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In this paper, two novel approaches for identifying the synchronous generator (SG) parameters are presented. The proposed approaches use an experimentally obtained armature current during the short-circuit test as input data. Both approaches are simple, cost-effective, and practical since they do not require any additional equipment for signal generation nor applying any of the advanced control techniques. The first approach relies on fitting the analytical total armature current waveform with the experimental one, while the second approach relies on matching the observed waveform with an analytical waveform of asymmetrical and alternating components. Instead of traditional graphical methods typically used in standardized procedures, this paper also proposes a novel hybrid metaheuristic algorithm to identify the SGs parameters. The experimental verification of the proposed methods is carried out on 100 MVA SG at the Bajina Basta hydropower plant (HPP) in Serbia.

Recently, IGCTs have shown a great potential for the soft-switching dc transformer applications based on LLC resonant converter. The zero-voltage switching that is present in this converter topology enables an operation of IGCTs without clamping circuit and with significantly higher switching frequencies, while maintaining the superiorly low conduction losses. The high voltage and current ratings of the IGCTs make them a preferred choice for an efficient bulk power transmission. This paper shows how gate units for IGCTs can be optimized for this soft-switching application. It presents a simplification of the driving topology and the design process for high switching frequencies. This results in low power consumption and a very small size of the gate unit. The prototype of the gate unit is built and experimentally validated in a 2.5 kV 0.75 kA resonant operation of the IGCT.

The bipolar pad is one of the most promising topologies in inductive power transfer systems for electric vehicles. However, there is scant literature on the analytical model of the bipolar pad. In this paper, a 3-D analytical model of the inductive power transfer system including a bipolar transmitter and a square receiver is developed based on the superposition of two 2-D subdomain analytical models. Ferrite and the aluminum shields with finite dimension are taken into account on the transmitter and receiver sides. An analytical calculation of the mutual inductance is then carried out with respect to the main parameters of the inductive power transfer system, namely the dimension of the coils, the conductivity and the permeability of the shield. Two study cases are demonstrated to highlight how the proposed method can accelerate speed up the pad design process. Calculation results of the proposed model are compared with both a finite element analysis model and experimental measurements, demonstrating that the proposed model is 9 times faster than the finite element analysis method. When comparing with the experimental results, computational error of the proposed model is less than 6% in most of the study cases

In this paper, a novel modular multilevel dc-dc converter with high power-density potential is proposed based on a cascaded string of voltage source submodules (VSM) merged with a current source submodule (CSM) that is configured to rapidly invert the polarity of the current in the VSM string. The proposed hybrid converter combines the i) modularity advantages of the modular multilevel converter (MMC) and ii) the high effective frequency advantages of the flying capacitor converter (FCC). Unlike other hybrid non-isolated MMC structures the proposed converter features a novel CSM stage enabling i) a near 50% reduction in string current, and ii) the input source and output load to be commonly grounded. In addition, the proposed operational approach enables self-balancing of the VSMs and a simple overall control structure. This article introduces the main operating principles of this novel topology termed the Current Shaping Modular Multilevel Forward Converter (CS-M2FC) along with a proposed control design. A peak efficiency of 97.1% is measured for a laboratory-scale prototype at 950-137V and 1.45 kW.

Considering the innately connected neutral point in a multi-functional converter system (MFCS) fed three-phase surface-mounted permanent magnet synchronous machine (SPMSM) drive, this paper develops its fault self-tolerance towards open-phase faults (OPFs) without assisted hardware. First, the operating principle of the MFCS fed SPMSM drive is explained by building a zero-sequence model. Second, to achieve fault-tolerant operations, an unconventional current vector trajectory is proposed, aiming at maintaining the torque and average input power invariable. The shape of the proposed trajectory tends to be like a heart rather than a circle with the rise of motor speed. Moreover, d-axis and zero-sequence currents vary with the position of rotor. By considering the limits of inverter voltage and current, the post-fault output capability of the drive is explicitly analyzed. Third, a new fault-tolerant control strategy is thus proposed, in which the OPF is detected by comparing the deviation between predictive and actual currents. To track the time-varying current references, a differential flatness-based current controller is further developed. Finally, experimental results are given to verify the effectiveness.

Pantograph slide is a vulnerable component because of the contact and friction between the pantograph and catenary (PAC) while the train runs with a high speed. Condition monitoring of the slide is a necessary and meaningful work to improve the operation efficiency and reduce the occurrence of accident. It is a hard work to measure the slide abrasion due to the dynamic and complicated environment. In this paper, an accurate edge extraction method based on image analysis is proposed, which aims at providing an image metric property based on vision measurement to detect the wear of slide. First, a subpixel edge detection method is proposed. The gradient in horizontal direction is first taken into account, which filters out the non-horizontal edges. Then, the upper and lower edges of the slide are located using the geometric restriction. The subpixel edge feature are also extracted in the image process, which can be used to the vision measurement system for monitoring the wear condition of the slide. The experiment results shows that the proposed algorithm can precisely detect the subpixel edges of the slide. The measurement error of the slide is less than 2 mm, which demonstrates that the proposed algorithm has a high accuracy in the large field of view.

In this paper, a new 5-level inverter is developed by inserting a switched-capacitor (SC) unit into the traditional 3-level neutral-point-clamped (NPC) inverter phase-leg. The SC unit consists of two capacitors and one bidirectional switch, all of which withstand a quarter of the dc input voltage. Whilst increasing the output levels, the performance is also improved in terms of power loss, common-mode voltage, switching stress dv/dt and output filter. Compared to other 5-level inverters like the conventional NPC and active NPC 5-level topologies, the new solution not only reduces the number of components and simplifies the design, but also has the advantage of self-balanced capacitor voltages. The analysis, simulation and experiment indicate that the proposed inverter is suitable for a wide range of applications like renewable source grid-connected interfaces and motor drivers. Simulation and experimental results of grid-connected operation verify that the new inverter is capable of providing both active and reactive power to the grid. Its excellent performance is also experimentally evaluated by a 1.2 kW prototype and the measured efficiency is above 97% for a wide range of load.

Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This paper proposes a knowledge-based, multi-physics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multi-objective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to trade-off smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the state-of-the-art model predictive controller to validate its superiority in terms of charging rapidity, enforcement of LIB thermal safety and life extension, as well as the computational tractability.

This paper demonstrates a vibration test for a resonant MEMS scanning system in operation to evaluate the vibration immunity for automotive lidar applications. The MEMS mirror has a reinforcement structure on the backside of the mirror, causing vibration coupling by a mismatch between the center of mass and the rotation axis. An analysis of energy variation is proposed, showing direction dependency of vibration coupling. Vibration influences are evaluated by transient vibration response and vibration frequency sweep using a single tone vibration for translational y- and z- axis. The measurement results demonstrate standard deviation (STD) amplitude and frequency errors are up to 1.64 % and 0.26 %, respectively, for 2 grms single tone vibrations on y axis. The simulation results also show a good agreement with both measurements, proving the proposed vibration coupling mechanism of the MEMS mirror. The phased locked loop (PLL) improves the STD amplitude and frequency errors to 0.91 % and 0.15 % for y axis vibration, corresponding to 44.4 % and 43.0 % reduction, respectively, showing the benefit of a controlled MEMS mirror for reliable automotive MEMS lidars., 9 pages, 11 figures, to be published in IEEE Transactions on Industrial Electronics

Current pulses are convenient to be actively implemented by a Battery Management System (BMS). However, the Short-Term Features (STF) from currentpulses originate from various sensors with uneven qualities, which hinders one powerful and strong learner with STF for the battery SOH estimation. This paper thus proposes an optimized weak learner formulation procedure for Lithium-ion (Li-ion) battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto Front-based Selection Strategy (PFSS) is designed to select the representative solutions from the non-dominated solutions fed by a Knee point driven Evolutionary Algorithm (KnEA), which guarantees both the diversity and accuracy of the weak learners. Afterwards, the weak learners, whose coefficients are obtained by Selfadaptive Differential Evolution (SaDE), are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of SOH estimation. The validation of the proposed method is proved by LiFePO4/C batteries under accelerated cycling ageing test including one mission profile providing Primary Frequency Regulation (PFR) service to the grid and one constant current profile.

This paper proposes an adaptive trajectory tracking control scheme for low-speed car-like vehicles with less efforts in tuning of the control gains. An interesting way of integrating adaptive control gains with consideration of steering saturation by using the backstepping technique is designed to enhance trajectory tracking while ensuring the commanded inputs within the input boundaries. The design of such adaptive control gains is also based on enhancing the convergence rate of tracking errors, especially for lateral deviation from the reference trajectory. It is further theoretically proven that, even under the influence of steering saturation, the proposed controller can make the closed-loop system approximately globally asymptotically stable at zero errors. Comparative MATLAB/ Simulink simulations and experimental tests based on Quanser latest self-driving car (QCar) have been conducted to verify the effectiveness of the proposed control scheme in accurate tracking without violating the input constraints.

In order to improve robustness and dynamic tracking performance, this paper proposes a deadbeat control method based on an enhanced predictive model and an exponential extended state observer (EDB-EESO). By incorporating parameter mismatches and load perturbation as lumped disturbance, extended state-space realizations of permanent magnet synchronous motor (PMSM) speed and stator current mathematical model are presented. To achieve higher robustness under the disturbance, the exponential extended state observer (EESO) is proposed based on the extended state-space realizations. Combining the EESO, the enhanced predictive model is designed to improve overshoot and settling time caused by the system inertial link. To eliminate the effect of time delay, a two-step predictive control method is adopted in the enhanced predictive model. Furthermore, the cost function of EDB-EESO is evaluated by utilizing the deadbeat control method and the stability of EDB-EESO is proved. The experimental results validate the strong robustness and excellent dynamic tracking performance of EDB-EESO in PMSM drives compared with conventional methods.

In this paper, we propose a hybrid framework using visual and force sensing for human-robot co-carrying tasks. Visual sensing is utilized to obtain human motion and an observer is designed for estimating control input of human, which generates robot's desired motion towards human's intended motion. An adaptive impedance-based control strategy is proposed for trajectory tracking with neural networks (NNs) used to compensate for uncertainties in robot's dynamics. Motion synchronization is achieved and this approach yields a stable and efficient interaction behavior between human and robot, decreases human control effort and avoids interference to human during the interaction. The proposed framework is validated by a co-carrying task in simulations and experiments.