Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell

Rahul Khajuria; Ravita Lamba; Rajesh Kumar; Srinivas Yelisetti

doi:10.1007/978-981-99-2279-6_53

Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell

Rahul Khajuria
, Ravita Lamba
, Rajesh Kumar
, Srinivas Yelisetti

Malaviya National Institute of Technology

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

6 Citations (Scopus)

Abstract

This article presents a study on the optimization of parameters of a solid oxide fuel cell (SOFC) by using seven different metaheuristic-based optimization techniques such as Differential Evolutionary algorithm (DE), Genetic Algorithm (GA), Evolutionary Programming (EP), Flower Pollination Algorithm (FPA), Evolutionary Strategies (ES), Memetic Algorithm (MA) and Coral Reefs Optimization (CRO). Optimization of a solid oxide fuel cell which is a complex, non-linear and multimodal problem, is solved by using these algorithms. Voltage-based objective function expressed as minimization of the sum of squared error has been developed in this study. Based on given optimization algorithms, the best value of unknown parameters of solid oxide fuel cell has been reported. Current–voltage, power and efficiency curves are shown in this study to match the estimated values with experimental values. Differential evolutionary algorithm (DE) is found to be best evolutionary algorithm having sum of squared error, mean and standard deviation of 8.33E-06, 1.57E-05 and 6.792E-06 respectively which are minimum among all other algorithm. Differential evolutionary algorithm predicts accurately among all other algorithms, used to find the unknown parameters of solid oxide fuel cell. Box plot shows the minimum median and lowest IQR which further validate the authenticity of DE among all evolutionary based algorithms to find unknown parameters of SOFC used in this study.

Original language	English
Title of host publication	Advances in Clean Energy and Sustainability - Proceedings of ICAER 2022
Editors	Suryanarayana Doolla, Zakir Hussain Rather, Venkatasailanathan Ramadesigan
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	605-613
Number of pages	9
ISBN (Print)	9789819922789
DOIs	https://doi.org/10.1007/978-981-99-2279-6_53
Publication status	Published - 2023
Externally published	Yes
Event	8th International Conference on Advances in Energy Research , ICAER 2022 - Mumbai, India Duration: 7 Jul 2022 → 9 Jul 2022

Publication series

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

Conference

Conference	8th International Conference on Advances in Energy Research , ICAER 2022
Country/Territory	India
City	Mumbai
Period	7/07/22 → 9/07/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Efficiency
Metaheuristic techniques
Parameter estimation
Power output
Solid oxide fuel cell

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

10.1007/978-981-99-2279-6_53

Cite this

Khajuria, R., Lamba, R., Kumar, R., & Yelisetti, S. (2023). Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell. In S. Doolla, Z. H. Rather, & V. Ramadesigan (Eds.), Advances in Clean Energy and Sustainability - Proceedings of ICAER 2022 (pp. 605-613). (Green Energy and Technology). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-2279-6_53

Khajuria, Rahul ; Lamba, Ravita ; Kumar, Rajesh et al. / Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell. Advances in Clean Energy and Sustainability - Proceedings of ICAER 2022. editor / Suryanarayana Doolla ; Zakir Hussain Rather ; Venkatasailanathan Ramadesigan. Springer Science and Business Media Deutschland GmbH, 2023. pp. 605-613 (Green Energy and Technology).

@inproceedings{d55b55b3ff394ab396e721d2731903b3,

title = "Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell",

abstract = "This article presents a study on the optimization of parameters of a solid oxide fuel cell (SOFC) by using seven different metaheuristic-based optimization techniques such as Differential Evolutionary algorithm (DE), Genetic Algorithm (GA), Evolutionary Programming (EP), Flower Pollination Algorithm (FPA), Evolutionary Strategies (ES), Memetic Algorithm (MA) and Coral Reefs Optimization (CRO). Optimization of a solid oxide fuel cell which is a complex, non-linear and multimodal problem, is solved by using these algorithms. Voltage-based objective function expressed as minimization of the sum of squared error has been developed in this study. Based on given optimization algorithms, the best value of unknown parameters of solid oxide fuel cell has been reported. Current–voltage, power and efficiency curves are shown in this study to match the estimated values with experimental values. Differential evolutionary algorithm (DE) is found to be best evolutionary algorithm having sum of squared error, mean and standard deviation of 8.33E-06, 1.57E-05 and 6.792E-06 respectively which are minimum among all other algorithm. Differential evolutionary algorithm predicts accurately among all other algorithms, used to find the unknown parameters of solid oxide fuel cell. Box plot shows the minimum median and lowest IQR which further validate the authenticity of DE among all evolutionary based algorithms to find unknown parameters of SOFC used in this study.",

keywords = "Efficiency, Metaheuristic techniques, Parameter estimation, Power output, Solid oxide fuel cell",

author = "Rahul Khajuria and Ravita Lamba and Rajesh Kumar and Srinivas Yelisetti",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 8th International Conference on Advances in Energy Research , ICAER 2022 ; Conference date: 07-07-2022 Through 09-07-2022",

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Khajuria, R, Lamba, R, Kumar, R & Yelisetti, S 2023, Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell. in S Doolla, ZH Rather & V Ramadesigan (eds), Advances in Clean Energy and Sustainability - Proceedings of ICAER 2022. Green Energy and Technology, Springer Science and Business Media Deutschland GmbH, pp. 605-613, 8th International Conference on Advances in Energy Research , ICAER 2022, Mumbai, India, 7/07/22. https://doi.org/10.1007/978-981-99-2279-6_53

Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell. / Khajuria, Rahul; Lamba, Ravita; Kumar, Rajesh et al.
Advances in Clean Energy and Sustainability - Proceedings of ICAER 2022. ed. / Suryanarayana Doolla; Zakir Hussain Rather; Venkatasailanathan Ramadesigan. Springer Science and Business Media Deutschland GmbH, 2023. p. 605-613 (Green Energy and Technology).

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

TY - GEN

T1 - Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell

AU - Khajuria, Rahul

AU - Lamba, Ravita

AU - Kumar, Rajesh

AU - Yelisetti, Srinivas

PY - 2023

Y1 - 2023

N2 - This article presents a study on the optimization of parameters of a solid oxide fuel cell (SOFC) by using seven different metaheuristic-based optimization techniques such as Differential Evolutionary algorithm (DE), Genetic Algorithm (GA), Evolutionary Programming (EP), Flower Pollination Algorithm (FPA), Evolutionary Strategies (ES), Memetic Algorithm (MA) and Coral Reefs Optimization (CRO). Optimization of a solid oxide fuel cell which is a complex, non-linear and multimodal problem, is solved by using these algorithms. Voltage-based objective function expressed as minimization of the sum of squared error has been developed in this study. Based on given optimization algorithms, the best value of unknown parameters of solid oxide fuel cell has been reported. Current–voltage, power and efficiency curves are shown in this study to match the estimated values with experimental values. Differential evolutionary algorithm (DE) is found to be best evolutionary algorithm having sum of squared error, mean and standard deviation of 8.33E-06, 1.57E-05 and 6.792E-06 respectively which are minimum among all other algorithm. Differential evolutionary algorithm predicts accurately among all other algorithms, used to find the unknown parameters of solid oxide fuel cell. Box plot shows the minimum median and lowest IQR which further validate the authenticity of DE among all evolutionary based algorithms to find unknown parameters of SOFC used in this study.

AB - This article presents a study on the optimization of parameters of a solid oxide fuel cell (SOFC) by using seven different metaheuristic-based optimization techniques such as Differential Evolutionary algorithm (DE), Genetic Algorithm (GA), Evolutionary Programming (EP), Flower Pollination Algorithm (FPA), Evolutionary Strategies (ES), Memetic Algorithm (MA) and Coral Reefs Optimization (CRO). Optimization of a solid oxide fuel cell which is a complex, non-linear and multimodal problem, is solved by using these algorithms. Voltage-based objective function expressed as minimization of the sum of squared error has been developed in this study. Based on given optimization algorithms, the best value of unknown parameters of solid oxide fuel cell has been reported. Current–voltage, power and efficiency curves are shown in this study to match the estimated values with experimental values. Differential evolutionary algorithm (DE) is found to be best evolutionary algorithm having sum of squared error, mean and standard deviation of 8.33E-06, 1.57E-05 and 6.792E-06 respectively which are minimum among all other algorithm. Differential evolutionary algorithm predicts accurately among all other algorithms, used to find the unknown parameters of solid oxide fuel cell. Box plot shows the minimum median and lowest IQR which further validate the authenticity of DE among all evolutionary based algorithms to find unknown parameters of SOFC used in this study.

KW - Efficiency

KW - Metaheuristic techniques

KW - Parameter estimation

KW - Power output

KW - Solid oxide fuel cell

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U2 - 10.1007/978-981-99-2279-6_53

DO - 10.1007/978-981-99-2279-6_53

M3 - Conference contribution

AN - SCOPUS:85163324128

SN - 9789819922789

T3 - Green Energy and Technology

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EP - 613

BT - Advances in Clean Energy and Sustainability - Proceedings of ICAER 2022

A2 - Doolla, Suryanarayana

A2 - Rather, Zakir Hussain

A2 - Ramadesigan, Venkatasailanathan

PB - Springer Science and Business Media Deutschland GmbH

T2 - 8th International Conference on Advances in Energy Research , ICAER 2022

Y2 - 7 July 2022 through 9 July 2022

ER -

Khajuria R, Lamba R, Kumar R, Yelisetti S. Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell. In Doolla S, Rather ZH, Ramadesigan V, editors, Advances in Clean Energy and Sustainability - Proceedings of ICAER 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 605-613. (Green Energy and Technology). doi: 10.1007/978-981-99-2279-6_53

Application of Metaheuristic Techniques in Optimal Parameter Estimation of Solid Oxide Fuel Cell

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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