Application of co-evolution RNA genetic algorithm for obtaining optimal parameters of SOFC model

Levenberg‐Marquardt backpropagation algorithm for parameter identification of solid oxide fuel cells

“…In conclusion, the identified parameters of ECM correspond to the weight of the first layer and the weight and bias of the third layer of FFANN-ECM, shown in Equations ( 13), ( 15) and (18). Meanwhile, the identified parameters of SSM correspond to the weight of the first layer and the weight and bias of the second layer of FFANN-SSM, illustrated in Equations ( 22), ( 23), and (25). Besides, other parameters in FFANN-ECM and FFANN-SSM are fixed in the whole training process.…”

“…19 Thus far, SOFCs have been widely applied in different fields, such as ships, 20 electric vehicles, 21,22 mobile power, 23 and so on. At the same time, a large number of technique researches including modeling analysis, 24 parameter estimation, 25 and fault diagnosis 26 of SOFC have been also developed. 27 Besides, accurate and reliable modeling of SOFC plays a significant role in simulation analysis, optimal control, 28 and behavior prediction, which typically relies on a precise identification and optimization of its several unknown parameters.…”

Parameter Identification for Solid Oxide Fuel Cell Models: Crucial Comments

“…In the study by Wei and Stanford, (2019), an optimized algorithm based on the chaotic binary shark smell optimization (CBSSO) algorithm is recommended, which alleviates the limitations of the optimization process and obtains satisfactory unknown parameter results, upon which superior performance in global search is fully verified. Furthermore, there are other meta-heuristic algorithms with excellent performance, such as interior search optimizer (ISO) (), differential evolution (DE) (Sarmah et al, 2017), co-evolution RNA genetic algorithm (coRNA-GA) (Wang et al, 2019), and simplified variant of competitive swarm optimizer (SCSO) (Xiong et al, 2020).…”

Grasshopper Optimization Algorithms for Parameter Extraction of Solid Oxide Fuel Cells

“…The meta-heuristic algorithm is independent of the system model and has fast convergence speed. Literature research shows various metaheuristic algorithms, such as chaotic binary shark odor optimizer (CBSSO) algorithm (Isa et al, 2019), bone particle swarm optimization (BPSO) algorithm (Ijaodola et al, 2019), adaptive differential evolution (ADE) algorithm, lunar flame optimization algorithm (MFO) (Wang et al, 2020), hybrid artificial bee colony algorithm (ABC), improved beetle antenna search (IBAS), vortex search algorithm (VSA) (Damo et al, 2019), multivariate optimization (MVO) (Wu et al, 2019a), flower pollination algorithm (FPA), ant optimization algorithm (ALO) (Nassef et al, 2019), gray wolf optimization (GWO), neural network optimization (NNO), and differential evolution (DE) (Masadeh et al, 2017). The overall algorithm can sample all areas of the search space at the same time, so the overall solution can be found simply by using the superhuman algorithm.…”