Future costs of fuel cell electric vehicles in California using a learning rate approach. (1st May 2018)

• Record Type: Journal Article
• Authors: Ruffini, Eleonora; Wei, Max
• Is Part Of: Energy. Volume 150(2018)
• Page Start: 329
• Page End: 341
• Publication Date: 2018-05-01
• Subjects: Fuel cell electric vehicle -- Hydrogen -- Cost of ownership -- Life cycle costs -- Learning rate; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.02.071
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Many countries and regions of the world are pursuing aggressive decarbonization policies in the transportation sector aiming to sharply reduce the sales of conventional gasoline and diesel-powered internal combustion engine vehicles (ICEVs). Zero emission vehicles (ZEVs), such as battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs), have zero tailpipe emissions, but still can be considerably more expensive than ICEVs, which is one key factor hampering their wide-scale adoption. Still, many decarbonization roadmaps and plans call for a large ramp up of ZEVs in coming years. For example, the International Energy Agency's aggressive 2DS high H2 scenario estimates 11 M BEVs and 4 M FCEVs sold in 2030 and California has also set aggressive sales targets under its ZEV Mandate. This paper provides a detailed life cycle cost analysis comparison for FCEV versus other vehicle technologies, assuming these international adoption scenarios are implemented using a learning rate approach. Results show that the fuel cell system is the key factor in making FCEV life cycle costs comparable to ICEV costs. With an 18% learning rate, FCEVs are estimated to be cost competitive with ICEVs by 2025, but with an 8% learning rate, this cost-competitive point is pushed out almost 25 years. Highlights: Fuel cell system learning rate is the key factor for FCEV cost competitiveness. FCEV cost of ownership trends for different adoption scenarios converge after 2030. These results are found to hold for a wide variety of sensitivity factors.