bCharge: Data-Driven Real-Time Charging Scheduling for Large-Scale Electric Bus Fleets

Wang, Guang; Xie, Xiaoyang; Zhang, Fan; Liu, Yunhuai; Stankovic, John A.

doi:10.1109/rtss.2018.00015

Cited by 79 publications

(48 citation statements)

References 27 publications

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“…They used a mixed integer linear programming at first, then, to reduce the computation time, they used a suboptimal strategy with a heuristic method. Wang et al [20] analyzed a real-world dataset of 16,359 EBs, 1400 bus lines and 5562 bus stops, which is obtained from the Chinese city Shenzhen. They designed "bCharge", which is a real-time charging scheduling system.…”

Section: Literature Review Of Large-scale Ebs Smart Charging Algorithmsmentioning

confidence: 99%

“…A centralized charging scheduling is operated by a central controller whereas decentralized charging scheduling is managed by individual users that optimize their own charging profiles. Some works have focused on the centralized overnight charging scheduling of EBs fleets [13][14][15][16][17][18][19][20][21]. Other works were dedicated to the optimization of charging infrastructure [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…The present paper differs from the referred ones [13][14][15][16][17][18][19][20][21][22][23][24][25] as it investigates more precisely the battery electro-thermal and aging behavior in order to minimize the battery aging cost. This work differs also from our previous results [26,27] that dealt with the reduction of the charging cost applied to a multi-objective small-scale EBs fleet and a mono-objective large-scale EBs fleet.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

et al. 2019

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Electro-mobility is increasing significantly in the urban public transport and continues to face important challenges. Electric bus fleets require high performance and extended longevity of lithium-ion battery at highly variable temperature and in different operating conditions. On the other hand, bus operators are more concerned about reducing operation and maintenance costs, which affects the battery aging cost and represents a significant economic parameter for the deployment of electric bus fleets. This paper introduces a methodological approach to manage overnight charging of an electric bus fleet. This approach identifies an optimal charging strategy that minimizes the battery aging cost (the cost of replacing the battery spread over the battery lifetime). The optimization constraints are related to the bus operating conditions, the electric vehicle supply equipment, and the power grid. The optimization evaluates the fitness function through the coupled modeling of electro-thermal and aging properties of lithium-ion batteries. Simulation results indicate a significant reduction in the battery capacity loss over 10 years of operation for the optimal charging strategy compared to three typical charging strategies.

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Section: Literature Review Of Large-scale Ebs Smart Charging Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

et al. 2019

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“…The suggested approach minimizes the number of buses necessary for the scheduled routes as well as the cost of the used energy. Wang et al also analyzed the bus fleet in Shenzhen [22]. They designed a new scheduling system called bCharge and tested it on a fleet of 16,359 buses.…”

Section: Introductionmentioning

confidence: 99%

“…They propose two real-time coordinated strategies to improve the charging costs by responding to the time-of-use electricity prices. Although the studies [21][22][23] deal with large-scale centralized depots, their optimization is based on electricity prices and its objective is to minimize the charging costs, not to minimize the peak demand. Hochbahn purchases electricity in advance and is not affected by the electricity price changes at the market.…”

Section: Introductionmentioning

confidence: 99%

Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots

2019

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The city of Hamburg has decided to electrify its bus fleets. The two public transportation companies in this city expect to operate up to 1500 buses by 2030. In order to accomplish this ambitious goal, both companies need to build an appropriate charging infrastructure. They have both decided to implement the centralized depot charging concept. Buses can therefore charge only at the depot and do not have the possibility for opportunity charging at intermediate stations. The load profile of such a bus depot is highly dependent on the charging schedule of buses. Without an intelligent scheduling system, the buses charge on demand as soon as they arrive to the depot. This can lead to an unevenly distributed load profile with high load peaks, which is problematic for the local grid as well as for the equipment dimensioning at the depot. Charging scheduling on large-scale bus depots is a relatively new and poorly researched topic. This paper addresses the issue and proposes two algorithms for charging scheduling on large-scale bus depots with the goal to minimize the peak load. The schedules created with the proposed algorithms were both tested and validated in the Bus Depot Simulator, a cosimulation platform used for bus depot simulations.

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Scheduling and Balancing of Electric Buses’ Charging Operations in Public Transportation

Naumann

Hübner

2021

Electric Mobility in Public Transport—Driving Towards Cleaner Air

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bCharge: Data-Driven Real-Time Charging Scheduling for Large-Scale Electric Bus Fleets

Cited by 79 publications

References 27 publications

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots

Scheduling and Balancing of Electric Buses’ Charging Operations in Public Transportation

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