Optimal Model Parameter Estimation of PEM Fuel Cell Using Mountaineering Team-Based Optimization

Pankaj Sharma; Rahul Khajuria; Rajesh Kumar; Ravita Lamba; Saravanakumar Raju

doi:10.1007/978-981-97-5419-9_11

Optimal Model Parameter Estimation of PEM Fuel Cell Using Mountaineering Team-Based Optimization

Pankaj Sharma
, Rahul Khajuria
, Rajesh Kumar
, Ravita Lamba
, Saravanakumar Raju

Vellore Institute of Technology
Malaviya National Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In this paper, a recently developed meta-heuristic (MH) algorithm, namely mountaineering team-based optimization (MTBO) has been applied to estimate seven unknown model parameters of proton exchange membrane (PEM) fuel cell. The mathematical model of the PEM fuel cell is considered as complex and nonlinear, and it contains several unknown parameters. Identification of these parameters is difficult using classical approaches. The MH algorithms are capable of solving complex problems. Therefore, MTBO has been used to identify the values of these parameters. A sum of squared error (SSE)-based objective function is considered and minimized in this study. The error is the difference between the experimental and estimated value of voltages. The polarization characteristics such as I-V and I-P curves have been used to evaluate the accuracy of the MTBO algorithm. Results obtained using MTBO have been compared with four competitive algorithms, namely Gray wolf optimization (GWO), Harris hawk optimization (HHO), differential evolution (DE), and whale optimization algorithm (WOA). The best value of SSE obtained by the MTBO algorithm is 1.1698E−02. Moreover, convergence curves and box plot study have been used to check the speed and reliability of the algorithm. Moreover, statistical study including mean, min, and standard deviation values is also performed to check the robustness of MTBO algorithm. It is concluded that MTBO performs better than other competitive algorithms in solving the problem of parameter estimation of PEM fuel cells.

Original language	English
Title of host publication	Advances in Clean Energy and Sustainability - Proceedings of the 9th International Conference on Advances in Energy Research
Editors	Sankara Sarma V. Tatiparti, Srinivas Seethamraju
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	117-128
Number of pages	12
ISBN (Print)	9789819754182
DOIs	https://doi.org/10.1007/978-981-97-5419-9_11
Publication status	Published - 2024
Externally published	Yes
Event	9th International Conference on Advances in Energy Research, ICAER 2023 - Mumbai, India Duration: 12 Dec 2023 → 14 Dec 2023

Publication series

Name	Green Energy and Technology
ISSN (Print)	1865-3529
ISSN (Electronic)	1865-3537

Conference

Conference	9th International Conference on Advances in Energy Research, ICAER 2023
Country/Territory	India
City	Mumbai
Period	12/12/23 → 14/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

BCS 500-W PEM fuel cell
Mountaineering team-based optimization
Parameter estimation
Polarization characteristics
Statistical study

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Industrial and Manufacturing Engineering
Management, Monitoring, Policy and Law

Access to Document

10.1007/978-981-97-5419-9_11

Cite this

Sharma, P., Khajuria, R., Kumar, R., Lamba, R., & Raju, S. (2024). Optimal Model Parameter Estimation of PEM Fuel Cell Using Mountaineering Team-Based Optimization. In S. S. V. Tatiparti, & S. Seethamraju (Eds.), Advances in Clean Energy and Sustainability - Proceedings of the 9th International Conference on Advances in Energy Research (pp. 117-128). (Green Energy and Technology). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-5419-9_11

Sharma, Pankaj ; Khajuria, Rahul ; Kumar, Rajesh et al. / Optimal Model Parameter Estimation of PEM Fuel Cell Using Mountaineering Team-Based Optimization. Advances in Clean Energy and Sustainability - Proceedings of the 9th International Conference on Advances in Energy Research. editor / Sankara Sarma V. Tatiparti ; Srinivas Seethamraju. Springer Science and Business Media Deutschland GmbH, 2024. pp. 117-128 (Green Energy and Technology).

@inproceedings{752124044e664470ac4837afb79cb353,

title = "Optimal Model Parameter Estimation of PEM Fuel Cell Using Mountaineering Team-Based Optimization",

abstract = "In this paper, a recently developed meta-heuristic (MH) algorithm, namely mountaineering team-based optimization (MTBO) has been applied to estimate seven unknown model parameters of proton exchange membrane (PEM) fuel cell. The mathematical model of the PEM fuel cell is considered as complex and nonlinear, and it contains several unknown parameters. Identification of these parameters is difficult using classical approaches. The MH algorithms are capable of solving complex problems. Therefore, MTBO has been used to identify the values of these parameters. A sum of squared error (SSE)-based objective function is considered and minimized in this study. The error is the difference between the experimental and estimated value of voltages. The polarization characteristics such as I-V and I-P curves have been used to evaluate the accuracy of the MTBO algorithm. Results obtained using MTBO have been compared with four competitive algorithms, namely Gray wolf optimization (GWO), Harris hawk optimization (HHO), differential evolution (DE), and whale optimization algorithm (WOA). The best value of SSE obtained by the MTBO algorithm is 1.1698E−02. Moreover, convergence curves and box plot study have been used to check the speed and reliability of the algorithm. Moreover, statistical study including mean, min, and standard deviation values is also performed to check the robustness of MTBO algorithm. It is concluded that MTBO performs better than other competitive algorithms in solving the problem of parameter estimation of PEM fuel cells.",

keywords = "BCS 500-W PEM fuel cell, Mountaineering team-based optimization, Parameter estimation, Polarization characteristics, Statistical study",

author = "Pankaj Sharma and Rahul Khajuria and Rajesh Kumar and Ravita Lamba and Saravanakumar Raju",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 9th International Conference on Advances in Energy Research, ICAER 2023 ; Conference date: 12-12-2023 Through 14-12-2023",

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Sharma, P, Khajuria, R, Kumar, R, Lamba, R & Raju, S 2024, Optimal Model Parameter Estimation of PEM Fuel Cell Using Mountaineering Team-Based Optimization. in SSV Tatiparti & S Seethamraju (eds), Advances in Clean Energy and Sustainability - Proceedings of the 9th International Conference on Advances in Energy Research. Green Energy and Technology, Springer Science and Business Media Deutschland GmbH, pp. 117-128, 9th International Conference on Advances in Energy Research, ICAER 2023, Mumbai, India, 12/12/23. https://doi.org/10.1007/978-981-97-5419-9_11

Optimal Model Parameter Estimation of PEM Fuel Cell Using Mountaineering Team-Based Optimization. / Sharma, Pankaj; Khajuria, Rahul; Kumar, Rajesh et al.
Advances in Clean Energy and Sustainability - Proceedings of the 9th International Conference on Advances in Energy Research. ed. / Sankara Sarma V. Tatiparti; Srinivas Seethamraju. Springer Science and Business Media Deutschland GmbH, 2024. p. 117-128 (Green Energy and Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Sharma, Pankaj

AU - Khajuria, Rahul

AU - Kumar, Rajesh

AU - Lamba, Ravita

AU - Raju, Saravanakumar

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - In this paper, a recently developed meta-heuristic (MH) algorithm, namely mountaineering team-based optimization (MTBO) has been applied to estimate seven unknown model parameters of proton exchange membrane (PEM) fuel cell. The mathematical model of the PEM fuel cell is considered as complex and nonlinear, and it contains several unknown parameters. Identification of these parameters is difficult using classical approaches. The MH algorithms are capable of solving complex problems. Therefore, MTBO has been used to identify the values of these parameters. A sum of squared error (SSE)-based objective function is considered and minimized in this study. The error is the difference between the experimental and estimated value of voltages. The polarization characteristics such as I-V and I-P curves have been used to evaluate the accuracy of the MTBO algorithm. Results obtained using MTBO have been compared with four competitive algorithms, namely Gray wolf optimization (GWO), Harris hawk optimization (HHO), differential evolution (DE), and whale optimization algorithm (WOA). The best value of SSE obtained by the MTBO algorithm is 1.1698E−02. Moreover, convergence curves and box plot study have been used to check the speed and reliability of the algorithm. Moreover, statistical study including mean, min, and standard deviation values is also performed to check the robustness of MTBO algorithm. It is concluded that MTBO performs better than other competitive algorithms in solving the problem of parameter estimation of PEM fuel cells.

AB - In this paper, a recently developed meta-heuristic (MH) algorithm, namely mountaineering team-based optimization (MTBO) has been applied to estimate seven unknown model parameters of proton exchange membrane (PEM) fuel cell. The mathematical model of the PEM fuel cell is considered as complex and nonlinear, and it contains several unknown parameters. Identification of these parameters is difficult using classical approaches. The MH algorithms are capable of solving complex problems. Therefore, MTBO has been used to identify the values of these parameters. A sum of squared error (SSE)-based objective function is considered and minimized in this study. The error is the difference between the experimental and estimated value of voltages. The polarization characteristics such as I-V and I-P curves have been used to evaluate the accuracy of the MTBO algorithm. Results obtained using MTBO have been compared with four competitive algorithms, namely Gray wolf optimization (GWO), Harris hawk optimization (HHO), differential evolution (DE), and whale optimization algorithm (WOA). The best value of SSE obtained by the MTBO algorithm is 1.1698E−02. Moreover, convergence curves and box plot study have been used to check the speed and reliability of the algorithm. Moreover, statistical study including mean, min, and standard deviation values is also performed to check the robustness of MTBO algorithm. It is concluded that MTBO performs better than other competitive algorithms in solving the problem of parameter estimation of PEM fuel cells.

KW - BCS 500-W PEM fuel cell

KW - Mountaineering team-based optimization

KW - Parameter estimation

KW - Polarization characteristics

KW - Statistical study

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Sharma P, Khajuria R, Kumar R, Lamba R, Raju S. Optimal Model Parameter Estimation of PEM Fuel Cell Using Mountaineering Team-Based Optimization. In Tatiparti SSV, Seethamraju S, editors, Advances in Clean Energy and Sustainability - Proceedings of the 9th International Conference on Advances in Energy Research. Springer Science and Business Media Deutschland GmbH. 2024. p. 117-128. (Green Energy and Technology). doi: 10.1007/978-981-97-5419-9_11