REC: Predictable Charging Scheduling for Electric Taxi Fleets

Zheng, Dong; Liu, Cong; Li, Yanhua; Bao, Jie; Gü, Yü; He, Tian

doi:10.1109/rtss.2017.00034

Cited by 38 publications

(27 citation statements)

References 16 publications

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“…Among these cities, the Chinese city Shenzhen has started the taxi electrification process from 2010 and achieved the largest ET network in the world by 2017 [9]. In particular, the number of ETs in Shenzhen has increased from 50 in 2010 to 12,518 in 2017 [26], and it is projected to be over 18,000 in 2020, becoming an ET-only taxi network.…”

Section: Introductionmentioning

confidence: 99%

Experience

Wang

Chen

Zhang

et al. 2019

The 25th Annual International Conference on Mobile Computing and Networking

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Due to the ever-growing concerns on the air pollution and energy security, many cities have started to update their taxi fleets with electric ones. Although environmentally friendly, the rapid promotion of electric taxis raises problems to both taxi drivers and governments, e.g., prolonged waiting/charging time, unbalanced utilization of charging infrastructures and reduced taxi supply due to the long charging time. In this paper, we make the first effort to understand the long-term evolving patterns through a five-year study on one of the largest electric taxi networks in the world, i.e., the Shenzhen electric taxi network in China. In particular, we perform a comprehensive measurement investigation called ePat to explore the evolving mobility and charging patterns of electric vehicles. Our ePat is based on 4.8 TB taxi GPS data, 240 GB taxi transaction data, and metadata from 117 charging stations, during an evolving process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover, ePat also explores the impacts of various contexts and benefits during the evolving process. Our ePat as a comprehensive investigation of the electric taxi network mobility and charging evolving has the potential to advance the understanding of the evolving patterns of electric taxi networks and pave the way for analyzing future shared autonomous vehicles.

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Section: Introductionmentioning

confidence: 99%

Experience

Wang

Chen

Zhang

et al. 2019

The 25th Annual International Conference on Mobile Computing and Networking

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“…during their daily operation. For each charging activity of EVs, there are three stages (i.e., traveling, waiting, and service), which can be extracted from GPS data [7,25,41].…”

Section: Charging Process Of Heterogeneous Ev Fleetsmentioning

confidence: 99%

“…The heterogeneous multiprocessor real-time scheduling includes two stages [33]: (i) assigning tasks to each processor and (ii) performing uniprocessor scheduling on each processor once tasks are assigned to processors. For the latter problem, it has been well-solved by using EDF algorithm [7], where the non-preemptive issue is also addressed. Since EVs have no deadlines, we set the charging finish time as the scheduling deadline.…”

Section: Sharedcharging Charging Scheduling Equivalentmentioning

confidence: 99%

“…Much existing research [7,24,25,27,50] focuses on how to choose the optimal locations for charging stations and how to assign charging points in each station to minimize the charging time of EVs considering some passenger demands or cost constraints. However, to our knowledge, almost all these works [7,25,28,41,44,[50][51][52] only consider one fleet (e.g., only e-taxis [7,25] or e-buses [44]). Different mobility patterns and charging patterns of heterogeneous EV fleets have not been considered based on real-world data.…”

Section: Introductionmentioning

confidence: 99%

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sharedCharging

Wang

Zhang

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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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.

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“…The EV charging rates were modeled via partial differential equations under three conditions: 1) EVs receive energy from the grid, 2) EVs are connected to the grid but not charging, and 3) EVs deliver energy to the grid, was considered in [22]. Optimal charging scheduling of taxi fleets was presented in [23] while taking into account the charging station locations, unpredictability and balancing issues. Although the aforementioned works highlight the importance of EV charging scheduling, energy management of both EV fleets and charging points and their impact on the electricity grid, both the route as well as the uncertainties involved during navigation of each EV were not part of their focus, while the navigation modes, emissions reduction during navigation, and the EV type (heterogeneous fleet) were also neglected.…”

mentioning

confidence: 99%

Efficient Automation of an HEV Heterogeneous Fleet Using a Two-Stage Methodology

Cerna

Pourakbari‐Kasmaei

Lehtonen

et al. 2019

IEEE Trans. Veh. Technol.

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An influential factor in enhancing the attendance services, mainly in commercial and emergency sectors, is the vehicular technology used to transport people, goods, or equipment. Although hybrid electric vehicles (HEVs) represent a sustainable transport alternative, the existing technical limitations such as battery and fuel capacities, and autonomy, among others, highlight the provision of an efficient automation tool. The tool can serve to enhance the operational performance of the HEV by selecting the proper driving mode (on fuel or electricity), and the navigation strategies to the delivery and charging points in urban areas. This paper proposes a two-stage methodology that allows the HEVs operators to automate the operational performance of a heterogeneous HEV fleet on a city map. Each stage is handled by its corresponding optimization model. In the first stage, the total navigation time and the battery lifetime of the fleet during the operation are optimized. In this stage, constraints related to charge-sustaining/charge-depleting modes, state of charge (SoC) of the HEVs battery, and deliveries schedules are taken into account. To this end, operating strategies related to the performance of different types of existing HEV technologies are anonymously considered. In the second stage, the best operating strategy among all the operating strategies is selected while considering the capacity of HEVs to deliver a given quantity of goods. Moreover, uncertainties during the HEV navigation are simulated considering the change in traffic density of the urban roads as a function of the levels of service (LOS). Results show that the proposed methodology establishes an efficient operational scheme for a HEVs fleet, ensuring a significant reduction of energy usage as well as mitigating the CO2 emissions.

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REC: Predictable Charging Scheduling for Electric Taxi Fleets

Cited by 38 publications

References 16 publications

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sharedCharging

Efficient Automation of an HEV Heterogeneous Fleet Using a Two-Stage Methodology

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