• 한국수소및신에너지학회논문집
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• 제34권3호
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• pp.246-255
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• 2023

Due to lack of hydrogen charging stations and hydrogen supply compared to the supply of hydrogen vehicles, social phenomena such as 2-hour queues and restrictions on charging capacity are occurring, which negatively affects the spread of hydrogen vehicles. In order to resolve these problems, it is essential to have a strategic operation of the hydrogen charging stations. To establish operational strategies, it is necessary to derive customer demand patterns and characteristics through the analysis of sales data. This study derived the demand patterns and characteristics of customers visiting hydrogen charging stations through data analysis from various perspectives, such as charging volume, charging speed, number of visits, and correlation with external factors, based on the hydrogen sales data of off-site hydrogen charging stations located in domestic residential areas.

• 전기학회논문지
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• 제65권4호
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• pp.547-554
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• 2016

Analyze the customer daily load patterns, be used to determine the optimal charging and discharging schedule which can minimize the electrical charges through the battery energy storage system(BESS) installed in consumers is an object of this paper. BESS, which analyzes the load characteristics of customer and reduce the peak load, is essential for optimal charging and discharging scheduling to save electricity charges. This thesis proposes optimal charging and discharging scheduling method, using particle swarm optimization (PSO) and penalty function method, of BESS for reducing energy charge. Since PSO is a global optimization algorithm, best charging and discharging scheduling can be found effectively. In addition, penalty function method was combined with PSO in order to handle many constraint conditions. After analysing the load patterns of target BESS, PSO based on penalty function method was applied to get optimal charging and discharging schedule.

• International Journal of Internet, Broadcasting and Communication
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• 제9권2호
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• pp.1-10
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• 2017

The high costs of electric vehicle supply equipment (EVSE) and installation are currently a stumbling block to the proliferation of electric vehicles (EVs). The cost-effective solutions are needed to support the expansion of charging infrastructure. In this paper, we develope EV charging system based on the big data analysis of the power consumption patterns. The developed EV charging system is consisted of the smart EV outlet, gateways, powergates, the big data management system, and mobile applications. The smart EV outlet is designed to low costs of equipment and installation by replacing the existing 220V outlet. We can connect the smart EV outlet to household appliances. Z-wave technology is used in the smart EV outlet to provide the EV power usage to users using Apps. The smart EV outlet provides 220V EV charging and therefore, we can restore vehicle driving range during overnight and work hours.

• 전기학회논문지
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• 제61권6호
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• pp.783-790
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• 2012

Electric Vehicles(EVs) and Plug-in Hybrid Electric Vehicles(PHEVs) which have the grid connection capability, represent an important power system issue of charging demands. Analyzing impacts EVs charging demands of the power system such as increased peak demands, developed by means of modeling a stochastic distribution of charging and a demand dispatch calculation. Optimization processes proposed to determine optimal demand distribution portions so that charging costs and demand can possibly be managed. In order to solve the problems due to increasing charging demand at the peak time, alternative electricity rate such as Time-of-Use(TOU) rate has been in effect since last year. The TOU rate would in practice change the tendencies of charging time at the peak time. Nevertheless, since it focus only minimizing costs of charging from owners of the EVs, loads would be concentrated at times which have a lowest charging rate and would form a new peak load. The purpose of this paper is that to suggest a scenario of load leveling for a power system operator side. In case study results, the vehicles as regular load with time constraints, battery charging patterns and changed daily demand in the charging areas are investigated and optimization results are analyzed regarding cost and operation aspects by determining optimal demand distribution portions.

• 전기학회논문지
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• 제67권11호
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• pp.1455-1462
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• 2018

As the interests in eco-friendly energy has increased, the interests in Electric Vehicles(EVs) are increasing as well. Moreover, due to the government's economic support for EVs, penetration level of it has rapidly increased. These sharp increases, however, induce various problems in distribution system, such as voltage/frequency variations, peak demand increasement, demand control, etc. To minimize these possible matters, lots of research have conducted. Nevertheless, most of it assumed extremely important factors, such as numbers and charging patterns of EVs. It inevitably results in errors in their research, and thus make it difficult to prevent the possible matters from EVs. In this paper, therefore, we use actual EVs charging data from KEPCO, and analysis and deduction of it were conducted. The simulations were carried out for four aspect(season, region, purpose).

• 한국정보통신학회논문지
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• 제26권2호
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• pp.313-316
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• 2022

In this paper, we implement a system that detects abnormalities in the charging data transmitted from the charger during the charging process of electric vehicles and controls them remotely. Using classification algorithms such as logistic regression, KNN, SVM, and decision trees, to do this, an analysis model is created that judges the data received from the charger as normal and abnormal. In addition, a model is created to determine the cause of the abnormality using the existing charging data based on the analysis of the type of charger abnormality. Finally, it is solved using unsupervised learning method to find new patterns of abnormal data.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.1-10
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• 2017

With the current global need for eco-friendly energies, the large scale use of Electric Vehicles (EVs) is predicted. However, the need to frequently charge EVs to an electrical power system involves risks such as rapid increase of demand power. Therefore, in this paper, we propose a practical smart EV charging scheme considering a Time-of-Use (ToU) price to prevent the rapid increase of demand power and provide load leveling function. For a more practical analysis, we conduct simulations based on the actual distribution system and driving patterns in the Republic of Korea. Results show that the proposed method provides a proper load leveling function while preventing a rapid increase of demand power of the system.

• 융합정보논문지
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• 제10권7호
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• pp.147-152
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• 2020

본 연구는 발파 설계에 있어서 발파공의 직경과 발파 패턴을 중심으로 비용 효과를 분석하기 위하여 수행되었다. 발파 패턴을 단일 직경 발파공으로 설계한 경우와 직경의 다른 2개의 발파공을 혼합하여 설계한 경우에 대하여 천공 시간, 장약 시간과 화약과 화공품 소모량을 비교 분석하였다. 소요 발파공 수는 단일 직경 발파공으로 설계할 경우와 직경이 다른 두 개의 발파공으로 설계할 경우 각각 138개와 93개로 나타났다. 직경이 다른 두 개의 발파공을 이용하여 설계한 경우, 단일 직경 발파공으로 설계한 경우보다 천공 시간은 139분이 단축되고 천공 효율은 25% 증가되었다. 규격이 다른 두 개의 발파공을 적용하여 설계한 경우, 작업 인원당 장약 단축 시간과 작업 효율 증가는 각각 22.5분과 33%로 분석되었다. 화약 소요량과 뇌관 소요량은 단일 규격 배열시 300개와 138개였으며, 혼합 규격 배열시 242개와 93개로 후자의 경우 각각 58개와 45개 적게 소요되는 것으로 나타났다. 직경이 다른 두 개의 발파공 혼합 설계 패턴은 발파 비용 절감의 잠재성을 가지고 있는 것으로 나타났다.

• 한국수소및신에너지학회논문집
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• 제30권4호
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• pp.356-361
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• 2019

Since the government announced the roadmap to revitalize the hydrogen economy, we are constantly making the effort to expand the use of fuel cell electric vehicles (FCEV) and hydrogen charging stations. There is however a significant issue to build and operate the hydrogen charging station due to the lack of the profit model. Many researchers believe that the supply of FCEV will be increased in the near future and finally ensure the economy of hydrogen charging stations. This study shows that the sales changes of hydrogen gas and consumption patterns by the operation of the hydrogen charging station in Changwon City. The results will be used as the evidence to support for operating the hydrogen charging station by private businesses and the validity of additional establishment of hydrogen charging stations.

• Journal of Power Electronics
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• 제12권1호
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• pp.1-9
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• 2012

This study investigates an application of the Hamming network-dual extended Kalman filter (DEKF) based on pattern recognition for high accuracy state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction at various temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for ten fresh Li-Ion cells at experimental temperatures are measured as representative patterns, together with cell model parameters. Through statistical analysis, the Hamming network is applied to identify the representative pattern that matches most closely with the pattern of an arbitrary cell measured at any temperature. Based on temperature-checking process, model parameters for a representative DCVT pattern can then be applied to estimate SOC/capacity and to predict SOH of an arbitrary cell using the DEKF. This avoids the need for repeated parameter measuremet.