International Institute for Hydrogen Materials Research
Recent publications
The increase of Plug-in Electric Vehicles (PEVs) penetration in distribution systems necessitates processing strategic assets in order to deal with their energy needs. A careful investigation into matters related to PEV charging management under actual circumstances can be regarded as the critical step towards enabling this process. Accordingly, this paper intends to design a practical controller capable of performing charging scheduling under uncertainties related to the lack of access to crucial PEV information accounting for departure time, energy requirement, and power demand nonlinearity. Although such an issue can be encountered when developing charging models for real-world conditions, it has not been adequately taken into consideration. The proposed controller carries out charging scheduling through a procedure with a set of effective straightforward algorithms, essential for actual applications. Particularly, it takes advantage of a Bayesian forecasting model that is able to efficiently predict charging energy demand according to car owner’s behavior. In addition, it employs a stochastic optimization framework to schedule PEV charging based on the dynamic electricity price and user preference. Several case studies are conducted to examine the performance of suggested controller in optimal scheduling by exploiting real data. The evaluation process is executed through a comparative analysis by using a deterministic method, as the ideal case, which exploits a full-information space. The results show that the proposed procedure can offer competitive charging schedules, which can minimize the cost while satisfying user desires. The designed controller can successfully manage PEV charging in the presence of stochastic phenomena with limited information access, and thus, enable physical implementations.
The partial atomic volume of hydrogen, vH, is a fundamentally important thermodynamic parameter of interstitial metal hydrides in which dissociated H occupies interstices in the metal lattice. Such an important property should be able to be reliably calculated by a suitable theory or model in order to explain and understand its origin. In practice, vH is typically obtained by means of ab initio calculations founded on density functional theory (DFT), where the equilibrium lattice constant at zero temperature is found by minimising the Born-Oppenheimer energy. While the absolute lattice constants calculated in this way depend quite strongly on the DFT scheme employed, the present work showed that vH is rather robust against differing calculational approaches, thus making a meaningful comparison of theory and experiment possible. Comparing vH for PdnH (0 < n < 8) calculated with DFT and obtained from in-situ neutron diffraction measurements revealed a significant discrepancy when octahedral-only interstitial occupancy was assumed. Calculations for PdH with mixed octahedral and tetrahedral occupancy gave a value for vH in agreement with experiment assuming that PdH contains 15–20% tetrahedral H.
This paper introduces a dynamic semiempirical model that predicts the degradation of a proton exchange membrane fuel cell (PEMFC) by introducing time-based terms in the model. The concentration voltage drop is calculated using a new statistical equation based on the load current and working time, whereas the ohmic and activation voltage drops are updated using time-based equations borrowed from the existing literature. Furthermore, the developed model calculates the membrane water content in the PEMFC, which indicates the membrane hydration state and indirectly diagnoses the flooding and drying faults. Moreover, the model parameters are optimized using a recently developed butterfly optimization algorithm. The model is simple and has a short runtime; therefore, it is suitable for monitoring. Voltage degradation under various loading currents was observed for long working hours. The obtained results indicate a significant degradation in PEMFC performance. Therefore, the proposed model is also useful for prognostics and fault diagnosis.
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Nicolas Armanet & Michel Bonnard