School of Natural Resources and Environment, University of Michigan , Ann Arbor, Michigan, USA.
Environ Sci Technol. 2013 Aug 20;47(16):9035-43. doi: 10.1021/es401008f. Epub 2013 Aug 1.
Environmental implications of fleet electrification highly depend on the adoption and utilization of electric vehicles at the individual level. Past research has been constrained by using aggregated data to assume all vehicles with the same travel pattern as the aggregated average. This neglects the inherent heterogeneity of individual travel behaviors and may lead to unrealistic estimation of environmental impacts of fleet electrification. Using "big data" mining techniques, this research examines real-time vehicle trajectory data for 10,375 taxis in Beijing in one week to characterize the travel patterns of individual taxis. We then evaluate the impact of adopting plug-in hybrid electric vehicles (PHEV) in the taxi fleet on life cycle greenhouse gas emissions based on the characterized individual travel patterns. The results indicate that 1) the largest gasoline displacement (1.1 million gallons per year) can be achieved by adopting PHEVs with modest electric range (approximately 80 miles) with current battery cost, limited public charging infrastructure, and no government subsidy; 2) reducing battery cost has the largest impact on increasing the electrification rate of vehicle mileage traveled (VMT), thus increasing gasoline displacement, followed by diversified charging opportunities; 3) government subsidies can be more effective to increase the VMT electrification rate and gasoline displacement if targeted to PHEVs with modest electric ranges (80 to 120 miles); and 4) while taxi fleet electrification can increase greenhouse gas emissions by up to 115 kiloton CO2-eq per year with the current grid in Beijing, emission reduction of up to 36.5 kiloton CO2-eq per year can be achieved if the fuel cycle emission factor of electricity can be reduced to 168.7 g/km. Although the results are based on a specific public fleet, this study demonstrates the benefit of using large-scale individual-based trajectory data (big data) to better understand environmental implications of fleet electrification and inform better decision making.
电动汽车在个人层面的推广和使用对车队电动化的环境影响有重要影响。过去的研究受到使用聚合数据的限制，即假设所有车辆都具有与聚合平均值相同的行驶模式。这忽略了个体出行行为的固有异质性，可能导致对车队电动化的环境影响的不切实际的估计。本研究使用“大数据”挖掘技术，分析了北京 10375 辆出租车一周内的实时车辆轨迹数据，以描述个体出租车的出行模式。然后，我们根据所描述的个体出行模式，评估了在出租车队中采用插电式混合动力汽车（PHEV）对生命周期温室气体排放的影响。结果表明：1）在当前电池成本、有限的公共充电基础设施和没有政府补贴的情况下，采用具有适度电动续航里程（约 80 英里）的 PHEV 可以实现最大的汽油替代量（每年 110 万加仑）；2）降低电池成本对提高行驶里程的电动化率（VMT）从而增加汽油替代量的影响最大，其次是多样化的充电机会；3）如果政府补贴针对电动续航里程为 80-120 英里的适度 PHEV，则可以更有效地提高 VMT 的电动化率和汽油替代量；4）尽管当前北京电网下出租车队的电气化可能会使温室气体排放增加高达 115 千吨 CO2-eq/年，但如果可以将电力燃料循环排放因子降低到 168.7g/km，则可以减少 36.5 千吨 CO2-eq/年的排放。虽然这些结果基于特定的公共车队，但本研究证明了使用大规模基于个体的轨迹数据（大数据）来更好地理解车队电动化的环境影响并为更好的决策提供信息的好处。
