PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data

Ashtari, Ali; Bibeau, Eric; Shahidinejad, Soheil; Molinski, Tom

doi:10.1109/tsg.2011.2162009

Cited by 263 publications

(140 citation statements)

References 14 publications

Supporting

Mentioning

128

Contrasting

Unclassified

Order By: Relevance

“…For case studies in this paper, energy consumption data of the Nissan Leaf PEV in [27] are used with q = 0.15 kWh/km (0.24 kWh/mile).…”

Section: Models Of Ev Returning-time and Charging Demandmentioning

confidence: 99%

Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids

Zhang

et al. 2017

Energies

View full text Add to dashboard Cite

A novel, fully decentralized strategy to coordinate charge operation of electric vehicles is proposed in this paper. Based on stochastic switching control of on-board chargers, this strategy ensures high-efficiency charging, reduces load variations to the grid during charging periods, achieves charge completion with high probability, and accomplishes approximate "valley-filling". Further improvements on the core strategy, including individualized power management, adaptive strategies, and battery support systems, are introduced to further reduce power fluctuation variances and to guarantee charge completion. Stochastic analysis is performed to establish the main properties of the strategies and to quantitatively show the performance improvements. Compared with the existing decentralized charging strategies, the strategies proposed in this paper can be implemented without any information exchange between grid operators and electric vehicles (EVs), resulting in a communications cost reduction. Additionally, it is shown that by using stochastic charging rules, a grid-supporting battery system with a very small energy capacity can achieve substantial reduction of EV load fluctuations with high confidence. An extensive set of simulations and case studies with real-world data are used to demonstrate the benefits of the proposed strategies.

show abstract

“…For case studies in this paper, energy consumption data of the Nissan Leaf PEV in [27] are used with q = 0.15 kWh/km (0.24 kWh/mile).…”

Section: Models Of Ev Returning-time and Charging Demandmentioning

confidence: 99%

Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids

Zhang

et al. 2017

Energies

View full text Add to dashboard Cite

show abstract

“…To date, the common methods to identify the NCBCparameters generally fall into two categories, i.e., the stochastic simulating methods [8][9][10][11][12][13] and the sub-metering methods [14][15][16][17]. For the stochastic simulating methods, travel patterns of internal combustion engine vehicles are used to simulate PEV charging behaviors and then to calculate NCBC-parameters [13].…”

Section: Introductionmentioning

confidence: 99%

Identification of charging behavior characteristic for large-scale heterogeneous electric vehicle fleet

Liu

2017

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

This paper aims to accurately identify parameters of the natural charging behavior characteristic (NCBC) for plug-in electric vehicles (PEVs) without measuring any data regarding charging request information of PEVs. To this end, a data-mining method is first proposed to extract the data of natural aggregated charging load (ACL) from the big data of aggregated residential load. Then, a theoretical model of ACL is derived based on the linear convolution theory. The NCBC-parameters are identified by using the mined ACL data and theoretical ACL model via the derived identification model. The proposed methodology is cost-effective and will not expose the privacy of PEVs as it does not need to install sub-metering systems to gather charging request information of each PEV. It is promising in designing unidirectional smart charging schemes which are attractive to power utilities. Case studies verify the feasibility and effectiveness of the proposed methodology.

show abstract

“…In [3][4][5], the trip distances, initial stage of change (SOC) and charging time were simulated by several independent probability distributions, and then the charging load model was established. In [6], based on the data provided by GPS devices, more accurate results were obtained by conditional probability distribution function. Besides, to consider the stochastic natures of EV transportation variables, a joint distribution function with copula functions was developed in [7].…”

Section: Introductionmentioning

confidence: 99%

“…However, although the methods in [4][5][6][7] are easy to implement, their results are not credible enough, as they lack effective methods to model the inherent randomness of EVs. Despite various improvements have been presented in [8][9][10][11], the behavior characteristics of EVs are still described by traditional analytical methods.…”

Section: Introductionmentioning

confidence: 99%

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Ban

2015

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

The usage of each private electric vehicle (PrEV) is a repeating behavior process composed by driving, parking, discharging and charging, in which PrEV shows obvious procedural characteristics. To analyze the procedural characteristics, this paper proposes a procedural simulation method. The method aggregates the behavior process regularity of the PrEV cluster to model the cluster's charging load. Firstly, the basic behavior process of each PrEV is constructed by referring the statistical datasets of the traditional private non-electric vehicles. Secondly, all the basic processes are set as a simulation starting point, and they are dynamically reconstructed by several constraints. The simulation continues until the steady state of charge (SOC) distribution and behavior regularity of the PrEV cluster are obtained. Lastly, based on the obtained SOC and behavior regularity information, the PrEV cluster's behavior processes are simulated again to make the aggregating charging load model available. Examples for several scenarios show that the proposed method can improve the reliability of modeling by grasping the PrEV cluster's procedural characteristics.

show abstract

PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data

Cited by 263 publications

References 14 publications

Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids

Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids

Identification of charging behavior characteristic for large-scale heterogeneous electric vehicle fleet

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Contact Info

Product

Resources

About