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Proton exchange membrane fuel cells (PEMFCs) have become the center of attention for energy conversion in many areas such as automotive industry, where they confront a high dynamic behavior resulting in their characteristics variation. In order to ensure appropriate modeling of PEMFCs, accurate parameters estimation is in demand. However, parameter estimation of PEMFC models is highly challenging due to their multivariate, nonlinear, and complex essence. This paper comprehensively reviews PEMFC models parameters estimation methods with a specific view to online identification algorithms, which are considered as the basis of global energy management strategy design, to estimate the linear and nonlinear parameters of a PEMFC model in real time. In this respect, different PEMFC models with different categories and purposes are discussed first. Subsequently, a thorough investigation of PEMFC parameter estimation methods in the literature is conducted in terms of applicability. Three potential algorithms for online applications, Recursive Least Square (RLS), Kalman filter, and extended Kalman filter (EKF), which has escaped the attention in previous works, have been then utilized to identify the parameters of two well-known semi-empirical models in the literature, Squadrito et. al and Amphlett et. al. Ultimately, the achieved results and future challenges are discussed.

Energy consumption reduction efforts in the residential buildings sector represent socio-economical, technological and environmental preoccupations which justify advanced scientific research. These lead to use inverse models to describe thermal behavior and to evaluate the energy consumption of buildings. Their principal goal is to provide supporting evidence of enhanced energy performances and predictions. More specifically, research questions are related to building thermal modeling which is the most appropriate in a smart grid context. In this context, the models are reviewed according to three categories. The first category is based on physical and basic principle modeling (white-box). The second offers a much simpler structure which is the statistical models (black-box). The black-box is used for prediction of energy consumption and heating/ cooling demands. Finally, the third category is a hybrid method (grey-box), which uses both physical and statistical modeling techniques. In this paper, we propose a detailed review and simulation of the main thermal building models. Our comparison and simulation results demonstrate that the grey-box is the most effective model for management of buildings energy consumption.

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