Energy and environmental impact of battery electric vehicle range in China

被引:111
作者
Yuan, Xinmei [1 ]
Li, Lili [2 ]
Gou, Huadong [1 ]
Dong, Tingting [3 ]
机构
[1] Jilin Univ, State Key Lab Automot Simulat & Control, Changchun 130025, Jilin, Peoples R China
[2] State Grid Energy Res Inst, Beijing 102209, Peoples R China
[3] Geely Automobile Inst, Hangzhou 311228, Zhejiang, Peoples R China
关键词
Electric vehicle; Driving range; Driving pattern; CO2; emmission; Grid-carbon intensity; GREENHOUSE-GAS EMISSIONS; LIFE-CYCLE ENERGY; PLUG-IN HYBRID; DRIVING PATTERNS; GHG EMISSIONS; POWER; ELECTRIFICATION; GENERATION; COST;
D O I
10.1016/j.apenergy.2015.08.001
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
To meet increasingly stringent emission legislation, electric vehicles are expected to offer promising sustainable mobility in the future. However, the driving range of battery electric vehicles (BEVs) is limited as compared with hybrid electric vehicles (HEVs). Additionally, the grid power supply in China is highly dependent on coal-based thermal power generation, which leads to high grid-carbon intensity and increased well-to-tank (WTT) emission for BEVs. Therefore, the tradeoff between electric vehicle driving range and environmental impact has become a critical problem in BEV development in China. In this study, a BEV model is built and validated. The energy consumption and well-to-wheel (WTW) CO2 emission rates of different driving ranges and test cycles are simulated. To determine the impact of driving patterns on BEV energy consumption, the distribution of vehicle energy consumption is analyzed and an analytical model is proposed to generalize the energy consumption of BEVs in standardized driving cycles to real-world driving with only two statistical characteristics: the average and the variance of the speeds. It is found that BEVs have a great advantage in terms of energy saving only at driving cycles with low average speeds and frequent stops. While driving at highway speeds, the energy consumption of BEVs can be very high. With an understanding of driving pattern impact, parameter variation analysis of the BEV WTW CO2 emission rates for different driving ranges is simulated. Simulation results show that the rolling coefficient and battery energy density have a significant impact on driving range, followed by the drag area. However, grid-carbon intensity is more efficient for reducing WTW CO2 emissions. Currently, optimization of the rolling coefficient and drag area is the most viable option for increasing the battery range and decreasing the WTW CO2 emission rate. Finally, to reduce the energy and environmental impact of BEVs in China, short driving ranges (<250 km) and low driving speeds (<80 km/h) are suggested for current BEVs, and optimization of the vehicle design and reduction of grid-carbon intensity are considered to be the most critical issues for the future application of BEVs. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:75 / 84
页数:10
相关论文
共 45 条
[21]   Influence of driving patterns on life cycle cost and emissions of hybrid and plug-in electric vehicle powertrains [J].
Karabasoglu, Orkun ;
Michalek, Jeremy .
ENERGY POLICY, 2013, 60 :445-461
[22]   Vehicle lightweighting vs. electrification: Life cycle energy and GHG emissions results for diverse powertrain vehicles [J].
Lewis, Anne Marie ;
Kelly, Jarod C. ;
Keoleian, Gregory A. .
APPLIED ENERGY, 2014, 126 :13-20
[23]   Power management strategy for a parallel hybrid electric truck [J].
Lin, CC ;
Peng, H ;
Grizzle, JW ;
Kang, JM .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2003, 11 (06) :839-849
[24]   Fleet view of electrified transportation reveals smaller potential to reduce GHG emissions [J].
Meinrenken, Christoph J. ;
Lackner, Klaus S. .
APPLIED ENERGY, 2015, 138 :393-403
[25]   Real CO2 emissions benefits and end user's operating costs of a plug-in Hybrid Electric Vehicle [J].
Millo, Federico ;
Rolando, Luciano ;
Fuso, Rocco ;
Mallamo, Fabio .
APPLIED ENERGY, 2014, 114 :563-571
[26]   Intelligent Hybrid Vehicle Power Control-Part I: Machine Learning of Optimal Vehicle Power [J].
Murphey, Yi Lu ;
Park, Jungme ;
Chen, Zhihang ;
Kuang, Ming L. ;
Masrur, M. Abul ;
Phillips, Anthony M. .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2012, 61 (08) :3519-3530
[27]  
National technical committee of auto standardization, 2005, 183862005 GBT NAT TE
[28]   Environmental impacts of hybrid, plug-in hybrid, and battery electric vehicles-what can we learn from life cycle assessment? [J].
Nordelof, Anders ;
Messagie, Maarten ;
Tillman, Anne-Marie ;
Soderman, Maria Ljunggren ;
Van Mierlo, Joeri .
INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT, 2014, 19 (11) :1866-1890
[29]   Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system [J].
Offer, G. J. ;
Howey, D. ;
Contestabile, M. ;
Clague, R. ;
Brandon, N. P. .
ENERGY POLICY, 2010, 38 (01) :24-29
[30]   Evaluation of motor characteristics for hybrid electric vehicles using the Hardware-in-the-Loop concept [J].
Oh, SC .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2005, 54 (03) :817-824