This paper presents novel current sensorless maximum-power point-tracking (MPPT) algorithms based on compensators/controllers and a single-input voltage sensor. The proposed MPPTs eliminate the expensive and noisy current sensor, which can significantly reduce the system cost and retain the advantages of the widely used MPPT algorithms, such as Incremental Conductance (IC) and Perturb and Observe (P&O) algorithms. Additionally, it is verified that the proposed algorithms, especially the proposed Current Sensorless V based on PI, can reach outstanding tracking factors (TFs) such as the IC and P&O based on PI algorithms. In this sense, the insertion of controllers inside the MPPT gives them adaptive characteristics, and the experimental TFs are in the remarkable range of more than 99%, with an average yield of 99.51% and a peak of 99.80%.

This manuscript presents current sensorless algorithms for maximum power point tracking (MPPT) in partially shaded photovoltaic (PV) systems. The necessity of a current sensor is eliminated with the use of mathematical modeling of the power electronics converter. This approach significantly reduces the implementation cost and the inherent disadvantages in the current sensor circuitry. MPPT techniques based on soft computing are employed, in addition to Perturb and Observe (P&O), due to their ability to explore a larger search space. This feature is advantageous because it minimizes convergence risk to a local maximum, a limitation of traditional techniques. Simulation and experimental results are presented and each algorithm is evaluated through different metrics, such as search time for the global maximum power point (GMPP) and efficiency. The tests consider dynamic irradiance profiles, producing a tracking factor (TF) above 99% and a remarkable fast convergence time.

This paper presents the power electronics architecture of an electric vehicle (EV) charging station operating as a DC microgrid with an AC grid interface. The grid-interface converter is implemented using an interleaved bidirectional DC–AC converter supporting bidirectional power exchange with the utility grid, as required for vehicle-to-grid (V2G) operation, while providing ancillary services to the utility grid. In addition to bidirectional power flow, the converter supports active power curtailment, reactive power compensation, and active power filtering, improving grid support and power quality. Within the DC microgrid, bidirectional converters manage the energy exchange between the DC bus, stationary battery storage, and electric vehicles. The EV charging stage is implemented using a set of parallel bidirectional dual active bridge (DAB) DC–DC converters, enabling galvanic isolation, high power density, and efficient bidirectional energy transfer between the DC bus and the vehicle batteries. To enhance the sustainability of the proposed system, a photovoltaic generation unit is connected to the DC bus through a dedicated interleaved power electronics converter. The control strategies and validation of the proposed architecture are presented with emphasis on converter operation and grid-support functionalities. The results demonstrate stable operation during battery charging and discharging, bidirectional power transfer between the EV battery and the DC bus through the DAB converter, bidirectional power exchange between the DC bus and the utility grid through the grid-interface converters, and a significant reduction in the total harmonic distortion (THD) of the grid currents while enabling advanced ancillary grid services.