Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas |
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| Institution: | 1. State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau, Macao 999078, Macau;2. Department of Transportation Engineering, University of California, Berkeley, Berkeley, CA 94720, USA;3. Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley CA 94720, USA;4. Transportation Sustainability Research Center, University of California, Berkeley, Berkeley, CA 94720, USA;5. Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA 94720, USA |
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| Abstract: | Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. We first adopt an agent-based simulation model, called BEAM, to describe the complex behaviors of both passengers and transportation systems in urban cities. BEAM simulates the driving, parking and charging behaviors of the AEV fleet with range constraints and identifies times and locations of their charging demands. Then, based on BEAM simulation outputs, we adopt a hybrid algorithm to site and size charging stations to satisfy the charging demands subject to quality of service requirements. Based on the proposed framework, we estimate the charging infrastructure demands and calculate the corresponding economics and carbon emission impacts of electrifying a ride-hailing AEV fleet in the San Francisco Bay Area. We also investigate the impacts of various AEV and charging system parameters, e.g., fleet size, vehicle battery capacity and rated power of chargers, on the ride-hailing system’s overall costs. |
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| Keywords: | Autonomous vehicle Electric vehicle Shared-use vehicle Ride-hailing service Charging system planning Agent-based simulation |
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