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Optimization of Power Control for Autonomous Hybrid Electric Vehicles With Flexible Power Demand

Journal of Autonomous Vehicles and Systems 2025年第2期5卷 021002页

作者： Kargar, mohammadali Song, Xingyong Department of Mechanical Engineering College of Engineering Texas A&M University College StationTX77843 United States Department of Engineering Technology and Industrial Distribution The Department of Mechanical Engineering The Department of Electrical and Computer Engineering College of Engineering Texas A&M University College StationTX77843 United States

technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degrees-of-freedom to the powertrain control while maintaining safety and drive comfort. The present research investigates the use of an approximate dynamic programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The concept of reachable sets is incorporated into the ADP framework to ensure safety, improve ride comfort, and enhance the accuracy of the optimization solution. The formulation is based on an autonomous hybrid electric vehicle, while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of flexible power demand, as addressed by the ADP method. The experiment demonstrates a 14.1% improvement in fuel economy compared to a scenario without flexible power demand. © 2024 by ASmE.

关键词： Dynamic programming

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Cyber Security of a Smart Power distribution System - Cyber Subsystem Use Case

Cyber Security of a Smart Power Distribution System - Cyber ...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Bobato, Vinicius Thongmai, Kumpanat Goulart, Ana Butler-Purry, Karen Texas A&m University Department of Engineering Technology and Industrial Distribution College StationTX United States Texas A&m University Department of Electrical and Computer Engineering College StationTX United States

ISBN: (纸本)9798350352528

Power distribution systems and communication networks are inter-dependent. Remote data collection and control of distributed energy resources (DERs), such as windmills and solar panels, employ not only remote terminal units (RTUs) but also Aggregators and Distributed Service Operators (DSOs). Aggregators are devices that communicate with a group of DERs and the DSO, which manages all DERs. These devices are located in different places and exchange data through a wide area network, i.e., the cyber sub-system. To help us understand this cyber sub-system and the impact of a cyber attack on a smart power distribution system, this paper presents a cybersecurity threat and detection use case implemented on a cyber-physical testbed. The power distribution system is simulated using Real Time Digital Simulator (RTDS) power simulator, while the communication network is emulated using Common Open Research Emulator (CORE). The use case simulates a peak shaving application, in which the DSO communicates with the Aggregator to find the capacity of four DERs to generate more power. During this process, an intruder performs a reconnaissance attack followed by a denial-of-service attack (DoS). © 2025 IEEE.

关键词： Power distribution networks

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Cyber Security Use Case on a Smart Power distribution System - Physical Subsystem

Cyber Security Use Case on a Smart Power Distribution System...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Thongmai, Kumpanat Bobato, Vinicius Butler-Purry, Karen Goulart, Ana Texas A&m University Department of Electrical and Computer Engineering College StationTX United States Texas A&m University Department of Engineering Technology and Industrial Distribution College StationTX United States

ISBN: (纸本)9798350352528

A Cyber-physical system (CPS) was simulated for peak shaving application to investigate vulnerabilities and re-silience of a power distribution system. The power system was implemented in the Real Time Digital Simulator (RTDS), including a remote terminal unit (RTU), a distribution transformer, and four Distributed Energy Resources (DERs). The communication system was implemented in Common Open Research Emulator (CORE). The cyber-attacks, Denial of Service (DoS) and man-in-the-middle (mITm), were simulated by attacking the communication between the Aggregator and DERs. Comparative analysis between normal system operation and post-attack operation revealed significant impacts. Cyber-physical system simulation provides valuable insights for researchers in developing detection and mitigation methods. © 2025 IEEE.

关键词： Cyber attacks

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Efficient Trotting of Soft Robotic Quadrupeds

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14562-14576页

作者： Arachchige, Dimuthu D. K. Sheehan, Tim Perera, Dulanjana m. mallikarachchi, Sanjaya Huzaifa, Umer Kanj, Iyad Godage, Isuru S. DePaul University School of Computing Jarvis College of Computing and Digital Media ChicagoIL60604 United States University of Illinois at Chicago Department of Mechanical and Industrial Engineering ChicagoIL60607 United States Texas A&M University Department of Multidisciplinary Engineering College StationTX77843 United States Texas A&M University Department of Engineering Technology and Industrial Distribution the J. Mike Walker '66 Department of Mechanical Engineering Department of Multidisciplinary Engineering College StationTX77843 United States

Soft robots hold significant potential in legged locomotion due to their inherent deformability, enabling enhanced adaptability to various environmental conditions and the generation of diverse locomotion gaits. While various soft robots have been proposed for terrestrial locomotion, research on dynamically-stable locomotion, such as trotting, with actuated soft bending limbs remains limited. We introduce a pneumatically-actuated soft quadruped featuring a soft body capable of a variety of dynamically-stable trotting locomotion. We utilize soft limb kinematics and parameterize fundamental limb locomotion to obtain quadrupedal locomotion trajectories for both linear and curvilinear motions. We also employ a physics-enabled dynamic model to optimize and evaluate trotting locomotion trajectories for dynamic stability. We further validate the stable locomotion trajectories through empirical experiments conducted on a soft quadruped prototype. The results demonstrate that the quadruped trots at a peak speed of 1.24 body lengths per second when traversing flat and uneven terrains, including slopes, cluttered areas, and naturalistic irregular surfaces. Furthermore, we compare the energy efficiency between trotting and crawling locomotion. The findings reveal that trotting is significantly more energy-efficient than crawling, with an average energy saving of up to 42%. © 2004-2012 IEEE.

关键词： Energy efficiency

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Learning Autonomy: Off-Road Navigation Enhanced by Human Input

arXiv

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arXiv 2025年

作者： Nagariya, Akhil Filev, Dimitar Saripalli, Srikanth Pandey, Gaurav Mechanical Engineering department of Texas A&M University United States Department of Engineering technology and Industrial Distribution of Texas A&M University United States

— In the area of autonomous driving, navigating off-road terrains presents a unique set of challenges, from unpredictable surfaces like grass and dirt to unexpected obstacles such as bushes and puddles. In this work, we present a novel learning-based local planner that addresses these challenges by directly capturing human driving nuances from real-world demonstrations using only a monocular camera. The key features of our planner are its ability to navigate in challenging off-road environments with various terrain types and its fast learning capabilities. By utilizing minimal human demonstration data (5-10 mins), it quickly learns to navigate in a wide array of off-road conditions. The local planner significantly reduces the real world data required to learn human driving preferences. This allows the planner to apply learned behaviors to real-world scenarios without the need for manual fine-tuning, demonstrating quick adjustment and adaptability in off-road autonomous driving technology. Copyright © 2025, The Authors. All rights reserved.

关键词： Off road vehicles

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Cyber Security Use Case on a Smart Power distribution System - Physical Subsystem

Cyber Security Use Case on a Smart Power Distribution System...

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Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Kumpanat Thongmai Vinicius Bobato Karen Butler-Purry Ana Goulart Department of Electrical and Computer Engineering Texas A&M University College Station TX Department of Engineering Technology and Industrial Distribution Texas A&M University College Station TX

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

关键词： Systems operation Power distribution Transformer cores Cyber-physical systems Transformers Software Real-time systems Servers Relays Cyberattack

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Cyber Security of a Smart Power distribution System - Cyber Subsystem Use Case

Cyber Security of a Smart Power Distribution System - Cyber ...

引用

Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Vinicius Bobato Kumpanat Thongmai Ana Goulart Karen Butler-Purry Department of Engineering Technology and Industrial Distribution Texas A&M University College Station TX Department of Electrical and Computer Engineering Texas A&M University College Station TX

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

关键词： Wide area networks Reactive power Power distribution Transformer cores Reconnaissance Transformers Real-time systems Communication networks Solar panels Prognostics and health management

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Generator Retirement Optimization for New York State Energy Transition: Balancing Cost, Reliability, and Emissions

Generator Retirement Optimization for New York State Energy ...

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2025 IEEE texas Power and Energy Conference, TPEC 2025

作者： Ahmadi, Seyed Ehsan Kabir, Elnaz Fattahi, mohammad marzband, mousa Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Texas A&M University Dept. of Engineering Technology & Industrial Distribution TX United States King Abdulaziz University Dept. of Electrical & Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331541125

New York State (NYS) is shifting towards renewable energy, targeting 70 % renewable generation by 2030 and a net-zero carbon power grid by 2040. This transition necessitates the systematic retirement of fossil fuel generators (FFGs), which have long supported grid stability but contribute significantly to CO2 emissions. However, FFG retirement introduces chal-lenges related to grid reliability, energy curtailment, transmission congestion, and peak load management, given the intermittent nature of renewable energy sources (RES). This paper presents an optimization model designed to develop an effective FFG retirement schedule that considers these operational complexities alongside RES integration and transmission constraints. Utilizing historical data on demand and weather patterns, the model evaluates multiple scenarios to balance cost, emissions reduction, and grid stability. The findings indicate that strategic retire-ment of 22 GW generation capacity of high-emission inefficient FFGs, combined with targeted transmission upgrades and RES expansion, can support NYS's energy targets by 2030. This framework offers valuable insights for policymakers aiming to achieve decarbonization while preserving grid resilience. © 2025 IEEE.

关键词： Low emission

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Fair machine Learning in Healthcare: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 493-507页

作者： Feng, Qizhang Du, mengnan Zou, Na Hu, Xia Texas A&M University Department of Computer Science & Engineering College StationTX77843 United States New Jersey Institute of Technology Department of Data Science NewarkNJ07102 United States University of Houston Department of Industrial Engineering HoustonTX77004 United States Rice University Department of Computer Science HoustonTX77251 United States

The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (mL) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problems is paramount to prevent further entrenchment of social injustices. In this survey, we analyze the intersection of fairness in mL and healthcare disparities. We adopt a framework based on the principles of distributive justice to categorize fairness concerns into two distinct classes: equal allocation and equal performance. We provide a critical review of the associated fairness metrics from a mL standpoint and examine biases and mitigation strategies across the stages of the mL lifecycle, discussing the relationship between biases and their countermeasures. The article concludes with a discussion on the pressing challenges that remain unaddressed in ensuring fairness in healthcare mL and proposes several new research directions that hold promise for developing ethical and equitable mL applications in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Cooperative Optimization of Grid-Edge Cyber and Physical Resources for Resilient Power System Operation

arXiv

引用

arXiv 2025年

作者： Huo, Xiang Sun, Shining Haque, Khandaker Akramul Homoud, Leen Al Goulart, Ana E. Davis, Katherine R. Department of Electrical & Computer Engineering Texas A&M University College StationTX77843 United States Department of Engineering Technology and Industrial Distribution Texas A&M University College StationTX77843 United States

The cooperative operation of grid-edge power and energy resources is crucial to improving the resilience of power systems during contingencies. However, given the complex cyber-physical nature of power grids, it is hard to respond timely with limited costs for deploying additional cyber and/or phyiscal resources, such as during a high-impact low-frequency cyber-physical event. Therefore, the paper examines the design of cooperative cyber-physical resource optimization solutions to control grid-tied cyber and physical resources. First, the operation of a cyber-physical power system is formulated into a constrained optimization problem, including the cyber and physical objectives and constraints. Then, a bi-level solution is provided to obtain optimal cyber and physical actions, including the reconfiguration of cyber topology (e.g., activation of communication links) in the cyber layer and the control of physical resources (e.g., energy storage systems) in the physical layer. The developed method improves grid resilience during cyberattacks and can provide guidance on the control of coupled physical side resources. Numerical simulation on a modified IEEE 14-bus system demonstrates the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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