Energy and Resources Group, University of California Berkeley, Berkeley, California 94720, United States.
Aspiring Citizens Cleantech, LLC, Singapore, aspiringcitizens.com.
Environ Sci Technol. 2020 Jun 16;54(12):7027-7033. doi: 10.1021/acs.est.0c01609. Epub 2020 May 29.
Mobility on-demand vehicle (MODV) services have grown explosively in recent years, threatening targets for local air pollution and global carbon emissions. Despite evidence that on-demand automotive fleets are ripe for electrification, adoption of battery electric vehicles (BEVs) in fleet applications has been hindered by lack of charging infrastructure and long charging times. Recent research on electrification programs in Chinese megacities suggests that top-down policy targets can spur investment in charging infrastructure, while intelligent charging coordination can greatly reduce requirements for battery range and infrastructure, as well as revenue losses due to time spent charging. Such capability may require labor policy reform to allow fleet operators to manage their drivers' charging behavior, along with collection and integration of several key data sets including (1) vehicle trajectories and energy consumption, (2) charging infrastructure installation costs, and (3) real-time charging station availability. In turn, digitization enabled by fleet electrification holds the potential to enable a host of smart urban mobility strategies, including integration of public transit with innovative transportation systems and emission-based pricing policies.
近年来,按需出行汽车(MODV)服务呈爆炸式增长,对当地空气污染和全球碳排放目标构成威胁。尽管有证据表明,按需汽车车队已经成熟,可以实现电气化,但车队应用中采用电池电动汽车(BEV)的情况受到充电基础设施不足和充电时间长的阻碍。最近对中国特大城市电气化计划的研究表明,自上而下的政策目标可以刺激充电基础设施投资,而智能充电协调可以大大降低对电池续航里程和基础设施的要求,以及因充电时间而导致的收入损失。这种能力可能需要劳动力政策改革,允许车队运营商管理其驾驶员的充电行为,同时收集和整合包括(1)车辆轨迹和能耗、(2)充电基础设施安装成本、(3)实时充电站可用性在内的几个关键数据集。反过来,车队电气化所带来的数字化有可能实现一系列智能城市交通策略,包括公共交通与创新交通系统的整合以及基于排放的定价政策。