Research Papers: Alternative Energy Sources

Optimizing the Reliability and Performance of Remote Vehicle-to-Grid Systems Using a Minimal Set of Metrics

[+] Author and Article Information
Annette G. Skowronska

Warren, MI 48397-5000;
Mechanical Engineering Department,
Oakland University,
Rochester, MI 48309

David J. Gorsich

Warren, MI 48397-5000

Vijitashwa Pandey

Industrial and Systems Engineering Department,
Oakland University,
Rochester, MI 48309

Zissimos P. Mourelatos

Mechanical Engineering Department,
Oakland University,
Rochester, MI 48309

1Corresponding author.

Contributed by the Internal Combustion Engine Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received January 3, 2015; final manuscript received March 28, 2015; published online April 27, 2015. Assoc. Editor: Stephen A. Ciatti.

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Energy Resour. Technol 137(4), 041204 (Jul 01, 2015) (7 pages) Paper No: JERT-15-1001; doi: 10.1115/1.4030317 History: Received January 03, 2015; Revised March 28, 2015; Online April 27, 2015

Vehicles connected to electric systems are considered “plug-in” vehicles. They can be an integral part of a microgrid. Ground vehicles have become more electrified over time, providing electrical power for the propulsion system (hybrid) and a complex suite of auxiliary power systems, enhancing their use in microgrids. Optimizing the microgrid system for performance and reliability considering many external loads and sources is a challenging problem. This is especially true when the plug-in vehicles may enter and leave the microgrid randomly becoming either sources or loads. The microgrid is a repairable system. Recent work has shown that multiple metrics are needed to fully account for the performance of repairable systems under uncertainty. In this paper, we propose a decision-based framework to design and maintain repairable systems for optimal performance and reliability using a set of metrics such as minimum failure free period (MFFP), number of failures in planning horizon, and cost. Optimal tradeoffs among a minimal set of metrics (MSOM) can be used in the design and maintenance of these systems. The optimal solution includes the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. Critical remote military installations with plug-in vehicles connected to the microgrids require careful consideration of cost and repair strategies because of logistical challenges in performing repairs and supplying necessary spare parts in unsafe locations. We show how a MSOM helps to solve the complex optimization problem of finding the best microgrid power management strategy considering performance, reliability, and cost.

Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.


Letendre, S., Denholm, P., and Lilienthal, P., 2006, “Electric and Hybrid Cars: New Load or New Resource?,” Public Utilities Frotnightly, http://www.fortnightly.com/pur_search_r.cfm
Malikopoulos, A., 2013, “Impact of Component Sizing in Plug-In Hybrid Electric Vehicles for Energy Resource and Greenhouse Emissions Reduction,” ASME J. Energy Resour. Technol., 135(4), p. 041201. [CrossRef]
Skowronska, A. G., Gorsich, D., Pandey, V., Mourelatos, Z. P., Mange, J., and Dunn, A., 2013, “Global Strategies for Optimizing the Reliability and Performance of a U.S. Army Mobile Power Transfer System,” Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), Troy, MI.
Kempton, W., and Tomic, J., 2005, “Vehicle-to-Grid Power Implementation: From Stabilizing the Grid to Supporting Large-Scale Renewable Energy,” J. Power Sources, 144(1), pp. 280–294. [CrossRef]
Kempton, W., and Tomic, J., 2005, “Vehicle-to-Grid Fundamentals: Calculating Capacity and Net Revenue,” J. Power Sources, 144(1), pp. 268–279. [CrossRef]
Kempton, W., Tomic, J., Letendre, S., Brooks, A., and Lipman, T., 2005, “Vehicle-to-Grid Power: Battery, Hybrid and Fuel Cell Vehicles as Resources for Distributed Electric Power in California, Davis, CA,” Institute of Transportation Studies, Report No. IUCD-ITS-RR 01-03.
Tomic, J., and Kempton, W., 2007, “Using Fleets of Electric-Drive Vehicles for Grid Support,” J. Power Sources, 168(2), pp. 459–468. [CrossRef]
Williams, B. D., and Kurani, K. S., 2006, “Estimating the Early Household Market for Light-Duty Hydrogen-Fuel-Cell Vehicles and Other ‘Mobile Energy’ Innovations in California: A Constraints Analysis,” J. Power Sources, 160(1), pp. 446–453. [CrossRef]
Williams, B. D., and Kurani, K. S., 2007, “Commercializing Light-Duty Plug-In/Plug-Out Hydrogen-Fuel-Cell Vehicles: Mobile Electricity Technologies and Opportunities,” J. Power Sources, 166(2), pp. 549–566. [CrossRef]
Kempton, W., and Kubo, T., 2000, “Electric-Drive Vehicles for Peak Power in Japan,” Energy Policy, 28(1), pp. 9–18. [CrossRef]
Pandey, V., Skowronska, A. G., Mourelatos, Z. P., Gorsich, D., and Castanier, M., 2013, “Reliability and Functionality of Repairable Systems Using a Minimal Set of Metrics: Design and Maintenance of a Smart Charging Microgrid,” ASME Paper No. DETC2013-12376. [CrossRef]
Elia, S., Gasulla, M., and De Francesco, A., 2012, “Optimization in Distributing Wind Generators on Different Places for Energy Demand Tracking,” ASME J. Energy Resour. Technol., 134(4), p. 041202. [CrossRef]
Whitefoot, J., Mechtenberg, A. R., Peters, D. L., and Papalambros, P. Y., 2011, “Optimal Component Sizing and Forward-Looking Dispatch of an Electric Microgrid for Energy Storage Planning,” ASME Paper No. DETC2011-48513. [CrossRef]
Haldar, A., and Mahadevan, S., 1999, Probability Reliability and Statistical Methods in Engineering Design, 1st ed., Wiley, New York.
Pandey, V., and Mourelatos, Z. P., 2013, “New Metrics to Assess Reliability and Functionality of Repairable Systems,” SAE Int. J. Mater. Manuf., 6(3), pp. 402–410. [CrossRef]
Kapur, K. C., and Lamberson, L. R., 1977, Reliability in Engineering Design, 1st ed., Wiley, New York.
Rigdon, S., and Basu, A., 2000, Statistical Methods for the Reliability of Repairable Systems, 1st ed., Wiley-Interscience, New York, p. 224.
Pandey, V., and Thurston, D., 2009, “Effective Age of Remanufactured Products: An Entropy Approach,” ASME J. Mech. Des., 131(3), p. 031008. [CrossRef]
Crow, L. H., 1974, Reliability Analysis for Complex, Repairable Systems in Reliability and Biometry, F.Proschan and R. J.Serfing, eds., SIAM, Philadelphia, PA, pp. 379–410.
Crow, L. H., 2012, Accessed Apr. 15, 2015, http://www.reliasoft.com/newsletter/v5i1/repairable.htm
Deb, K., Pratap, A., Agrawal, S., and Meyarivan, T., 2002, “A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II,” IEEE Trans. Evolutionary Comput., 6(2), pp. 182–197. [CrossRef]


Grahic Jump Location
Fig. 1

Schematic of load and source profiles over time. Sources are turned off and on (dispatched) to closely match the load. The load is dispatched if the maximum supply cannot meet it.

Grahic Jump Location
Fig. 2

MTBF represents the expected time to failure correctly only for symmetric distributions (center pdf, solid line). Most systems fail before MTBF for right-skewed distributions and after MTBF for left-skewed distributions.

Grahic Jump Location
Fig. 3

Availability is high if the ratio of repair time to operation time is small. It depends on whether the system is in steady-state or initial (transient) state of operation.

Grahic Jump Location
Fig. 4

Schematic of the SCMG

Grahic Jump Location
Fig. 5

Power management protocol for microgrid

Grahic Jump Location
Fig. 6

Pareto front over MFFP, number of failures, and cost




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In