Modeling and computational experiment to obtain optimized neural network for battery thermal management data

Afzal, Asif and Bhutto, Javed Khan and Alrobaian, Abdulrahman and Kaladgi, Abdul Razak and Khan, Sher Afghan (2021) Modeling and computational experiment to obtain optimized neural network for battery thermal management data. Energies, 14 (7370). pp. 1-19. ISSN 1996-1073

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Abstract

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 have 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.

Item Type:	Article (Journal)
Uncontrolled Keywords:	neural network; battery; heat transfer; activation functions; hidden layers
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1 Motor vehicles
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User:	Prof. Dr. Sher Afghan Khan
Date Deposited:	08 Nov 2021 08:45
Last Modified:	08 Nov 2021 08:45
URI:	http://irep.iium.edu.my/id/eprint/93515

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