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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 article presents a novel technique to enhance the reliability and robustness of the IMPC for electric vehicle 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.

In this paper, a new control technique called inverse model predictive control (IMPC) is proposed for power electronic converters. The proposed IMPC technique is primarily based on the inverse of the conventional finite set model predictive control. The key advantage of this design is that it avoids predicting the controlled states for all possible switching-state vectors, while maintaining the advantage of adhering to multiple constraints and achieving multiple objectives. IMPC predicts the optimal control signal that minimizes the cost function and converts it into a switching vector. Unlike classical MPC, the computational time required for IMPC is significantly reduced which makes it easily applicable to all types and levels of power electronics converters. The proposed method does not depend on the number of possible switching vectors and is compatible with low-cost microcontrollers commonly used for industrial applications. Additionally, the proposed control inherits the benefits of MPC, such as the ability to achieve its objectives while adhering to various constraints, minimal parameters tuning requirements, and reduced computational time. To demonstrate the effectiveness of the proposed technique, experimental results are presented for a five-level active neutral point clamped flying capacitor inverter (ANPC-FC) as a case study.

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 an adaptive and fault-tolerant communication method for controlling parallel-connected active front-end rectifiers (AFRs). The proposed method relies on the principle of master-slave communication based on the controller area network bus protocol. The master unit is responsible for generating current reference signal and for sharing it with the slaves, while the slave units are solely responsible for generating the power based on the received reference set points. The master can be selected and changed automatically based on a negotiation algorithm among the connected rectifiers. Then, if a communication fault occurs in the master, another master is chosen by the slaves. On the other hand, slaves’ communication faults are tolerated by switching to communication-less mode of operation with online estimation of the current reference signal instead of receiving it over the communication network. The proposed algorithms are validated by simulation and experimentally on a prototype with three parallel AFRs.

This article proposes a digital twin (DT)-based diagnosis and fault-tolerant control for T-type three-level rectifiers. To develop the DT, a dense deep neural network (DNN) machine learning approach is used. The DT 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 or for sensorless control. The OSF detection and localization algorithm is implemented based on the dynamic response difference between the physical system and its DT. First, the OSF is detected and localized based on the grid current dynamics, where each switch fault generates a specific pattern in the current dynamics. OSF is tolerated by changing the switching function based on the location of the fault. Second, the voltage and current sensor fault is detected when the DT 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 DT 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 paper proposes an adaptive control technique (ACT) for a three-phase, three-level, T-type rectifier based on online disturbance estimation and compensation. The proposed solution also regulates the DC-link voltage and grid currents under uncertainties, disturbances, measurement noises, and unbalanced grid voltages without cascaded control. The proposed controller consists of two layers; the first one is responsible for estimating nonlinearities and model uncertainties based on Kalman Filter Algorithm (KFA). The second layer is responsible for controlling both grid currents and DC link voltage using Linear Quadratic Regulator (LQR). The proposed controller is analyzed theoretically, validated experimentally, and the performance of the proposed controller is compared with two other controllers. The simulation and experimental results prove the superiority of the proposed controller and show that the proposed controller can ensure fast-tracking performance with almost zero steady-state error. The proposed controller has the ability to overcome severe disturbances such as AC and DC side disturbances, measurement noises, and mathematical model’s uncertainties even up to 400%.