Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm

Abstract

The process of constructing a reliable mathematical model of solid oxide fuel cell (SOFC) is a challenge due to its complex nature. This paper proposes a new methodology incorporated a recent meta-heuristic algorithm named parasitism-predation algorithm (PPA) to estimate the optimal parameters of SOFC equivalent circuit. Two experiments are conducted in this work; the first one comprises four measured datasets for a commercial enhanced cylindrical SOFC manufactured by Siemen Energy. While the second series consists of five measured datasets for a theoretical 5 KW ð Þ dynamic SOFC stack with 96 connected cells. The collected datasets are measured at different operating conditions. An excessive comparative study is presented with other optimizers of comprehensive learning particle swarm optimization (CLPSO), improved PSO with difference mean with perturbation (DMP_PSO), heterogeneous CLPSO (HCLPSO), locally informed PSO (LIPS), modified CSO with tri-competitive mechanism (MCSO), opposition-based learning competitive PSO (OBLCPSO), ranking-based biased learning swarm optimizer (RBLSO), competitive swarm optimizer (CSO), hybrid Jaya with DE (JayaDE), and social learning PSO (SLPSO). Furthermore, statistical analyses of the ranking tests, multiple sign tests, Friedman tests, and ANOVA are performed. The obtained results confirmed the proposed PPA's competence in constructing a reliable model of SOFC as it provides the least mean square error (MSE) between the measured and estimated characteristics of 2.164e À6 in the first series of experiments at 1073 K, in contrast, the most peer (CLPSO) provides 5.57e À6 . Similarly, in the second series of experiments, PPA achieves lease MSE of 7.17e À2 at 973 K; meanwhile, the most peer (CLPSO) attains 5.44e À1 .

An Improved Comprehensive Learning - Particle Swarm Optimization - Extended Kalman Filtering Method for the Online High-Precision State of Charge and Model Parameter Co-Estimation of Lithium-Ion Batteries

“…When compared to the Thevenin model, the simple structure of the second-order RC model and straightforward operation are better able to represent the static and dynamic characteristics of the battery. 37,38 In light of this, z E-mail: wangshunli1985@qq.com parameter identification is performed using the second-order RC model, as demonstrated in Fig. 1.…”

Parameter Estimation of Two Spiking Neuron Models With Meta-Heuristic Optimization Algorithms

“…A genetic algorithm and particle swarm optimization are the main optimization algorithms used to identify the parameters of spiking neural models in the literature (Rossant et al, 2010 ; Lynch and Houghton, 2015 ). Recently, several other nature-inspired optimization algorithms have been presented and proven to give better results in many applications (Yousri et al, 2018 , 2021 ). Examples of these algorithm are the cuckoo search optimizer (Gandomi et al, 2013 ), the marine predator algorithm (Faramarzi et al, 2020 ), and the gray wolf optimizer (Yousri et al, 2020 ) (check Abd Elaziz et al, 2021 for a recent review of these algorithms).…”