“… Fig. 60 Thermal behaviour of stretchable battery cell with optimal design and original design [ 134 ] …”
Section: Btms Investigation Based On Different Approachesmentioning
confidence: 99%
“…It was found that case 3 will be a better choice in terms of thermal performance of battery and energy consumption. Yang et al [134] investigated numerically the thermal performance of Lithium cobalt oxide (LiCoO2) stretchable type of battery cell to optimize the maximum temperature and temperature difference between the cells. The optimization was carried by using multiple objective particle swarm optimization (MOPSO), Non dominating sorting genetic algorithm type III (NSGA-III), and strength Pareto evolutionary algorithm-II (SPEA-II).…”
Section: Pcm and Composite Pcmmentioning
confidence: 99%
“…Figure 66 above shows that with decrease in the inlet temperature of water flowing through the channel the average temperature of battery surface also decreases while the discharge rate of battery and Fig. 60 Thermal behaviour of stretchable battery cell with optimal design and original design [134] mass flow rate of water were kept constant. Such behaviour clearly indicates that water inlet temperature is much more significant compare to its mass flow rate.…”
Section: Water Coolant With Mini-channelmentioning
“… Fig. 60 Thermal behaviour of stretchable battery cell with optimal design and original design [ 134 ] …”
Section: Btms Investigation Based On Different Approachesmentioning
confidence: 99%
“…It was found that case 3 will be a better choice in terms of thermal performance of battery and energy consumption. Yang et al [134] investigated numerically the thermal performance of Lithium cobalt oxide (LiCoO2) stretchable type of battery cell to optimize the maximum temperature and temperature difference between the cells. The optimization was carried by using multiple objective particle swarm optimization (MOPSO), Non dominating sorting genetic algorithm type III (NSGA-III), and strength Pareto evolutionary algorithm-II (SPEA-II).…”
Section: Pcm and Composite Pcmmentioning
confidence: 99%
“…Figure 66 above shows that with decrease in the inlet temperature of water flowing through the channel the average temperature of battery surface also decreases while the discharge rate of battery and Fig. 60 Thermal behaviour of stretchable battery cell with optimal design and original design [134] mass flow rate of water were kept constant. Such behaviour clearly indicates that water inlet temperature is much more significant compare to its mass flow rate.…”
Section: Water Coolant With Mini-channelmentioning
“…Each neuron with different weight is calculated by mathematical function. [33] Due to the high precision and excellent data noisy tolerance, ANN has been successfully applied in the research of battery management, such as the state of charge estimation, [34] state of health assessment, [35] battery temperature prediction, [36] BTMS optimization, [37][38][39] and so on.…”
To ensure the normal operation of a battery pack, a battery thermal management system (BTMS) is required to control the temperature of batteries. Herein, the method using an artificial neural network (ANN) combined with a genetic algorithm (GA) is proposed to optimize the thermal performance of air phase change material (PCM) cooling based BTMS. The ANN is applied to describe the relationship between BTMS parameters (inlet air velocity, inlet air temperature, PCM thickness, battery unit spacing, and discharge rate) and battery pack thermal characteristics. The results show that the PCM thickness and battery unit spacing have little effect on the battery temperature. Then, the optimal parameter combinations of BTMS are solved by GA with the goal of minimizing the maximum temperature. The maximum relative error between simulation and prediction is 0.484 °C, which is only 1.3835% of the simulated value. The optimal parameter combinations help to slow down the temperature rise of the battery pack and delay the phase transition of PCM. The results indicate that the developed model can accurately describe the relationship between the BTMS parameters and battery temperature, which provides a time‐saving and efficient method for the optimal design of BTMS.
“…Sequentially, a macro-scale model of 3 D shell woven composite is established and then the critical buckling load responses of the corresponding buckling woven composite plate are extracted with different type of cutout. Second, because of the simplicity of deployment and strong optimization capabilities, several optimization algorithms are used to handle the variables design in engineering applications (Chen et al 2019; Kitayama et al , 2006; Li et al , 2009; Park and Kim, 2011; Wang et al 2017; Yang et al 2019) to develop an optimized process to meet the lightweight requirements (Liu et al , 2013; Liu et al , 2016). In this section, different surrogate models and efficient global optimization (EGO) are integrated to predict buckling load value with woven architecture.…”
Purpose
The purpose of this paper is to investigate the mechanical properties’ behaviors of woven composite cut-out structures with specific parameters. Because of the complexity of micro-scale and meso-scale structure, it is difficult to accurately predict the mechanical material behavior of woven composites. Numerical simulations are increasingly necessary for the design and optimization of test procedures for composite structures made by the woven composite. The results of the proposed method are well satisfied with the results obtained from the experiment and other studies. Moreover, parametric studies on different plates based on the stacking sequences are investigated.
Design/methodology/approach
A multi-scale modeling approach is suggested. Back-propagation neural networks (BPNN), radial basis function (RBF) and least square support vector regression are integrated with efficient global optimization (EGO) to reduce the weight of assigned structure. Optimization results are verified by finite element analysis.
Findings
Compared with other similar studies, the advantage of the suggested strategy uses homogenized properties behaviors with more complex analysis of woven composite structures. According to investigation results, it can be found that 450/−450 ply-orientation is the best buckling load value for all the cut-out shape requirements. According to the optimal results, the BPNN-EGO is the best candidate for the EGO to optimize the woven composite structures.
Originality/value
A multi-scale approach is used to investigate the mechanical properties of a complex woven composite material architecture. Buckling of different cut-out shapes with the same area is surveyed. According to investigation, 45°/−45° ply-orientation is the best for all cut-out shapes. Different surrogate models are integrated in EGO for optimization. The BPNN surrogate model is the best choice for EGO to optimization difficult problems of woven composite materials.
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