The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.

Development of small hydropower plants depends on its economic and financial feasibility, which must be evaluated by cost estimates before construction. Cost of electromechanical (EM) equipment represents the major portion of the total plant budget. The paper presents new cost estimation approach for EM equipment of hydropower plants. Compared to the literature approaches, the proposed cost correlation is elaborated basing on dataset of hydropower plants located in different continents. Furthermore, the transport cost has been considered of continental factors. Mono-and multi-objective genetic algorithm (GA) optimization have been both applied and compared to determine the best cost correlation. The accuracy of this best cost correlation is validated by using statistical analysis tools to compare it with the equation deduced from the best model reviewed in the literature. The results have shown that the bivariate correlation, viz., the Pearson Product Moment Correlation Coefficient (PPMCC), is slightly improved from 98 % to 99 %. The Mean Square Relative Error (MSRE) is substantially improved from 4.47 % to 1.82 %, and the Utmost Square Relative Error (USRE) from 45.2 % to 11.4 %. Indeed, the proposed cost correlation gives a more accurate estimate of EM equipment cost of hydropower plants in different continents.

The study concerns the winding head thermal design of electrical machines in difficult thermal environments. The new approach is adapted for all basic shapes and solves the thermal behaviour of a random wire layout. The model uses the nodal method but does not use the common homogenization method for the winding slot. The layout impact can be precisely studied to find different hotspots. To achieve this a Delaunay triangulation provides the thermal links between adjoining wires in the slot. Voronoï tessellation gives a cutting to estimate thermal conductance between adjoining wires. This thermal behaviour is simulated in cell cutting and it is simplified with the thermal bridge notion to obtain a simple solving of these thermal conductances. The boundaries are imposed on the slot borders with Dirichlet condition. Then solving with many Dirichlet conditions is described. Some results show different possible applications with rectangular and round shapes, one ore many boundaries, different limit condition values and different layouts. The model can be integrated into a larger model that represents the stator to have best results.

Shunting locomotives exhibit a wide and unpredictable range of power profiles. This unpredictability makes it impossible to rely on offline optimizations or predictive methods combined with online optimization. To maintain optimal performance across this broad range of operating conditions, the online control strategy must be robust. This article proposes a robust method to determine the optimal parameter combinations for an online energy management strategy of a hybrid fuel cell battery shunting locomotive, ensuring optimality across all scenario conditions. The first step involves extracting a statistically representative subspace for simulation, both in terms of parameter combinations and scenario conditions. A response surface model (numerical twin) is then constructed to extrapolate results across the entire space based on this simulated subspace. Using this model, the optimal solution is identified through metaheuristic algorithms (minimization search). Finally, the proposed solution is validated against a set of expert-defined scenarios. The result of the methodology ensures robust optimization across an infinite number of scenarios by minimizing the impact on both the fuel cell and the battery, without increasing mission costs.

The expansion of mobile networks generates high energy demand, especially in remote areas. In Benin, the use of diesel generators in these areas leads to pollution and energy losses on site in the event of fuel shortages. To solve these problems, the integration of renewable energy sources such as solar in mobile base stations is a promising solution. The objective of this study is to optimize the integration of intermittent renewable sources, powering the base transceiver stations (BTS). This leads to the reduction of CO2 gaze emissions and the reliability of power supply to mobile networks in remote areas. The work is based on optimizing a PV/Diesel/battery hybrid system in terms of energy production to permanently power the telecommunication systems. To achieve this goal, the NSGA-II genetic algorithm is employed, taking into account a non-linear load profile that perfectly reflects the energy demand of a BTS site. The results show that the optimal power system for BTS in Benin is composed of a 12 kW diesel generator and 50 solar panels with a peak power of 540 Wc and 30 modular 48V/150Aah batteries. This system would emit 3571.3282 kg/year CO2 with a Lost Power System Probability (LPSP) equal to 4%. The average monthly consumption of the existing site, which is the subject of this study and operates on a diesel generator, is 1500 liters per month, or 18,000 liters per month. This diesel consumption results in annual pollution of 53460 tons of CO2. With the integration of solar energy in this system, the theoretical results show a consumption of 1202 liters of diesel for a production of greenhouse gases of 3571 tonnes of CO2. We note a significant reduction of about 93% on the consumption of fuel oil and on the production of greenhouse gases.

This article presents the results of the suitability assessment and identification of favorable sites for photovoltaic solar power plants connected to the electricity grid in northern Benin. The integration of renewable energies into Benin's energy park constitutes a major challenge for which those in power are seeking an optimal solution. The north of the country has strong solar potential, we based our study on this region in order to evaluate the technical and economic feasibility of photovoltaic power plants. The method adopted consists first of analyzing the opportunity to choose potential sites through the combination of a geographic information system (GIS) and a hierarchical analytical process (AHP) approach. In this process, nine (9) factors were weighted, namely solar irradiation, grid connection infrastructure, topography, land cover and use, surface and soil characteristics, environmental risks, flooding, restricted areas and distances from the road and power grid to determine potential sites. Then thanks to the Technique of Order of Preference by Similarity with the Ideal Solution (TOPSIS), the distances were optimized for the identification of the most favorable sites. The results of the application of the selection criteria applied to the northern zone around the substations of the Beninese Energy Electrical located in Bembereke, Djougou, Kandi, Natitingou and Parakou made it possible to identify 25 potential sites then 15 favorable sites. This hybrid method has the advantage of determining both favorable sites and the capacities of the resulting power plants.