Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm

Afzal, Asif; Mokashi, Imran; Khan, Sher Afghan; Abdullah, Nur Azam; Azami, Muhammad Hanafi

doi:10.1080/10407782.2020.1845560

Cited by 35 publications

(11 citation statements)

References 41 publications

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“…Therefore, the response surface methodology (RSM) analysis was performed to comprehensively evaluate the effects of these parameters and their interactions on the cooling performance. RSM employs a second‐order polynomial equation in Equation () to fit the experimental data, then establishes a regression function for the targets 41

y goodbreak= a_{0} goodbreak+ \sum_{i = 1}^{k} a_{i} x_{i} goodbreak+ \sum_{i = 1}^{k} a_{ii} x_{i}^{2} goodbreak+ \sum_{1 \leq i < j}^{k} a_{ij} x_{i} x_{ij} goodbreak+ ε

where

y

is the target value,

x_{i}

is the independent variables,

k

means the number of independent variables,

a_{0}

a_{i}

a_{ii}

, and

a_{ij}

are coefficients which can be obtained via multiple regression, and

ε

denotes the experimental residual.…”

Section: Resultsmentioning

confidence: 99%

Design and optimization of a novel reverse layered air‐cooling battery management system using U and Z type flow patterns

Lan

et al. 2022

Intl J of Energy Research

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Summary Battery thermal management technology is critical to the lifespan and performance of the lithium‐ion battery packs. In this work, a reverse layered series cooling scheme with a U/Z type flow pattern is introduced to enhance the temperature uniformity of the forced‐air cooling BTMS. The cooling performance of the current design is investigated and compared with that of the parallel cooling for the dynamic unsteady heat generation process. It is demonstrated that the proposed cooling scheme provides good cooling performance and uniform temperature distribution. Then, the geometric structure of the proposed cooling scheme with a U‐type inlet/outlet manifold is optimized by the response surface methodology and the Cuckoo search algorithm. In comparison with the initial design, the maximum temperature of the optimized design is reduced by 0.97°C and the temperature difference is reduced by up to 36.4% (1.23°C) with the pressure drop increased merely by 1.28 Pa.

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y goodbreak= a_{0} goodbreak+ \sum_{i = 1}^{k} a_{i} x_{i} goodbreak+ \sum_{i = 1}^{k} a_{ii} x_{i}^{2} goodbreak+ \sum_{1 \leq i < j}^{k} a_{ij} x_{i} x_{ij} goodbreak+ ε

where

y

is the target value,

x_{i}

is the independent variables,

k

means the number of independent variables,

a_{0}

a_{i}

a_{ii}

, and

a_{ij}

are coefficients which can be obtained via multiple regression, and

ε

denotes the experimental residual.…”

Section: Resultsmentioning

confidence: 99%

Design and optimization of a novel reverse layered air‐cooling battery management system using U and Z type flow patterns

Lan

et al. 2022

Intl J of Energy Research

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“…150 to 160 MPa ranges of friction pressure and 6 to 10 s of friction time are performed in all experimental methods. Figure 15(b) is shown the 6 s of friction time and 150 MPa of friction pressure combinations getting good bulged shapes in the machining process [33,34]. Figure 15(c) is shown the 7 s of friction time and 160 MPa of friction pressure combinations getting a good bonded effect in the machining process.…”

Section: Optimization Parameters and Design Variablesmentioning

confidence: 98%

Investigation of friction welding parameters of AISI 304L/Ti-6AL-4V joints

Kumar

Babu

Saleh

et al. 2022

Mater. Res. Express

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The present paper focuses on evaluating the bonding strength interfaces between the contact materials such as AISI 304L and Ti-6Al-4V via friction welding (FW). Experimental tests are performed by considering the process parameters such as friction time, rotational speed and friction pressure, mechanical properties were evaluated. The rotational speed of 1800, 1600, 2100, 2300 and 2500 rpm were considered in the study. The response surface methodology (RSM) is used to predict the outcomes of the work. Experimental tests were revealed that the friction pressure of 160 MPa, 7 sec of friction time and speed of 2300rpm are the optimal parameters based on the joint strength. Also, observed that the aluminum interlayer thickness of 1.25 µm on the Ti-6Al-4V side and 1.38 µm on the AISI 304L side. The tensile strength of 143.39 MPa was noticed at the interface area. The RSM response generates the curved potential line frequency range with 6.5% elongations. Results confirm that, the response surface methodology outcomes and experimental values were in close agreement.

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“…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%

A Review on Battery Modelling Techniques

Tamilselvi¹,

Gunasundari²,

Karuppiah³

et al. 2021

Sustainability

Self Cite

115

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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.

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Optimization and analysis of maximum temperature in a battery pack affected by low to high Prandtl number coolants using response surface methodology and particle swarm optimization algorithm

Cited by 35 publications

References 41 publications

Design and optimization of a novel reverse layered air‐cooling battery management system using U and Z type flow patterns

Design and optimization of a novel reverse layered air‐cooling battery management system using U and Z type flow patterns

Investigation of friction welding parameters of AISI 304L/Ti-6AL-4V joints

A Review on Battery Modelling Techniques

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