考虑时间相关性的电动汽车全生命周期碳排放量预测

doi:10.19799/j.cnki.2095-4239.2021.0715

摘要/Abstract

摘要：

电动汽车碳排放影响因素较多，导致其精确预测难度较大。为此，本工作提出考虑时间相关性的电动汽车全生命周期碳排放量预测方法。从全生命周期理论出发，计量边际发电量、线损率、煤炭生产环节产生的碳排放量、电煤运输环节产生的碳排放量与发电环节的碳排放量，并将时间相关性应用到电动汽车行驶特征分析，计算直接碳排放量，以此完成考虑时间相关性的电动汽车全生命周期碳排放量预测。实验结果表明：在车辆上坡、下坡、拥堵与正常行驶情况下的碳排放量预测准确度较高，满足电动汽车碳排放量预测需求。

Abstract:

Because of several contributing factors, it is difficult to correctly anticipate electric vehicle carbon emissions. As a result, a strategy for predicting carbon emissions from electric cars during their whole life cycle is suggested, taking into account temporal correlation. On the whole life cycle theory, measuring marginal electricity, and coal production line loss, suggestions produce carbon emissions, and the carbon emissions formed by coal transportation link power link's carbon footprint, and applies time correlation to the analysis of characteristics of the electric car driving, calculate direct carbon emissions, to complete consideration of time correlation prediction in the whole life cycle of the electric car carbon emissions. The experimental findings reveal that the proposed measuring technique has high accuracy in carbon emission forecast under vehicle uphill, downhill, congestion, and regular driving scenarios, which satisfies the requirement for electric vehicle carbon emission prediction.

Key words: time correlation, electric vehicles, full life cycle, carbon emissions, forecast, marginal power generation

中图分类号:

U 491

武光华, 李宏胜, 李飞, 陈博, 张世科. 考虑时间相关性的电动汽车全生命周期碳排放量预测[J]. 储能科学与技术, 2022, 11(7): 2206-2212.

Guanghua WU, Hongsheng LI, Fei LI, Bo CHEN, Shike ZHANG. Research on the prediction of carbon emissions in the whole life cycle of electric vehicles considering time correlation[J]. Energy Storage Science and Technology, 2022, 11(7): 2206-2212.

图/表 9

图1

图2

图3

表1

图4

图5

图6

图7

表2

参考文献 20

1	杨捷, 曹子健. 电动汽车储能V2G模式的成本与收益分析[J]. 储能科学与技术, 2020, 9(S1): 45-51.
	YANG J, CAO Z J. Cost and benefit analysis of EV energy storage through V2G[J]. Energy Storage Science and Technology, 2020, 9(S1): 45-51.
2	张骞, 武小兰, 白志峰, 等. 电动汽车混合储能系统自适应能量管理策略研究[J]. 储能科学与技术, 2020, 9(3): 878-884.
	ZHANG Q, WU X L, BAI Z F, et al. Research on adaptive energy management strategy of hybrid energy storage system in electric vehicles[J]. Energy Storage Science and Technology, 2020, 9(3): 878-884.
3	江东, 王娣, 林刚, 等. 特斯拉汽车产业规划布局对中国电能耗和碳排放的影响评估[J]. 科技导报, 2020, 38(16): 140-145.
	JIANG D, WANG D, LIN G, et al. Impact assessment of Tesla's automotive industry planning and layout for China's resource environment[J]. Science & Technology Review, 2020, 38(16): 140-145.
4	严岿, 陈行. 纯电动汽车和传统汽车使用周期效益比较研究[J]. 武汉理工大学学报(交通科学与工程版), 2021, 45(1): 177-181.
	YAN K, CHEN H. Comparative study on life cycle benefits of pure electric vehicles and traditional vehicles[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2021, 45(1): 177-181.
5	蒋浩, 林舜江, 卢艺, 等. 考虑时间相关性的电动汽车充电站负荷概率建模及场景生成[J]. 电力建设, 2020, 41(2): 47-57.
	JIANG H, LIN S J, LU Y, et al. Load probability modeling and scenario generation for electric vehicle charging station considering time correlation[J]. Electric Power Construction, 2020, 41(2): 47-57.
6	梁晶, 盛慧敏, 吕靖. 环渤海地区物流产业集聚对物流碳排放影响的实证研究[J]. 生态经济, 2020, 36(9): 38-43.
	LIANG J, SHENG H M, LV J. Empirical study on the logistics industry agglomeration to the carbon emission of logistics in the Bohai rim region[J]. Ecological Economy, 2020, 36(9): 38-43.
7	闫梦, 王聪. 基于多尺度集成模型预测碳交易价格——以广州碳排放交易中心为例[J]. 技术经济与管理研究, 2020(5): 19-24.
	YAN M, WANG C. Prediction of carbon trading price based on multi-scale integrated model—Take Guangzhou carbon emission trading centre as an example[J]. Journal of Technical Economics & Management, 2020(5): 19-24.
8	平智毅, 吴学兵, 吴雪莲. 长江经济带碳排放效率的时空差异及其影响因素分析[J]. 生态经济, 2020, 36(3): 31-37.
	PING Z Y, WU X B, WU X L. Spatial-temporal differences and its influencing factors of carbon emission efficiency in the Yangtze River economic belt[J]. Ecological Economy, 2020, 36(3): 31-37.
9	王珂珂, 牛东晓, 甄皓, 等. 基于WOA-ELM模型的中国碳排放预测研究[J]. 生态经济, 2020, 36(8): 20-27.
	WANG K K, NIU D X, ZHEN H, et al. Forecast of carbon emissions in China based on WOA-ELM model[J]. Ecological Economy, 2020, 36(8): 20-27.
10	王志远, 王守相, 陈海文, 等. 考虑空间相关性采用LSTM神经网络的光伏出力短期预测方法[J]. 电力系统及其自动化学报, 2020, 32(5): 78-85.
	WANG Z Y, WANG S X, CHEN H W, et al. Short-term photovoltaic output forecasting method using LSTM neural network with consideration of spatial correlation[J]. Proceedings of the CSU-EPSA, 2020, 32(5): 78-85.
11	胡健, 秦玉杰, 焦提操, 等. 泛在电力物联网环境下考虑碳排放权约束的VPP理性调峰模型[J]. 电力系统保护与控制, 2020, 48(3): 49-57.
	HU J, QIN Y J, JIAO T C, et al. Rational peak shaving model of VPP considering carbon emission rights constraints in ubiquitous power Internet of Things environment[J]. Power System Protection and Control, 2020, 48(3): 49-57.
12	王勇, 许子易, 张亚新. 中国超大城市碳排放达峰的影响因素及组合情景预测——基于门限-STIRPAT模型的研究[J]. 环境科学学报, 2019, 39(12): 4284-4292.
	WANG Y, XU Z Y, ZHANG Y X. Influencing factors and combined scenario prediction of carbon emission peaks in megacities in China: Based on threshold-STIRPAT model[J]. Acta Scientiae Circumstantiae, 2019, 39(12): 4284-4292.
13	慈铁军, 马皓璨, 杜恒, 等. 电煤供应链碳排放分析与预测[J]. 电力科学与工程, 2021, 37(9): 62-70.
	CI T J, MA H C, DU H, et al. Analysis and forecast of carbon emission in power-coal supply chain[J]. Electric Power Science and Engineering, 2021, 37(9): 62-70.
14	钟少芬, 郭晓娟, 刘煜平, 等. 基于STRPAT模型的碳排放情景分析[J]. 科技管理研究, 2019, 39(17): 253-258.
	ZHONG S F, GUO X J, LIU Y P, et al. Scenario analysis on carbon emission based on the STIRPAT model[J]. Science and Technology Management Research, 2019, 39(17): 253-258.
15	宋杰, 陈振宇, 杨阳, 等. 考虑资源相关性和不确定性的负荷需求响应决策研究[J]. 电力建设, 2019, 40(6): 132-138.
	SONG J, CHEN Z Y, YANG Y, et al. Research on load demand response decision considering resource correlation and uncertainty[J]. Electric Power Construction, 2019, 40(6): 132-138.
16	朱瑞金, 郭威麟, 龚雪娇. 考虑天然气和电负荷之间相关性的短期电负荷预测[J]. 电力系统及其自动化学报, 2019, 31(8): 27-32.
	ZHU R J, GUO W L, GONG X J. Short-term power load forecasting considering correlations between natural gas and power load[J]. Proceedings of the CSU-EPSA, 2019, 31(8): 27-32.
17	徐龙, 王力, 刘莹, 等. 基于多源数据的公交车能耗碳排放测算模型[J]. 交通运输系统工程与信息, 2020, 20(3): 174-181.
	XU L, WANG L, LIU Y, et al. Calculation model of bus energy consumption and CO₂ emission based on multi-source data[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(3): 174-181.
18	刘朝晖, 张为民, 金希, 等. 制造过程中的广义碳排放计量方法研究[J]. 机械科学与技术, 2019, 38(4): 544-550.
	LIU Z H, ZHANG W M, JIN X, et al. Accounting method of extended carbon emissions in manufacturing process[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(4): 544-550.
19	郝旭, 王贺武, 李伟峰, 等. 基于中国电网结构及一线典型城市车辆出行特征的PHEV二氧化碳排放分析[J]. 环境科学, 2019, 40(4): 1705-1714.
	HAO X, WANG H W, LI W F, et al. Analysis of PHEV CO₂ emission based on China's grid structure and travelling patterns in mega cities[J]. Environmental Science, 2019, 40(4): 1705-1714.
20	王幼松, 黄旭辉, 闫辉. 地铁盾构区间物化阶段碳排放计量分析[J]. 土木工程与管理学报, 2019, 36(3): 12-18, 47.
	WANG Y S, HUANG X H, YAN H. Quantitative analysis of embodied carbon emission in metro shield tunnel[J]. Journal of Civil Engineering and Management, 2019, 36(3): 12-18, 47.

序号	名称	参数
1	单位	ppm^①、mg/m³、ppb^②、pphm^③、%VOL、 %LEL(部分可切换)
2	响应时间	≤20 s
3	测量精度	≤±3%
4	回复时间	≤20 s
5	零点漂移	≤±1%
6	重复性	≤±1%
7	测量方式	泵吸式，内置微型抽气泵
8	检测方式	实时检测、定时检测可设置
9	存储模式	实时存储、定时存储可设置；可存储数据 120000组，可在屏幕上查看历史数据

时间	累计碳排放量/kg
时间	实际值	预测值
9∶00	4.144	4.143
9∶30	3.543	3.541
10∶00	3.612	3.611
10∶30	3.857	3.855
11∶00	3.896	3.896
11∶30	4.265	4.265
12∶00	6.033	6.032
12∶30	6.193	6.193
13∶00	5.812	5.812
13∶30	4.179	4.177
14∶00	3.537	3.536
14∶30	3.471	3.471
15∶00	3.645	3.643

[1]	刘芊彤, 邢远秀. 基于VMD-PSO-GRU模型的锂离子电池剩余寿命预测[J]. 储能科学与技术, 2023, 12(1): 236-246.
[2]	李江峰, 李帅旗, 阮先轸, 徐磊, 张孝春, 宋文吉, 冯自平. 纯电动汽车CO₂ 热泵空调及整车热管理概述[J]. 储能科学与技术, 2022, 11(9): 2959-2970.
[3]	邓林旺, 冯天宇, 舒时伟, 张子峰, 郭彬. 锂离子电池快充策略技术研究进展[J]. 储能科学与技术, 2022, 11(9): 2879-2890.
[4]	耿晓倩, 徐玉杰, 黄景坚, 凌浩恕, 张雪辉, 孙爽, 陈海生. 先进压缩空气储能系统全生命周期能耗及二氧化碳排放[J]. 储能科学与技术, 2022, 11(9): 2971-2979.
[5]	黄鹏, 聂枝根, 陈峥, 舒星, 沈世全, 杨继鹏, 申江卫. 基于优化Elman神经网络的锂电池容量预测[J]. 储能科学与技术, 2022, 11(7): 2282-2294.
[6]	易顺民, 谢林柏, 彭力. 基于VF-DW-DFN的锂离子电池剩余寿命预测[J]. 储能科学与技术, 2022, 11(7): 2305-2315.
[7]	燕乔一, 吴锋, 陈人杰, 李丽. 锂离子电池负极石墨回收处理及资源循环[J]. 储能科学与技术, 2022, 11(6): 1760-1771.
[8]	王芳, 王峥, 林春景, 张国振, 张贵平, 马天翼, 刘磊, 刘仕强. 新能源汽车动力电池安全失效潜在原因分析[J]. 储能科学与技术, 2022, 11(5): 1411-1418.
[9]	张岩, 韩伟, 宋闯, 杨双义. 含电动汽车的光储充一体化电站设施规划与运行联合优化[J]. 储能科学与技术, 2022, 11(5): 1502-1511.
[10]	王军, 阮琳, 邱彦靓. 锂离子电池低温快速加热方法研究进展[J]. 储能科学与技术, 2022, 11(5): 1563-1574.
[11]	郭斌, 邢洁, 姚飞, 景小敏. 基于双层规划模型的用户侧混合储能优化配置[J]. 储能科学与技术, 2022, 11(2): 615-622.
[12]	肖浩逸, 何晓霞, 梁佳佳, 李春丽. 一种基于模态分解和机器学习的锂电池寿命预测方法[J]. 储能科学与技术, 2022, 11(12): 3999-4009.
[13]	张宇杰, 汪兴兴, 朱昱, 倪红军, 邓业林. 宽温度范围内方形三元锂电池倍率放电性能[J]. 储能科学与技术, 2022, 11(12): 3950-3956.
[14]	周思飞, 李骏, 王小飞, 张道明, 薛浩亮. 锂电池电解液电导率模型研究进展[J]. 储能科学与技术, 2022, 11(11): 3688-3698.
[15]	史永胜, 李锦, 任嘉睿, 张凯. 基于WOA-XGBoost的锂离子电池剩余使用寿命预测[J]. 储能科学与技术, 2022, 11(10): 3354-3363.