An Optimal Battery Charging and Schedule Control Strategy for Electric Bus Rapid Transit

Álvarez, Semaria Ruiz; Arroyo, Nicolas; Acosta, Andres; Portilla, Christian; Espinosa, Jairo

doi:10.1049/ic.2018.0004

Cited by 5 publications

(9 citation statements)

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“…The first one, which is more reduced than second, is used to compare the wear obtained using both proposed approaches. The second study case, which contains the data demand described in Ruiz et al (2018), is used to apply the proposed control algorithm for a fleet of EVs.…”

Section: Study Casementioning

confidence: 99%

“…The study case used to apply the algorithm of Figure 1 includes the traffic network, and the passengers demand described by Ruiz et al (2018), which presents a study case applicable for the city of Medellín, Colombia. The urban traffic microsimulation to calculate the required travels and power consumption in them was performed using the software SUMO.…”

Section: First Study Casementioning

confidence: 99%

“…The urban traffic microsimulation to calculate the required travels and power consumption in them was performed using the software SUMO. The electric vehicles considered are BRTs Type 1, as described in Ruiz et al (2018), whose energy capacity is 324 kWh. The data of the required travels and power consumption in them were taken from Ruiz et al (2018), taking into account only the travels performed by BRTs Type 1.…”

Section: First Study Casementioning

confidence: 99%

“…The electric vehicles considered are BRTs Type 1, as described in Ruiz et al (2018), whose energy capacity is 324 kWh. The data of the required travels and power consumption in them were taken from Ruiz et al (2018), taking into account only the travels performed by BRTs Type 1. The electricity price time series and the power limit for the feeder of the charging station were also taken from the aforementioned study.…”

Section: First Study Casementioning

confidence: 99%

“…The decision variables calculated are the charging and discharging power and the route assignment for each EV. The control strategy is applied to the study case proposed by Ruiz, Arroyo, Acosta, Portilla, and Espinosa (2018).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal vehicle-to-grid strategy for a fleet of EVs considering the batteries aging

Espinosa

Álvarez

2019

Ing. Inv.

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The use of electric vehicles for public transportation is a practice that has spread widely in recent years, in response to the needs ofreducing global polluting gases emissions and decrease vehicle ownership. However, nowadays some issues need to be addressedto provide this type of services with electric vehicles, such as the low-profit margins that can be achieved by the fleet operators.This paper addresses this issue giving to the public EVs the feature of providing power to the electrical network. Hence, a controlsystem based on optimization is proposed to perform profitable management of the charge and discharge of the EVs that are usedto provide public transport services by an operator, which also have the possibility of delivering power to the electrical network.Furthermore, this control system will also take into account the batteries wear cost.

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Section: Study Casementioning

confidence: 99%

Section: First Study Casementioning

confidence: 99%

Section: First Study Casementioning

confidence: 99%

Section: First Study Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal vehicle-to-grid strategy for a fleet of EVs considering the batteries aging

Espinosa

Álvarez

2019

Ing. Inv.

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V2G Ancillary Services Management Strategy for EVs with Solar Powered Charging Stations based on artificial Intelligence Algorithms

2023

ijSmartGrid

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This research proposes a vehicle-to-grid strategy based on dynamic optimization for a fleet of public transportation Electric Vehicles (EVs) whose charging station is jointly powered by the conventional electrical network and photovoltaic renewable sources. Utilizing two neural networks, the proposed algorithm predicted future energy expenditure of Electric Vehicles (EVs) and the power generation potential of the renewable sources. The goal was to optimize dynamically the EVs' decision-making, encompassing their charging-discharging schedules, power exchange with the electrical network, and travel dispatch. The analysis considered the EVs' capacity for selling energy and providing frequency reserve ancillary services. Consequently, this proposal enables the estimation of fleet management plans by considering the daily average congestion level in the analysis zone, the required departure schedules of the vehicles in the fleet, and the past measures of solar radiation at the site. These variables serve as inputs for the prediction algorithms. The mathematical model of dynamic optimization was formulated as a convex Mixed-Integer problem and was solved using the iterative branch and cut method. The findings revealed that the most profitable options for the EVs' owners include selling energy and providing downward regulation ancillary services. Moreover, as the solution's viability relied on the accuracy of the prediction algorithms' outputs, two high precision neural networks, with an error rate lower than 2%, have been employed.

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A Planning Method for Partially Grid-Connected Bus Rapid Transit Systems Operating with In-Motion Charging Batteries

Díez

Restrepo

2021

Energies

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This paper presents an electrical infrastructure planning method for transit systems that operate with partially grid-connected vehicles incorporating on-board batteries. First, the state-of-the-art of electric transit systems that combine grid-connected and battery-based operation is briefly described. Second, the benefits of combining a grid connection and battery supply in Bus Rapid Transit (BRT) systems are introduced. Finally, the planning method is explained and tested in a BRT route in Medellin, Colombia, using computational simulations in combination with real operational data from electric buses that are currently operating in this transit line. Unlike other methods and approaches for Battery Electric Bus (BEB) infrastructure planning, the proposed technique is system-focused, rather than solely limited to the vehicles. The objective of the technique, from the vehicle’s side, is to assist the planner in the correct sizing of batteries and power train capacity, whereas from the system side the goal is to locate and size the route sections to be electrified. These decision variables are calculated with the objective of minimizing the installed battery and achieve minimum Medium Voltage (MV) network requirements, while meeting all technical and reliability conditions. The method proved to be useful to find a minimum feasible cost solution for partially electrifying a BRT line with In-motion Charging (IMC) technology.

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An Optimal Battery Charging and Schedule Control Strategy for Electric Bus Rapid Transit

Cited by 5 publications

References 0 publications

Optimal vehicle-to-grid strategy for a fleet of EVs considering the batteries aging

Optimal vehicle-to-grid strategy for a fleet of EVs considering the batteries aging

V2G Ancillary Services Management Strategy for EVs with Solar Powered Charging Stations based on artificial Intelligence Algorithms

A Planning Method for Partially Grid-Connected Bus Rapid Transit Systems Operating with In-Motion Charging Batteries

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