• 대한전기학회논문지:시스템및제어부문D
• /
• 제49권8호
• /
• pp.432-441
• /
• 2000

It is difficult to analyze the charging characteristics of the lead-acid battery, because of the influences by various non-linear and time-variant parameters. In this study, the charging characteristics of high capacity industrial lead-acid battery 630 Ah was investigated through experiments with respect to the variations of temperature and the aged state of battery during the charging process. The database of those characteristics is established from the results of experiments, and the fuzzy logic charging algorithm is suggested using them. The results of experiment shows that the industrial lead-acid batteries can be always fully charged within the saved charging time by the proposed charging control algorithm adapting to the variations of charging condition. This new charging concept will be useful for developing the advanced battery charger improving the efficiency of storing electricity.

• 전력전자학회논문지
• /
• 제18권6호
• /
• pp.507-514
• /
• 2013

In this paper, the proposed charging algorithm is converted from the charging mode to compensate the transient state in the solar battery charging system. The maximum power point tracking (MPPT) control methods and the various charging algorithms for the optimal battery charging are reviewed. The proposed algorithm has excellent transient characteristics compare to the previous algorithm by adding the optimal control method to compensate the transient state when the charging mode switches from the constant current mode to the constant voltage mode based on the conventional constant-current constant-voltage (CC-CV) charging algorithm. The effectiveness of the proposed method has been verified by simulations and experimental results.

• 한국신재생에너지학회:학술대회논문집
• /
• 한국신재생에너지학회 2005년도 춘계학술대회
• /
• pp.370-373
• /
• 2005

In this research, we presented the regenerative braking algorithms considering fuel economy and charging oftenness, and also analyzed these algorithms. The first algorithm was the regenerative braking algorithm for the ideal recovery of kinetic energy. The HEV using this algorithm had high fuel economy, on the other hand frequent charging was occurred. The second algorithm was the regenerative braking algorithm for reduction of the charging oftenness. Using this algorithm, the HEV had the low charging oftenness and small loss of fuel economy.

• Journal of Electrochemical Science and Technology
• /
• 제9권3호
• /
• pp.184-194
• /
• 2018

In this paper, we propose a rapid-charging system for the lithium-ion battery (LIB) packs used in electric forklifts. The battery offers three benefits: reduced charge time, prolonged battery life, and increased charging efficiency. A rapid-charging algorithm and DC/DC converter topology are proposed to achieve these benefits. This algorithm is developed using an electrochemical model, which controls the maximum charging current limit depending on the cell voltage and temperature. The experimental use of a selected 18650 LIB cell verified the prolongation of battery life on use of the algorithm. The proposed converter offers the same topological merits as a conventional resonant converter but solves the light-load regulation problem of conventional resonant converters by adopting pulse-width modulation. A 6.6-kW converter and charging algorithm were used with a forklift battery pack to verify this method's operational principles and advantages.

• ETRI Journal
• /
• 제41권3호
• /
• pp.326-336
• /
• 2019

A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a wide-spread research problem. In this paper, we propose a demand-based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to-be-charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K-means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on-demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.

• 전기전자학회논문지
• /
• 제22권2호
• /
• pp.455-459
• /
• 2018

In this paper, proposed a multi-channel charging control strategy for electric vehicle. This control strategy can adjust the charging power according to the calculated state-of-charge (SOC). Electric vehicle (EV) charging system using Particle Swarm Optimization (PSO) algorithm is proposed. A stochastic optimization algorithm technique such as PSO in the time-of-use (TOU) price used for the energy cost minimization. Simulation results show that the energy cost can be reduced using proposed method.

• 한국위성정보통신학회논문지
• /
• 제12권4호
• /
• pp.95-99
• /
• 2017

식 구간에서 인공위성이 필요로 하는 모든 전력을 공급하는 배터리의 수명은 인공위성의 수명과 직결된다. 배터리의 수명은 배터리 충전 방식에 의해 영향을 받으므로, 배터리 충전을 제어하는 전력제어유닛은 배터리 수명을 고려하여 설계되어야 한다. 배터리 충전은 낮 구간에서 태양전지로부터 생성된 전력을 전력제어유닛의 충전 전류 제어를 통해 이루어진다. 계절마다 다른 식 구간의 빈도를 고려하고 배터리 과충전 방지를 위하여 배터리 충전관련 변수들은 계절에 따라 각각 다르게 설계되어야 한다. 또한 충전 시 과전류 충전과 과전압 충전을 방지하기 위해 배터리 충/방전 상태, 충전전류 양, 배터리 전압, 배터리 용량, 배터리 온도, 배터리 셀 전압 등을 모니터링 하여 충전 전류를 제어한다. 인공위성에서는 각 조건을 반영하여 충전 전류를 제어하는 tapering 방식을 사용한다. 본 논문에서는 tapering 충전 방식을 적용한 인공위성 전력제어유닛의 배터리 충전 알고리즘을 설계한다. 설계된 알고리즘을 위성에 업로드 할 수 있는 코드로 변환하고 구축된 위성환경에서의 시험을 통해 동작을 검증한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권3호
• /
• pp.551-569
• /
• 2024

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2019년도 춘계학술발표대회
• /
• pp.60-61
• /
• 2019

Drones are opening new horizon as a major Internet-of-Things (IoT) player which is a network of objects. Drone needs to charge itself during providing services from the charging stations. If there are lots of drones and one charging station, then it is a critical situation to decide which drone should get charged first and make order of priorities for drones to get charged sequentially. In this paper, we propose an efficient scheduling algorithm for drone power charging (ESADPC), in which charging station would have a scheduler to decide which drone can get charged earlier among many other drones. Simulation results have showed that our algorithm reduces the deadline miss ration and turnaround time.

• KEPCO Journal on Electric Power and Energy
• /
• 제6권1호
• /
• pp.59-64
• /
• 2020

Scheduling of electric vehicles and optimizing for charging waiting time have been critical. Meanwhile, it is challengeable to exploit the fluctuating data from electric vehicles in real-time. We introduce an optimal routing algorithm and a simulator with electric vehicles obeying the Poisson distribution of the observed information about time, space and energy-demand. Electric vehicle routing is updated in every cycle even it is already set. Also, we suggest an electric vehicle routing algorithm for minimizing total trip time, considering a threshold of the waiting time. Total trip time and charging waiting time are decreased 34.3% and 86.4% respectively, compared to the previous algorithm. It can be applied to the information service of charging stations and utilized as a reservation service.