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IJAT Vol.12 No.6 pp. 806-813
doi: 10.20965/ijat.2018.p0806
(2018)

Paper:

Forecasting Life Cycle CO2 Emissions of Electrified Vehicles by 2030 Considering Japan’s Energy Mix

Keita Ishizaki and Masaru Nakano

Graduate School of System Design and Management, Keio University
4-1-1 Hiyoshi, Kohoku-ku, Yokohama 223-8526, Japan

Corresponding author

Received:
April 28, 2018
Accepted:
September 17, 2018
Published:
November 5, 2018
Keywords:
electricity generation mix, air conditioning, clean energy vehicle, life cycle assessment
Abstract

This paper presents a comprehensive life-cycle analysis of CO2 (LCCO2) emissions from automobiles using a hybrid life-cycle inventory approach to predict the growth of electrified vehicles in Japan. Herein, the hybrid electric vehicle (HEV), plug-in HEV (PHEV), and battery electric vehicle (BEV) versions of the mass-produced Toyota Prius hatchback are analyzed, considering the automobile-usage environment in Japan. In particular, a breakeven analysis of HEV vs. PHEV vs. BEV is conducted in terms of LCCO2 emissions that are affected by (i) outside air temperature and (ii) CO2 emissions during power generation from the present day up to 2030. Our results show that HEV has the lowest LCCO2 emissions when the current thermal-power-dependent electricity generation mix (average for 2012–2014) is considered, followed in order by PHEV and BEV. However, it is predicted that in 2030, PHEV will have the lowest LCCO2 emissions, followed in order by HEV and BEV, as it is anticipated that nuclear and renewable energy sources will be widely available by 2030. PHEV is expected to gain popularity by 2030. Regarding BEV, large quantities of CO2 emissions are emitted during battery production. Furthermore, due to the domestic electricity generation mix from the present day up to 2030, the LCCO2 emissions of BEV will exceed those of HEV and PHEV.

Cite this article as:
K. Ishizaki and M. Nakano, “Forecasting Life Cycle CO2 Emissions of Electrified Vehicles by 2030 Considering Japan’s Energy Mix,” Int. J. Automation Technol., Vol.12 No.6, pp. 806-813, 2018.
Data files:
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