Battery thermal management: An optimization study of parallelized conjugate numerical analysis using Cuckoo search and Artificial bee colony algorithm

Afzal, Asif; Samee, Abdus; Jilte, Ravindra; Islam, Md. Tariqul; Manokar, A. Muthu; Razak, Kaladgi Abdul

doi:10.1016/j.ijheatmasstransfer.2020.120798

Cited by 67 publications

(13 citation statements)

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“…The importance of determining the best ANN architecture is critical because it has a significant impact on the results [50][51][52]. The optimisation of ANN variables is achieved by minimising the mean square error (MSE) after examining a large number of distinctly configured neural networks [53]. The appropriate ANN framework that was chosen in this analysis has 30 neurons in the hidden layer for the prediction of UTS as shown in Figure 24.…”

Section: Results Of Ann Modellingmentioning

confidence: 99%

Influence of Heat Treatment and Reinforcements on Tensile Characteristics of Aluminium AA 5083/Silicon Carbide/Fly Ash Composites

et al. 2021

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The effect of reinforcements and thermal exposure on the tensile properties of aluminium AA 5083–silicon carbide (SiC)–fly ash composites were studied in the present work. The specimens were fabricated with varying wt.% of fly ash and silicon carbide and subjected to T6 thermal cycle conditions to enhance the properties through “precipitation hardening”. The analyses of the microstructure and the elemental distribution were carried out using scanning electron microscopic (SEM) images and energy dispersive spectroscopy (EDS). The composite specimens thus subjected to thermal treatment exhibit uniform distribution of the reinforcements, and the energy dispersive spectrum exhibit the presence of Al, Si, Mg, O elements, along with the traces of few other elements. The effects of reinforcements and heat treatment on the tensile properties were investigated through a set of scientifically designed experimental trials. From the investigations, it is observed that the tensile and yield strength increases up to 160 °C, beyond which there is a slight reduction in the tensile and yield strength with an increase in temperature (i.e., 200 °C). Additionally, the % elongation of the composites decreases substantially with the inclusion of the reinforcements and thermal exposure, leading to an increase in stiffness and elastic modulus of the specimens. The improvement in the strength and elastic modulus of the composites is attributed to a number of factors, i.e., the diffusion mechanism, composition of the reinforcements, heat treatment temperatures, and grain refinement. Further, the optimisation studies and ANN modelling validated the experimental outcomes and provided the training models for the test data with the correlation coefficients for interpolating the results for different sets of parameters, thereby facilitating the fabrication of hybrid composite components for various automotive and aerospace applications.

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Section: Results Of Ann Modellingmentioning

confidence: 99%

Influence of Heat Treatment and Reinforcements on Tensile Characteristics of Aluminium AA 5083/Silicon Carbide/Fly Ash Composites

et al. 2021

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“…Later NN D (deep NN) model is also analyzed with different hidden layers and neurons in each layer to obtain the optimized network for different activation functions. As reported in a few works, the Nu avg data pertinent to battery thermal management is highly nonlinear [8,22,25,28,32,33]. The entire data is sorted carefully and compiled to enable the intelligent algorithms to predict an essential aspect of battery systems.…”

Section: Resultsmentioning

confidence: 99%

Modelling and Computational Experiment to Obtain Optimized Neural Network for Battery Thermal Management Data

et al. 2021

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The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number (Nuavg) data using four activations functions. The battery Nuavg is highly nonlinear as reported in the literature, which depends mainly on flow velocity, coolant type, heat generation, thermal conductivity, battery length to width ratio, and space between the parallel battery packs. Nuavg is modeled at first using only one hidden layer in the network (NN1). The neurons in NN1 are experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN1. Similarly, deep NN (NND) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nuavg. RSME (root mean square error) and R-Squared (R2) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN1 and NND both accurately predict the battery data. Six neurons in the hidden layer for NN1 give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN1 model. In NND, the optimized model is obtained at different hidden layers and neurons for each activation function. The Sigmoidal and Gaussian functions outperformed the Tanh and Linear functions in an NN1 model. The linear function, on the other hand, was unable to forecast the battery data adequately. The Gaussian and Linear functions outperformed the other two NN-operated functions in the NND model. Overall, the deep NN (NND) model predicted better than the single-layered NN (NN1) model for each activation function.

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“…The PSO algorithm has good local search capabilities and strong convergence characteristics, 62–65 while the CS algorithm is highly random and has a strong ability to avoid the local optimal solution and global searchability. 66–68 In recent years, the PSO-CS algorithm has been applied in many fields, and many scholars have also verified that the PSO-CS coupling algorithm has many advantages. 69–74 By combining the PSO algorithm with the CS algorithm, each particle in the PSO algorithm is moving toward the current global optimum and the current local optimum.…”

Section: Methodsmentioning

confidence: 99%

Motion comfort analysis of tight-fitting sportswear from multi-dimensions using intelligence systems

Cheng

Wang

Zeng

et al. 2022

Textile Research Journal

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Focusing on the “human–sport-clothing” system, this paper analyzed the influence of combinations of different tights and sport states on human body parts and overall body comfort from multiple dimensions. However, the motion state and some fabric parameters are non-numerical parameters, which could not be used for model analysis directly. In addition, there are too many numerical fabric parameters whose relationships are complicated, and it is difficult for general models to deal with these relationships, resulting in low accuracy of the comfort prediction model. Moreover, when using the artificial neural network to study comfort, it has some difficulties in expressing comfort and low prediction accuracy. To solve these problems, One-Hot was used to encode non-numerical parameters, and then intelligent algorithms were adopted to deal with these complex fabric parameters. Finally, a comfort prediction model was established in combination with an adaptive fuzzy reasoning system. The results showed that different fabric combinations and motion states had significant effects on local comfort (comfort of specific human body parts) and global comfort (whole body comfort). Moreover, the prediction model with non-numerical parameters has higher accuracy than the model without non-numerical parameters, which indicated that the prediction accuracy of the model had been improved after the introduction of One-Hot coding, so the non-numerical parameters cannot be ignored. The particle swarm optimization algorithm-cuckoo search algorithm-adaptive network-based fuzzy inference system hybrid model was superior to the particle swarm optimization algorithm-adaptive network-based fuzzy inference system and cuckoo search algorithm-adaptive network-based fuzzy inference system model in predicting local comfort and global comfort.

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Battery thermal management: An optimization study of parallelized conjugate numerical analysis using Cuckoo search and Artificial bee colony algorithm

Cited by 67 publications

References 64 publications

Influence of Heat Treatment and Reinforcements on Tensile Characteristics of Aluminium AA 5083/Silicon Carbide/Fly Ash Composites

Influence of Heat Treatment and Reinforcements on Tensile Characteristics of Aluminium AA 5083/Silicon Carbide/Fly Ash Composites

Modelling and Computational Experiment to Obtain Optimized Neural Network for Battery Thermal Management Data

Motion comfort analysis of tight-fitting sportswear from multi-dimensions using intelligence systems

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