• Proceedings of the KIPE Conference
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• 2012.07a
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• pp.271-272
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• 2012

전류 적산법(Coulomb counting, ampere counting)을 이용한 배터리 SOC(State-of-Charge) 추정 방법은 상용화된 IC를 사용할 수 있기에 구현이 간단하고 SOC 정의를 통해 배터리 사용 가능한 시간을 쉽게 예측할 수도 있다. 하지만 초기 SOC 오류와 누적되는 전류 정보의 오차로 인해 추정이 실패하는 단점이 존재하기 때문에 이를 해결해주는 알고리즘이 필요하다. 본 논문에서는 전류 적산법 기반의 배터리 SOC 추정 회로에 확장 칼만 필터(EKF, Extended Kalman Filter)를 접목하여 전류 적산법을 이용하였을 때 나타날 수 있는 오차 누적을 줄이는 알고리즘을 제안한다. 또한 실험을 통해 제안된 배터리 SOC 추정 회로의 성능을 확인해본다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.939-949
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• 2017

Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Proceedings of the KIPE Conference
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• 2011.11a
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• pp.299-300
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• 2011

상이한 전기화학적 특성을 가진 단위 셀들을 미리 선별하여 팩의 안전한 운용 및 배터리 관리 시스템의 성능 향상을 위해 스크리닝(screening)은 필수적이다. 그러므로, 본 논문에서는 이산 웨이블릿 변환(DWT;discrete wavelet transform)을 이용한 리튬 이온 배터리 스크리닝 방법을 제안한다. 제안된 방식은 축소된 하이브리드 자동차용 전류프로파일을 통해 얻어진 충방전 전압을 이산 웨이블릿 변환에 적용하여 저주파 전압 성분과 고주파 전압 성분으로 분리하고, 각 단계별로 얻어진 성분들의 통계처리를 실시하여 스크리닝을 구현한다. 특히, 마지막 단계에서의 저주파 전압 성분과 고주파 전압 성분은 배터리의 State-of-health(SOH)를 예측하기 위한 성분으로 정의된다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.545-550
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• 2007

최근 들어, 모바일 장치의 성능이 향상되고 보급률이 증대됨에 따라 모바일 장치를 그리드 자원으로 이용하기 위한 모바일 그리드가 등장하였다. 그러나 모바일 장치가 가지는 무선기기로써의 제약사항 즉, 무선 통신의 불안정성, 이동으로 인한 연결 끊김 등의 문제와 배터리의 제약은 모바일 그리드를 구성하는데에 많은 어려움을 야기한다. 따라서 본 논문에서는 이러한 제약사항을 극복할 수 있는 환경적인 요소를 고려하여 학교나 회사와 같이 안정적인 무선통신 환경을 제공하고 베터리 충전이 용의한 네트워크 그룹을 가정하였다. 그리고 제한된 성능을 발휘하는 모바일 장치에서 독립적인 소규모 작업의 효율적인 수행을 위해 성능예측 기반 작업 스케줄링 기법을 제시하였다. 이 기법은 네트워크 그룹 내의 모바일 장치의 이용 패턴이 규칙적으로 나타내는 특성을 이용한다. 제안하는 스케줄링 기법에서는 하나의 네트워크 그룹의 성능을 그 그룹에 속한 모바일 장치들의 성능의 합으로 정의하고 시간에 따라 변화하는 모바일 장치들의 성능을 예측하기 위해 기존에 수집된 성능 정보의 통계를 이용한다. 그리고 본 기법은 그리드와 네트워크 그룹, 네트워크 그룹과 모바일 장치 사이의 작업 분배시 예측된 성능 정보를 이용함으로써 네트워크 그룹의 사용률을 높여 전체 작업의 최종 응답시간을 줄일 수 있다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.05a
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• pp.226-230
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• 2010

In this paper, the performance of an electric powered small Unmanned Aerial Vehicle which has a battery and electric motor is analysed. Aerodynamic data is obtained through flight test and flight performance is predicted. As a result, we present the optimum flight speed for the maximum endurance and predict endurance and range according to the variation of flight speed.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.55-63
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• 2014

Up to date, numerous studies on wireless sensor networks have been performed to overcome the Energy-Constraint of the sensor system. Existing schemes for estimating the residual energy have considered only voltage of sensor system. However battery performance in the real is affected by temperature and load. In this paper we introduce more accurate scheme, for the use in wireless sensor node, based on the interpolation of lookup tables which allow for temperature and load characteristics, as well as battery voltage.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.141-146
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• 2014

This paper suggests a way of generating one-day load curves for performance improvement of peak shaving in a power system. This Peak Shaving algorithm is a long-term scheduling algorithm of PMS (Power Management System) for BESS (Battery Energy Storage System). The main purpose of a PMS is to manage the input and output power from battery modules placed in a power system. Generally, when a Peak Shaving algorithm is used, a difference occurs between predict load curves and real load curves. This paper suggests a way of minimizing the difference by making predict load curves that consider weekly normalization and seasonal load characteristics for smooth energy charging and discharging.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.12
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• pp.819-828
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• 2022

Sensitivity of aircraft sizing depending on battery performance was studied for a generic quad tilt rotor type electric vertical takeoff and landing vehicle. The mission requirements proposed by Uber Elevate and NASA were used for initial sizing, and the calculated gross weight is ranged between 5,000lb and 11,000lb for battery specific energy range of 200-400Wh/kg in pack level and continuous discharge rate range of 4-5C. For the assumed gross weight of 7,000lb, the required battery performance was calculated with two different criteria: available power and energy, and the effects of battery specific energy and discharge rate are analyzed. The maximum discharge rate is also recommended considering failure cases such as one battery pack inoperative and one prop rotor inoperative.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.469-477
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• 2009

In mobile systems, energy efficiency is critical to extend battery life. Therefore, power consumption should be taken into account to develop software in addition to performance, Efficient software design in power and performance is possible if accurate power prediction is accomplished during the execution of software, In this paper, power estimation model is developed using statistical analysis, The proposed model analyzes processor behavior Quantitatively using the data of performance monitoring events and power consumption collected by executing various benchmark programs, And then representative hardware events on power consumption are selected using hierarchical clustering, The power prediction model is established by regression analysis in which the selected events are independent variables and power is a response variable, The proposed model is applied to a PXA320 mobile processor based on Intel XScale architecture and shows average estimation error within 4% of the actual measured power consumption of the processor.