Energy structures from non-conventional energy source has become highly demanded nowadays. In this way, the maximum power extraction from photovoltaic (PV) systems has attracted the attention, therefore an optimization technique is necessary to improve the performance of solar systems. This article proposes the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter. The procedure of the ABC MPPT algorithm is using data values from PV module, the P-V characteristic is identified and the optimal voltage is selected. Then, the MPPT strategy is applied to obtain the voltage reference for the outer PI control loop, which in turn provides the current reference to the predictive digital current programmed 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 general system 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 to the conventional perturb and observe method, results show the proposed method archives a much superior performance.

With the increasing penetration of distributed energy resources into islanded microgrids, a grid-forming inverter (GFI) has become the key element interfacing renewable energy sources. Usually, the GFI is employed with an output filter to minimize the harmonic content achieving high-quality output voltage regulation. To this end, model-predictive control (MPC) has been widely proposed to control the output voltage of GFI systems due to the high-quality performance, fast control response, and straightforward handling of constraints. However, an accurate model of the system is required for the conventional MPC to avoid a suboptimal performance under uncertainties. To overcome this known drawback, a novel model-free predictive control is proposed in this article. Consequently, the output voltage of an $LCL$ -filtered GFI is regulated without the knowledge of the physical model.

A Maximum Power Point Tracking (MPPT) algorithm is proposed based on the assumption that a simplified three-parameter photodiode-based model can provide an excellent approximation of a PV module i-v curve around its maximum power point (MPP). Procedures to obtain the MPP coordinates and the three parameters of the approximated i-v curve from experimental online measurements, analytical and Newton-Raphson iterative calculations are thoroughly described. Initializing the model as well as optimizing it to operate faster by identifying only subsets of the model parameters provides excellent MPPT efficiency in both static and dynamical MPPT situations. The performance of the proposed algorithm has been verified in comparison with other well-known MPPT methods using the software-in-the-loop approach. Next, its performance has been evaluated by using the MATLAB-based hardware-in-the loop experimental setup that provides the required reproducibility of the different synthetic and real irradiance and temperature profiles considered.

This article proposes a continuous-control-set model-predictive control (CCS-MPC) designed explicitly for second-order dc/dc converters, such as the boost, buck, buck–boost, and noninverting buck–boost converters. These converters share a bilinear dynamic structure that allows designing a generalized control. In contradistinction to the conventional finite-control-set approach, the proposed control uses a pulsewidth modulation that allows a continuous control set with constant switching frequency. The modulation index's saturation is considered an inequality constraint in the optimization model. The proposed control guarantees stability in the sense of Lyapunov. This property is proven by using the passive structure of these converters. Experimental results on the four converters mentioned previously show the superior performance of the proposed control in terms of robustness, offset, computation time in the central processing unit, and, of course, stability.

The 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.

This 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.