feasibility from taxi travel patterns: The case study of New York City, USA

    Transport. Res. Part C: Emerg. Technol.

    (2018)
  • E. Hyytiä et al.

    Non-myopic vehicle and route selection in dynamic DARP with travel time and workload objectives

    Computers & Operations Research

    (2012)
  • J. Jung et al.

    Stochastic dynamic itinerary interception refueling location problem with queue delay for electric taxi charging stations

    Transport. Res. Part C: Emerg. Technol.

    (2014)
  • S.R. Kancharla et al.

    Incorporating driving cycle based fuel consumption estimation in green vehicle routing problems

    Sustainable Cities and Society

    (2018)
  • C.S. Liao et al.

    The electric vehicle touring problem

    Transport. Res. Part B: Methodol.

    (2016)
  • M.A. Masmoudi et al.

    The dial-a-ride problem with electric vehicles and battery swapping stations

    Transport. Res. Part E: Logist. Transp. Rev.

    (2018)
  • C. Peprah et al.

    A system view of smart mobility and its implications for Ghanaian cities

    Sustainable Cities and Society

    (2019)
  • S.M. Rezvanizaniani et al.

    Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility

    Journal of Power Sources

    (2014)
  • N. Sathaye

    The optimal design and cost implications of electric vehicle taxi systems

    Transport. Res. Part B: Methodol.

    (2014)
  • H.R. Sayarshad et al.

    A scalable non-myopic dynamic dial-a-ride and pricing problem for competitive on-demand mobility systems

    Transport. Res. Part C: Emerg. Technol.

    (2018)
  • X. Yuan et al.

    Method for evaluating the real-world driving energy consumptions of electric vehicles

    Energy

    (2017)
  • X. Zhang et al.

    Optimal dispatch of electric vehicle batteries between battery swapping stations and charging stations

    IEEE Power and Energy Society General Meeting

    (2016)
  • R. Zhang et al.

    Mesoscopic model framework for estimating electric vehicles’ energy consumption

    Sustainable Cities and Society

    (2019)
  • A. Abdellah Chehri et al.

    Autonomous vehicles in the sustainable cities, the beginning of a green adventure

    Sustainable Cities and Society

    (2019)
  • Alternative Fuels Data Center. Retrieved from...
  • BattSwap Company,...
  • Cited by (41)

    • The electric on-demand bus routing problem with partial charging and nonlinear function

      2023, Transportation Research Part C: Emerging Technologies
    • Comparative analysis of comprehensive benefits of Beijing's taxi electrification paths

      2023, Transportation Research Part D: Transport and Environment
    • A review of siting, sizing, optimal scheduling, and cost-benefit analysis for battery swapping stations

      2022, Energy
      Citation Excerpt :

      To alleviate congestions at BSSs, station assignment and routing of EVs are necessary to maximize the utilization of BSSs. Some studies developed EV routing models to reduce energy consumption of EVs, traffic congestion and overall operation costs of BSSs [56–59]. However, the influence of the service capacities and locations of BSSs on the routing was ignored in these models.

    View all citing articles on Scopus
    View full text