This article presents a data-driven methodology for modeling lithium-ion batteries, which includes the estimation of the open-circuit voltage and state of charge. Using the proposed methodology, the dynamics of a battery cell can be captured without the need for explicit theoretical models. This approach only requires the acquisition of two easily measurable variables: the discharge current and the terminal voltage. The acquired data are used to build a linear differential system, which is algebraically manipulated to form a space-state representation of the battery cell. The resulting model was tested and compared against real discharging curves. Preliminary results showed that the battery’s state of charge can be computed with limited precision using a model that considers a constant open-circuit voltage. To improve the accuracy of the identified model, a modified recursive least-squares algorithm is implemented inside the data-driven method to estimate the battery’s open-circuit voltage. These last results showed a very precise tracking of the real battery discharging dynamics, including the terminal voltage and state of charge. The proposed data-driven methodology could simplify the implementation of adaptive control strategies in larger-scale solutions and battery management systems with the interconnection of multiple battery cells.

This article presents a data-driven methodology for modeling DC–DC power electronic converters. Using the proposed methodology, the dynamics of a converter can be captured, thereby eliminating the need for explicit theoretical modeling methods. This approach only requires the acquisition of fundamental measurements: currents through inductors and voltages across capacitors. The acquired data are used to construct a linear difference system, which is algebraically manipulated to form a state–space representation of the converter under analysis. Three DC–DC converter topologies were analyzed, and their resulting models were tested and compared with simulation data, yielding an average error deviation of approximately $2\%$ for current signals and $4\%$ for voltage signals, demonstrating precise tracking of the actual dynamics. The proposed data-driven methodology could simplify the implementation of adaptive control strategies in larger-scale solutions or in the interconnection of multiple converters.

This work introduces a boost converter with quadratic gain. Its main advantage compared to well-known similar quadratic boost converters is that it requires capacitors with a relatively small capacitance and inductors with small inductance, leading to a reduction in the size or stored energy while performing a power conversion of similar power rating and the same switching ripples in both the input current and the output voltage. It is inspired by the recently introduced ISB converter and uses a specific PWM method. This results in achieving switching ripple constraints while using smaller energy storage elements (capacitors and inductors). The updated converter offers the same voltage gain compared to the conventional quadratic boost topology with the benefit of compact component sizes. While it has more passive elements, they are of reduced size. An analysis of energy storage revealed that this new converter uses only half the energy in inductors and 14% in capacitors when compared to specific design parameters.

This paper introduces a controller designed for transformerless inverters connected to the grid via an inductor-capacitor-inductor (LCL) output filter. The controller incorporates a virtual ground (VG) connection, providing a straightforward solution to mitigate common-mode current (CMC), i.e., to reduce the leakage ground current issue. Specifically, the focus is on a three-level, three-phase NPC inverter with split DC-link capacitors. The VG is established by connecting the star point of the LCL filter capacitors to the midpoint of the DC-link. This topology presents specific challenges, including voltage imbalances in the DC-link capacitors and resonances in the homopolar components of the signals within the LCL filter. The proposed controller not only addresses the goal of current injection but also ensures, on average, the balance of DC-link capacitor voltages (or equivalently, the regulation of the neutral point). Additionally, it provides the necessary damping for the homopolar components of the LCL filter signals. The performance of the proposed control scheme has been experimentally tested in a 2 kW three-phase NPC inverter prototype.

This article presents a data-driven modeling methodology applied to a battery-based power system comprising a power converter and an electric machine. The proposed method captures the dynamics describing the complete system and allows the identification of its parameters without the need for any explicit theoretical model of the components. In particular, the proposed approach considers the battery as the supplying element of a broader system comprising power electronics converters and direct-current motors, paying special attention to the battery open-circuit voltage curve estimation. This approach successfully yields a state-space representation that optimally describes the more essential variables, such as motor speed and output voltages of the converter and battery. Consequently, the proposed approach allows the generation of higher-order models representing transient and rapid dynamics and facilitates the identification of parameters that define reduced-order models describing slower dynamics. This streamlines the implementation of adaptive control strategies, providing an effective tool for their development and execution.