Pricing and free periodic maintenance service decisions for an electric-and-fuel automotive supply chain using the total cost of ownership

Narges Mohammadzadeh, Seyed Hessameddin Zegordi and Ehsan Nikbakhsh

Applied Energy, 2021, vol. 288, issue C, No S0306261921000362

Abstract: Although subsidies have had significant impacts on the electric vehicle (EV) market share, many governments have planned to eliminate subsidies. There is a concern that unsubsidized EVs reduce the EV market share, significantly. However, purchasing an EV instead of a fuel vehicle (FV) might impose a lower total cost of ownership (TCO) on customers, depending on their vehicle usage. In this case, supply chains could optimize their decisions considering which vehicle is affordable for each customer class from the view of TCO. This study investigates optimal pricing and free periodic maintenance service (FPMS) decisions in a two-stage electric-and-fuel automotive supply chain, considering TCO to estimate vehicle market shares under customer classification with different vehicle usage patterns. Two bi-level models are developed and solved through Karush-Kuhn-Tucker equations and a reformulation-and-decomposition algorithm. Sensitivity analyses are performed considering various scenarios on energy prices and ownership periods. Results indicate that the high-usage customers are more likely to purchase an EV if the ownership period is the same for all classes. However, if low-usage customers keep the vehicle for a longer period than the others, they are more likely to purchase an EV. Both providing FPMSs by the manufacturer instead of the retailer and increasing the fuel price over time with a higher rate, compared with the electricity price and the inflation rate, improve the EV market share and reduce the total fuel consumption and emissions. Investment to produce EVs is not economical for a high price of electricity while having low fuel prices.

Keywords: Electric vehicles; Total cost of ownership; Automotive supply chain; Pricing decision; Bi-level programming; Decomposition algorithm (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2021.116471

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