This paper proposes a digital twin-based diagnosis and fault tolerant control for T-type three-level rectifiers. To develop the digital twin (DT), a dense deep neural network (DNN) machine learning approach is used. The digital twin is trained offline using a set of experimental data and updated online to get the maximum possible accuracy. Then, the DT is used for the diagnosis and tolerance of open-switch faults (OSFs) and faults related to voltage and current sensors (VACSF) or for sensorless control. The open-switch fault detection and localization algorithm is implemented based on the dynamic response difference between the physical system and its digital twin. First, the open-switch fault is detected and localized based on the grid current dynamics, where each switch fault generates a specific pattern in the current dynamics. Open-switch fault is tolerated by changing the switching function based on the location of the fault. Second, the voltage and current sensors fault is detected when the digital twin provides a specific amplitude of currents while the physical sensors do not provide a correct measurement. This case is tolerated by feeding back the grid currents or voltages from the digital twin as an alternative to the physical sensors. The proposed technique has low overhead, enhances the reliability of the power converter, and is applicable for sensorless mode of control. Experimental investigations are conducted to validate the proposed concept.

This article introduces a set of innovative solutions designed to enhance the scalability of ac–dc side operations in fast electric vehicle (EV) charging stations. The proposed solutions focus on streamlining and regulating the connection process for integrating a new active front-end rectifier (AFR) with existing AFRs at the high-voltage dc-link side, without requiring detailed knowledge of their specifications, such as control algorithms, switching frequencies, topologies, and modulation techniques. Neglecting these specifications can result in significant adverse effects, particularly on grid currents within the ac–dc side of the charging station. Conversely, these specifications have minimal impact on the dc–dc side. Therefore, this article primarily concentrates on addressing the scalability challenges associated with the ac–dc side of fast EV charging stations. Experimental analysis is conducted to assess the consequences of underestimating any specification, followed by the proposal of a general solution to compensate for or reduce each effect. The proposed methods effectively address the critical demand for scalability in fast EV chargers. Furthermore, these techniques are implemented using low-cost microcontrollers and validated experimentally through the utilization of three parallel-connected AFRs.

This article proposes a sensorless self-tuning control algorithm for a three-phase T-type rectifier. The proposed sensorless algorithm consists of three layers including an observer, which estimates grid voltages or currents based on the Kalman filter algorithm, an online estimator, which estimates the state-space matrix parameters, and a linear quadratic tracker (LQT), which is the main controller in this article. On the other hand, the performance of the LQT depends on the gains that need to be adjusted according to the system parameters, which is a challenge. To overcome this problem, a self-tuning method based on recursive least square algorithm is also proposed to tune the gains of LQT online. An experimental prototype is established, and the results, including the steady state and dynamic performances, are carried out and presented to validate the effectiveness of the proposed controller.

This paper presents an approach for enhancing the reliability and robustness of electric vehicle (EV) chargers, particularly the DC-DC side of the EV chargers, by using the Inverse Model Predictive Control (IMPC). IMPC, a recently introduced control method for power electronic converters, leverages the strengths of Model Predictive Control (MPC), while minimizing its computational burden. IMPC excels in managing sophisticated and nonlinear systems, controlling multiple objectives, and adhering to various constraints. However, the effectiveness of conventional IMPC is heavily dependent on the accurate dynamic model of the power converter. This dependency makes IMPC susceptible to uncertainties and disturbances. To address this challenge, the proposed method employs an adaptive estimation strategy utilizing a recursive least square algorithm for online dynamic model estimation. This real-time estimated model enables IMPC to predict optimal switching states with improved reliability. The proposed control technique is designed to provide constant power, constant current, and constant voltage modes, with the ability to seamlessly transition between them. The efficacy of this technique is demonstrated through extensive simulations and experimental validation for a dual active bridge (DAB) converter. This adaptive method underscores the potential of IMPC for practical EV charging scenarios, ensuring reliable and high-performance charging.

This article proposes a grid voltage-sensorless open-switch fault detection and fault-tolerant control for a three-phase three-level T-type rectifier. First, the parameters of the rectifier's mathematical model are estimated online. Then, changes in the estimated parameters are observed and analyzed to detect and localize the fault based on the amount of parameters’ change and which parameter is changed. When a fault is detected and localized, the fault-tolerant control switches to an alternative switching function to allow the converter to operate satisfactorily. In addition, a robust control algorithm is proposed to handle all switching functions and topological changes. Unlike cascaded controllers, the proposed controller uses a single-loop control where the current reference generation and current-tracking processes are done using a single controller. The design of such a controller depends on estimating the relationship between the dc-link voltage, grid voltages, and grid currents. Experimental investigations are conducted to validate the concept and prove its high-quality operation.

Inverse model predictive control (IMPC) is a control technique that was recently proposed for power electronic converters. IMPC inherits the advantages of model predictive control (MPC) in terms of ability to handle complex and nonlinear systems and achieving multiple control objectives, while adhering to various constraints. Unlike MPC, IMPC offers a significantly reduced computational burden by omitting the iterative computations of the cost functions and states predictions. Nevertheless, both IMPC and MPC rely significantly on the dynamic model of the power converter. This makes them susceptible to uncertainties and disturbances. This paper presents a novel technique to enhance the reliability and robustness of the IMPC for electric vehicle (EV) chargers by treating the converter's dynamic model as a black box. Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. The key benefit of the proposed technique is the utilization of an accurate and real-time estimated dynamic model, which facilitates a reliable states prediction by the IMPC. The effectiveness of the proposed technique is demonstrated through extensive simulations and experimental validation for a three-phase three-level T-type rectifier.