Fuel cells are sources of clean energy which have become a key enabling technology in a wide spectrum of applications, ranging from automotive and aerospace applications to power supply for off-grid communities. The adequate functioning of a fuel cell requires permanent electrical power delivery to its load, operating at its maximum possible efficiency, even under load variations. Controlling the operating point of the fuel cell to manage changes in load conditions allows extending its service life. Several variables must be monitored and/or controlled to achieve optimal operating conditions of the fuel cell. This work deals with the design of a linear-quadratic-Gaussian, LQG, state-space controller for a proton exchange membrane fuel cell. The LQG controller is commonly used in fuel cell applications because it features an observer which can reconstruct states that are needed for the control strategy and that many times are difficult or too expensive to measure. The tuning of the parameters of the controller is performed by means of genetic algorithms procedures. The goal of the optimization is to prevent low levels of reactant gases due to sudden increases in the load. This will avoid damages to the membrane and other components of the stack while improving the overall performance of the system. The open loop and closed loop system response are presented using the lineal and non-lineal model of the plant. The response of the compensated system using the LQG controller is compared to the response using a basic state space controller, designed by the pole placing method, to assess the robustness of the LQG controller under disturbances. The results demonstrate the ability of the genetic algorithm technique to design a controller that can help preserving the integrity of the fuel cell while optimizing its performance.
The optimal performance of a micro-power piezoelectric generator for power harvesting from ambient vibrations strongly depends on the appropriate coupling among its components such as the piezoelectric element, the electrical circuit interface and the load. This coupling is governed by the different types of physical interaction phenomena occurring between such subsystems. A piezoelectric micro-power generator typically consists of a layer of active material deposited on a substrate that convert the mechanical energy from ambient vibrations into electrical energy, an interfacing circuit that usually rectifies this electrical energy and the electrical load where the harvested energy can be stored for later use or spent directly in an application. So far the research efforts in the literature have focused on the performance optimization of each of these subsystems independently, in many cases in an analytical form. Unfortunately, this approach implies a simplification of the models, ruling out most of the complex effects embedded in the dynamic behavior of the system, which does not guarantee optimal performance for the whole device once all its parts are put together. Performance is reduced in the whole device due to different effects such as dynamic loading and impedance mismatch, among others. In order to study the interaction between the subsystems of a micro-power generator, this research proposes a methodology that, by implementing the model for all components on a common a platform, allows for simultaneous analysis and design. A case study is presented and the results demonstrate the potential of the technique for cross-layer optimization of micro-power generators in connection with their associated electronics circuitry.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.