The conventional rotor flux estimation method has issues of dc offset and harmonics, which are caused by initial rotor flux, detection errors, etc. To eliminate these defects, one improved nonlinear flux observer is proposed for sensorless control of permanent magnet synchronous machine (PMSM). First, the rotor position estimation method based on PMSM rotor flux observation is studied. Meanwhile, the limitations of the traditional rotor flux estimators, i.e., the saturation of pure integrator, phase shift, and amplitude attenuation of a low-pass filter are analyzed. Then, two novel flux observers, second-order generalized integral flux observer (SOIFO) and second-order SOIFO, are designed for the rotor flux estimation of PMSM. Based on second-order generalized integrator (SOGI) structure, the SOIFO can limit the dc component to a certain value. Furthermore, the second-order SOIFO is developed from the SOGI, which is characterized with effective dc and harmonics attenuation capability. With the second-order SOIFO, even without magnitude and phase compensation, the dc offset and harmonics of estimated rotor flux could be well eliminated. Therefore, the speed and rotor position can be estimated accurately. All the performances of the four methods are analyzed by transfer functions and Bode diagrams. Finally, the new sensorless control strategy is validated by comprehensive experimental results.

This paper presents a Lagrange multiplier-based adaptive droop control to mitigate distribution power loss of parallel-connected distributed energy resource (DER) systems in DC microgrids. The distribution power loss comprising line loss and converter loss can be modelled as a quadratic function of the output currents of the DER systems, which can be optimized by the tertiary-layer Lagrange multiplier method to obtain the optimal output current references for the secondary-layer adaptive droop control. The output currents are compensated by the adaptive droop control to provide output voltage references for the primary-layer local dual-loop control, which is a conventional local control scheme for the regulations of grid-connected DC/DC converters. Both simulation and experimental results validate that the proposed control strategy can reduce the distribution power loss of parallel-connected DER systems in 48 V DC microgrids as compared to the conventional control strategy by only optimizing the line loss in different cases.

Hybrid modular multilevel converters (MMCs) have exhibited superior power density and cost-effectiveness than the conventional half-bridge submodule-based MMC (HB-MMC), while still maintaining the hassle of using high-voltage DC supporting capacitors or non-standard transformers. To alleviate the hassle, an asymmetric hybrid phase leg MMC (AHPL-MMC) is proposed in this paper. The presented AHPL-MMC consists of a half-bridge submodule-based phase leg and two full-bridge submodule-based hybrid phase legs. This asymmetric configuration reduces bridge arm inductance, the number of submodules, and total capacitance, thereby improving the converter's power density and cost-effectiveness. Additionally, the proposed converter can operate across the full range of modulation indices and power factors with the features of DC fault blocking and low DC current ripples. The operating principles, parameter design, and performance comparison of the AHPL-MMC are provided in this paper. Close-loop energy balancing control methods are further proposed for the hybrid phase legs and the conventional phase leg, respectively. The effectiveness of the proposed AHPL-MMC is validated by both simulation and experimental results.

In this article, a secure, sustainable, and fast charging strategy based on an advanced electro-thermal-degradation (ETD) model is proposed for lithium-ion batteries. In this ETD model, temperature, state-of-charge, and state-of-health are strongly coupled. It combines the simplicity of the equivalent circuit model and the high accuracy of the random forest (RF) algorithm. The RF algorithm is used to address uncertainties, such as measurement errors, parameter drifts, and parasitic effects, all of which may otherwise compromise model accuracy. The degradation model is derived from the Arrhenius equation and enhanced through data fitting techniques. To minimize charging time while respecting degradation constraints, we employ a data-driven Bayesian optimization (BO) approach with a constraint-based expectation improvement acquisition function. The proposed control strategy is implemented in a dual-loop structure, with an outerloop temperature control to ensure thermal safety and an innerloop current control to achieve sustainable and fast charging based on the BO. Experimental results demonstrate the effectiveness of the proposed strategy, showing that the ETD model achieves accurate temperature estimation across a wide range of operating conditions.

Distributed hierarchical control with first-order observers has been widely adopted to mitigate the false data injection (FDI) attacks on distributed energy resources (DERs) in ac microgrids. However, the conventional direct feedback control requires neighboring data feedback to ensure the robustness of distributed observers and suffers from inaccurate detections of high-order FDI attacks. To address those issues, a distributed proportional-integral observer (DPIO)-based hierarchical control is proposed. The proposed control is designed by adopting the generalized proportional-integral observers (GPIOs) which have been successively adopted in robotics, motor drives and dc/dc converters. It comprises a consensus-based secondary control, a droop control, and a local dual-loop control. Compared to the conventional control schemes, the proposed control scheme exhibits superior dynamic tracking performance, higher robustness against disturbance, and simpler algorithms and implementations. Both simulation and OPAL-RT-based hardware-in-loop experiments validate the effectiveness and enhanced performance of the proposed control to detect and eliminate FDI attacks in various cases.

Nonuniform temperatures in lithium-ion battery modules, caused by manufacturing inconsistencies, vibrations, and unequal line resistances, lead to uneven current distribution and accelerated degradation of the battery. Existing thermal management methods face challenges in achieving real-time cell-level balancing due to limited intercell modeling, high computational cost, and lack of closed-loop control. This article proposes a model predictive temperature balancing control (MPTBC) strategy based on a scalable 2-D thermal network model (TNM) that captures intercell thermal coupling and enables real-time prediction with reduced computational cost. A physics-informed neural network (PINN) models the nonlinear internal resistance, with Bayesian optimization (BO) used to efficiently identify optimal parameters. The MPTBC is implemented on a four-module, high-power-density, single-input multioutput (SIMO) switched-capacitor (SC) converter. Experiments validate the TNM accuracy and demonstrate that MPTBC effectively minimizes cell-to-cell temperature differences.