Name: Optimization of Latin Hypercube Sampling and Adaptive Simulated Annealing for Temperature Control in Fluid Dynamics
Uploaded: 2023-11-03T14:40:00
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optimization of latin hypercube sampling and adaptive simulated annealing for temperature control in fluid dynamics

Optimizing Battery Heat Management in Dusty Environments

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 

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

This study aims to solve the problem of the cell temperature rise and the performance decline caused by the dusty particulate matter covering the surface of the cell through allocation of airflow velocities at the less of battery cooling box under the goal of low energy consumption. An optimization method simultaneously considering energy consumption and thermal performance of battery management system has been established. And it can be widely used to improve the lifecycle of the battery pack with minimum operating cost.

Watch this Scientific Journal Video about Optimization of Latin Hypercube Sampling and Adaptive Simulated Annealing for Temperature Control in Fluid Dynamics at JoVE.com

Optimization of Latin Hypercube Sampling and Adaptive Simulated Annealing for Temperature Control in Fluid Dynamics  

Begin by opening an empty spreadsheet to create a table with rows named inlet1, inlet2, and inlet3 in the first column. Save the file as sampling.xlsx. Run the optimization software and drag the spreadsheet icon onto the single arrow of task one.Then double click the spreadsheet icon to open the Component Editor Excel window. Click the Browse button to import sampling.xlsx. Then click add this mapping to map inlet1, inlet2, and inlet3 to A1, A2, and A3 as parameters.Click OK to return to the initial window. Drag the DOE icon into task one and double click on it to open the Component Editor DOE window. Select Optimal Latin Hypercube.And in the general window, set the number of points to 15. Navigate to the factors window and set 5.5 as the upper limit and five as the lower limit for A1, A2, and A3.Switch to the design matrix window and click generate to produce random sampling points for different inlet velocities. Close the optimization software.Combine the predictor variables x1, x2, and x3 of the velocity arrays and y of the temperature arrays to form a new table. Save the table as sample. txt and import it to fit a response surface model.Rerun the optimization software and drag the approximation icon onto the single arrow of task one. Double click the task one icon to pop up the Component Editor Approximation window to select the response surface model. Navigate to the Data File window and import the sample.txt file containing the prediction variables. Switch to the Parameters window and click Scan to open the parameters in the data file window where the predictor variables of x1, x2, and x3 are defined as input and y as output. Next, go to the Technique Options window and select Quadratic in polynomial order.Switch to the Error Analysis Options window and select cross validation in the error analysis method. Then switch to the View Data window and click Initialize Now to obtain the coefficients of the quadratic linear regression equation, Click the Error Analysis button to open the Approximation Error Analysis window. Check if the errors, meet the acceptable standards for each error type.Close the Approximation Component window. Drag the optimization icon into task one and double click it to open the Component Editor Optimization window. Then select adaptive simulated annealing in the optimization technique.Navigate to the variables window and set 5.5 as the upper limit and five as the lower limit. Switch to the objectives window and select the y parameter before closing the Component Editor Optimization window. Finally, click the run optimization button and wait for the optimization results.Our square analysis showed that the second order polynomial response surface approximation model had a good fitting accuracy. The maximum temperature obtained through optimization was 309.39 kelvin with specific airflow velocities at the inlets. The optimized airflow velocities led to a lower maximum temperature of 309.39 kelvin as compared to the initial case.The sum of airflow velocities of the optimized case is less than the non-optimized cases. However, the maximum temperature does not increase with decreasing airflow velocity. Further, the flow line distribution after optimization becomes wider.Factor x1 has the greatest influence on temperature, while factors x2 and x3 have similar effects.

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JoVE Journal

Engineering

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 ...