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

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  • Asif Afzal

    (Department of Mechanical Engineering, P. A. College of Engineering, Visvesvaraya Technological University, Mangaluru 574153, India
    Department of Mechanical Engineering, School of Technology, Glocal University, Delhi-Yamunotri Marg, SH-57, Mirzapur Pole, Saharanpur District, Saharanpur 247121, India)

  • Javed Khan Bhutto

    (Department of Electrical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha 61421, Saudi Arabia)

  • Abdulrahman Alrobaian

    (Department of Mechanical Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia)

  • Abdul Razak Kaladgi

    (Department of Mechanical Engineering, P. A. College of Engineering, Visvesvaraya Technological University, Mangaluru 574153, India)

  • Sher Afghan Khan

    (Department of Mechanical Engineering, Faculty of Engineering, International Islamic University, Kuala Lumpur 50728, Malaysia)

Abstract

The focus of this work is to computationally obtain an optimized neural network (NN) model to predict battery average Nusselt number ( Nu avg ) data using four activations functions. The battery Nu avg 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. Nu avg is modeled at first using only one hidden layer in the network (NN 1 ). The neurons in NN 1 are experimented from 1 to 10 with activation functions: Sigmoidal, Gaussian, Tanh, and Linear functions to get the optimized NN 1 . Similarly, deep NN (NN D ) was also analyzed with neurons and activations functions to find an optimized number of hidden layers to predict the Nu avg . RSME (root mean square error) and R-Squared (R 2 ) is accessed to conclude the optimized NN model. From this computational experiment, it is found that NN 1 and NN D both accurately predict the battery data. Six neurons in the hidden layer for NN 1 give the best predictions. Sigmoidal and Gaussian functions have provided the best results for the NN 1 model. In NN D, 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 NN 1 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 NN D model. Overall, the deep NN (NN D ) model predicted better than the single-layered NN (NN 1 ) model for each activation function.

Suggested Citation

  • Asif Afzal & Javed Khan Bhutto & Abdulrahman Alrobaian & Abdul Razak Kaladgi & Sher Afghan Khan, 2021. "Modelling and Computational Experiment to Obtain Optimized Neural Network for Battery Thermal Management Data," Energies, MDPI, vol. 14(21), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7370-:d:672817
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    References listed on IDEAS

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    1. Benmouna, A. & Becherif, M. & Boulon, L. & Dépature, C. & Ramadan, Haitham S., 2021. "Efficient experimental energy management operating for FC/battery/SC vehicles via hybrid Artificial Neural Networks-Passivity Based Control," Renewable Energy, Elsevier, vol. 178(C), pages 1291-1302.
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    Cited by:

    1. Ernest Agyemang & Joseph Awetori Yaro, 2023. "Knowledge, Attitudes, and Perception as Predictors of COVID-19 Safety Practices of Ride-Hailing Operators in Ghana: A Cross-Sectional Study," IJERPH, MDPI, vol. 20(5), pages 1-21, March.
    2. Rajib Mahamud & Chanwoo Park, 2022. "Theory and Practices of Li-Ion Battery Thermal Management for Electric and Hybrid Electric Vehicles," Energies, MDPI, vol. 15(11), pages 1-45, May.

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