Battery State Estimation Algorithm for High-Capacity Lithium Secondary Battery for EVs Considering Temperature Change Characteristics

Jinho Park,Byoungkuk Lee,Do-Yang Jung,Dong-Hee Kim 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.5

In this paper, we studied the state of charge (SOC) estimation algorithm of a high-capacity lithium secondary battery for electric vehicles (EVs) considering temperature characteristics. Nonlinear characteristics of high-capacity lithium secondary batteries are represented by differential equations in the mathematical form and expressed by the state space equation through battery modeling to extract the characteristic parameters of the lithium secondary battery. Charging and discharging equipment were used to perform characteristic tests for the extraction of parameters of lithium secondary batteries at various temperatures. An extended Kalman filter (EKF) algorithm, a state observer, was used to estimate the state of the battery. The battery capacity and internal resistance of the high-capacity lithium secondary battery were investigated through battery modeling. The proposed modeling was applied to the battery pack for EVs to estimate the state of the battery. We confirmed the feasibility of the proposed study by comparing the estimated SOC values and the SOC values from the experiment. The proposed method using the EKF is expected to be highly applicable in estimating the state of the high-capacity rechargeable lithium battery pack for electric vehicles.

리튬 배터리 등가모델의 정확도 개선을 위한 SOC 계수 보정법

이대건(Dae-Gun Lee),정원재(Won-Jae Jung),장종은(Jong-Eun Jang),박준석(Jun-Seok Park) 대한전자공학회 2017 전자공학회논문지 Vol.54 No.4

BMS 에너지 효율 향상을 위한 리튬 배터리 SOC 계수 보정 알고리즘

이대건,정원재,임세미,채형일,박준석 한국정보통신설비학회 2016 한국정보통신설비학회 학술대회 Vol.2016 No.09

This paper describes a battery equivalent model’s SOC(state of charge) coefficient calibration method to improve the accuracy of the battery equivalent model. The battery SOC difference between each cells is a major factor of decreasing the battery life. BMS (battery management system) has been developed in order to reduce the inter-cell battery SOC deviation. however, since the general battery cell balancing techniques operate by measuring only the voltage of the battery cell, they can not keep up with the electrical characteristics due to variation of the internal resistance of the capacitor. In this paper, we propose the battery equivalent model’s SOC coefficient calibration algorithm : (1)choses the battery and battery model, (2)measure the battery charging and discharging characteristics, (3)extracting the resistance and the capacitor value on the simulation, following the actual battery charge and discharge characteristic curve. in this paper, we use a 3.7 V, 280 mAh and 650 mAh lithium battery. And a RC Tank based battery equivalent model is adopted. The maximum error of charging and discharging characteristic between measurement and modeling is approximately 2.13 %.

Battery State Estimation Algorithm for High-Capacity Lithium Secondary Battery for EVs Considering Temperature Change Characteristics

Park, Jinho,Lee, Byoungkuk,Jung, Do-Yang,Kim, Dong-Hee The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.5

In this paper, we studied the state of charge (SOC) estimation algorithm of a high-capacity lithium secondary battery for electric vehicles (EVs) considering temperature characteristics. Nonlinear characteristics of high-capacity lithium secondary batteries are represented by differential equations in the mathematical form and expressed by the state space equation through battery modeling to extract the characteristic parameters of the lithium secondary battery. Charging and discharging equipment were used to perform characteristic tests for the extraction of parameters of lithium secondary batteries at various temperatures. An extended Kalman filter (EKF) algorithm, a state observer, was used to estimate the state of the battery. The battery capacity and internal resistance of the high-capacity lithium secondary battery were investigated through battery modeling. The proposed modeling was applied to the battery pack for EVs to estimate the state of the battery. We confirmed the feasibility of the proposed study by comparing the estimated SOC values and the SOC values from the experiment. The proposed method using the EKF is expected to be highly applicable in estimating the state of the high-capacity rechargeable lithium battery pack for electric vehicles.

확률적 머신러닝 모델기반의 리튬이온배터리 파라미터 추정 알고리즘

김민호(Minho Kim),송민석(Minseok Song),임정택(Jeongtaek Lim),함경선(Kyung Sun Ham),이도헌(DOHEON LEE),김태형(Taehyoung Kim) 한국에너지학회 2024 에너지공학 Vol.33 No.1

In this study, a new lithium-ion battery performance degradation model and a stochastic machine learning model-based lithium-ion battery parameter estimation method were proposed and verified through actual battery degradation cycle experiment data. The proposed parameter estimation method based on a stochastic machine learning model requires less battery model operation time compared to other methods, enabling efficient parameter estimation. The lithium-ion battery performance degradation model is an equivalent circuit-based model, but it reflects various electrochemical phenomena, including side reactions on the surface of the anode active material, including the formation of a solid electrolyte interphase (SEI) layer, the loss of positive electrode active material due to mechanical stress-induced fatigue failure is included, and the corresponding decrease in the amount of cyclable lithium. In the proposed method of estimating the parameters of a lithium-ion battery model, a probabilistic machine learning model that can estimate battery model parameters from sensible data such as voltage and current is developed and used to generate virtual experiment data. We proposed a technique for learning and finding optimal battery model parameters based on the learned model. The developed performance degradation model and parameter estimation method were verified based on actual experimental data. Since it is impossible to observe the inside of the battery, correct answers to the battery parameters cannot be obtained, so the model and parameter estimation algorithm are indirectly verified through errors of voltage and temperature. As a result of the verification, the errors in voltage and temperature were found to be 0.676% and 0.207%, respectively.

리튬이온 배터리 화재의 충전율 효과에 대한 모사연료 모델링

김신우(Sin-Woo Kim),이의주(Eui-Ju Lee) 한국화재소방학회 2024 한국화재소방학회논문지 Vol.38 No.1

최근 발생하는 이상기후와 환경 문제로 친환경 에너지 수요가 급증하고 있으며 효율적인 에너지 사용을 위해 배터리 사용량이 급격하게 증가하였다. 하지만 배터리 화재 사고 또한 증가하고 있어 보다 안전한 사용과 화재특성을밝히기 위해 많은 연구가 진행되고 있었다. 배터리의 많은 종류로 인해 모든 경우의 실험을 수행하기 어렵기 때문에배터리 화재를 모사하기 위해 다양한 방법이 시도되고 있다. 이 연구는 배터리 열폭주 현상의 모델링 방법을 제시하기 위해 충전율의 효과와 배터리 가연물을 모사하는 방식을 제시하였다. 배터리의 구성물질을 기반으로 모사연료3가지를 선정하였으며, 배터리 화재의 충전율이 증가할 때 일어나는 효과를 모사하기 위해 산소의 농도를 높이는방식을 사용하였다. 그 결과 산소농도의 증가로 온도와 압력이 상승하였으며, 배터리의 열폭주로 나타나는 폭발을적절히 모사할 수 있었다. 특히 모사연료 중 고온에서 반응하는 C2H6는 배터리의 물리적 폭발에 기여할 뿐만 아니라H2와 CH4등으로 열분해 되어 화학적 폭발의 위험도 함께 증가하는 특성이 있음을 확인하였다. Recently, the demand for eco-friendly energy sources, including batteries, has increased rapidly owing to growing climateand environmental problems. However, battery fire accidents are also increasing, and many researchers are focusing on thesafe use of batteries and their flammability characteristics. Owing to the multiple battery types, conducting experiments isdifficult in all cases, so various methods to model battery fires are being attempted. This study focuses on the effect ofcharge states and models surrogate fuels for battery thermal runaway. Three surrogate fuels were selected based on thebattery components, and a method of increasing the oxygen concentration was adapted to model the effect that occurs whenthe charge states of a battery fire increases. As a result, the temperature and pressure increased in all cases, and it appearsthat the explosion caused by the battery thermal runaway can be simulated by the model. One of the results of this studyis that ethane exposed to a high temperature not only contributes to the physical explosion of the battery but also increasesthe risk of chemical explosion as it decomposes in to H2 and CH4.

리튬 이온 배터리의 열적 특성을 고려한 전기자동차 시뮬레이션 모델링 및 성능 분석

최원세(Wonse Choi),박태상(Taesang Park) 한국자동차공학회 2020 한국자동차공학회 부문종합 학술대회 Vol.2020 No.7

With growing concerns over fossil fuel depletion and the increasing price of crude oil, hybrid electric vehicles (HEVs), electric vehicles (EVs) and fuel cell vehicles (FCEVs) have gained more interest as a mode of transportation. Especially EV is spotlighted because platform is simple and can be easily tuned with different size motors, batteries and gear ratio. Among electric vehicle components, batteries play an increasingly critical role because the performance of EV is highly dependent on battery capacity. Specially the battery temperature is critical factor for battery operating performance. In order to investigate EV performance according to battery temperatures, this paper builds an electro-thermal model of Li-ion battery and EV powertrain using 0D/1D simulation code, AVL CRUISE M. The EV powertrain model is validated with energy consumption and 0-100km/h performance. Also, 3D CFD model of battery using AVL FIRE M is modeled which uses the same electro-thermal model and parameters with 1D battery model. This model will be used as battery cells of battery module in future research to model the battery thermal management systems (BTMS) in 0D/1D and 3D.

전기자동차 배터리 모델링 및 파라미터 최적화 기법 연구

김일송 전력전자학회 2020 전력전자학회 논문지 Vol.25 No.3

This paper presents the methods for the modeling and parameter optimization of the electric vehicle battery. The state variables of the battery are defined, and the test methods for battery parameters are presented. The state-space equation, which consists of four state variables, and the output equation, which is a combination of to-be-determined parameters, are shown. The parameter optimization method is the key point of this study. The least square of the modeling error can be used as an initial value of the multivariable function. It is equivalent to find the minimum value of the error function to obtain optimal parameters from multivariable function. The SIMULINK model is presented, and the 10-hour full operational range test results are shown to verify the performance of the model. The modeling error for 25 degrees is approximately 1% for full operational ranges. The comments to enhance modeling accuracy are shown in the conclusion.

유체 유동 배열에 따른 원통형 리튬이온 배터리팩의 유랭식 성능 특성에 관한 수치적 연구

한정우,황성국,쿠날 산딥 가루드,이명성,이무연 한국산학기술학회 2022 한국산학기술학회논문지 Vol.23 No.6

The life of lithium-ion batteries decreases because of high heat generation during discharging at high temperatures. Lithium-ion batteries need to maintain an optimal temperature using an effective cooling system. Recently, indirect cooling using cold plates has been employed, but there are disadvantages to this cooling, such as an increase in thermal resistance and coolant leakage. Accordingly, research is being conducted on the direct oil cooling of lithium-ion batteries to overcome these drawbacks. In this study, numerical analysis was conducted to analyze the oil cooling performance and pressure drop characteristics for different fluid flow arrangements. The electrochemical heat generation of a cylindrical lithium-ion battery was calculated using the NTGK model in ANSYS Fluent commercial code. The Y and U parameters of the NTGK model were obtained through a constant current discharge experiment at five discharge rates, and the numerical analysis results were verified at a 2C-rate. Numerical analysis was conducted considering four inlet and outlet cases for a cylindrical lithium-ion battery pack. The results showed that the cooling performance and pressure drop are affected by the battery busbar, and the proposed optimal fluid flow arrangement showed the maximum temperature and temperature difference of 32.41°C and 5.15°C, respectively. 리튬이온 배터리는 방전 시 전기화학 반응으로 인하여 열이 발생하고, 고온 상태에서는 열화현상에 의해 수명이 감소한다. 따라서 리튬이온 배터리는 최적 온도를 유지하는 것이 중요하며, 적정한 냉각 시스템이 요구된다. 최근 자동차용 리튬이온 배터리 팩의 용량 증가로 인하여 수랭식 냉각판을 이용한 간랭식이 주로 사용되고 있으나, 냉각수 누수 위험성 열저항 증가 등의 단점이 있다. 이러한 단점을 극복하고자 절연 유체를 사용해 리튬이온 배터리를 직접 냉각하는 연구가 이루어지고 있다. 본 논문에서는 유체 유동 배열에 따른 유랭식 성능 특성 및 압력강하를 분석하기 위해 수치해석 연구를 진행하였다. 원통형 리튬이온 배터리의 전기화학 발열량을 수치적으로 계산하기 위해 상용코드인 ANSYS fluent의 MSMD battery model인 NTGK model을 이용하였다. 5개 방전속도 조건에서 정전류 방전 실험을 통해 NTGK model의 Y, U 파라미터를 구하였으며, 2C-rate에서 수치해석 값을 검증하였다. 해석은 4S4P 배열의 18650 원통형 리튬이온 배터리팩에 대해 4가지 입구 및 출구 조건에서 진행하였다. 해석 결과 냉각성능 및 압력강하는 배터리 버스바에 영향을 받았으며, 최적 유동 배열의 최고온도와 온도편차는 각각 32.41℃, 5.15℃로 나타났다.

폐배터리 블랙 매스(black mass) 회수를 위한 파쇄/분급 공정 분석 및 2종 혼합물의 수학적 분쇄 모델링

김관호,이훈 한국자원리싸이클링학회 2022 資源 리싸이클링 Vol.31 No.6

The use of lithium-ion batteries increases significantly with the rapid spread of electronic devices and electric vehicle and thereby an increase in the amount of waste batteries is expected in the near future. Therefore, studies are continuously being conducted to recover various resources of cathode active material (Ni, Co, Mn, Li) from waste battery. In order to recover the cathode active material, black mass is generally recovered from waste battery. The general process of recovering black mass is a waste battery collection ― discharge ― dismantling ― crushing ― classification process. This study focus on the crushing/classification process among the processes. Specifically, the particle size distribution of various samples at each crushing/ classification step were evaluated, and the particle shape of each particle fraction was analyzed with a microscope and SEM (Scanning Electron Microscopy)-EDS(Energy Dispersive Spectrometer). As a result, among the black mass particle, fine particle less than 74 μm was the mixture of cathode and anode active material which are properly liberated from the current metals. However, coarse particle larger than 100 μm was present in a form in which the current metal and active material were combined. In addition, this study developed a PBM(Population Balance Model) system that can simulate two-species mixture sample with two different crushing properties. Using developed model, the breakage parameters of two species was derived and predictive performance of breakage distribution was verified. 리튬이온 배터리의 사용은 전자기기 및 전기차 등의 생산량 증가로 인해 사용량이 크게 증가하고 있으며, 이와 맞물려 향후 폐배터리의발생량 증가도 예상된다. 따라서 폐배터리를 구성하고 있는 여러 유가 자원 중 Ni, Co, Mn, Li 등이 함유되어 있는 양극 활물질이 매우 중요한 유가 자원으로, 이를 재활용하기 위한 많은 연구가 진행되고 있다. 양극 활물질 회수를 위해서 일반적으로 폐배터리로부터 블랙 매스(Black mass)를 회수하고, 이를 처리하여 주요 금속 자원을 회수한다. 블랙 매스를 회수하는 공정은 폐배터리를 수거-방전-해체-파쇄-분급의 순서로 이루어지며, 본 연구에서는 블랙 매스 회수를 위한 파쇄/분급 공정을 분석하였다. 파쇄/분급 공정을 통해 다양한 공정 산물의 입도 특성을 분석하고, 이 과정에서 생산된 산물의 입도별 형상을 현미경 및 SEM(Scanning Electron Microscopy)-EDS(Energy Dispersive Spectrometer)로 분석하였다. 분석 결과 블랙 매스로 회수되는 입자 중 74 μm의 미세한 입자들은 양극/음극 활물질이 전극으로부터 단체분리되어 존재하였지만, 100 μm 이상의 입자들은 전극과 활물질이 붙어있는 상태에서 파쇄에 의해 입도가 감소되어 존재함을 확인하였다. 또한 배터리의 특징인 2종 혼합물(전극과 활물질)이 결합되어 있는 시료에 대해 파분쇄 특성을 모사할 수 있는 PBM(Population Balance Model) 을 개발하였으며, 2종 혼합물의 분쇄 상수를 도출하고 입도 분포 예측 성능을 검증하였다.