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This paper proposes a novel error correction grey prediction model for degradation prediction of renewable energy storages. The proposed approach uses an error correction factor ψ to eliminate the inherent error of the original grey model (GM), and at the same time retain the original simplicity and fast prototyping. In addition, due to the uncertainty and complexity of failure mechanisms, a trigonometric residual modification is considered in order to well-describe the influence of operating conditions or cyclic fluctuation on the renewable energy storages. Two experimental case studies, including lithium-ion battery and fuel cell aging tests, are performed to validate the performance of the proposed method. In particular, the accuracy of the proposed method is investigated for different prediction horizon lengths, in order to further demonstrate its effectiveness and robustness. It is worth mentioning that the proposed method can ensure the accuracy of the remaining useful life estimation in the case of long-term forecasting, and thus, the maintenance management and corrective action of renewable energy storages can be scheduled earlier, leading to more effective cost minimization and risk mitigation. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient two-dimensional (2-D) steady-state model for fuel cell real-time control implementation. Both the fluid and electrochemical physical domains are considered in the proposed real-time model. The fuel cell under-rib convection is fully described by considering the geometry of serpentine channel. In addition, in order to solve the implicit activation voltage loss and further explore the computational performance, three numerical root-searching algorithms: bisection, secant, and Newton–Raphson methods are applied to the proposed implicit iterative solver and compared. The preferred secant method has been proven to effectively improve both the efficiency and robustness performance of the proposed real-time fuel cell model. Moreover, a computational fluid dynamic based COMSOL fuel cell model is used to validate the calculation accuracy. Furthermore, the practical feasibility of the presented real-time model has been verified using an RT-LAB simulator platform from Opal-RT. [ABSTRACT FROM AUTHOR]

This paper presents a novel two-dimensional real-time modeling approach for a proton exchange membrane fuel cell (PEMFC) based on a tridiagonal matrix algorithm (Thomas algorithm). The Thomas algorithm consists of a forward elimination and a backward substitution, its arithmetic complexity of computations being much lower than the Gaussian elimination. In order to use this advanced numerical solver, the differential equations of reactant gas convection and diffusion phenomena in serpentine channels are transformed into a tridiagonal equations system. In addition, a three-level bisection algorithm has been developed to solve spatial physical quantities distribution for electrochemical domain. The real-time computing methods developed in this paper are then implemented in C language for a fast execution time in a real-time processor. The proposed real-time model is experimentally validated using a 1.2 kW Ballard NEXA fuel cell system, and its practical feasibilities in advanced real-time control for PEMFC systems have been experimentally demonstrated in an RT-LAB real-time simulator. [ABSTRACT FROM PUBLISHER]

In this paper, a novel degradation prediction model for proton-exchange-membrane fuel cell (PEMFC) performance is proposed based on a multiphysical aging model with particle filter (PF) and extrapolation approach. The proposed multiphysical aging model considers major internal physical aging phenomena of fuel cells, including fuel cell ohmic losses, reaction activity losses, and reactants mass transfer losses. Furthermore, in order to obtain accurate values of electrochemical activation losses under a variable load profile, a bisection solver is presented to solve the implicit Butler–Volmer equation. The proposed aging model is initialized at first by fitting the PEMFC polarization curve at the beginning of lifetime. During the prediction process, the aging dataset is then divided into two parts, learning and prediction phases. The PF framework is used to study the degradation characteristics and update the aging parameters during the learning phase. The suitable fitting curve functions are then selected to satisfy the degradation trends of trained aging parameters, and used to further extrapolate the future values of aging parameters in the prediction phase. By using these extrapolated aging parameters, the prediction results are thus obtained from the proposed aging model. Three experimental validations with different aging testing profiles have been performed. The results demonstrate the robustness and advantages of the proposed prediction method. [ABSTRACT FROM AUTHOR]

Dynamic variable coupling analysis is an important method to properly design a control structure for complex multivariable and multi-physical dynamic systems, such as fuel cells. The fuel cell is an electrochemical energy conversion device which includes different inter-coupled dynamic phenomena in electrical, fluidic, and thermal domains. In order to achieve optimized fuel cell performance, different operation variables, such as fuel cell temperature, inlet air flow rate, hydrogen pressure, and membrane water content, need to be properly controlled. In this paper, variable coupling analyses of fuel cell dynamic behaviors are presented and discussed based on a proton exchange membrane fuel cell (PEMFC) dynamic model, which considers in particular the transient phenomena in both fluidic and thermal domain. The analyses of dynamic phenomena step responses are conducted using the relative gain array for various control input variables. Quantitative analyses of coupling effects in different physical domains are shown and discussed. The analysis results can be used to optimize the controller design for fuel cell system. [ABSTRACT FROM PUBLISHER]

In this paper, a robust model-based battery state-of-charge (SOC) estimating algorithm is proposed with a novel approach based on combination of multimodels data-fusion technique and particle filter (PF). The proposed method is particularly adapted for SOC estimation under real-time conditions and the presence of measurement noise. In this innovative approach, multiple battery models have been used in order to accurately estimate a battery SOC. During the estimation process, the measured battery terminal voltage is compared with the multiple battery models output to generate individual residual, which is then used to calculate the weight of estimated value from each battery model. This weight, which represents the accuracy of observation equation of each battery model, is inversely proportional to the residual. The estimated SOC values from different models are then fused and the weights of estimated values from each battery model are adjusted dynamically using PF and weighted average methodology, in order to calculate the final SOC estimation of the battery. For each proposed battery model, the corresponding parameter-tuning strategies are also presented. In addition, the proposed method has been validated by experimental results. The results demonstrate that the proposed multimodels-based algorithm can be implemented effectively for real-time application, and achieve better accuracy than single model-based methods. [ABSTRACT FROM AUTHOR]