Nusselt number analysis from a battery pack cooled by different fluids and multiple back-propagation modelling using feed-forward networks

Mokashi, Imran; Afzal, Asif; Khan, Sher Afghan; Abdullah, Nur Azam; Azami, Muhammad Hanafi; Jilte, Ravindra; Samuel, Olusegun David

doi:10.1016/j.ijthermalsci.2020.106738

Cited by 74 publications

(30 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Normalized root mean square was used to check and evaluate the performance of the ANN [ 33 ]. Three inputs (L: load, S: speed, D: distance) were used to construct the ANN architecture for all the samples in which weight loss (w) was the output, and the hidden layer indicated that an interaction between the neurons was not visible [ 54 , 55 , 56 , 57 ].…”

Section: Resultsmentioning

confidence: 99%

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

Lightweight composite materials have recently been recognized as appropriate materials have been adopted in many industrial applications because of their versatility. The present research recognizes the inclusion of ceramics such as Gr and B4C in manufacturing AMMCs through stir casting. Prepared composites were tested for hardness and wear behaviour. The tests’ findings revealed that the reinforced matrix was harder (60%) than the un-reinforced alloy because of the increased ceramic phase. The rising content of B4C and Gr particles led to continuous improvements in wear resistance. The microstructure and worn surface were observed through SEM (Scanning electron microscope) and revealed the formation of mechanically mixed layers of both B4C and Gr, which served as the effective insulation surface and protected the test sample surface from the steel disc. With the rise in the content of B4C and Gr, the weight loss declined, and significant wear resistance was achieved at 15 wt.% B4C and 10 wt.% Gr. A response surface analysis for the weight loss was carried out to obtain the optimal objective function. Artificial neural network methodology was adopted to identify the significance of the experimental results and the importance of the wear parameters. The error between the experimental and ANN results was found to be within 1%.

show abstract

Section: Resultsmentioning

confidence: 99%

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ANN used 520 experimental results, split into 3 segments: the training set (65% data), the test set (25% data), and the validation set (10% data). 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].…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The actual behaviour of the battery is represented using this model and accurate results are obtained from it. The parameters are easily detectable in this model, and are made to compare with different types of battery manufacturers' data [99][100][101][102].…”

Section: Charge/discharge Rate and Soc Calculations Using Grey Box Modellingmentioning

confidence: 99%

“…GA was the first metaheuristic optimisation algorithm proposed by Holland et al in 1988 based on Darwin's principle of natural selection. GA is a suitable algorithm for the parameter's extraction of this model in terms of its complexity and the large number of its parameters [102][103][104][105]. GA imitates the process of biological evolution, including selection, crossover and mutation based on the principle that good individuals survive and breed good individuals [86].…”

Section: Parameter Extraction Of the Grey Box Modelling Using Bio-inspired Algorithmsmentioning

confidence: 99%

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

Self Cite

115

View full text Add to dashboard Cite

The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

show abstract

Nusselt number analysis from a battery pack cooled by different fluids and multiple back-propagation modelling using feed-forward networks

Cited by 74 publications

References 55 publications

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

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

A Review on Battery Modelling Techniques

Contact Info

Product

Resources

About