An optimization and analysis framework for TCO minimization of plug-in hybrid heavy-duty electric vehicles

D.P.H. Kolodziejak (Corresponding author), T.H. Pham (Corresponding author), T. Hofman, S. Wilkins

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)
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This paper develops an optimization framework to minimize the Total Cost of Ownership (TCO) for Plug-in Hybrid Electric Vehicles (PHEVs). In this paper, TCO is the summation of operational and main vehicle powertrain components cost. The developed optimization framework is formulated via combining convex optimization and Dynamic Programming technique. This framework aims at minimizing TCO by optimizing not only the sizing of the main powertrain components but also the powertrain topology. Using the developed optimization framework, this paper elaborates relevant design factors for a considered bus application namely: i) the value of equipping a HEV with plug-in functionality; ii) the effect of battery aging and replacement cost; iii) the sensitivity to fuel and electricity cost; Simulation results show that the TCO can be reduced by having plug-in functionality in the HEVs. However, this may not hold if the electricity price (in Euros/kWh) is higher than certain times of the fuel price (in Euros/kWh), e.g. 2.25 for the simulated cases in this paper. Simulation results also suggest that it is more profitable to equip the vehicle with a big enough battery to avoid replacing it during the vehicle economical life.
Original languageEnglish
Pages (from-to)484-491
Number of pages8
Issue number5
Publication statusPublished - 2019
Event9th IFAC International Symposium on Advances in Automotive Control, AAC 2019 - Orléans, France
Duration: 24 Jun 201927 Jun 2019
Conference number: 9

Bibliographical note

Funding Information:
This work is supported by ORCA project addressing topic GV-03-2016, “System and cost optimised hybridisation of road vehicΛes” of the Green VehicΛe work programme.


  • plug-in hybrid electric vehicles
  • optimization
  • total cost of ownership
  • component sizing
  • energy management


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