• 전력전자학회논문지
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• 제24권1호
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• pp.16-24
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• 2019

The performance of the battery management system (BMS) algorithm is important for ensuring the stability and efficient operation of battery packs. Such a performance is determined by the internal parameters of the electrical equivalent circuit model (EECM). This study proposes a performance improvement and verification of battery parameters for the BMS algorithm using electrical experiments and tools. The parameters were extracted through electrical characteristic experiments, and an EECM based on Ah counting was designed. Simulation results using the EECM were compared with actual experimental data to determine the best parameter extraction method.

• 한국전기전자재료학회논문지
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• 제32권1호
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• pp.58-63
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• 2019

Lithium-ion batteries used for IT, automobiles, and industrial energy-storage devices have battery management systems (BMS) to protect the battery from abnormal voltage, current, and temperature environments, as well as safety devices like, current interruption device (CID), fuse, and vent to obtain positive temperature coefficient (PTC). Nonetheless, there are harmful to human health and property and damage the brand image of the manufacturer because of smoke, fire, and explosion of lithium battery packs. In this paper, we propose a systematic protection algorithm combining battery temperature, over-current, and interconnection between protection elements to prevent copper deposition, internal short circuit, and separator shrinkage due to frequent and instantaneous over-current discharges. The parameters of the proposed algorithm are suggested to utilize the experimental data in consideration of battery pack operating conditions and malicious conditions.

• 전력전자학회논문지
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• 제26권4호
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• pp.241-248
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• 2021

A battery state-of-health (SOH) estimation algorithm using a machine learning-based linear regression method is proposed for estimating battery aging. The proposed algorithm analyzes the change trend of the open-circuit voltage (OCV) curve, which is a parameter related to SOH. At this time, a section with high linearity of the SOH and OCV curves is selected and used for SOH estimation. The SOH of the aged battery is estimated according to the selected interval using a machine learning-based linear regression method. The performance of the proposed battery SOH estimation algorithm is verified through experiments and simulations using battery packs for electric vehicles.

• 한국전기전자재료학회논문지
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• 제31권6호
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• pp.422-426
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• 2018

The lithium-ion battery pack of an electric vehicle (EV) deserves to be considered for an alternative use within smart-grid infrastructure. Despite the long automotive service life, EV batteries retain over 70~80% of their initial capacity. These battery packs must be managed for their reliability and safety. Therefore, a battery management system (BMS) should use specific algorithms to measure and estimate the status of the battery. Most importantly, the BMS of a grid-connected energy storage system (ESS) must ensure that the lithium-ion battery does not catch fire or explode due to an internal short from uncontrolled dendrite growth. In other words, the BMS of a lithium-ion battery pack should be capable of detecting the battery's status based on the electrochemical reaction continuously until the end of the battery's lifespan. In this paper, we propose a new protection algorithm for a dendritic lithium battery. The proposed algorithm has applied a parameter from battery pack aging results and has control power managing.

• 한국에너지공학회:학술대회논문집
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• 한국에너지공학회 2010년도 춘계학술 발표회
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• pp.149-149
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• 2010

• 마이크로전자및패키징학회지
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• 제30권2호
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• pp.33-42
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• 2023

전기자동차 산업은 전 세계적 환경규제 정책과 더불어 각 국 정부의 지원이 맞물려 성장이 가속화 되고 있다. 따라서 전기자동차용 배터리에 대한 수요는 지속적으로 증가할 것으로 예상된다. 전기자동차 배터리 시스템은 다수의 배터리 셀 및 모듈을 전기적으로 연결시켜 하나의 배터리 팩으로 적용된다. 이러한 배터리 셀 및 모듈을 접합하는 기술은 성능, 용량 및 안정성에 직접적인 영향을 주기 때문에 매우 중요하다. 따라서 기계적, 전기적 특성 등 여러 기준들을 고려하여 견고하게 조립되어야 한다. 각각의 접합 기술은 서로 다른 장점과 한계를 가지고 있으며, 배터리 셀에 적용할 때에는 몇 가지 기준이 고려되어야 한다. 본 논문에서는 다양한 배터리 셀 형태에 따른 접합기술의 적용 현황을 조사하고, 저항 용접 및 레이저, 초음파 등 대표적 접합기술에 대한 특징과 장단점을 제공하고자 한다.

• Journal of Power Electronics
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• 제19권4호
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• pp.858-868
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• 2019

With the widespread use of modern clean energy, lithium-ion batteries have become essential as a more reliable energy storage component in the energy Internet. However, due to the difference in monomers, some of the battery over-charge or over-discharge in battery packs restrict their use. Therefore, a novel multiphase interleaved converter for reducing the inconsistencies of the individual cells in a battery pack is proposed in this paper. Based on the multiphase converter branches connected to each lithium battery, this circuit realizes energy transferred from any cell(s) to any other cell(s) complementarily. This flexible interlaced converter is composed of an improved bi-directional Buck-Boost circuit that is presented with its own available control method. A simulation model based on the PNGV model of fundamental equalization is built with four cells in PSIM. Simulation and experimental results demonstrate that converter and its control achieve simple and fast equalization. Furthermore, a comparison of traditional methods and the HNFABC equalization is provided to show the performance of the converter and the control of lithium-based battery stacks.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2024년도 제69차 동계학술대회논문집 32권1호
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• pp.339-341
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• 2024

본 논문에서는 전기 자동차 배터리 팩 설계에서 성능 예측을 위해 전산유체해석 및 Long Short-Term Memory (LSTM)를 활용한다. 두 계산 모두의 예측이 상당한 유사성을 나타내며, 전산유체해석은 시스템 유체 역학을 고려한 상세한 물리 모델을 제공하고, LSTM은 시계열 데이터를 기반으로 한 딥러닝 모델로 효과적으로 패턴을 파악, 향후 온도 상승을 예측한다. 결과는 두 접근 모두가 효과적인 예측을 제공하며 향후 전기 자동차 배터리 팩 설계 및 최적화에서 종합적인 접근의 필요성을 강조한다. 특히, LSTM 기반 예측에 소요되는 시간은 계산 유체 역학의 약 25%로, 약 일주일 정도로 빠르게 확인 가능하다. 이는 현대 산업 환경에서 시간적 효율성이 중요한 측면을 강조하며, 계산 유체 역학의 상세한 물리 모델링과 LSTM의 빠른 예측 속도를 결합한 설계 방법론을 제안한다.

• 한국수소및신에너지학회논문집
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• 제31권6호
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• pp.622-629
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• 2020

The performance and cost of electric vehicles (EVs) are much influenced by the performance and service life of the Li-ion battery system. In particular, the cell performance and reliability of Li-ion battery packs are highly dependent on their operating temperature. Therefore, a novel battery thermal management is crucial for Li-ion batteries owing to heat dissipation effects on their performance. Among various types of battery thermal management systems (BTMS'), the phase change material (PCM) based BTMS is considered to be a promising cooling system in terms of guaranteeing the performance and reliability of Li-ion batteries. This work is mainly concerned with the basic research on PCM based BTMS. In this paper, a basic experimental study on PCM based battery cooling system was performed. The main purpose of the present study is to present a comparison of two PCM-based cooling systems (n-Eicosane and n-Docosane) of the unit 18650 battery module. To this end, the simplified PCM-based Li-ion battery module with two 18650 batteries was designed and fabricated. The thermal behavior (such as temperature rise of the battery pack) with various discharge rates (c-rate) was mainly investigated and compared for two types of battery systems employing PCM-based cooling. It is considered that the results obtained from this study provide good fundamental data on screening the appropriate PCMs for future research on PCM based BTMS for EV applications.

• 전력전자학회논문지
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• 제25권5호
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• pp.376-384
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• 2020

An energy storage system is composed of lithium-ion batteries in modern applications. Batteries are regarded as storage devices for renewable and residual energy. The failure of batteries can cause the performance reduction and explosion of battery systems. High maintenance cost is essential when dealing with the problem of battery safety. Therefore an accurate health diagnosis is required to ensure the high reliability of battery systems. A battery pack is a combination of single cells in series and parallel connections. A battery pack has to consider various factors to assess battery health. Battery health involves conventional factors and additional factors, such as cell-to-cell imbalance. For large applications, state-of-health (SOH) can be inaccurate because of the lack of factors that indicate the state of the battery pack. In this study, six characterization factors are proposed for improving the SOH estimation of battery packs. The six proposed characterization factors can be regarded as health indicators (HIs). The six HIs are applied to the principal component analysis (PCA) algorithm. To reflect information regarding capacity, voltage, and temperature, the PCA algorithm extracts new degradation factors by using the six HIs. The new degradation factors are applied to a multiple regression model. Results show the advancement and improvement of SOH estimation.