sharedCharging

Abstract

Our society is witnessing a rapid vehicle electrification process. Even though being environmental-friendly, electric vehicles have not reached their full potentials due to prolonged charging time. Moreover, unbalanced spatiotemporal charging demand/supply along with the uneven number of charging stations between heterogeneous fleets make electric vehicle management more challenging, e.g., surplus charging stations across a city for electric buses but limited charging stations in some regions for electric taxis, which severely limit the charging performance of the whole electric vehicle network in a city. In this paper, we first analyze a large-scale real-world dataset from two heterogeneous electric vehicle fleets in the Chinese city Shenzhen. We investigate their mobility and charging patterns and then verify the practicability and necessity of shared charging. Based on the insights we found, we design a generic real-time shared charging scheduling system called sharedCharging to improve overall charging efficiency for heterogeneous electric vehicle fleets. Our sharedCharging also considers sophisticated real-world constraints, e.g., station spaces, availability of charging points, real-time timetable guarantee, etc. More importantly, we take the electric bus and electric taxi fleets as a concrete example of heterogeneous electric vehicle fleets given their different operating patterns. We implement and evaluate sharedCharging with streaming data from over 13,000 electric taxis and 16,000 electric buses, coupled with the charging station data in the Chinese city Shenzhen, which is the largest public electric vehicle network in the world. The evaluation results demonstrate that the proposed sharedCharging reduces the waiting time by 63.5% and reduces the total charging time by 15% on average for e-taxis.

Modeling the preference of electric shared mobility drivers in choosing charging stations

“…Moreover, the validity of the EVSE occupancy rate and the SEV queuing time is confirmed by the real-world practice of the fully electrified taxi fleet in Shenzhen, China. In this case, an EVSE occupancy rate from 10% to 80% [56] and a queuing time of 10 min to 40 min [57] are considered to be plausible. Despite the different taxi market settings between NYC and Shenzhen, we believe that the charging dynamics in our simulation platform are permissible in the real-world ESMS, which can sufficiently justify our baseline scenario.…”

Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties

“…We use real-world E-taxi data from Shenzhen city to conduct experiments. Three different data sets [6], [32] including E-taxi GPS data (vehicle ID, locations, time and speed, etc), transaction data (vehicle ID, pick-up and dropoff time, pick-up and drop-off location, travel distance, etc) and charging station data (locations, the number of charging points, etc) are used to build an E-AMoD system simulator as the training and evaluation environment. To test the robustness of our proposed robust method, we inject a Gaussian noise follows N (0, 1) into the state when testing vehicle balancing methods.…”

“…At each time interval [t, t + 1), passengers' ride requests and low-battery EAV's charging needs emerge in each region. After the locations and status of each EAV are observed and updated, a local controller assigns available EAVs to pick up existing passengers in the request queue according to specific trip assignment algorithms, such as methods designed in the literature [31], and assigns EAVs that need to be charged to charging stations [32]. Then the predicted passenger demand and available charging spots at each region for time interval [t, t + 1) are updated, and a system-level EAV balancing decision is calculated according to the algorithm designed in this work.…”

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

“…Several data types are considered in these mechanisms, including real-time location, intended route, battery level, and station availability, to ensure the drivers are not detoured from their intended route [27], [30]. Although disclosing such information poses privacy concerns for the driver's location and vehicle tracking, the privacy requirements of such mechanisms are not sufficiently studied in the literature.…”