This paper introduces a novel mechanism for online allocation with multi-phase, non-separable regularizers, termed Cap-and-Penalize (CnP), inspired by real-world applications such as cap-and-tax policies in carbon pricing. The CnP regularizer models a multi-phase cost structure, imposing a monotone convex penalty when total allocation exceeds a predefined level (soft cap) and enforcing a strict limit (hard cap) beyond which allocation is prohibited. Our contributions are twofold: (1) we propose an online mechanism for CnP-regularized allocation without per-step resource constraints, which operates as a simple and intuitive posted-price mechanism, but achieves the best-possible guarantee among all possible online algorithms; (2) we tackle the more complex setting with per-step resource constraints by decomposing the regularizer into local components, yielding a similar mechanism with time-dependent marginal pricing functions. To establish the tightness of our results in both settings, we introduce a representative function-based approach that transforms the lower-bound proof into the problem of solving an ordinary differential equation with boundary conditions. We believe that this technique has the potential to be applied to other similar online optimization problems.

AI, especially deep learning algorithm, has proved its potential in wind power prediction; however, the lack of explainability is the main concern to address and this work is the first to investigate the emerging Liquid Neural Network (LNN) to provide necessary transparency in wind power prediction. LNN utilizes the mathematical abstraction of C.elegans and demonstrates liquid/robust behavior in learning and estimation for unseen data. For comparative analysis, the LNN family (i.e., closed form continuous (CfC), Liquid Time Constant) and state-of-the-art recurrent networks (e.g., LSTM and GRU) and 1D-CNN are considered, and the CfC neural network provides the best results on unseen data. CfC models with fully connected layers using only 25 neurons have provided superior results for wind power prediction in different time spans, resolutions, and number of variables.

Multimotor drives have become increasingly important in modern industrial applications due to their ability to provide superior performance, efficiency, and flexibility compared to single-motor systems. Hence, this article presents an overview of recent advancements in multimotor drives, focusing on three main areas: structural diversity, advanced control, and emerging challenges and solutions. First, the various structural configurations of multimotor drives are summarized, which include parallel, cascaded, and hybrid configurations. The features as well as component motors and converters of each configuration are discussed, along with the selection rules of a particular configuration for a given application. Second, from the perspective of different performance requirements, the advanced control technologies used for multimotor drives are discussed. Then, this article highlights the technical challenges associated with multimotor drives, including coordination control, mutual interference, communication, interdependent fault diagnosis, and power quality. Meanwhile, viable solutions to these challenges are summarized. Finally, a discussion of the future directions and opportunities for further research and development in the field of multimotor drives is presented. Through this article, scholars and engineers can gain a comprehensive understanding of current and future developments in multimotor drives, contributing to continued research in this field and facilitating successful integration into various applications.

Regenerative braking energy (RBE) utilization plays a vital role in improving the energy efficiency of electrified railways. To date, various power flow control-based solutions have been developed to recycle the RBE for utilization within railway power systems (RPSs). In this paper, an overview of the state-of-the-art power flow control-based solutions for RBE utilization in AC electrified railways is presented. It provides a technical analysis of four primary power flow control-based solutions for RBE utilization, including power sharing-based, energy feedback-based, energy storage-based, and composite solutions. The critical architectures of power flow conditioners for each solution are analyzed in depth. Meanwhile, the power flow control strategies for these solutions are reviewed from the perspectives of power flow management and converter control. From the industrial point of view, the critical challenges associated with fault protection, economy, and environmental impact are discussed. In addition, future trends are comprehensively elaborated from internal and extended improvements. This comprehensive review provides an insightful understanding of the technology readiness, constraints, and perspectives regarding the power flow control-based RBE utilization in electrified railways, contributing to bridging the gaps between academic research and industry implementation.

Featured with a more complicated configuration, the modelling, analysis, and derivation of multiport converters (MPCs) requires much more effort than conventional two-port converters. The duality principles from circuit theory and graph theory can serve well as a powerful tool to deal with these challenges. However, a fundamental property of duality has been missing in the power electronics community for over 40 years, i.e., different dual MPC topologies can come from the same original MPC, even for those with planar circuits. And this indeed limits our understanding of the MPCs. To fill this gap, the missing theoretical foundations are provided in this work, forming the generalized duality principles for systematic modelling, analysis, and derivations of MPCs. The theoretical foundations are firstly presented through advanced concepts in graph theory. Then, extensive MPCs are selected as examples to validate the feasibility of this theory. It is shown that a 3-port non-isolated MPC can have 8 different duals (for MPCs with more ports, this number will go even higher) and these duals are related to each other by common electrical relationships. Therefore, their modelling, analysis, and operation design can be achieved in a systematic way.

Outdoor point-cloud object localization is an essential processing step for urban scene analysis and modeling in numerous applications, especially in land surveying and site analysis. Given the increasingly use of Terrestrial Laser Scanning (TLS) and LiDAR 3D data acquisition, numerous annotated point-cloud datasets are available and can be used to evaluate computer vision and machine learning based algorithms. Nevertheless, current point-cloud datasets mainly focus on object detection and classification in autonomous driving or urban planning type of applications, and share redundant object classes, e.g., trees, vehicles and pedestrians, which limit their usefulness for land surveying and site analysis. This paper introduces a novel 3D benchmark dataset LiSurveying, which is a large-scale point-cloud dataset with over a billion points and uncommon urban object categories in complex outdoor environments. Our dataset incorporates more urban object classes than existing datasets. Its instances have diverse point densities, shapes and dimensions, which also impose a challenge for point-cloud detection and classification algorithms. We conducted baselines experiments for point-cloud classification using machine learning classifiers and deep learning methods on different subsets of the LiSurveying dataset, and we are able to demonstrate that the various types of object classes, number of instances per class, distribution of object points, and variety of complex scenes, make this LiSurveying benchmark dataset suitable for evaluating 3D point-cloud classification, semantic segmentation, and object detection algorithms.

Medium-voltage high-power converters are usually modulated using low switching frequency techniques such as Selective Harmonic Elimination - Pulse Width Modulation (SHE-PWM) in order to improve their efficiency. To calculate the firing angles, SHE-PWM transcendental equations are usually solved by offline calculation methods, whose computational burden depends on the number of firing angles or eliminated harmonics and the required accuracy in the solutions. Consequently, with the aim of reducing this computational burden, this paper presents an analysis that estimates the maximum accuracy required in the calculated SHE-PWM solutions considering the main non-idealities of the converter. In this sense, a systematic methodology to evaluate the error in the harmonic amplitudes due to control and dead time effects has been developed, providing an optimal error tolerance for the calculation methods that solve the SHE-PWM problem.

This article presents a comprehensive, multidisciplinary overview of the development of powertrain technologies for energy-efficient heavy-duty earthmoving machines. The heavy-duty earthmoving equipment industry has been among the biggest contributors to emissions globally. However, due to high power demand and multidisciplinary powertrain structures, improving the energy efficiency of heavy-duty mobile machines has been a pressing and challenging task in the industry. To cope with this challenge, hydraulics and power electronics (PE) have been the key driving forces. As such, the relative developments in both fields are covered in this article. For hydraulics, developments of efficient hydraulic circuits will be overviewed in detail along with the introduction of hydraulic energy recovery technologies. In addition, developments of PE architectures in hybrid and electrified machines will be introduced. Furthermore, potential medium-voltage dc mining site power distribution and the valves of wide bandgap devices will also be discussed with the hope to open up new research opportunities in PE. Moreover, emerging hybrid electrohydraulic drive technology is introduced. Based on the overview in this article, it is anticipated that electrohydraulic hybridization will be the future trend in the earthmoving machine industry. Deeper collaboration between the two areas is desirable.

A precisely parameterized battery model is the prerequisite of the model-based management of lithium-ion battery. However, the unexpected sensing of noises may discount the identification of model parameters in practical applications. This article focuses on the noise effect compensation and online parameter identification for the widely used equivalent circuit model. A novel degree of freedom (DOF) eliminator is proposed and combined with the Frisch scheme in a recursive fashion, for the first time, to coestimate the noise statistics and unbiased model parameters. A computationally tractable numerical solver is further proposed for the DOF eliminator to improve the real-time performance. Simulations and experiments are performed to validate the proposed method from theoretical to practical perspective. Results show that the proposed method can effectively mitigate the noise-induced identification biases and outperform the existing methods in terms of the accuracy and the robustness to noise corruption.

Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.

This paper discusses harmonic compensation schemes using distributed generation (DG) units. As most DG units are connected to the grid with interfacing converters, the harmonic compensation functions can be realized through flexible control of these converters. Both current controlled and voltage controlled DG systems are considered in this paper. Details on how to implement the harmonic compensation function on those systems are presented. Additionally, the recently proposed hybrid control method (HCM) is also discussed. It shows that the HCM has better performance and is more flexible. Selective simulated and experimental results are provided to verify the feasibility of the HCM approach.

The increased number of nonlinear residential loads in today's typical home and power electronics based distributed generation (DG) systems is a growing concern for the utility companies due to the power quality issues. However, properly controlled DG-grid interfacing converters are able to improve the distribution system power quality. Thus increased number of DG systems can effectively be utilized to address the power quality concern raised by increased nonlinear residential loads. This paper is mainly focused on the distribution system harmonic control through the DG-grid interfacing converters. An in-depth analysis and comparison of different compensation schemes based on the virtual harmonic impedance concept are carried out. The analysis results are verified by simulation of a test residential distribution system.