일부 분석 및 최적화 소프트웨어는 칭화대학교, 건국대학교, 전남대학교, 목포대학교, 치바대학교에서 지원합니다.

Optimizing Battery Heat Management in Dusty Environments

<ol>
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저자는 공개할 것이 없습니다.

본 연구에서 사용된 BTMS는 공랭식 시스템을 기반으로 구축되었으며, 이는 비용이 저렴하고 구조가 단순하기 때문이다. 열 전달 용량이 낮기 때문에 공냉식 시스템의 성능은 액체 냉각 시스템 및 상변화 재료 냉각 시스템보다 낮습니다. 그러나, 액체 냉각 시스템은 냉매 누출의 단점이 있고, 상변화 물질 냉각 시스템은 질량이 높고 에너지 밀도가 낮다(29). 이러한 냉각 시스템에는...

자동차 산업의 급속한 발전으로 전통적인 연료 차량은 재생 불가능한 자원을 많이 소비하여 심각한 환경 오염과 에너지 부족을 초래합니다. 가장 유망한 솔루션 중 하나는 전기 자동차(EV)1,2의 개발입니다.
EV에 사용되는 전원 배터리는 전기화학 에너지를 저장할 수 있으며, 이는 기존 연료 차량을 대체하는 열쇠입니다. EV에 사용되는 전원 배터리에는 리튬 이온 배터리(LIB), 니켈 금속 수소화물 배터리(NiMH) 및 전기 이중층 커패시터(EDLC)3가 포함됩니다. 다른 배터리에 비해 리튬 이온 배터리는 높은 에너지 밀도, 고효율 및 긴 수명 주기 4,5,6,7과 같은 장점으로 인해 현재 EV의 에너지 저장 장치로 널리 사용되고 있습니다.
그러나 화학 반응열과 줄열로 인해 급속 충전 및 고강도 방전 시 많은 양의 열이 축적되고 배터리 온도가 상승하기 쉽습니다. LIB의 이상적인 작동 온도는 20-40 °C 8,9입니다. 배터리 스트링의 배터리 간 최대 온도 차이는 5°C10,11을 초과해서는 안 됩니다. 그렇지 않으면 배터리 간의 온도 불균형, 노화 가속화, 과열, 화재, 폭발 등과 같은 일련의 위험이 발생할 수 있습니다12. 따라서 해결해야 할 중요한 문제는 좁은 시간 내에 배터리 팩의 온도와 온도 차이를 제어할 수 있는 효율적인 배터리 열 관리 시스템(BTMS)을 설계하고 최적화하는 것입니다.
일반적인 BTMS에는 공랭식, 수냉식 및 상변화 물질 냉각이 포함된다13. 이러한 냉각 방법 중에서, 공랭식은 비용이 저렴하고 구조가 단순하기 때문에 널리 사용된다(14). 공기의 제한된 비열 용량으로 인해 공랭식 시스템의 배터리 셀 간에 고온 및 큰 온도 차이가 발생하기 쉽습니다. 공랭식 BTMS의 냉각 성능을 향상시키기 위해서는 효율적인 시스템(15,16,17)을 설계할 필요가 있다. Qian et al.18은 배터리 팩의 최대 온도 및 온도 차이를 수집하여 시리즈 공랭식 배터리 팩의 셀 간격을 최적화하는 데 사용되는 해당 베이지안 신경망 모델을 훈련했습니다. Chen et al.19는 Z형 병렬 공랭식 시스템에서 입구 발산 플레넘과 출구 수렴 플레넘의 폭을 최적화하기 위해 뉴턴 방법과 유동 저항 네트워크 모델을 사용했다고 보고했습니다. 그 결과 시스템의 온도 차이가 45% 감소한 것으로 나타났습니다. Liu et al.20은 J-BTMS에서 5개 그룹의 냉각 덕트를 샘플링하고 앙상블 대리 기반 최적화 알고리즘으로 셀 간격의 최적 조합을 얻었습니다. Baveja et al.21은 패시브 밸런스 배터리 모듈을 모델링했으며, 이 연구는 모듈 수준 패시브 밸런싱에 대한 열 예측의 효과와 그 반대의 효과를 설명했습니다. Singh et al.22은 결합된 전기화학-열 모델링을 사용하여 설계된 강제 대류 공기 냉각과 함께 캡슐화된 상변화 물질을 사용하는 배터리 열 관리 시스템(BTMS)을 조사했습니다. Fan et al.23은 미세유체 응용 분야에서 높은 인식을 가진 각형 유형 리튬 이온 배터리에 더 안전한 온도 범위를 제공하기 위해 다단계 Tesla 밸브 구성으로 구성된 액체 냉각판을 제안했습니다. Feng et al. 24는 변동 계수 방법을 사용하여 입구 유량과 배터리 간극이 다른 방식을 평가했습니다. Talele et al.25는 난방 필름의 최적 배치를 기반으로 잠재적으로 생성된 열을 저장하기 위해 벽 강화 파이로 라이닝 단열재를 도입했습니다.
공냉식 BTMS를 사용하면 금속 먼지 입자, 광물 먼지 입자, 건축 자재 먼지 입자 및 외부 환경의 기타 입자가 송풍기에 의해 공냉식 BTMS로 유입되어 배터리 표면이 DPM으로 덮일 수 있습니다. 방열 계획이 없으면 지나치게 높은 배터리 온도로 인해 사고가 발생할 수 있습니다. 시뮬레이션 후 지정된 기류 속도와 먼지가 없는 환경에서 배터리 팩의 최대 온도를 먼지가 많은 환경에서의 예상 온도로 사용합니다. 첫째, C-rate는 배터리가 지정된 시간 내에 정격 용량을 방출할 때 필요한 전류 값을 나타내며, 이는 데이터 값에서 배터리 정격 용량의 배수와 같습니다. 이 논문에서 시뮬레이션은 2C 속도 방전을 사용합니다. 정격 용량은 10Ah이고 공칭 전압은 3.2V입니다. 인산철리튬(LiFePO4)은 양극 재료로 사용되며 탄소는 음극 재료로 사용됩니다. 전해질에는 전해질 리튬염, 고순도 유기용매, 필요한 첨가제 및 기타 원료가 포함되어 있습니다. OLHA를 통해 흡입구의 다양한 속도 조합을 나타내는 랜덤 어레이를 결정하고, 곡선 피팅의 정확도를 확인하는 조건으로 배터리 팩의 최대 온도와 흡입구 유속 조합 사이의 2차 함수를 설정하였다. 라틴 하이퍼큐브(LH) 설계는 McKay et al.26에 의해 제안된 이후 많은 컴퓨터 실험에 적용되었습니다. LH는 N x p-행렬 L로 주어지며, 여기서 L의 각 열은 1에서 N까지의 정수의 치환으로 구성됩니다. 본 논문에서는 계산 부담을 줄이기 위해 최적의 라틴어 하이퍼큐브 샘플링 방법을 사용한다. 이 방법은 층화 샘플링을 사용하여 샘플링 지점이 모든 샘플링 내부를 포함할 수 있도록 합니다.
다음 단계에서는 에너지 소모량을 동시에 고려한 조건에서 ASAM을 기준으로 먼지가 많은 환경에서 배터리 팩의 최대 온도를 낮추도록 입구 유속 조합을 최적화했습니다. 적응 시뮬레이션 어닐링 알고리즘은 광범위하게 개발되어 많은 최적화 문제27,28에서 널리 사용되었습니다. 이 알고리즘은 특정 확률로 최악의 해를 받아 국소 최적해에 갇히는 것을 피할 수 있습니다. 글로벌 최적해는 수용 확률과 온도를 정의하여 달성됩니다. 이 두 매개 변수를 사용하여 계산 속도를 조정할 수도 있습니다. 마지막으로, 최적화의 정확도를 확인하기 위해 최적의 결과를 시뮬레이션 소프트웨어에서 얻은 해석 결과와 비교했습니다.
본 논문에서는 더스트 커버로 인해 온도가 상승하는 배터리 팩에 대해 배터리 박스의 입구 유량에 대한 최적화 방법을 제안한다. 목적은 에너지 소비가 적은 경우 먼지로 덮인 배터리 팩의 최대 온도를 먼지로 덮이지 않은 배터리 팩의 최대 온도 이하로 낮추는 것입니다.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Ansys-Workbench</td>
			<td>ANSYS</td>
			<td>N/A</td>
			<td>Multi-purpose finite element method computer design program software.https://www.ansys.com</td>
		</tr>
		<tr>
			<td>Isight</td>
			<td>Engineous Sogtware</td>
			<td>N/A</td>
			<td>Comprehensive computer-aided engineering software.https://www.3ds.com</td>
		</tr>
		<tr>
			<td>NVIDIA GPU</td>
			<td>NVIDIA</td>
			<td>N/A</td>
			<td>An NVIDIA GPU is needed as some of the software frameworks below will not work otherwise. https://www.nvidia.com</td>
		</tr>
		<tr>
			<td>Software</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>SOLIDWORKS</td>
			<td>Dassault Systemes</td>
			<td>N/A</td>
			<td>SolidWorks provides different design solutions, reduces errors in the design process, and improves product quality 
			www.solidworks.com</td>
		</tr>
	</tbody>
</table>

optimization of an air-based heat management system for dusty particulate matter-covered lithium-ion battery packs

본 연구는 저에너지 소비를 목표로 배터리 냉각 박스 입구의 기류 속도 할당을 통해 셀 표면을 덮고 있는 먼지가 많은 미립자 물질로 인한 셀 온도 상승 및 성능 저하 문제를 해결하는 것을 목표로 합니다. 지정된 기류 속도와 먼지가 없는 환경에서 배터리 팩의 최대 온도를 먼지가 많은 환경에서의 예상 온도로 사용합니다. 먼지가 많은 환경에서 배터리 팩의 최대 온도는 시뮬레이션 소프트웨어에서 구성된 해석 모델의 경계 조건인 다양한 입구 기류 속도에서 해결됩니다. 흡입구의 다양한 기류 속도 조합을 나타내는 어레이는 최적의 라틴 하이퍼큐브 알고리즘(OLHA)을 통해 무작위로 생성되며, 여기서 원하는 온도 이상의 온도에 해당하는 속도의 하한 및 상한이 최적화 소프트웨어에서 설정됩니다. 최적화 소프트웨어의 피팅 모듈을 사용하여 속도 조합과 최대 온도 사이의 대략적인 QRSM을 설정합니다. QRSM은 ASAM을 기반으로 최적화되며, 최적의 결과는 시뮬레이션 소프트웨어에서 얻은 해석 결과와 잘 일치합니다. 최적화 후 중간 흡입구의 유량이 5.5m/s에서 5m/s로 변경되고 총 기류 속도가 3% 감소합니다. 본 프로토콜은 이미 확립된 배터리 관리 시스템의 에너지 소비량과 열 성능을 동시에 고려한 최적화 방법을 제시하며, 최소한의 운영 비용으로 배터리 팩의 수명 주기 향상에 널리 사용될 수 있다.

With the rapid development of the automobile industry, traditional fuel vehicles consume a lot of non-renewable resources, resulting in serious environmental pollution and energy shortage. One of the most promising solutions is the development of electric vehicles (EVs)1,2.
The power batteries used for EVs can store electrochemical energy, which is the key to replacing traditional fuel vehicles. Power batteries used in EVs include lithium-ion battery (LIB), nickel-metal hydride battery (NiMH), and electric double-layer capacitor (EDLC)3. Compared to the other batteries, lithium-ion batteries are currently widely used as energy storage units in EVs owing to their advantages such as high energy density, high efficiency, and long life cycle4,5,6,7.
However, due to chemical reaction heat and Joule heat, it is easy to accumulate a large amount of heat and increase the battery temperature during rapid charging and high-intensity discharging. The ideal operating temperature of LIB is 20-40 &#176;C8,9. The maximum temperature difference between the batteries in a battery string should not exceed 5 &#176;C10,11. Otherwise, it may lead to a series of risks such as temperature imbalance between the batteries, accelerated aging, even overheating, fire, explosion, and so on12. Therefore, the critical issue to be resolved is designing and optimizing an efficient battery thermal management system (BTMS) that can control the temperature and temperature difference of the battery pack within a narrow.
Typical BTMS include air cooling, water cooling, and phase change material cooling13. Among these cooling methods, the air cooling type is widely used because of its low cost and simplicity of the structure14. Due to the limited specific heat capacity of air, high temperature and large temperature differences are easy to occur between battery cells in air-cooled systems. In order to improve the cooling performance of air-cooled BTMS, it is necessary to design an efficient system15,16,17. Qian et al.18 collected the battery pack&#39;s maximum temperature and temperature difference to train the corresponding Bayesian neural network model, which is used to optimize cell spacings of the series air-cooled battery pack. Chen et al.19 reported using the Newton method and the flow resistance network model for optimization of the widths of the inlet divergence plenum and the outlet convergence plenum in the Z-type parallel air-cooled system. The results showed a 45% reduction in the temperature difference of the system. Liu et al.20 sampled five groups of the cooling ducts in the J-BTMS and obtained the best combination of cell spacings by the ensemble surrogate-based optimization algorithm. Baveja et al.21 modeled a passively balanced battery module, and the study described the effects of thermal prediction on module-level passive balancing and vice versa. Singh et al.22 investigated a battery thermal management system (BTMS) that used encapsulated phase change material along with forced convective air cooling designed using the coupled electrochemical-thermal modeling. Fan et al.23 proposed a liquid cooling plate comprising a multi-stage Tesla valve configuration to provide a safer temperature range for a prismatic-type lithium-ion battery with high recognition in microfluidic applications. Feng et al. 24 used the coefficient of variation method to evaluate the schemes with different inlet flow rates and battery clearances. Talele et al.25 introduced wall-enhanced pyro lining thermal insulation to store potential generated heating based on optimal placement of heating films.
When one uses air-cooling BTMS, metal dust particles, mineral dust particles, building materials dust particles, and other particles in the external environment will be brought into the air-cooling BTMS by the blower, which can cause the surface of the batteries to be covered with DPM. If there is no heat dissipation plan, it may cause accidents due to the excessively high battery temperature. After simulation, we take the maximum temperature of the battery pack in a specified airflow velocity and dust-free environment as the expected temperature in a dusty environment. First, C-rate refers to the current value required when the battery releases its rated capacity within the specified time, which is equal to a multiple of the battery&#39;s rated capacity in the data value. In this paper, the simulation uses 2C rate discharge. The rated capacity is 10 Ah, and the nominal voltage is 3.2 V. Lithium iron phosphate (LiFePO4) is used as the positive electrode material, and carbon is used as the negative electrode material. The electrolyte has electrolyte lithium salt, a high-purity organic solvent, necessary additives, and other raw materials. The random array representing the different velocity combinations at the inlets was determined through the OLHA, and a 2nd order function between the maximum temperature of the battery pack and the inlet flow velocity combination was set up under the condition of checking the accuracy of the curve fitting. Latin hypercube (LH) designs have been applied in many computer experiments since they were proposed by McKay et al.26. An LH is given by an N x p-matrix L, where each column of L consists of a permutation of the integers 1 to N. In this paper, the optimal Latin hypercube sampling method is used to reduce the computational burden. The method uses stratified sampling to ensure that the sampling points can cover all the sampling internals.
In the following step, the inlet flow velocity combination was optimized to decrease the maximum temperature of the battery pack in a dusty environment based on the ASAM under the condition of considering energy consumption simultaneously. The adaptive simulated annealing algorithm has been extensively developed and widely used in many optimization problems27,28. This algorithm can avoid getting trapped in a local optimum by accepting the worst solution with a certain probability. The global optimum is achieved by defining the acceptance probability and temperature; the calculation speed can also be adjusted by using these two parameters. Finally, for checking the accuracy of the optimization, the optimal result was compared with the analysis result obtained from the simulation software.
In this paper, an optimization method for the inlet flow rate of the battery box is proposed for the battery pack whose temperature rises due to dust cover. The purpose is to reduce the maximum temperature of the dust-covered battery pack to below the maximum temperature of the non-dust-covered battery pack in the case of low energy consumption.

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption 

먼지가 많은 미립자 물질로 덮인 리튬 이온 배터리 팩을 위한 공기 기반 열 관리 시스템 최적화

프로토콜에 따라 배터리 팩의 최대 온도를 얻기 위해 모델링, 메싱 및 시뮬레이션을 포함하는 처음 세 부분이 가장 중요합니다. 그런 다음 샘플링을 통해 기류 속도를 조정하고 마지막으로 최적화를 통해 최적의 유량 조합을 얻습니다.
그림 9는 다양한 환경에서의 배터리 팩 온도 분포 비교를 보여주고, <...

Watch this Scientific Journal Video about Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption at JoVE.com

여기에서는 먼지가 많은 미립자 물질로 덮인 배터리 열 관리 시스템에 해당하는 근사 QRSM(2차 응답 표면 모델)을 최적화하고 시스템 흡입구의 공기 흐름 속도 조합을 조정하여 온도 강하를 충족하기 위한 적응형 시뮬레이션 어닐링 방법(ASAM)을 제시합니다.

This study aims to solve the problem of the cell temperature rise and performance decline caused by dusty particulate matter covering the surface of the ...

본 연구는 낮은 에너지 소비를 목표로 배터리 냉각 박스의 적은 부분에서 기류 속도를 할당하여 셀 표면을 덮고 있는 먼지가 많은 미립자 물질로 인한 셀 온도 상승 및 성능 저하 문제를 해결하는 것을 목표로 합니다. 배터리 관리 시스템의 에너지 소비와 열 성능을 동시에 고려한 최적화 방법이 확립되었습니다. 또한 최소한의 운영 비용으로 배터리 팩의 수명을 개선하는 데 널리 사용할 수 있습니다.

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Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs

Transient Stability and System Controls

SCIENTISTS IN ACTION

868 조회수.  Wenzhou University. 본 연구는 낮은 에너지 소비를 목표로 배터리 냉각 박스의 적은 부분에서 기류 속도를 할당하여 셀 표면을 덮고 있는 먼지가 많은 미립자 물질로 인한 셀 온도 상승 및 성능 저하 문제를 해결하는 것을 목표로 합니다. 배터리 관리 시스템의 에너지 소비와 열 성능을 동시에 고려한 최적화 방법이 확립되었습니다. 또한 최소한의 운영 비용으로 배터리 팩의 수명을 개선하는 데...

비디오: Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption 

This study aims to solve the problem of the cell temperature rise and performance decline caused by dusty particulate matter covering the surface of the cell through the allocation of airflow velocities at the inlets of the battery cooling box under the goal of low energy consumption. We take the maximum temperature of the battery pack at a specified airflow velocity and dust-free environment as the expected temperature in a dusty environment. The maximum temperature of the battery pack in a dusty environment is solved at different inlet airflow velocities, which are the boundary conditions of the analysis model constructed in the simulation software. The arrays representing the different airflow velocity combinations of inlets are generated randomly through the optimal Latin hypercube algorithm (OLHA), where the lower and upper limits of velocities corresponding to the temperatures above the desired temperature are set in the optimization software. We establish an approximate QRSM between the velocity combination and the maximum temperature using the fitting module of the optimization software. The QRSM is optimized based on the ASAM, and the optimal result is in good agreement with the analysis result obtained by the simulation software. After optimization, the flow rate of the middle inlet is changed from 5.5 m/s to 5 m/s, and the total airflow velocity is decreased by 3%. The protocol here presents an optimization method simultaneously considering energy consumption and thermal performance of the battery management system that has been established, and it can be widely used to improve the life cycle of the battery pack with minimum operating cost.

Here, we present the adaptive simulated annealing method (ASAM) to optimize an approximate quadratic response surface model (QRSM) corresponding to a dusty particulate matter-covered battery heat management system and fulfill the temperature drops back by adjusting the airflow velocities combination of system inlets.

연구

공학

Simulation Analysis for Evaluating Temperature and Airflow Dynamics in Battery Packs 

To begin the fluid flow analysis, drag Fluid Flow from the toolbox and Analysis Systems into the project schematic zone. Then hold airFEM mesh, batteryFEM mesh, and dpmFEM mesh fluid flow with the left mouse button and move them to fluid flow setup. Right click on Fluid Flow, then set up and select Update to enter the Setup window.Confirm the validity of the FEM model and check if the mesh has a negative volume. Now, enter the setting interface of the Viscous model and the Radiation model and select the K-epsilon model and the Discrete Ordinates model respectively. Change the fluid type of the numbered battery domains to the solid type.Then on the Solid window, double click each battery domain to change the DPM material to the battery material. Subsequently, choose the source terms item and check the highlighted source terms to add an energy source by assigning the number and the number of energy sources and selecting constant type to input the source value. Change the fluid type of numbered DPM domains to solid type.Next, convert the type of all renamed surfaces, including the inner surfaces of the air domain, all sides of the battery domains, and DPM domains from the default wall to the interface. To generate mesh interfaces, click the Mesh Interfaces and enter the Create and Edit Mesh Interfaces Window. Match the cavity surfaces to all sides, except the upper sides of the battery domains and the lower sides of the DPM domains.Name and number them as interface1 to interface11 respectively. Then match the upper sides of the battery domains and the lower sides of the DPM domains. Name and number them as interface12 to interface22 respectively.To assign the outer border surface as the wall thermal boundary, set the heat transfer coefficient as five in the mixed thermal condition. Then change the material from default aluminum to the previously self-defined battery box material. In the Velocity Inlet window, set airflow velocities of all inlets to five meters per second.Then set the gauge pressure of the outlet to zero in the Pressure Outlet window. Next, set the state of the computing domain with an initial temperature of 300 kelvin and solution initialization type, best standard initialization. Set the number of iterations to 2000 and click Calculate to begin the simulation.To enter the CFD Post window, double click Fluid Flow followed by Results. Then from the toolbox, double click the icon of contour. In the location selector, choose all sides of the batteries and switch from pressure to temperature.Then click Apply to generate the temperature contour of the batteries. Click File, then Export. To select the temperature of the selected variables, then click the dropdown button of the locations to select the battery domains.Click OK followed by the Save button to quit. Battery pack temperature variation at different inlet airflow velocities demonstrated that the maximum battery pack temperature decreases with the increase of inlet airflow velocity. The comparison of the battery pack temperature distribution and the second battery temperature distribution in different environments showed that the temperature of the battery increases under dusty conditions due to the low thermal conductivity of DPM.