A Degradation-Informed Battery-Swapping Policy for Fleets of Electric or Hybrid-Electric Vehicles

Ahmad Almuhtady (), Seungchul Lee (), Edwin Romeijn (), Michael Wynblatt () and Jun Ni ()
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Ahmad Almuhtady: Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109
Seungchul Lee: Department of Human and Systems Engineering, Ulsan National Institute of Science and Technology, Ulsan, 689-798, Korea
Edwin Romeijn: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Michael Wynblatt: Eaton Corporation-Innovation Center, Southfield, Michigan 48076
Jun Ni: Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109

Transportation Science, 2014, vol. 48, issue 4, 609-618

Abstract: Motivated by high oil prices, several large fleet companies initiated future plans to hybridize their fleets to establish immunity of their optimized business models against severe oil price fluctuations, and adhere to increasing awareness of environmentally friendly solutions. The hybridization projects increased maintenance costs especially for costly and degradable components such as Li-ion batteries. This paper introduces a degradation-based resource allocation policy to optimally utilize batteries on fleet level. The policy, denoted as degradation-based swapping optimization, incorporates optimal implementation of swapping and substitution actions throughout a plan of finite-time horizon to minimize projected maintenance costs. The swapping action refers to the interchange in the placement of two batteries within a fleet. The substitution action refers to the replacement of degraded batteries with new ones. The policy takes advantage of the different degradation rates of the state of health of the batteries because of different loading conditions, achieving optimal placement at different time intervals throughout the plan horizon. A mathematical model for the policy is provided. The optimization of the generated model is studied through several algorithms. Numerical results for sample problems are obtained to illustrate the capability of the proposed policy in establishing substantial savings in the projected maintenance costs compared to other policies.

Keywords: intelligent maintenance; swapping policy; resource allocation policy; fleet electrification and hybridization; electric delivery vehicles (EDV); genetic algorithm; simulated annealing; branch and bound (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (10)

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