pwc-archive/papers-with-abstracts huggingface dataset:
Gallium Nitride (GaN) based High-Power Multilevel H-Bridge Inverter for Wireless Power Transfer of Electric Vehicles
Authors: Javad Chevinly, Shervin Salehi Rad, Elias Nadi, Bogdan Proca, John Wolgemuth, Anthony Calabro, Hua Zhang, Fei Lu
Date: 2024-05-18 00:00:00

This paper presents a design and implementation of a high-power Gallium Nitride (GaN)-based multilevel Hbridge inverter to excite wireless charging coils for the wireless power transfer of electric vehicles (EVs). Compared to the traditional conductive charging, wireless charging technology offers a safer and more convenient way to charge EVs. Due to the increasing demand of fast charging, high-power inverters play a crucial role in exciting the wireless charging coils within a wireless power transfer system. This paper details the system specifications for the wireless charging of EVs, providing theoretical analysis and a control strategy for the modular design of a 75-kW 3-level and 4-level Hbridge inverter. The goal is to deliver a low-distortion excitation voltage to the wireless charging coils. LTspice simulation results, including output voltage, Fast Fourier Transform (FFT) analysis for both 3-level and 4-level H-bridge inverters, are presented to validate the control strategy and demonstrate the elimination of output harmonic components in the modular design. A GaNbased inverter prototype was employed to deliver a 85-kHz power to the wireless charging pads of the wireless power transfer system. Experimental results at two different voltage and power levels, 100V-215W and 150V-489W, validate the successful performance of the GaN inverter in the wireless charging system.
A Simulation-Optimization Framework for Developing Wind-Resilient AAM Networks
Authors: Emin Burak Onat, Shangqing Cao, Raiyan Rizwan, Xuan Jiang, Mark Hansen, Raja Sengupta, Anjan Chakrabarty
Date: 2024-05-17 00:00:00
Tasks: Management, Scheduling

Environmental factors pose a significant challenge to the operational efficiency and safety of advanced air mobility (AAM) networks. This paper presents a simulation-optimization framework that dynamically integrates wind variability into AAM operations. We employ a nonlinear charging model within a multi-vertiport environment to optimize fleet size and scheduling. Our framework assesses the impact of wind on operational parameters, providing strategies to enhance the resilience of AAM ecosystems. The results demonstrate that wind conditions exert significant influence on fleet size even for short-distance flights, their impact on fleet size and energy requirements becomes more pronounced over longer distances. Efficient management of fleet size and charging policies, particularly for long-distance networks, is needed to accommodate the variability of wind conditions effectively.
Affine Transformation-based Perfectly Undetectable False Data Injection Attacks on Remote Manipulator Kinematic Control with Attack Detector
Authors: Jun Ueda, Jacob Blevins
Date: 2024-05-17 00:00:00

This paper demonstrates the viability of perfectly undetectable affine transformation attacks against robotic manipulators where intelligent attackers can inject multiplicative and additive false data while remaining completely hidden from system users. The attacker can implement these communication line attacks by satisfying three Conditions presented in this work. These claims are experimentally validated on a FANUC 6 degree of freedom manipulator by comparing a nominal (non-attacked) trial and a detectable attack case against three perfectly undetectable trajectory attack Scenarios: scaling, reflection, and shearing. The results show similar observed end effector error for the attack Scenarios and the nominal case, indicating that the perfectly undetectable affine transformation attack method keeps the attacker perfectly hidden while enabling them to attack manipulator trajectories.
Game-theoretic Energy Management Strategies With Interacting Agents in Formula 1
Authors: Giona Fieni, Marc-Philippe Neumann, Alessandro Zanardi, Alberto Cerofolini, Christopher H. Onder
Date: 2024-05-17 00:00:00
Tasks: Bilevel Optimization, energy management, Management

This paper presents an interaction-aware energy management optimization framework for Formula 1 racing. The considered scenario involves two agents and a drag reduction model. Strategic interactions between the agents are captured by a Stackelberg game formulated as a bilevel program. To address the computational challenges associated with bilevel optimization, the problem is reformulated as a single-level nonlinear program employing the Karush-Kuhn-Tucker conditions. The proposed framework contributes towards the development of new energy management and allocation strategies, caused by the presence of another agent. For instance, it provides valuable insights on how to redistribute the energy in order to optimally exploit the wake effect, showcasing a notable difference with the behavior studied in previous works. Robust energy allocations can be identified to reduce the lap time loss associated with unexpected choices of the other agent. It allows to recognize the boundary conditions for the interaction to become relevant, impacting the system's behavior, and to assess if overtaking is possible and beneficial. Overall, the framework provides a comprehensive approach for a two-agent Formula 1 racing problem with strategic interactions, offering physically intuitive and practical results.
Neuroscheduling for Remote Estimation
Authors: Marcos M. Vasconcelos, Yifei Zhang
Date: 2024-05-17 00:00:00
Tasks: Scheduling

Many modern distributed systems consist of devices that generate more data than what can be transmitted via a communication link in near real time with high-fidelity. We consider the scheduling problem in which a device has access to multiple data sources, but at any moment, only one of them is revealed in real-time to a remote receiver. Even when the sources are Gaussian, and the fidelity criterion is the mean squared error, the globally optimal data selection strategy is not known. We propose a data-driven methodology to search for the elusive optimal solution using linear function approximation approach called neuroscheduling and establish necessary and sufficient conditions for the optimal scheduler to not over fit training data. Additionally, we present several numerical results that show that the globally optimal scheduler and estimator pair to the Gaussian case are nonlinear.
Efficient Recovery of Sparse Graph Signals from Graph Filter Outputs
Authors: Gal Morgenstern, Tirza Routtenberg
Date: 2024-05-17 00:00:00

This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, which is often referred to as the identification of the source of a diffused sparse graph signal, is seminal in the field of graph signal processing (GSP). Sparse graph signals can be used in the modeling of a variety of real-world applications in networks, such as social, biological, and power systems, and enable various GSP tasks, such as graph signal reconstruction, blind deconvolution, and sampling. In this paper, we assume double sparsity of both the graph signal and the graph topology, as well as a low-order graph filter. We propose three algorithms to reconstruct the support set of the input sparse graph signal from the graph filter output samples, leveraging these assumptions and the generalized information criterion (GIC). First, we describe the graph multiple GIC (GM-GIC) method, which is based on partitioning the dictionary elements (graph filter matrix columns) that capture information on the signal into smaller subsets. Then, the local GICs are computed for each subset and aggregated to make a global decision. Second, inspired by the well-known branch and bound (BNB) approach, we develop the graph-based branch and bound GIC (graph-BNB-GIC), and incorporate a new tractable heuristic bound tailored to the graph and graph filter characteristics. In addition, we propose the graph-based first order correction (GFOC) method, which improves existing sparse recovery methods by iteratively examining potential improvements to the GIC cost function by replacing elements from the estimated support set with elements from their one-hop neighborhood. In addition, we investigate the application of our graph-based sparse recovery methods in blind deconvolution scenarios where the graph filter is unknown.
Model-free fast charging of lithium-ion batteries by online gradient descent
Authors: Hamed Taghavian, Malin Andersson, Mikael Johansson
Date: 2024-05-17 00:00:00

A data-driven solution is provided for the fast-charging problem of lithium-ion batteries with multiple safety and aging constraints. The proposed method optimizes the charging current based on the observed history of measurable battery quantities, such as the input current, terminal voltage, and temperature. The proposed method does not need any detailed battery model or full-charging training episodes. The theoretical convergence is proven under mild conditions and is validated numerically on several linear and nonlinear battery models, including single-particle and equivalent-circuit models.
Distributed Model Predictive Control for Asynchronous Multi-agent Systems with Self-Triggered Coordinator
Authors: Qianqian Chen, ShaoYuan Li
Date: 2024-05-17 00:00:00
Tasks: Model Predictive Control

This paper investigates the distributed model predictive control for an asynchronous nonlinear multi-agent system with external interference via a self-triggered generator and a prediction horizon regulator. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enable the robustness of the system. Then, the trigger interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade-off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the multi-agent system is achieved under asynchronous communication. Finally, the recursive feasibility and stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.
Two-Stage Stochastic Optimal Power Flow for Microgrids With Uncertain Wildfire Effects
Authors: Sifat Chowdhury, Yu Zhang
Date: 2024-05-16 00:00:00

Large-scale power outages caused by extreme weather events are one of the major factors weakening grid resilience. In order to prevent the critical infrastructure from cascading failure, power lines are often proactively de-energized under the threat of a progressing wildfire. In this context, the potential of microgrid (MG) functioning in islanded mode can be exploited to enhance the resiliency of the power grid. However, there are numerous uncertainties originating from these types of events and an accurate modeling of the MG is required to harness its full potential. In this paper, we consider the uncertainty in line outages depending on fire propagation and reduced solar power generation due to the particulate matter in wildfire smoke. We formulate a two-stage stochastic MG optimal power flow problem by utilizing a second-order cone relaxation of the DistFlow model. Leveraging an effective approximation of the resistive heat gain, we separate the complicating constraints of dynamic line rating from the resulting optimization problem. Extensive simulation results corroborate the merits of our proposed framework, which is tested on a modified IEEE 22-bus system.
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks
Authors: YuHan Liu, Roland Tóth, Maarten Schoukens
Date: 2024-05-16 00:00:00
Tasks: State Space Models

Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical PGNN, the penalization of the physics-guided part is at the output level, which leads to a conservative result as systems with highly similar state-transition functions, i.e. only slight differences in parameters, can have significantly different time-series outputs. Furthermore, the classical PGNN cost function regularizes the model estimate over the entire state space with a constant trade-off hyperparameter. In this paper, we introduce a novel model augmentation strategy for nonlinear state-space model identification based on PGNN, using a weighted function regularization (W-PGNN). The proposed approach can efficiently augment the prior physics-based state-space models based on measurement data. A new weighted regularization term is added to the cost function to penalize the difference between the state and output function of the baseline physics-based and final identified model. This ensures the estimated model follows the baseline physics model functions in regions where the data has low information content, while placing greater trust in the data when a high informativity is present. The effectiveness of the proposed strategy over the current PGNN method is demonstrated on a benchmark example.
Deep Koopman Operator-Informed Safety Command Governor for Autonomous Vehicles
Authors: Hao Chen, Xiangkun He, Shuo Cheng, Chen Lv
Date: 2024-05-16 00:00:00
Tasks: Autonomous Vehicles

Modeling of nonlinear behaviors with physical-based models poses challenges. However, Koopman operator maps the original nonlinear system into an infinite-dimensional linear space to achieve global linearization of the nonlinear system through input and output data, which derives an absolute equivalent linear representation of the original state space. Due to the impossibility of implementing the infinite-dimensional Koopman operator, finite-dimensional kernel functions are selected as an approximation. Given its flexible structure and high accuracy, deep learning is initially employed to extract kernel functions from data and acquire a linear evolution dynamic of the autonomous vehicle in the lifted space. Additionally, the control barrier function (CBF) converts the state constraints to the constraints on the input to render safety property. Then, in terms of the lateral stability of the in-wheel motor driven vehicle, the CBF conditions are incorporated with the learned deep Koopman model. Because of the linear fashion of the deep Koopman model, the quadratic programming problem is formulated to generate the applied driving torque with minimal perturbation to the original driving torque as a safety command governor. In the end, to validate the fidelity of the deep Koopman model compared to other mainstream approaches and demonstrate the lateral improvement achieved by the proposed safety command governor, data collection and safety testing scenarios are conducted on a hardware-in-the-loop platform.
Introducing advanced hybrid coupling: Non-discriminatory coalescence of flow-based and net transfer capacity calculation regions
Authors: David Schönheit, Ivan Marjanović
Date: 2024-05-16 00:00:00

Flow-based market coupling is substantially altering the computation of cross-zonal capacities for the trade of electricity in the vast majority of European markets. The main benefit of the flow-based method is improved accuracy by better representing the impact of cross-zonal trade on the power flows in transmission grids. Some borders, adjacent to flow-based capacity regions, are represented through net transfer capacities during market coupling. Under the current standard hybrid coupling, the utilization of grid elements in the flow-based regions due to the predicted trade across such borders is not available for trades between flow-based zones. The flow-based representation is not limited to the given capacity calculation region, but can be extended to also model the impact of trade with other regions. This so-called advanced hybrid coupling replaces the priority inherently given to trade across net transfer capacity-coupled borders by introducing virtual bidding zones. These map the effect of non-flow-based borders on line capacities in the flow-based regions, enabling the market coupling optimization to prioritize trade between flow-based bidding zones and trade across non-flow-based borders. This paper explains the mechanism of advanced hybrid coupling and how it is modeled mathematically. Based on a test network, a case study shows to what extent and why advanced hybrid coupling leads to welfare gains during market coupling and lower congestion management costs in the flow-based region.
Collaborative planning of integrated hydrogen energy chain multi-energy systems: A review
Authors: Xinning Yia, Tianguang Lua, Jing Li, Shaocong Wu
Date: 2024-05-16 00:00:00
Tasks: Articles

Most planning of the traditional hydrogen energy supply chain (HSC) focuses on the storage and transportation links between production and consumption ends. It ignores the energy flows and interactions between each link, making it unsuitable for energy system planning analysis. Therefore, we propose the concept of a hydrogen energy chain (HEC) based on the HSC, which emphasizes the interactions between different types of energy flows in the production, compression, storage, transportation, and application links of hydrogen. The HEC plays a crucial role in mitigating fluctuations of renewable energy and facilitating the optimal allocation of heterogeneous energy sources across time and space. Effective collaborative planning models that consider HEC are essential for the optimal configuration of multi-energy systems (MESs), which guarantees high-efficiency operation and the economic and environmental friendliness of the system. This paper presents a systematic review of recent articles on collaborative planning of integrated hydrogen energy chain multi-energy systems (HEC-MESs). First, we introduce the basic framework of HEC-MES, focusing on the current research status of the production, compression, storage, transportation, and application links in HEC. Furthermore, we review technology types of hydrogen energy for planning and summarize the typical forms of HEC in MESs. Then, the following sections outline the models and methods for collaborative planning of HEC-MES. They include detailed analyses of covered sector types, spatial and temporal scopes of planning, uncertainties, model formulations, and solution methods. Finally, the paper concludes by summarizing the research gaps identified in current articles and outlining directions for future research.
Adaptive tracking MPC for nonlinear systems via online linear system identification
Authors: Tatiana Strelnikova, Johannes Köhler, Julian Berberich
Date: 2024-05-16 00:00:00
Tasks: LEMMA, Model Predictive Control

This paper presents an adaptive tracking model predictive control (MPC) scheme to control unknown nonlinear systems based on an adaptively estimated linear model. The model is determined based on linear system identification using a moving window of past measurements, and it serves as a local approximation of the underlying nonlinear dynamics. We prove that the presented scheme ensures practical exponential stability of the (unknown) optimal reachable equilibrium for a given output setpoint. Finally, we apply the proposed scheme in simulation and compare it to an alternative direct data-driven MPC scheme based on the Fundamental Lemma.
Structural Impact of Grid-Forming Inverters on Power System Coherency
Authors: Sayak Mukherjee, Ramij Raja Hossain, Kaustav Chatterjee, Soumya Kundu, Kyung-bin Kwon, Sameer Nekkalapu, Marcelo Elizondo
Date: 2024-05-15 00:00:00

This paper addresses the following fundamental research question: how does the integration of grid-forming inverters (GFMs) replacing conventional synchronous generators (SGs) impact the slow coherent eigen-structure and the low-frequency oscillatory behavior of future power systems? Due to time-scale separated dynamics, generator states inside a coherent area synchronize over a fast time-scale due to stronger coupling, while the areas themselves synchronize over a slower time scale. Our mathematical analysis shows that due to the large-scale integration of GFMs, the weighted Laplacian structure of the frequency dynamics is preserved, however, the entries of the Laplacian may be significantly modified based on the location and penetration levels of the GFMs. This can impact and potentially significantly alter the coherency structure of the system. We have validated our findings with numerical results using the IEEE 68-bus test system.
Predictive Energy Management for Battery Electric Vehicles with Hybrid Models
Authors: Yu-Wen Huang, Christian Prehofer, William Lindskog, Ron Puts, Pietro Mosca, Göran Kauermann
Date: 2024-05-15 00:00:00
Tasks: energy management, Management

This paper addresses the problem of predicting the energy consumption for the drivers of Battery electric vehicles (BEVs). Several external factors (e.g., weather) are shown to have huge impacts on the energy consumption of a vehicle besides the vehicle or powertrain dynamics. Thus, it is challenging to take all of those influencing variables into consideration. The proposed approach is based on a hybrid model which improves the prediction accuracy of energy consumption of BEVs. The novelty of this approach is to combine a physics-based simulation model, which captures the basic vehicle and powertrain dynamics, with a data-driven model. The latter accounts for other external influencing factors neglected by the physical simulation model, using machine learning techniques, such as generalized additive mixed models, random forests and boosting. The hybrid modeling method is evaluated with a real data set from TUM and the hybrid models were shown that decrease the average prediction error from 40% of the pure physics model to 10%.
Frequency stabilization of self-sustained oscillations in a sideband-driven electromechanical resonator
Authors: B. Zhang, Yingming Yan, X. Dong, M. I. Dykman, H. B. Chan
Date: 2024-05-14 00:00:00

We present a method to stabilize the frequency of self-sustained vibrations in micro- and nanomechanical resonators. The method refers to a two-mode system with the vibrations at significantly different frequencies. The signal from one mode is used to control the other mode. In the experiment, self-sustained oscillations of micromechanical modes are excited by pumping at the blue-detuned sideband of the higher-frequency mode. Phase fluctuations of the two modes show near perfect anti-correlation. They can be compensated in either one of the modes by a stepwise change of the pump phase. The phase change of the controlled mode is proportional to the pump phase change, with the proportionality constant independent of the pump amplitude and frequency. This finding allows us to stabilize the phase of one mode against phase diffusion using the measured phase of the other mode. We demonstrate that phase fluctuations of either the high or low frequency mode can be significantly reduced. The results open new opportunities in generating stable vibrations in a broad frequency range via parametric downconversion in nonlinear resonators.