• 关键词：生命周期  能源结构优化  排放因子  排放模型

• 基金项目：四川大学引进人才启动基金（No.JS20100324507093）；教育部新世纪人才支持计划资助项目（No.NCET-10-0578）

• 宋宁
• 四川大学建筑与环境学院, 成都 610065

• 张凯山
• 四川大学建筑与环境学院, 成都 610065

• 摘要：针对我国城市能源供需矛盾日益恶化、环境污染日益严重的现状，优化城市机动车能源结构，缓解城市机动车对传统能源的使用压力，减少尾气排放，具有十分重要的意义.因此，本文以成都市为例，采用线性优化技术，通过确定各种车用能源生命周期内的能源消耗及排放、各种能源车辆的经济成本等，分析得到成都市整个城区范围内能耗最低、排放最小的各种能源机动车数量比例.结果表明，成都市各种能源机动车的数量比例随所选目标函数的不同而有所变化.为实现总能耗最小，各种能源机动车的比例按汽油、柴油、纯电动、电力/汽油混合动力、CNG单燃料、CNG/汽油双燃料、LPG单燃料、及LPG/汽油双燃料等划分应分别68.6%、11.4%、3.7%、6.1%、5.0%、5.0%、0.1%、0.1%.与机动车数量比例优化前相比，优化后总能源消耗量、石油消耗量分别平均减少了11.5%、13.8%.除PM₁₀及SO_x排放量有所增加外，其他气体的排放均有所减少，如CO₂、VOC、CO及NO_x的平均排放量分别减少了10.6%、13.5%、13.5%及16.5%.这是因为机动车能源结构优化后，纯电动汽车及混合燃油汽车的比例有所增加，由于这两类汽车都需要经常充电从而增加了电网的压力.而我国发电技术主要采用火力发电，发电过程将会产生大量的PM₁₀及SO_x.本研究的成果可用于环境决策者制定有效的交通能源政策，达到城市可持续发展的目的.

• Abstract：The energy demand continues to increase as the energy storage is being used up and air pollution due to vehicle emissions is getting worse. Hence, it is critical to reduce vehicular energy consumption and emissions. The objectives of this paper are to optimize the vehicular energy structures in the urban area in order for minimizing energy consumption and emissions. Using Chengdu city as an example, a linear programming was used to determine the percentage of vehicle in the fleet based on life cycle emissions, energy consumption, and cost. The analysis showed that the population penetration rates by different fuel types of vehicles vary by the selected objective function. For example, in order for a minimum total energy consumption, the vehicle population penetration rates for different fuel types of gasoline, diesel, electric, electric/gasoline hybrid, CNG, CNG/gasoline hybrid, LPG, LPG/gasoline hybrid should be 68.6%, 11.4%, 3.7%, 6.1%, 5.0%, 5.0%, 0.1%, and 0.1%, respectively. After the vehicular energy structure was optimized, on average, the total energy consumption and oil consumption would reduce by 11.5% and 13.8%, respectively. While CO₂、VOC、CO and NO_x emissions would reduce by 10.6%,13.5%,13.5%, and 16.5%, respectively. Except for PM₁₀ and SO_x, gaseous emissions would largely reduce. For PM₁₀ and SO_x, emissions slightly increased due to the amount increase for electric and mixed fuel vehicles (less emissions) were outcompeted by increased needs for power generation (more emissions). The findings could be used for decision making regarding transportation and energy policy for sustainable development.

• Key words：life cycle  energy structure optimization  emission factors  emission models

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