Examinando por Autor "González-Castaño, Catalina"
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Ítem A bidirectional versatile buck–boost converter driver for electric vehicle applications(MDPI, 2021-08) González-Castaño, Catalina; Restrepo, Carlos; Kouro, Samir; Vidal-Idiarte, Enric; Calvente, JavierThis work presents a novel dc-dc bidirectional buck–boost converter between a battery pack and the inverter to regulate the dc-bus in an electric vehicle (EV) powertrain. The converter is based on the versatile buck–boost converter, which has shown an excellent performance in different fuel cell systems operating in low-voltage and hard-switching applications. Therefore, extending this converter to higher voltage applications such as the EV is a challenging task reported in this work. A high-efficiency step-up/step-down versatile converter can improve the EV powertrain efficiency for an extended range of electric motor (EM) speeds, comprising urban and highway driving cycles while allowing the operation under motoring and regeneration (regenerative brake) conditions. DC-bus voltage regulation is implemented using a digital two-loop control strategy. The inner feedback loop is based on the discrete-time sliding-mode current control (DSMCC) strategy, and for the outer feedback loop, a proportional-integral (PI) control is employed. Both digital control loops and the necessary transition mode strategy are implemented using a digital signal controller TMS320F28377S. The theoretical analysis has been validated on a 400 V 1.6 kW prototype and tested through simulation and an EV powertrain system testing. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Ítem A Composite DC–DC Converter Based on the Versatile Buck–Boost Topology for Electric Vehicle Applications(MDPI, 2022-07) González-Castaño, Catalina; Restrepo, Carlos; Flores-Bahamonde, Freddy; Rodriguez, JoseThe composite converter allows integrating the high-efficiency converter modules to achieve superior efficiency performance, becoming a prominent solution for electric transport power conversion. In this work, the versatile buck–boost dc–dc converter is proposed to be integrated into an electric vehicle composite architecture that requires a wide voltage range in the dc link to improve the electric motor efficiency. The inductor core of this versatile buck–boost converter has been redesigned for high voltage applications. The versatile buck–boost converter module of the composite architecture is in charge of the control stage. It provides a dc bus voltage regulation at a wide voltage operation range, which requires step-up (boost) and step-down (buck) operating modes. The PLECS thermal simulation of the composite architecture shows a superior power conversion efficiency of the proposed topology over the well-known classical noninverting buck–boost converter under the same operating conditions. The obtained results have been validated via experimental efficiency measures and experimental transient responses of the versatile buck–boost converter. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 4.4 kW powertrain is presented using a PLECS RT Box 1 device. The HIL simulation results verified the accuracy of the theoretical analysis and the effectiveness of the proposed architecture. © 2022 by the authors.Ítem A fast converging hybrid mppt algorithm based on abc and p&o techniques for a partially shaded pv system(MDPI, 2021-09) Restrepo, Carlos; Yanẽz-Monsalvez, Nicolas; González-Castaño, Catalina; Kouro, Samir; Rodriguez, JoseAmong all the conventional maximum power point tracking (MPPT) techniques for a photovoltaic (PV) system that have been proposed, incremental conductance (INC) and perturb and observe (P&O) are the most popular because of their simplicity and ease of implementation. However, under partial shading conditions (PSCs), these MPPT algorithms fail to track the global maximum power point (GMPP) and instead converge into local maximum power points (LMPPs), resulting in considerable PV power loss. This paper presents a new hybrid MPPT technique combining the artificial bee colony (ABC) and P&O algorithms named ABC-P&O. The P&O technique is used to track the MPP under uniform irradiance, and only during irradiance variations is the ABC algorithm employed. The effectiveness of the proposed hybrid algorithm at tracking the GMPP, under both uniform and nonuniform irradiance conditions, was assessed by hardware-in-the-loop (HIL) tests employed by a dc–dc boost converter. Then, the ABC-P&O strategy was applied to obtain the voltage reference for the outer PI control loop, which provided the current reference to the discrete-time sliding-mode current control. The ABC-P&O algorithm has a reasonable computational cost, allowing the use of a commercial, low-priced digital signal controller (DSC) with outer voltage and inner current control loops. Many challenging tests validated that the proposed ABC-P&O technique converges fast to the GMPP with high efficiency and superior performance under different PSCs.Ítem A Sensorless Inverse Optimal Control Plus Integral Action to Regulate the Output Voltage in a Boost Converter Supplying an Unknown DC Load(Institute of Electrical and Electronics Engineers Inc., 2023) Montoya, Oscar Danilo; Gil-González, Walter; Riffo, Sebastián; Restrepo, Carlos; González-Castaño, CatalinaThis study utilizes inverse optimal control (IOC) theory to address the issue of output voltage regulation in a boost converter feeding an unknown direct current (DC) load. The proposed approach involves developing a general feedback control law through IOC to ensure asymptotic stability in closed-loop operation, with the added advantage of incorporating an integral gain without compromising stability. Two estimators are introduced to minimize the number of sensors required for implementing the IOC controller with integral action. The first estimator, based on the immersion and invariance (I&I) method, determines the current demand of the DC load by measuring the boost converter's output voltage. While the second estimator, using the disturbance observer (DO) method, estimates the voltage input value by measuring the inductor's current flow. Both methods guarantee exponential convergence to the precise value of the estimated variable, irrespective of the initial estimation points. Experimental validation using varying DC loads and estimation techniques confirms the proposed IOC approach's effectiveness and robustness in regulating voltage for DC loads connected to a boost converter. Furthermore, the proposed controller is compared to the sliding mode control and presents a better performance with a more straightforward design, and the stability in closed-loop ensured.Ítem An mppt strategy based on a surface-based polynomial fitting for solar photovoltaic systems using real-time hardware(MDPI AG, 2021-01) González-Castaño, Catalina; L. Lorente-Leyva, Leandro; Muñoz, Javier; Restrepo, Carlos; H. Peluffo-Ordóñez, DiegoThis paper presents an optimal design of a surface-based polynomial fitting for tracking the maximum power point (MPPT) of a photovoltaic (PV) system, here named surface-based polynomial fitting (MPPT-SPF). The procedure of the proposed MPPT-SPF strategy is based on a polynomial model to characterize data from the PV module with a global fit. The advantage of using polynomials is that they provide a good fit within a predefined data range even though they can diverge greatly from that range. The MPPT-SPF strategy is integrated with a DC-DC boost converter to verify its performance and its interaction with different control loops. Therefore, the MPPT strategy is applied to the reference outer PI control loop, which in turn provides the current reference to the inner current loop based on a discrete-time sliding current control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardware-in-the-loop system to obtain the results. The proposed strategy does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared with a conventional perturb and observe method to prove its effectiveness under demanding tests. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.Ítem Dynamic modeling of a proton-exchange membrane fuel cell using a gaussian approach(MDPI, 2021-11) González-Castaño, Catalina; Lorente-Leyva, Leandro L.; Alpala, Janeth; Revelo-Fuelagán, Javier; Peluffo-Ordóñez, Diego H.; Restrepo, CarlosThis paper proposes a Gaussian approach for the proton-exchange membrane fuel cell (PEMFC) model that estimates its voltage behavior from the operating current value. A multi-parametric Gaussian model and an unconstrained optimization formulation based on a conventional non-linear least squares optimizer is mainly considered. The model is tested using experimental data from the Ballard Nexa 1.2 kW fuel cell (FC). This methodology offers a promising approach for static and current-voltage, characteristic of the three regions of operation. A statistical study is developed to evaluate the effectiveness and superiority of the proposed FC Gaussian model compared with the Diffusive Global model and the Evolution Strategy. In addition, an approximation to the exponential function for a Gaussian model simplification can be used in systems that require real-time emulators or complex long-time simulations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Ítem Model Predictive Control for Stabilization of DC Microgrids in Island Mode Operation(MDPI, 2022-09) Murillo-Yarce, Duberney; Riffo, Sebastián; Restrepo, Carlos; González-Castaño, Catalina; Garcés, AlejandroDC microgrid (DCMG) is a promising technology for integrating distributed resources, such as solar generation and energy storage devices, that are intrinsically DC. Recently, model predictive control (MPC) is one of the control techniques that has been widely used in microgrid applications due to its advantages, such as transient response and flexibility to nonlinearity inclusion. MPC applications can be centralized, distributed, or decentralized based on the communication architecture. A major disadvantage of the centralized model predictive control (CMPC) is the high computational effort. This paper proposes a CMPC for DCMG stabilization that uses the admittance matrix of a reduced DCMG in the prediction equation and the one-step prediction horizon to decrease the computational effort. The proposed CMPC also replaces the hierarchical architecture primary and secondary controls, achieving voltage or power regulation. A hardware-in-the-loop (HIL) tool, known as RT-Box 2, has been used to emulate an 8-node DC microgrid with versatile buck–boost converters at the supply and power consumption nodes. The proposed predictive control exhibited better performance in comparison with the averaged voltage control in the HIL experiments.