Optimal recharging scheduling for urban electric buses: A case study in Davis

Wang, Yusheng; Huang, Yongxi; Xu, Jiuping; Barclay, Nicole

doi:10.1016/j.tre.2017.01.001

Cited by 243 publications

(107 citation statements)

References 24 publications

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“…Moreover, Sinhuber et al [20] proposed that the BEB energy storage could be modular to adapt the bus for changing mission profiles. Wang et al [21] determined that the recharging time window of electric city buses should be from 5 to 10 min in a real-world study in Davis. The annual recharging costs varied only slightly within this time window.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Shorter recharge times substantially increased the recharging activity costs and longer recharges conflicted with the bus schedule. As the recharging activity costs are about 80% of the total annual costs [21], the variation in ECR needs to be quantified in order to limit the charging time variation.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Even though the ECR of EVs and BEBs has been studied before, the impact of variation in multiple operation factors on the variation in the ECR of BEBs has not been accurately quantified. As Wang et al [21] mentioned, the optimal charging time window is only 5 min in some cases. Hence, the impact of operation factor variance on ECR and internal resistance of the battery (IRB) is crucial in order to dimension minimal, yet reliable energy storage and charging systems.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Hence, the impact of operation factor variance on ECR and internal resistance of the battery (IRB) is crucial in order to dimension minimal, yet reliable energy storage and charging systems. Our intention is to extend the analysis of previous real-world BEB studies of Milton Keynes [16], California [17], and Davis [21] with a detailed sensitivity analysis. The sensitivity analysis of energy usage is vital for large-scale BEB commercialization, because unexpected driving behavior, route characteristics, or the use of auxiliary devices has led to a shorter range than promised by the BEB manufacturer [27].…”

Section: State-of-the-artmentioning

confidence: 99%

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Energy Uncertainty Analysis of Electric Buses

et al. 2018

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Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.

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Section: State-of-the-artmentioning

confidence: 99%

Section: State-of-the-artmentioning

confidence: 99%

Section: State-of-the-artmentioning

confidence: 99%

Section: State-of-the-artmentioning

confidence: 99%

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Energy Uncertainty Analysis of Electric Buses

et al. 2018

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“…For this, based on a discrete event simulation, the EB energy consumption was evaluated, taking into account load and friction forces, as well as different data from a case study of Curitiba, Brazil, such as passenger demand, bus, speed, distances, and route elevation profiles. The authors of [95] studied the planning decisions for siting and sizing EB charging stations. For this, an optimization charging scheduling framework was developed, which minimized the annual system operating costs, including a recharging wait cost as a penalty term.…”

Section: Electric Bus (Eb) Approachesmentioning

confidence: 99%

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

et al. 2019

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The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.

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Electric Bus Scheduling and Optimal Charging

Messaoudi¹,

Oulamara²

2019

Lecture Notes in Computer Science

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Optimal recharging scheduling for urban electric buses: A case study in Davis

Cited by 243 publications

References 24 publications

Energy Uncertainty Analysis of Electric Buses

Energy Uncertainty Analysis of Electric Buses

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

Electric Bus Scheduling and Optimal Charging

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