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Original Articles

Plug-In Hybrid Electric Vehicles’ Potential for Urban Transport in China: The Role of Energy Sources and Utility Factors

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Pages 145-157 | Received 20 Jan 2012, Accepted 08 Oct 2012, Published online: 21 Aug 2014
 

Abstract

To investigate the energy consumption and emissions of plug-in hybrid electric vehicles (PHEVs) in China in 2020, we undertake a “Well-to-Wheel” lifecycle energy consumption and carbon emission analysis using the ‘Greenhouse Gases, Regulated Emissions and Energy Use in Transport’ model from the U.S. Argonne National Laboratory. We find that PHEVs would reduce energy consumption by 37.5% and GHG emissions by 35%, when compared to current gasoline vehicles under the predicted 2020 electricity-generation mix. These savings would be higher under cleaner electricity-generation mixes. These benefits are not substantially affected by changes in travel distances, battery ranges, or charging frequencies.

Acknowledgments

The authors are indebted to four anonymous reviewers of the International Journal of Sustainable Transportation for comments and suggestions that greatly improved this article. Thanks are also due to anonymous reviewers and attendees of the 12th Meeting of the World Conference on Transport Research Society, Lisbon, July 11-15, 2010 and the Transportation Research Board 90th Annual Meeting, Washington DC, January 23-27, 2011.

Notes

1Just to put subsidies in context, that for a BYD E6 would be about one-third of the vehicle price, assuming a price of roughly 37,000 RMB per vehicle, as shown on the manufacturer's website (BYD 2012).

2As a side note, some PHEVs are actually more expensive than pure EVs because of larger batteries. A Volt is more expensive than a Leaf, for example.

3The version used here is GREET 1.8c and is downloadable for free from http://www.transportation.anl.gov/modeling_simulation/GREET/.

4Feedstock is defined as energy resources for fuel/electricity products.

5Our results are related to a small sample, which may not be necessarily representative of the Chinese population as a whole. Having said that, our 331 survey responses are extremely similar to the 1163 responses of a survey carried out by DTTL (Citation2011), which we use for comparison purposes in Section 2.2.1. Both sets of findings are also in line with Wang et al. (Citation2006 p. 28).

6GREET 1.8c.0 assumes a fuel-production energy efficiency in the U.S. for 2020 of 92%.

7On 25 May 2010 China Daily reported that China's annual crude-oil refining capacity may rise by 50% by 2015 (http://www.chinadaily.com.cn/bizchina/2010-05/25/content_9890521.htm).

a International Energy Agency (2010).

b Zhang et al. (2007, p. 94).

c The electricity-generation mixes “2020 slightly cleaner” and “All nuclear” are unlikely and were used for sensitivity and comparison purposes only.

8The “Auto.Sohu” website is a Chinese website owned by a private company that specializes on car reviews, car information, car news, car surveys and car data: http://auto.sohu.com/20100224/n270407525.shtml

10Axsen and Kurani (Citation2009) conducted an Internet-based survey of 2,373 new car-buying households in the U.S. and found that fuel economy appears to be the most important characteristic for potential buyers of PHEVs in their sample. Similarly, 52% of our respondents thought that lower operating costs were an important advantage of HEVs and EVs.

11Of respondents 38% stated that the minimum distance per charge they would find acceptable would be 200 km, and 33% stated that it would over 200 km. Thirty-one per cent of our respondents stated that the lowest vehicle maximum speed they would find acceptable would be 100 km/h and 57% stated it would be 150 km/h.

12This assumption is common in the literature. Even the documents produced by the Society of Automotive Engineers (SAE) in the U.S. assume that batteries are only recharged once a day (Bradley and Quinn Citation2010). Axsen and Kurani (Citation2010) evaluate different re-charging patterns and, not surprisingly, conclude that PHEV electricity use could be increased through policies supporting non-home recharging opportunities, although this increase would occur during daytime hours and would therefore potentially increase peak electricity demand.

13Graham and Glasiter (2004) review a number of studies conducted for different countries.

14For the fuel cycle of baseline ICEs, the electricity is consumed in both processes of crude oil recovery and gasoline production. For instance, in the crude oil recovery process, the electricity consumption is 3872Btu for every million Btu of petroleum, which accounts for 19% of total energy consumption in this process. As the electricity is largely generated from the combustion of coal, the crude oil recovery and fuel production involve coal consumption. Tables A3 and A4 provide absolute values of fuel cycle energy consumption and emissions of IVE vehicles and PHEVs, as well as changes of PHEV values relative to ICE values for the 2020 predicted electricity mix and for the all nuclear mix, respectively.

15Duvall et al. (Citation2007) estimate GHG reductions in the whole of the U.S. for the year 2050 as a result of low, medium and high market penetration of PHEVs. Although they report results under a number of different assumptions, they report them in billion metric tons of CO2e rather than percentage changes. Also their analysis concerns marginal emission reductions rather than total emission reductions. The model they use is the National Electric System Simulation Integrated Evaluator (NESSIE), developed at the Electric Power Research Institute. The NESSIE models the U.S. electricity sector from 2010 to 2050. For all those reasons comparisons with the Chinese case in the present study are not straightforward.

Note. Numbers in parenthesis correspond to % changes relative to 2009 ICE vehicles (current gasoline vehicles) under the relevant electricity mix. Source: Estimates produced by the authors using GREET 1.8c, with UF = 0.69.

*The state of charge is the percentage of available electricity capacity to the battery's total storage capacity. It is the equivalent of a fuel gauge for the battery pack (0% = empty; 100% = full). Source: Rousseau et al. (2007).

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