Accurate, efficient and reliable parameter extraction of PEM fuel cells using shuffled multi-simplexes search algorithm

Jin, Li; Gao, Xiankun; Cui, Yan; Hu, Jianjun; Xu, Guangming; Zhang, Zhiping

doi:10.1016/j.enconman.2020.112501

Cited by 29 publications

(21 citation statements)

References 64 publications

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“…The Nernst equation was first proposed by the German chemist Walther Hermann Nernst [30] to determine the electromotive force in electrochemical cells. Under the standard condition with an environmental temperature of 298.15 K and atmospheric pressure of 1 atm, the standard electrode potential is 1.229 V, with F being a Faraday constant of 96,485 As/mol, and n representing the number of electrons per unit mole during the chemical reaction in a cell [31][32][33]:…”

Section: Proton Exchange Membrane Modeling and Theorymentioning

confidence: 99%

“…In the above equation, the value of 1.229 indicates the ideal electric potential energy under the standard condition, T represents cell temperature, and P H2 and P O2 each represents the effective partial pressure of hydrogen and oxygen, respectively. If H 2 and O 2 are the reactants, then the partial pressure can be calculated using Equation (5) and (6); however, if the reactants are H 2 and the air, the effective partial pressure of P O2 must then be calculated with Equation (7) [31]:…”

Section: Proton Exchange Membrane Modeling and Theorymentioning

confidence: 99%

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Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

2021

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As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 · 10−4). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules.

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Section: Proton Exchange Membrane Modeling and Theorymentioning

confidence: 99%

Section: Proton Exchange Membrane Modeling and Theorymentioning

confidence: 99%

Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

2021

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“…The fuel cell is with 225 A maximum cell current, and operating partial pressures lie between 1 and 5 bar. Table 4 present the approximated values obtained by Bézier curve method compared with the algorithms MRFO [38], FOA [25], ICA [25], SFLA [25], SMSA [26] and SSO [32]. Figure 13a,b show the reconstructed V-I and P-I curves of NedStackPS6 PEMFC stack using the approximated value using Bézier curve method compared to experimental data and MRFO and FOA algorithms.…”

Section: Case Study 3: Nedstackps6 Pemfc Stackmentioning

confidence: 99%

“…On the other hand, the meta-heuristic algorithms have prospered over the last few years, and have been effectively used to overcome several challenging optimization difficulties in specific various fields. In the literature, numerous meta-heuristic optimization techniques have been proposed to approximate the FC characteristic curves, including Shuffled Frog-Leaping Algorithm (SFLA), Firefly Optimization Algorithm (FOA), Imperialist Competitive Algorithm (ICA) [25], Shuffled Multi-Simplexes Search Algorithm (SMSA) [26], Hybrid Grey Wolf Optimization (HGWO) [27], Hybrid Vortex Search Algorithm and Differential Evolution [28], Eagle Strategy [8], Cuckoo Search Algorithm with Explosion Operator (CAEO) [29], Neural Network Optimizer (NNO) [30], Shark Smell Optimizer (SSO) [31], Slap Swarm Optimizer (SSO) [32], Grasshopper Optimization (GO) [33], Grey Wolf Optimizer (GWO) [34], Hybrid Teaching Learning Based Optimization-Differential Evolution [35], Hybrid Adaptive Differential Evolution Algorithm [36], Evolutionary Strategy [37], Genetic Algorithm (GA) and Manta Rays Foraging Optimizer (MRFO) [38], Transferred Adaptive Differential Evolution (TADE) [39], Adaptive Differential Evolution Algorithm (ADEA) [40], and Harmony Search Algorithm (HSA) [41]. In particular, these nature-inspired or artificial swarm intelligence have their own benefits and drawbacks, in which someone can overcome an unsolvable problem and not solve another one.…”

Section: Introductionmentioning

confidence: 99%

“…To check the efficiency of the used technique and the performance of the obtained results, a statistical analysis is conducted to assess the precision and accuracy of the approximated V-I and P-I curves. The results obtained are compared to latest methods, such as the Whale Optimization Algorithm (WOA) [14], GRG-1 [22], GRG-2 [22], FOA [25], ICA [25], SFLA [25], SMSA [26], SSO [32], Modified Artificial Ecosystem Optimizatio (MAEO) [49], GA [38], MRFO [38], Grouping-based Global Harmony Search GGHS [50], Improved Harmouny Search (HIS) [50], Particle Swarm Optimization with adaptive inertia Weight (PSO-w) [50], and Chaotic Harris Hawks Optimization (CHHO) [51]. To evaluate the performance of the proposed method, various error parameters are computed, examined, and compared, such as Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Bias Error (MBE), and Autocorrelation Function (ACF).…”

Section: Introductionmentioning

confidence: 99%

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Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique

2020

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Accurate modelling of the fuel cell characteristics curve is essential for the simulation analysis, control management, performance evaluation, and fault detection of fuel cell power systems. However, the big challenge in fuel cell modelling is the multi-variable complexity of the characteristic curves. In this paper, we propose the implementation of a computer graphic technique called Bézier curve to approximate the characteristics curves of the fuel cell. Four different case studies are examined as follows: Ballard Systems, Horizon H-12 W stack, NedStackPS6, and 250 W proton exchange membrane fuel cells (PEMFC). The main objective is to minimize the absolute errors between experimental and calculated data by using the control points of the Bernstein–Bézier function and de Casteljau’s algorithm. The application of this technique entails subdividing the fuel cell curve to some segments, where each segment is approximated by a Bézier curve so that the approximation error is minimized. Further, the performance and accuracy of the proposed techniques are compared with recent results obtained by different metaheuristic algorithms and analytical methods. The comparison is carried out in terms of various statistical error indicators, such as Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Bias Errors (MBE), and Autocorrelation Function (ACF). The results obtained by the Bézier curve technique show an excellent agreement with experimental data and are more accurate than those obtained by other comparative techniques.

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Accurate PEM fuel cells parameters estimation using hybrid artificial bee colony differential evolution shuffled complex optimizer

Hachana

2021

Intl J of Energy Research

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Summary To get efficient current/voltage (I/V) polarization curves, the parameter identification of the proton exchange membrane (PEM) fuel cells (FCs) model based on experimental datasets and meta‐heuristic algorithms remains an active research field during the past few years. Meanwhile, estimating those parameters accurately is still a challenge. In this work, a new hybridized approach is presented to identify the PEMFC model parameters denominated the artificial bee colony differential evolution shuffled complex (ABCDESC) optimizer. In the proposed algorithm, the double execution of the probabilities evaluation and the selection strategy of ABC optimizer enables to have better exploitation phase without getting stuck into the local optimum. The sum of squared errors (SSE)‐based objective function is used to perform the optimization as it is the well used in the literature. In order to assess this new hybridized approach, a comparative study with the latest published techniques is carried out using six typical test benchmarking PEMFCs modules extensively utilized in the literature. In this context, the reached SSE values and the standard deviations among other challenging methodologies are very competitive with the best convergence speed and reduced number of function evaluations. The ABCDESC algorithm reaches a standard deviation/CPU run time (STD/CRT) of 8.4690e−16/0.298 second, 1.3275e−16/0.290 second, 1.9721e−14/0.356 second, 4.3889e−15/0.343 second, 1.4388e−12/0.49 second, and 1.6398e−17/0.326 second for 250 W, BCS 500 W, NedStack PS6, Ballard Mark V, Horizon H‐12, and Modular SR‐12 stacks; respectively. The comparison results indicate the successful use of the proposed ABCDESC optimizer to characterize the PEMFC model accurately.

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Accurate, efficient and reliable parameter extraction of PEM fuel cells using shuffled multi-simplexes search algorithm

Cited by 29 publications

References 64 publications

Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique

Accurate PEM fuel cells parameters estimation using hybrid artificial bee colony differential evolution shuffled complex optimizer

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