Numerical and artificial neural network inspired study on step-like-plenum battery thermal management system

• Extended study on step-like plenum of BTMS was done for additional number of steps. • Investigation of low and high temperature of cooling air at inlet was considered. • Effect of air velocity at inlet of the systems was also investigated. • ANN model was trained using data generated from CFD simulation. • Optimum design and process parameters were obtained using the developed ANN model. This study leverage numerical simulation (NS) and artificial neural network (ANN) capabilities to carry out additional investigations on step-like plenum battery thermal management system (BTMS). Different cooling strategies have been developed over the years in BTMSs’ design. Yet, air-cooling strategies still remains relevant, especially in battery-powered aircrafts, where light-weight is important and air is the preferred cooling fluid. Hence, additional study become necessary especially on the step-like plenum design to provide more insight on the performance of the design by considering several number of step, varied air inlet temperature and velocity. Computational fluid dynamics (CFD) approach was employed to obtained results for different number of step; N s = 1, 3, 4, 7, 9, 15 and 19, varied air inlet temperature; T i = 278, 298 and 318 K , and varied air inlet velocity; V i = 3, 3.5, 4, 5 and 6 m / s . Artificial Neural Network (ANN) approach was then employed to predict the BTMSs’ performance for additional values of T i and V i . Minimum temperature ( T min ), maximum temperature ( T max ), maximum temperature difference (Δ T max ) and pressure drop (Δ P ) were computed. By comparing the CFD results with the result predicted by the ANN, the percentage difference, for the entire dataset were 0.01 %, 0.005 %, 1 % and 0.14 % for T max , T min Δ T max and Δ P , respectively. Based on the optimum design parameters predicted using ANN, for T max = 299.24 comprises N s = 4, V i = 6 m / s and T i = 278 K , while for Δ P , comprises N s = 1, V i = 3 m / s and T i = 318 K .

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Publication Details

Journal
International Journal of Thermofluids
Published
2024-10-02
DOI
https://doi.org/10.1016/j.ijft.2024.100897
Citations
3
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.61
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article

Numerical and artificial neural network inspired study on step-like-plenum battery thermal management system

Olanrewaju M. Oyewola, Emmanuel T. Idowu
3 citations
International Journal of Thermofluids
Advanced Battery Technologies Research
0.61
article

Numerical and artificial neural network inspired study on step-like-plenum battery thermal management system

Olanrewaju M. Oyewola, Emmanuel T. Idowu
article en
3 citations

Abstract

• Extended study on step-like plenum of BTMS was done for additional number of steps. • Investigation of low and high temperature of cooling air at inlet was considered. • Effect of air velocity at inlet of the systems was also investigated. • ANN model was trained using data generated from CFD simulation. • Optimum design and process parameters were obtained using the developed ANN model. This study leverage numerical simulation (NS) and artificial neural network (ANN) capabilities to carry out additional investigations on step-like plenum battery thermal management system (BTMS). Different cooling strategies have been developed over the years in BTMSs’ design. Yet, air-cooling strategies still remains relevant, especially in battery-powered aircrafts, where light-weight is important and air is the preferred cooling fluid. Hence, additional study become necessary especially on the step-like plenum design to provide more insight on the performance of the design by considering several number of step, varied air inlet temperature and velocity. Computational fluid dynamics (CFD) approach was employed to obtained results for different number of step; N s = 1, 3, 4, 7, 9, 15 and 19, varied air inlet temperature; T i = 278, 298 and 318 K , and varied air inlet velocity; V i = 3, 3.5, 4, 5 and 6 m / s . Artificial Neural Network (ANN) approach was then employed to predict the BTMSs’ performance for additional values of T i and V i . Minimum temperature ( T min ), maximum temperature ( T max ), maximum temperature difference (Δ T max ) and pressure drop (Δ P ) were computed. By comparing the CFD results with the result predicted by the ANN, the percentage difference, for the entire dataset were 0.01 %, 0.005 %, 1 % and 0.14 % for T max , T min Δ T max and Δ P , respectively. Based on the optimum design parameters predicted using ANN, for T max = 299.24 comprises N s = 4, V i = 6 m / s and T i = 278 K , while for Δ P , comprises N s = 1, V i = 3 m / s and T i = 318 K .

International Journal of ThermofluidsVol. 24
University of Alaska Fairbanks (US), Ajayi Crowther University (NG)
Affordable and clean energy
Openalex Percentile: Top 33%
Advanced Battery Technologies Research
0.61
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