Demand profile study of battery electric vehicle under different charging options

Marra, Francesco; Yang, Guangya; Larsen, Elfinn; Rasmussen, Claus; You, Shi

doi:10.1109/pesgm.2012.6345063

Cited by 133 publications

(97 citation statements)

References 14 publications

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“…The EVs are preferably kept operating in the range SOC = [0.2, 0.9] due to battery life concerns [15] 1) from Figs. 6 and 7 it can be seen that both controllers are quite good at staying within the QoS limits of the DSO and EV owners, which can be seen in the fact that none of the controllers have non-delivery and η is small.…”

Section: Comparison and Discussion Of Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Performance assessment of aggregation control services for demand response

Bondy

Costanzo

Heussen

et al. 2014

IEEE PES Innovative Smart Grid Technologies, Europe

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Abstract-Aggregation algorithms that provide services to the grid via demand side management are moving from research ideas to the market. With the diversity of the technology delivering such services, it becomes essential to establish transparent performance standards from a service delivery perspective. This paper formulates performance measures and an index to evaluate in hindsight the quality of service delivery by an aggregator, both with respect to ancillary service and asset management service. The index is based on requirements formulated in service contracts and provides an overall assessment of the quality of service provided by an aggregation control algorithm. By a detailed case study we present and an application of the index, comparing the performance of two different control architectures for demand side management delivering a distribution grid service.

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Section: Comparison and Discussion Of Resultsmentioning

confidence: 99%

“…For the formulation of the mathematical models we refer to [15] for the battery model and to [16] for the building space heating model (modified, as proposed in [13]). For the modeling of the services, we apply the method described in Sec.III-B.…”

Section: A the Reference Case: Without Units Coordinationmentioning

confidence: 99%

Performance assessment of aggregation control services for demand response

Bondy

Costanzo

Heussen

et al. 2014

IEEE PES Innovative Smart Grid Technologies, Europe

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“…We performed experiments on the changes in airflow according to the HVAC inflow conditions, with the changes in the temperature reflecting the operating conditions according to changes in the winter temperature, and changes in the rated voltage of the battery according to the model of electric car. The detailed conditions are provided in Table 3 [18]. When the PTC heater is started up, an inrush current higher than the normal operating current flows; hence, it is important to ensure electrical stability.…”

Section: Experiments Conditions and Methodsmentioning

confidence: 99%

Heating Performance Characteristics of High-Voltage PTC Heater for an Electric Vehicle

Park

Kim

2017

Energies

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High-voltage positive temperature coefficient (PTC) heaters have a high heating capacity and are fast acting; thus, they function as the actual main heating equipment in electric cars, and considerable research is devoted to improving their heating performance and efficiency. We evaluated the heating performance of a high-voltage PTC heater for an electric car by building a closed-loop-type test system including an air channel, environment chamber, DC power supply, and data acquisition system, and designed an initial prototype with general characteristics. Using this test system, we analyzed the heating performance characteristics of the heater as a function of changes in the blower airflow, ambient temperature, and battery voltage. We changed the geometrical variables of the heater and conducted an analysis to improve the heating performance and output density of the initial prototype. Based on the heating performance of the initial prototype and its geometrical variables, we designed an improved prototype and compared its heating performance and output density with that of the initial prototype. As a result, we achieved a heating capacity of 5.52 kW, a pressure drop of 48.2 Pa, and an efficiency of 98%, whereas the output density was 3.45 kW/kg, which is a 24% improvement over the initial prototype.

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“…Then, the recharging rate of ϕth charger is fixed and given as a constant, ε ϕ , where ϕ ∈ Φ = {1, 2, 3}. In the real world, the charging time increases for last 10%-20% of the battery capacity in the recharging processes [50]; for simplification reasons, the last assumption is presented. Finally, the charging services are performed by a homogeneous fleet of mobile charging vehicles with a fixed battery capacity, Q, and energy consumption rate, h. Neubauer et al [51] found that the travel ranges of BEVs can vary by up to 60%, depending on the weather, temperature, use of in-car heater, speed, and so on.…”

Section: Optimization Frameworkmentioning

confidence: 99%

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

2018

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Abstract:Increasing attention is being paid to the technology of battery electric vehicles (BEVs) because of their environmental friendliness. However, their short range, extended recharging times, and insufficient charging facilities hinder the improvement in the market share of BEVs. As a remedy, this paper presents a novel approach to providing a service for the battery charge replenishment of BEVs. Instead of using traditional alternative methods by only providing a charging service in a fixed location, such as battery-swapping and charging lanes, the novel charge replenishment is provided by mobile charging vehicles (MCVs), which could offer a charging service at any time and at location requested. To consider the limited running range and the opportunity to recharge from MCVs, as well as to determine the location strategy of multiple types of plug-in charging facility locations and the routing plan of the MCVs simultaneously, the location routing problem (LRP) that can integrate two decision levels, with a strategic level (location) and tactical level (routing), is applied. Then, we present the multiple types of plug-in charging facilities' location-routing problem with time windows for mobile charging vehicles (MTPCF-LRPwTW-MCVs), and formulate the MTPCF-LRPwTW-MCVs as a mixed integer linear program for the convenience of solving. To demonstrate the model, test instances are designed and computational results are presented. Furthermore, sensitivity analyses on battery capacity, recharging rate, and so on, are also examined. The results show that with the increase of the battery capacity or the improvement of the charging rate of the charging facilities, the service efficiency of the MCVs can reasonably be improved. Therefore, the proposed method could be used in real world problems.

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Demand profile study of battery electric vehicle under different charging options

Cited by 133 publications

References 14 publications

Performance assessment of aggregation control services for demand response

Performance assessment of aggregation control services for demand response

Heating Performance Characteristics of High-Voltage PTC Heater for an Electric Vehicle

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

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