Enterprise Risk and Resilience of Electric-Vehicle Charging Infrastructure and the Future Mobile Power Grid

  • 18 Accesses


Purpose of Review

This paper provides a review of advances in the enterprise risk and resilience management of electric vehicle charging infrastructures. The works reviewed address the interactions of electric vehicles with power grids through coordinated networks of bidirectional chargers, or vehicle-to-grid technology, and the enterprise resilience of infrastructure that supports logistics systems that use electric vehicles.

Recent Findings

The latest research identifies potential for revenue generation, e.g., through frequency regulation and demand charge management, when electric vehicle fleets connect to vehicle-to-grid infrastructure. Other leading research evaluates the impact of a variety of emergent and future conditions on mobile power grid initiatives utilizing methods of resilience analytics and risk management.


The examination of research reveals a potential for mitigating initial investment costs. It also suggests how further work is beneficial to develop reliable power demand prediction models and scheduling techniques to maximize vehicle-to-grid technology. Scenario-based analysis and risk and resilience management may assist in infrastructure development until economic factors make electric vehicles a preferred option.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.


  1. 1.

    Joselow M. U.S. reaches its 1 millionth EV. Does it matter? In: E&E news climatewire. 2018. .

  2. 2.

    Shahan Z. Electric vehicle sales up 130% in 2018, 210% in q4 2018. Clean technica: In; 2019..

  3. 3.

    Konish L. Can you save money and the planet by owning a tesla or another electric car? CNBC impact investing: In; 2019..

  4. 4.

    Larocque M. City of Brockton adding 14th electric car to battery-powered fleet. In: The enterprise. 2019;. .

  5. 5.

    Gillis J. Electric cars are finally starting to take off. Congress should keep them affordable. The Washington Post opinions: In; 2019..

  6. 6.

    Kempton W, Tomic J. Vehicle-to-grid power fundamentals: calculating capacity and net revenue. J of Power Sources. 2005;144:268–79.

  7. 7.

    Kirby BJ. Frequency regulation basics and trends. U.S. Department of Energy Office of Scientific and Technical Information: In; 2004. .

  8. 8.

    Merrill CH, Lam VH, Van Vleet MJ, Chatti MS, Brannon MC, Connelly EB, et al. Modeling and simulation of fleet vehicle batteries for integrated logistics and grid services. IEEE Systems and Inf Eng Des Symp. 2015:255–60.

  9. 9.

    Fitzsimmons JD, Kritzer SJ, Muthiah VA, Parmer JJ, Rykal TJ, Stone MT, et al. Simulation of an electric vehicle fleet to forecast availability of grid balancing resources. IEEE Systems and Inf Eng Des Symp. 2016:205–10.

  10. 10.

    Wang J, Purewal J, Graetz J, Soukiazian S, Tataria H, Verbrugge MW. Degradation of lithium ion batteries employing graphite negatives and nickel-cobalt-manganese oxide + spinel manganese oxide positives: part 2, chemical-mechanical degradation model. J of Power Sources. 2014.

  11. 11.

    Sweeney JC, Ledwith MC, Costello DS, Lin JS, Brown DN, Leimback TR, et al. Deployment of advanced bidirectional chargers to lower total cost of ownership of electric-vehicle fleets. IEEE Systems and Inf Eng Des Symp. 2017:312–7.

  12. 12.•

    Walton IA, Diduch KW, Kim AH, Lockett SB, O’Rourke IV JM, Sinha T, et al. Statistical analysis of building energy load profiles to assess site-specific feasibility of demand charge management. IEEE systems and Inf Eng Des Symp. 2018;231-236. doi: Study that discusses the importance of facility selection in realizing business opportunities for vehicle-to-grid demand charge management.

  13. 13.

    Hamilton MC, Lambert JH, Connelly EB, Barker K. Resilience analytics with disruption of preferences and lifecycle cost analysis for energy microgrids. Reliabiliy Engineering and Syst Saf. 2016;150:11–21.

  14. 14.•

    Almutairi A, Wheeler JP, Slutzky DL, Lambert JH. Integrating stakeholder mapping and risk scenarios to improve resilience of cyber-physical-social networks. Risk Analysis. 2019. doi: Formulation of a framework to account for influences of scenarios and multiple groups of stakeholders on priorities with a case study for an enterprise operating an electric-vehicle fleet.

  15. 15.

    Connelly EB, Lambert JH, Thekdi SA. Robust investments in humanitarian logistics and supply chains for disaster resilience and sustainable communities. Nat Hazards Rev. 2016;17(1):04015017.

  16. 16.

    Quenum A, Thorisson H, Wu D, Lambert JH. Resilience of business strategy to emergent and future conditions. J of Risk Res. 2019.

  17. 17.

    Collier ZA, Lambert JH. Time management of infrastructure recovery schedules by anticipation and valuation of disruptions. ASCE-ASME J of Risk and Uncertain in Engineering Systems, Part A: Civ Engineering. 2018;4(2):04018012.

  18. 18.••

    Almutairi A, Thorisson H, Wheeler JP, Slutzky DL, Lambert JH. Scenario-based preferences in development of advanced mobile grid services and a bidirectional charger network. ASCE-ASME J of Risk and Uncertain in Engineering Systems, Part A: Civ Engineering. 2018;4(2):04018017. doi: Study describing a risk-informed methodology for prioritizing stakeholders’ future planning initiatives across the influence of emergent and future conditions to help decision makers focus their efforts on the most highly ranked and most robust future planning initiatives.

  19. 19.••

    Thorisson H, Alsultan M, Hendrickson D, Polmateer TL, Lambert JH. Addressing schedule disruptions in business processes of advanced logistics systems. Systems Engineering. 2018. doi: Development of a framework for identifying sources of risk and evaluating their potential to disrupt schedules in a business process model.

  20. 20.

    Thorisson H, Lambert JH, Cardenas JJ, Linkov I. Resilience analytics with application to power grid of a developing region. Risk Anal. 2016;37(7):1268–86.

  21. 21.•

    Collier ZA, Lambert JH. Managing obsolescence of systems containing embedded hardware and software. Frontiers of Engineering Management. 2019. doi: Study describing an approach to manage and forecast obsolescence of systems with embedded hardware and software using non-probabilistic scenarios coupled with decision analysis.

Download references


This work is support in part by the National Science Foundation NSF #1848669 - Assessing International Collaboration Opportunities for Science and Technology Innovation: Methods and Approaches and NSF IUCRC #1747767 - Center for Hardware and Embedded System Security and Trust (CHEST)) and the Commonwealth Center for Advanced Logistics Systems (CCALS).

Author information

Correspondence to James H. Lambert.

Ethics declarations

Conflict of Interest

Daniel J. Andrews, Thomas L. Polmateer, John P. Wheeler, David L. Slutzky, and James H. Lambert declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Transportation

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Andrews, D.J., Polmateer, T.L., Wheeler, J.P. et al. Enterprise Risk and Resilience of Electric-Vehicle Charging Infrastructure and the Future Mobile Power Grid. Curr Sustainable Renewable Energy Rep (2020).

Download citation


  • Vehicle-to-grid
  • Energy mobility
  • Frequency regulation
  • Charge management
  • Risk analysis
  • Systems engineering
  • Deep uncertainties
  • Emergent conditions