Energy Consumption and CO2 Emission Model for Hybrid
Vehicles in Real Traffic Conditions

doi:10.16097/j.cnki.1009-6744.2022.06.032

Abstract

Abstract: Due to the difference in energy consumption and emission factors between hybrid vehicles and conventional vehicles, there is uncertainty in the quantitative assessment of energy consumption and emission in automobile traffic road networks. This study establishes a model to measure the energy consumption and CO2 emission factors of hybrid vehicles in real traffic conditions, based on the vehicle specific power (VSP) as a parameter to characterize the coupling relationship from vehicle driving activities to energy consumption and emission. This model introduces the rotational speed of the internal combustion engine to distinguish whether it is operating or not, calculates the average energy consumption rate corresponding to the VSP in the engine operating mode, and develops the VSP distribution that can analyze the energy consumption and emission generation mechanism of hybrid vehicles. The accuracy of the model was verified by collecting fuel consumption data from hybrid vehicles test in typical driving cycles, and the model was applied to measure the fuel consumption and CO2 emission factors of hybrid vehicles at different average speeds using the real driving trajectory data in Beijing. The results show that the average relative errors between the model-measured energy consumption emission factors and the real values are 3.7% and - 1.7% in the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWY), respectively, which are 5.6% and 4.3% less than those of the original conventional model. In the real traffic condition, the measurement method of conventional vehicles will lead to the underestimation of fuel consumption and CO2 emissions when the average speed of hybrid vehicles is in the high-speed, and the overestimation of fuel consumption and CO2 emissions when the average speed is at a low level.

Key words: urban traffic, traffic emission model, vehicle specific power, hybrid vehicles, CO2 emission

摘要： 由于混合动力汽车与传统燃油车的能耗排放因子具有差异性，导致机动车交通路网能耗排放的量化评估存在不确定性。本文建立混合动力汽车在实际交通状态中的能耗和CO2排放因子测算模型，基于车辆比功率VSP(Vehicle Specific Power)作为车辆行驶状态与能耗排放之间耦合关系的表征参数。通过引入内燃机转速区分内燃机开启和关闭工作状态，并计算内燃机开启状态下VSP对应的平均能耗率，同时，建立能够解析混合动力汽车能耗排放产生机理的VSP分布。通过收集典型行驶工况下车辆测试油耗数据和北京市车辆实际行驶轨迹数据，验证了模型的准确性，并应用模型测算混合动力汽车不同速度区间下的油耗和CO2排放因子。研究结果表明：在城市行驶工况(UDDS)和高速行驶工况(HWY)中，模型测算能耗排放因子与真实值的平均相对误差分别为3.7%和-1.7%，与不考虑内燃机开启状态相比，测算误差减少5.6%和4.3%；在实际交通状态下，采用传统燃油车的测算方法会导致混合动力汽车行驶平均速度为高速区间时油耗和CO2排放量被低估，当行驶平均速度为低速区间时油耗和CO2排放量会被高估。

关键词: 城市交通, 交通排放模型, 比功率, 混合动力汽车, CO2排放

CLC Number:

U121

PENG Fei, SONG Guo-hua, YIN Hang, YU Lei. Energy Consumption and CO2 Emission Model for Hybrid Vehicles in Real Traffic Conditions[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(6): 316-326.

彭飞, 宋国华, 尹航, 于雷. 面向实际交通状态的混合动力汽车能耗和CO2排放模型[J]. 交通运输系统工程与信息, 2022, 22(6): 316-326.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.06.032

http://www.tseit.org.cn/EN/Y2022/V22/I6/316

References

[1] 程颖, 陈艳艳, 刘莹, 等. 城市交通排放高分辨率分析方法研究: 北京实证[J]. 交通运输系统工程与信息, 2018, 18(2): 236-244. [CHENG Y, CHEN Y Y, LIU Y, et al. A method of high-resolution of urban traffic emission for pollution control: A case study in Beijing[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(2): 236-244.]

[2] US Environment Protection Agency (EPA). Overview of EPA's motor vehicle emission simulator (MOVES3) [R]. Washington DC: US EPA, 2021.

[3] 单肖年, 刘皓冰, 张小丽, 等. 基于MOVES模型本地化的轻型车排放因子估计方法[J]. 同济大学学报(自然科学版), 2021, 49(8): 1135-1143, 1201. [SHAN X N, LIU H B, ZHANG X L, et al. Localization of light-duty vehicle emission factor estimation based on MOVES[J]. Journal of Tongji University (Natural Science), 2021, 49 (8): 1135-1143, 1201.]

[4] 黄文伟, 强明明, 孙龙林, 等. 基于MOVES的车辆排放因子测试[J]. 交通运输工程学报, 2017, 17(1): 140- 148. [HUANG W W, QIANG M M, SUN L L, et al. Emission factor measurement of vehicles based on MOVES[J]. Journal of Traffic and Transportation Engineering, 2017, 17(1): 140-148.]

[5] 吕晨, 张哲, 陈徐梅, 等. 中国分省道路交通CO2排放因子[J]. 中国环境科学, 2021, 41(7): 3122-3130. [LV C, ZHANG Z, CHEN X M, et al. Study on CO2 emission factors of road transport in Chinese provinces[J]. China Environmental Science, 2021, 41(7): 3122-3130.]

[6] 胥耀方, 于雷, 宋国华. 基于遗传算法的运行模式分布模型及排放测算 [J]. 中国环境科学, 2016, 36(12): 3548-3559. [XU Y F, YU L, SONG G H. GA-based approach to modeling operating mode distributions and estimating emissions[J]. China Environmental Science, 2016, 36(12): 3548-3559.]

[7] 张双红, 于雷, 宋国华. 基于工况分布的重型环卫货车NOX排放模型[J]. 交通运输系统工程与信息, 2019, 19 (3): 222- 229. [ZHANG S H, YU L, SONG G H. NOx emission model for heavy-duty refuse trucks based on operating modes[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3): 222-229.]

[8] 樊守彬, 田灵娣, 张东旭, 等. 基于实际道路交通流信息的北京市机动车排放特征[J]. 环境科学, 2015, 36 (8): 2750-2757. [FAN S B, TIAN L D, ZHANG D X, et al. Emission characteristics of vehicle exhaust in Beijing based on actual traffic flow information[J]. Environmental Science, 2015, 36(8): 2750-2757.]

[9] 李笑语, 吴琳, 邹超, 等. 基于实时交通数据的南京市主次干道机动车排放特征分析[J]. 环境科学, 2017, 38(4): 1340-1347. [LI X Y, WU L, ZHOU C, et al. Emission characteristics of vehicle exhaust in artery and collector roads in Nanjing based on real-time traffic data [J]. Environmental Science, 2017, 38(4): 1340-1347.]

[10] 黎明, 宋国华, 靳秋思, 等. 路网机动车排放因子测算与不确定性分析[J]. 哈尔滨工业大学学报, 2017, 49 (3): 132-137. [LI M, SONG G H, JIN Q S, et al. Development emission factor for road network and uncertainty analysis[J]. Journal of Harbin Institute of Technology, 2017, 49(3): 132-137.]

[11] 吴克寒, 赵一新, 唐夕茹. 考虑道路属性特征的中观排放模型研究[J]. 交通运输系统工程与信息, 2019, 19 (6): 20-25, 31. [WU K H, ZHAO Y X, TANG X R, Mesolevel vehicle emission model based on road section characteristics[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(6): 20-25, 31.]

[12] 禹文林, 葛蕴珊, 王欣, 等. 混合动力汽车实际道路行驶排放特性研究[J]. 汽车工程, 2018, 40(10): 1139- 1145. [YU W L, GE Y S, WANG X, et al. Research on the real driving emission characteristics of hybrid electric vehicles [J]. Automotive Engineering, 2018, 40 (10): 1139-1145.]

[13] HOLMEN B A, SENTOFF K M. Hybrid-electric passenger car carbon dioxide and fuel consumption benefits based on real-world driving[J]. Environment Science Technology, 2015, 49 (16): 10199-10208.

[14] ZHAI H B, FREY H C, ROUPHAIL N M. Development of a modal emissions model for a hybrid electric vehicle [J]. Transportation Research Part D: Transport and Environment, 2011, 16 (6): 444-450.

[15] MITCHELL M K, HOLMEN B A. Hybrid-electric passenger car energy utilization and emissions: Relationships for real-world driving conditions that account for road grade[J]. Science of the Total Environment, 2020, 738: 139692

[16] JIMENEZ J L. Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing[D]. Cambridge: University of Cambridge, 1999.

[17] US Environment Protection Agency (EPA). Greenhouse gas and energy consumption rates for onraod vehicles in MOVES3 [R]. Washington DC: US EPA, 2020.

Energy Consumption and CO2 Emission Model for Hybrid Vehicles in Real Traffic Conditions

面向实际交通状态的混合动力汽车能耗和CO2排放模型

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