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
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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
UR - http://www.scopus.com/inward/record.url?scp=85163324128&partnerID=8YFLogxK
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
SP - 605
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 -
