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For gallium nitride (GaN) based triangular current mode (TCM) applications, the dead-time has a significant effect on the switching loss. However, previous GaN high electron mobility transistor (HEMT) models focus on the turn-on/off process without fully considering the effect of the dead-time. Hence, this article analyzes the switching transients under the superfluous and insufficient dead-time and evaluates the dead-time loss with an improved GaN HEMT model. The proposed model improves an existing GaN HEMT model by adding the voltage rising/falling time of the gate driver, the dynamic threshold voltage of Schottky-type GaN HEMTs, and an equivalent gate-drain capacitance obtained from the datasheet. Verified by a GaN-based double-pulse test, the proposed model can more accurately calculate the gate-source voltage and the self-commutated reverse conduction voltage. Verified by a GaN-based TCM Buck converter, the proposed model can predict the dead-time loss well and has higher simulation accuracy for the turn-on process induced by the inappropriate dead-time. [ABSTRACT FROM AUTHOR]

This article reveals that the saturation block (SB) used with the power-angle control loop can enhance the transient stability of grid-forming (GFM) inverters. Three methods of using the SB in the power-angle loop are examined based on power-angle curves. It is found that all can enhance the transient stability of GFM inverters by either decreasing the acceleration area or increasing the deceleration area. At the same time, the threshold value selection of SB in the three schemes is also explained, considering the tradeoff between the transient stability and the response speed. A holistic comparison of the three schemes recommends the scheme with the SB used for power error adopted by the GFM inverters. Finally, experimental tests confirm the effectiveness and correctness of the analysis. [ABSTRACT FROM AUTHOR]

Data centers require high efficiency and high power-density dc–dc converter with wide input range and galvanic isolation. To overcome the drawbacks of traditional single-stage LLC and two-stage solutions, a novel partial power regulation topology with wide input range is proposed in this article. The proposed topology delivers the load power through a three-ports LLC-based dc transformer regulated DCX (DCX). A pulsewidth modulation (PWM) converter is cascaded with one of the input ports of the DCX and then in series with the other input port to regulate the output voltage. Since a single transformer and a partial power PWM converter are employed, high power density can be achieved. Furthermore, only a portion of the load power is transferred by the PWM converter, the efficiency sacrifice is reduced. By introducing an appropriate negative current, the zero voltage switching of the PWM is also achieved. Furthermore, when the input voltage approaches the lower or the upper limit, the switching frequency of the PWM regulator can be ultralow. As a result, switching losses and core losses are further reduced. Finally, a prototype with 190—475 V input and 12 V/500 W output is demonstrated, which achieves a peak efficiency of up to 97.2% and a power density of 153 W/in3. [ABSTRACT FROM AUTHOR]

Conventional fast Fourier transform analysis is limited in conducted electromagnetic interference (EMI) prediction for triangle current mode (TCM) based converters. Several prediction strategies based on EMI receiver modeling have been published for this issue, but the prediction range was below 1 MHz. Such a narrow frequency range cannot meet the demands of EMI prediction for MHz converter. In this article, a more accurate differential mode (DM) EMI prediction is proposed for MHz TCM-based single-phase inverter. The accurate drain-to-source voltage model considering the TCM resonant transition is established for different TCM-derived strategies at first. Passive components of the propagation path are also modeled with the measured impedance for more accurate transfer gain. An improved envelope detector model of the EMI receiver is proposed for an accurate quasi-peak spectrum. The prediction range can be extended to 5 MHz. Moreover, the EMI of TCM, hybrid triangle current mode, and sinusoidal triangle current mode are analyzed and compared with the established prediction strategy. The worst spectrum can be determined, and the DM filter can be designed without repetitive measurements. Finally, A 500 W MHz single-phase inverter prototype is built, and the evaluation of the measured conducted EMI spectrum and the EMI filter design can verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

This letter analyzes the impact of hybrid voltage- and power-based synchronization control on the transient stability of grid-forming (GFM) inverters. It is found that the added voltage-based synchronization loop exploits the phase angle difference to resist frequency deviation, whose function is similar to the damper winding of synchronous generators. By resembling the large-signal model of such hybrid synchronization control (HSC) to the power-angle swing equation, it is proved that the HSC brings higher damping to the system and thus, enhances the transient stability of GFM inverters. Further, a comparative study of two active power controllers, i.e., the droop controller with a low-pass filter and the proportional-integral (PI) controller, is given. It is revealed that the HSC with the droop controller equivalently decreases the active power reference, which leads to better transient stability behavior than that of using the PI controller. The theoretical findings are corroborated by experimental tests. [ABSTRACT FROM AUTHOR]

Accurate trajectory prediction of surrounding vehicles enables lower risk path planning in advance for autonomous vehicles, thus promising the safety of automated driving. A low-risk and high-efficiency path planning approach is proposed for autonomous driving based on the high-performance and practical trajectory prediction method. A long short-term memory (LSTM) network is trained and tested using the highD dataset, and the validated LSTM is used to predict the trajectories of surrounding vehicles combining the information extracted from vehicle-to-vehicle (V2V) technology. A risk assessment and mitigation-based local path planning algorithm is proposed according to the information of predicted trajectories of surrounding vehicles. Two driving scenarios are extracted and reconstructed from the highD dataset for validation and evaluation, i.e., an active lane-change scenario and a longitudinal collision-avoidance scenario. The results illustrate that the risk is mitigated and the driving efficiency is improved with the proposed path planning algorithm comparing to the constant-velocity prediction and the prediction method of the nonlinear input–output (NIO) network, especially when the velocity and trajectory with sudden changes. Note to Practitioners—This article was motivated by the problem of promising the safety decision-making and path planning through accurate environment prediction. There are two main parts included in this article. First, this article proposed one pragmatic approach to predict the environment movement correctly based on the long short-term memory (LSTM) approach. The prediction performance of LSTM was compared with nonlinear input–output (NIO). The results showed that the LSTM approach has a significant advantage in motivation prediction of the surrounded vehicles during path planning. The second part of this article is to make the decision and realize local path planning based on the risk assessment. The potential field-based approach is implemented on the risk assessment based on these accurate predictions. Some primary results demonstrate that the decision-making algorithm performs better under the accurate prediction model. The results also show that the safety and driving efficiency of the ego vehicle were improved by tracking the trajectory, which was planned based on the risk assessment. The only concern for the real-time application is the computation time; in future, we will figure it out how to further reduce the computation time. [ABSTRACT FROM AUTHOR]

Integrated buck-boost-LLC (IBBL) dcconverter is a good solution for high efficiency and high power density applications. However, the bridge leg integration causes asymmetrical zero-voltage-switching process and asymmetrical secondary current. The method of high quality factor or symmetric phase shift modulation proposed in the existing research can improve the asymmetrical current issue under heavy load, but is ineffective for light load conditions. This article proposes a novel Interleaved IBBL converter to improve the asymmetry issue for whole load range. Interleaving makes the secondary current be symmetric naturally. In addition, a comprehensive optimal design method for phase-shift angle and choke inductor is proposed to minimize the total power loss of all primary elements. A 600 W/1 MHz prototype verifies that the proposed IIBBL converter achieves symmetric secondary current in whole load range, and it achieves 95.7% efficiency and 180 W/in3 under the proposed comprehensive optimal design method. [ABSTRACT FROM AUTHOR]

In this study, a decision-making and motion planning controller with continuous action space is constructed in the highway driving scenario based on deep reinforcement learning. In the decision-making and planning problem, the goal is to achieve the safety, efficiency, and comfort of automated vehicles. In the driving scenario, the surrounding vehicles are controlled by the intelligent driver model and a general model (minimizing overall braking induced by lane change, MOBIL), which enables them to react to the environment and mimic the vehicle interactions on the highway. Given the uncertainties in the driving conditions, a specific deep reinforcement learning technique, called soft actor-critic, is used to solve the decision-making and planning problem with continuous action space. Simulation results show that the proposed method can solve the decision-making and motion planning problem in the interactive traffic environment to carry out safe lane-change maneuvers and cruise at high speed. In addition, two control policies are developed with different weights on safety, efficiency, and comfort. [ABSTRACT FROM AUTHOR]

Benefited from the fast switching speed, Gallium nitride (GaN) high electron mobility transistors (HEMTs) have been widely used in high switching frequency converters. However, due to the significant correlation between the turn-off time and operating conditions (more than 20 times difference of the turn-off time between the high load current and small load current for GaN HEMTs), using a fixed dead time will introduce extra dead-time (DT) losses in inverters where the output voltage and current are constantly varying. Therefore, this article proposes a high-efficiency adaptive method to dynamically adjust the GaN-inverter DT with operating conditions. An improved transient model including the parasitic inductance and output voltage with bidirectional solution flow has been proposed for GaN HEMTs to increase the accuracy of DT adjustment. Based on this model, the dynamic DT adjustment can be realized without extra sensors for GaN-inverters. Using the dynamic DT adjustment, the experimental results show a peak efficiency of 98.25% in a 1200 W inverter with triangular current mode modulation. Compared with using the fixed DT, the dynamic DT method can reduce the power loss by 26.7% under full load and 49.14% under light load. [ABSTRACT FROM AUTHOR]

This article presents a framework to determine driving style and design a driver steering model considering driver characteristics. First, principal component analysis (PCA) and $K$ -means clustering are utilized to classify 30 participants into cautious, moderate, and aggressive drivers. Subsequently, a generic steering model is established based on the model predictive control method. Thereafter, the maximum lateral acceleration is extracted as a crucial indicator to represent driver characteristics, and it is calibrated through probabilistic models using the dataset, which consists of the classified drivers. Besides, point estimation model and interval estimation model are leveraged to determine driving style and adjust constraints in the stochastic programming-based steering model. Finally, simulation experiments present the variations of actual output trajectories between the aggressive drivers and the cautious drivers. [ABSTRACT FROM AUTHOR]

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning (RL) framework to transform the driving tasks in the intersection environments. The driving missions at the unsignalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making policy learned from one driving task is transferred through three transfer rules in another driving mission and evaluated. Simulation results reveal that the decision-making strategies related to similar tasks are transferable and have a high success rate. It indicates that the presented control framework could reduce time consumption and realize online implementation. Therefore, the transfer RL concept is helpful for establishing the real-time decision-making policy for autonomous vehicles. [ABSTRACT FROM AUTHOR]

Ethical issues remain a significant concern for future large-scale deployment of autonomous vehicles. Although machine ethics have been developed for a few decades, ethical decision making in autonomous driving imposes more complex and emerging challenges to which some dedicated efforts from academia, policymakers, and automakers have been devoted in the past few years. This paper reviews the efforts and progress associated with the various aspects of ethical challenges in autonomous vehicles. Based on the critical review, it is clear that a consistent solution for ethical challenges does not currently exist and recommendations are provided, to potentially encourage the involving players to collaborate to tackle the remaining challenges. [ABSTRACT FROM AUTHOR]

Practical superconducting materials and technologies have been enabled with availability for large scale devices and applications. Various large scale and strong magnetic field applications of superconductors have been developed, and a series of those applications are explored with technical details mainly including i) applied superconducting materials and their characteristics; ii) superconducting magnets and their techniques; iii) large and advanced electromagnetic devices and applications. The applied superconductivity, application technology, and availability are especially focused and verified with the trend of large scale applications’ development prospection. [ABSTRACT FROM AUTHOR]

Advanced algorithms can promote the development of energy management strategies (EMSs) as a key technology in hybrid electric vehicles (HEVs). Reinforcement learning (RL) with distributed structure can significantly improve training efficiency in complex environments, and multi-threaded parallel computing provides a reliable algorithm basis for promoting adaptability. Dedicated to trying more efficient deep reinforcement learning (DRL) algorithms, this paper proposed a deep q-network (DQN)-based energy and emission management strategy (E&EMS) at first. Then, two distributed DRL algorithms, namely asynchronous advantage actor-critic (A3C) and distributed proximal policy optimization (DPPO), were adopted to propose EMSs, respectively. Finally, emission optimization was taken into account and then distributed DRL-based E&EMSs were proposed. Regarding dynamic programming (DP) as the optimal benchmark, simulation results show that three DRL-based control strategies can achieve near-optimal fuel economy and outstanding computational efficiency, and compared with DQN, two distributed DRL algorithms have improved the learning efficiency by four times. [ABSTRACT FROM AUTHOR]

Automated sleep staging is essential to assess sleep quality and treat sleep disorders, so the issue of electroencephalography (EEG)-based sleep staging has gained extensive research interests. However, the following difficulties exist in this issue: 1) how to effectively learn the intrinsic features of salient waves from single-channel EEG signals; 2) how to learn and capture the useful information of sleep stage transition rules; 3) how to address the class imbalance problem of sleep stages. To handle these problems in sleep staging, we propose a novel method named SleepFC. This method comprises convolutional feature pyramid network (CFPN), cross-scale temporal context learning (CSTCL), and class adaptive fine-tuning loss function (CAFTLF) based classification network. CFPN learns the multi-scale features from salient waves of EEG signals. CSTCL extracts the informative multi-scale transition rules between sleep stages. CAFTLF-based classification network handles the class imbalance problem. Extensive experiments on three public benchmark datasets demonstrate the superiority of SleepFC over the state-of-the-art approaches. Particularly, SleepFC has a significant performance advantage in recognizing the N1 sleep stage, which is challenging to distinguish.