Wireless Power Transfer (WPT) technology has gained significant attention in recent years due to its potential for efficient and convenient energy transmission without physical connections. While most literature discusses WPT converters operating at resonance, this paper investigates system behavior under non-resonant conditions caused by deviations in resonant capacitor values or change in operating frequency. In addition, a study is conducted to examine the impact of mutual inductance variations—caused by changes in coil distance—on system performance under both resonant and non-resonant conditions. A Dual Active Bridge (DAB) topology is considered for the power conversion stage, with different compensation methods—Series-Series (S-S), LCL-S, and LCCL-S. The paper proposes modulation strategies to maintain stable output power across varying operating conditions. Simulations conducted in MATLAB validate the effectiveness of these modulation techniques in conserving output power across parameter variations and re-establishing system efficiency.

An uninterruptible power supply (UPS) application requires a DC/AC converter to connect AC loads to the battery DC power source. Most inverters used for such application are multi-stage including a bulky component in the DC link leading to decreased efficiency. Single stage inverters offer the advantages of increased power density, higher efficiency, compact size and reduced weight. This paper presents the design and control of a high-frequency, high-efficiency single-stage isolated DC/AC inverter for UPS applications. The discussed inverter employs a structure and a topology inspired from Dual Active bridge (DAB). The proposed topology allows bidirectional power flow. First, the modeling and the control of the proposed structure is developed. Then, the effectiveness of the proposed inverter and the control algorithms are validated through simulations.

With environmental pollution widespread and a critical shortage of fossil fuels, the development of electric vehicles powered by green energy sources becomes crucial for tackling this issue. Especially noteworthy is the hybrid electric vehicle that utilizes fuel cells and supercapacitors, which is the most effective option. Numerous optimization challenges exist in finding power sharing algorithms between fuel cell (FC) and supercapacitor (SC) aimed to maximize the system's efficiency while upholding the supercapacitor's state of charge (SOC) boundaries. However, most of the existing models don't consider the FC dynamic impact caused by several factors such as FC membrane hydration, temperature, and hydrogen pressure which can affects negatively on performance of the system. The paper presents inner loop control algorithm that aims to manage the power-sharing between FC and SC sources guided by the decision input power sharing coefficient. Additionally, the paper shows an outer loop control algorithm that performs two functions: the 1stis to identify the FC model specifically the curve that links the efficiency to the output power of the FC. The 2ndseeks to optimize both overall efficiency and the operational management of the supercapacitor's SOC. These objectives are integrated within the suggested adaptive multiple prediction horizon dynamic power-sharing model predictive control (MPC) algorithm and utilized for an open-end winding induction motor (OEWIM) fed by the power sources through a dual traditional inverter.

International audience

Modular Multilevel Converters (MMCs) are widely used in medium to high voltage/power applications such as in energy transformation and grid connected systems. MMCs benefit from being redundant and allowing transformer-less operations. Unfortunately, they suffer from having a high cost due to the high number of sub-modules (SMs). Moreover, they are hard to control. In order to operate the MMCs properly, the load current, the circulating current and the capacitor voltages should be controlled. The usual control methods are synthesized in order to control the average lower and upper arm capacitor voltage as well as the load current. However, in some special cases, the capacitor values could change due to the aging of the capacitors, or the SM may stop working completely. Thus, creating an imbalance in the capacitor voltages of each arm in the system. This paper presents a new control scheme that allows to control the voltage of the upper and lower arms separately while controlling the load current at the same time. The usual and the new proposed controllers are developed and compared through software simulation. Finally, the conclusion is drawn.

Squirrel cage induction motors are widely used in the industrial field as they present many advantages compared to other types of electric machines. They are self-starting, need little maintenance, can work in harsh environment and are relatively cheap. Many industrial applications requiring current or speed control of the machine need the knowledge of the machine parameters. Therefore, induction motor parameter identification algorithms are developed taking in consideration the accuracy of the identification process which impacts on the quality of the control and thereby the overall system. These algorithms are usually sensitive to measurement noise thus there usage in noisy industrial environment is not desirable. This paper presents three induction motor offline identification algorithms and test their accuracy and their ability to work in noisy environment. The first method subject to the study is a Least Mean Square (LMS) based identification method working at standstill, the second is a newly introduced method robust toward noises and the third is the classic no-load and blocked rotor identification method. Simulation results are presented and finally conclusions are drawn.

On-line system identification is generally used to find the mathematical or frequency model of a system for control applications. In particular, the cross-correlation technique is nowadays used for nonparametric identification of linear or linearized systems such as switching power converters. This method is however limited to systems with close time constants, therefore, it cannot be applied to systems with different time constants in a large bandwidth, such as machine drives. This paper presents a modified cross-correlation method. It first provides an improved calculation technique for the parameters of the pseudo-random binary signal used as input signal for identification. It then modifies the initial cross-correlation technique allowing the identification of systems with different time constants in a large bandwidth. Simulation results using MATLAB/Simulink validate the feasibility of the proposed technique in order to implement it using FPGAs.