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Reconfigurable Smart Grid Management for Electric Vehicle Charging and the Integration of Renewable Energy Sources using Pied Kingfisher Optimizer

Reconfigurable Smart Grid Management for Electric Vehicle Ch...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： M. Sivaramkrishnan M. Siva Ramkumar Kaparthi Uday Mohammad Kanan C. Mohan Raj Jayant Giri Dept of EEE Karpagam College of Engnieering India Dept of ECE SNS College of Technology EIE Department CVR College of Engineering Ibrahimpatanam Hyderabad Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan Department or EEE Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Coimbatore Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Electric Vehicle (EV) charging management under the Smart Grid (SG) framework with incorporated Renewable Energy Sources (RESs) functions to advance Energy Management (EM). The operational cost of EV charging varies because renewable energy availability can change so this affects the financial performance of charging operations. Integration of RESs with the SG becomes challenging for operational system efficiency particularly when renewable energy generation levels are low. To overcome these drawbacks, this manuscript proposes an efficient approach for SG management for EV charging with the integration of RESs. The suggested method is Pied Kingfisher Optimizer (PKO). The main aim of the suggested technique is to minimize the operational expenses of EV charging as well as maximize the efficiency of the SG system. The PKO is used to optimize the scheduling and allocation of energy resources within the SG system. By then, the suggested approach is implemented in MATLAB and contrasted with several other methods that are currently in use. The suggested technique outperforms all existing approaches such as Bald Eagle Search (BES), Red Kite Optimization (RKO), Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), and Coati Optimization Algorithm-Constitutive Artificial Neural Network (COA-CANN). The suggested approach delivers superior performance than conventional methods by reducing operational costs to 638,322$ under coordinated charging and 629,745$ under smart charging without impacting system performance metrics.

关键词： Renewable energy sources Costs System performance Artificial neural networks Power system stability Stability analysis Smart grids Wind turbines Resource management Smart charging

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Electric Vehicle Charging Navigation Using Waterwheel Plant Algorithm for Coordinating Smart Grids and Intelligent Transportation Systems

Electric Vehicle Charging Navigation Using Waterwheel Plant ...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： M. Siva Ramkumar M. Sivaramkrishnan Kaparthi Uday Mohammad Kanan C. Mohan Raj Jayant Giri Dept of ECE SNS College of Technology Dept of EEE Karpagam College of Engnieering India EIE Department CVR College of Engineering Ibrahimpatanam Hyderabad Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan Department or EEE Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Coimbatore Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The efficient charging of electric vehicles (EVs) remains essential to create sustainable mobility while EVs transform into crucial elements of upcoming transportation systems. The present EV charging navigation systems deal with three main issues including grid congestion and inadequate charging station (CS) selection and delays from traffic *** paper proposes a novel Waterwheel Plant Algorithm (WWPA) for optimizing EV charging navigation within the Smart Grid (SG) and Intelligent Transportation Systems (ITS). The suggested method strives to enhance SG-EV coordination through demand balancing in addition to charge price reduction and decreased grid congestion levels. WWPA algorithm serves as the proposed method to optimize EV charging schedules. The MATLAB platform serves as the implementation environment for the proposed method which demonstrates superior performance than Multi-agent Reinforcement Learning (MARL), Deep Reinforcement Learning (DRL), Coati-Northern Goshawk Optimization (Coati-NGO) and Grey Sail Fish Optimization (GSFO). Results demonstrate that the WWPA algorithm reduces average charging expenses by 18.14% throughout diverse charging operations as it produces superior performance than existing algorithms operating in the SG ITS.

关键词： Navigation Heuristic algorithms Transportation Transforms Traffic control Electric vehicle charging Smart grids Delays Vehicle dynamics Testing

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Enhancing Thermal Properties: A Comprehensive Study on Cellulose Nanocrystal Nanofluids 2nd

Enhancing Thermal Properties: A Comprehensive Study on Cellu...

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2nd International Conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Kareem Ahmad Aqeel, Ahmad Abdul Hajjaj, Sami Mohamed, Hassan Gomaa, Mohamed R. Alsboul, Monther Selangor Kajang43000 Malaysia School of Robotics Xi’an Jiaotong-Liverpool University 111 Taicang Avenue Taicang Jiangsu Province Suzhou215400 China School of Engineering and Technology Sunway University Selangor Darul Ehsan No. 5 Jalan Universiti Bandar Sunway47500 Malaysia Selangor Kajang43000 Malaysia Mechanical Engineering Department Faculty of Engineering Al-Hussein Bin Talal University Ma’an71111 Jordan Physics Department College of Science Al Hussein Bin Talal University Maan71111 Jordan

ISBN: (纸本)9789819639489

Nanofluids, characterized by a base fluid infused with nanoparticles, have gained substantial interest due to their superior thermal properties, primarily enhanced heat transfer capabilities. This study investigates the dispersion of cellulose nanocrystal (CNC) nanoparticles in base fluid and its subsequent impact on thermal properties. CNC nanoparticles obtained from Blue Goose Biorefineries Inc. Were dispersed in distilled water and ethylene glycol (EG) at varying volume concentrations (0.25%, 0.50%, 0.75%). The prepared nanofluids underwent thorough stability analyses utilizing techniques such as ultrasonication. The CNC nanofluids ‘ thermal conductivity, density, viscosity, and specific heat capacity were systematically measured using instrumentation. The study found that adding cellulose nanocrystal (CNC) nanoparticles to an 80:20 water-ethylene glycol mixture significantly improves the thermal conductivity, viscosity, and density of the base fluid. Thermal conductivity increased linearly with higher CNC concentrations (0.25%, 0.5%, 0.75%) and temperatures (25–55 ℃), peaking at 0.815 W/m·K at 55 ℃ with 0.75% CNC. Viscosity decreased with temperature but increased with higher CNC concentrations. Density also increased with CNC concentration and decreased with temperature, while specific heat capacity showed minimal changes with concentration but a slight increase with temperature. These enhancements make CNC nanofluids beneficial for heat transfer applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Viscosity

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FAST-LIVO2 on Resource-Constrained Platforms: LiDAR-Inertial-Visual Odometry with Efficient Memory and Computation

arXiv

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arXiv 2025年

作者： Zhou, Bingyang Zheng, Chunran Wang, Ziming Zhu, Fangcheng Cai, Yixi Zhang, Fu Laboratory Department of Mechanical Engineering University of Hong Kong Hong Kong Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

This paper presents a lightweight LiDAR-inertial-visual odometry system optimized for resource-constrained platforms. It integrates a degeneration-aware adaptive visual frame selector into error-state iterated Kalman filter (ESIKF) with sequential updates, improving computation efficiency significantly while maintaining a similar level of robustness. Additionally, a memory-efficient mapping structure combining a locally unified visual-LiDAR map and a long-term visual map achieves a good trade-off between performance and memory usage. Extensive experiments on x86 and ARM platforms demonstrate the system’s robustness and efficiency. On the Hilti dataset, our system achieves a 33% reduction in per-frame runtime and 47% lower memory usage compared to FAST-LIVO2, with only a 3 cm increase in RMSE. Despite this slight accuracy trade-off, our system remains competitive, outperforming state-of-the-art (SOTA) LIO methods such as FAST-LIO2 and most existing LIVO systems. These results validate the system’s capability for scalable deployment on resource-constrained edge computing platforms. Copyright © 2025, The Authors. All rights reserved.

关键词： Kalman filters

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Large-Scale Distributed Multidisciplinary Design Optimization of the NASA Lift-Plus-Cruise Air Taxi Concept

Large-Scale Distributed Multidisciplinary Design Optimizatio...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： van Schie, Sebastiaan P. C. Ruh, Marius L. Fletcher, Andrew Warner, Michael Sperry, Mark Scotzniovsky, Luca Orndorff, Nicholas C. Xiang, Ru Yan, Jiayao Zhao, Han Krokowski, Joshua J. Chen, J.S. Sarojini, Darshan Gill, Hyunjune Lee, Seongkyu Tagg, Andrew Anderson, Ryan Green, Eric Joseph, Cibin Ning, Andrew Cheng, Zeyu Cao, Zhi Mi, Chunting Guibert, Alexandre T. R. Cronk, Ashley Kim, H. Alicia Meng, Y. Shirley Silva, Christopher Hwang, John T. University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Mechanical and Aerospace Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Structural Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Aerospace and Ocean Engineering Department Virginia Tech 1600 Innovation Dr BlacksburgVA24060 United States Department of Mechanical and Aerospace Engineering University of California Davis 1 Shields Avenue DavisCA95616 United States Mechanical Engineering Brigham Young University ProvoUT84602 United States Department of Electrical & Computer Engineering San Diego State University 5500 Campanile Dr San Diego92182 United States Structural Engineering Department University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Materials Science & Engineering Program University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60607 United States NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Large-scale gradient-based Multidisciplinary Design Optimization (MDO) can aid in the exploration of high-dimensional design spaces for novel air vehicle concepts, thereby leading to more efficient and economic designs. This paper builds on past works where we demonstrated large-scale physics-based MDO capabilities and applied these to NASA’s lift-plus-cruise electric air taxi concept. We extend this comprehensive mid-fidelity system-level optimization problem with high(er)-fidelity subsystem-level optimizations of various aircraft systems and mission phases, with the aim of further enhancing the accuracy and scope of the aforementioned system-level optimization problem: 1) Power minimization during the transition mission phase;2) Electrical powertrain topology optimization;3) Cell chemistry modeling and thermomechanical battery pack topology optimization;4) Shell-based coupled aero-elastic wing structure optimization. An Analytical Target Cascading-like distributed MDO architecture allows us to couple the system- and subsystem-level optimizations in order to arrive at a consistent and feasible design. We find that the system-level design is most heavily impacted by the reduced battery pack-level energy densities that stem from power peaks in the mission profile. These power peaks seem to result from the inefficient lift rotor blade designs that are needed to satisfy noise constraints. We draw conclusions based on trends in our results and give recommendations for future MDO studies of electrical air taxi vehicle concepts. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Shape optimization

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Sanam Narejo Muhammad Taimoor Khan Muhammad Zakir Shaikh Lubna Luxmi Dhirani Bhawani Shankar Chowdhery Department of Computer Systems Mehran Univerity of Engineering and Technology Jamshoro Pakistan National Centre for Robotics Automation and Artificial Intelligence Mehran Univerity of Engineering and Technology Jamshoro Pakistan Mechanical Engineering and energy efficiency School of Industrial Engineering University of Malaga Malaga Spain Department of Electronic and Computer Engineering University of Limerick Limerick Ireland

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car’s navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car’s driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car’s evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations.

关键词： Navigation Tracking Neural networks Decision making Reinforcement learning Radar tracking Topology Sensors Automobiles Vehicle dynamics

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Influence of directional solidification parameters on the amount of quasicrystalline phase of an AlCuFe alloy

Journal of Alloys and Metallurgical Systems

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Journal of Alloys and Metallurgical Systems 2025年 9卷

作者： dos Santos Barros, Thayza Pacheco Caluête, Rafael E. da Silva, Aline Karla Barbosa de Oliveira, Danniel Ferreira Feitosa, Francisco Riccelly Pereira de Lima, Bruno Alessandro Silva Guedes de Lima, Danielle Guedes Federal University of Paraíba Graduate Program in Mechanical Engineering PB João Pessoa58051-900 Brazil Federal University of Paraíba Department of Mechanical Engineering PB João Pessoa58051-900 Brazil Federal University of Paraíba Department of Materials Engineering João Pessoa58051-900 Brazil

In this work, a casting processing route via unidirectional solidification is proposed, where the severity of cooling can be increased to maximize the amount of quasicrystalline phase. For this, the alloy with nominal composition Al62.5Cu25Fe12.5 was melted and solidified unidirectionally under optimized processing conditions. Our results evidenced a dependence of the quasicrystalline phase percentage, obtained for an AlCuFe alloy, as a function of the position of the ingot and the type of mold used during the directional solidification process. This analysis expands the spectrum of studies on structural properties, for alloys of the AlCuFe system, indicating the cooling conditions, during the directional solidification process, necessary to maximize the amount of quasicrystalline phase. © 2024 The Authors

关键词： Iron alloys

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Modulated magnetic and piezoresponse phase by applied electric field in 2D layered materials at room temperature

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Results in Physics 2025年 70卷

作者： Lim, Joong Yeon Kim, Seonghwan Kim, Young-Seong Department of Mechanical Robotics and Energy Engineering Dongguk University Jung-gu Seoul 04620 South Korea Department of Mechanical and Manufacturing Engineering Schulich School of Engineering University of Calgary Calgary T2N 1N4 AB Canada

A hybrid perovskite material with a Two-dimensional (2D) layered (C4H9NH3)2PbIBr3 crystal structure is presented that exhibits magnetoelectricity and piezoelectricity coupling properties at room temperature. The inter... 详细信息

A hybrid perovskite material with a Two-dimensional (2D) layered (C₄H₉NH₃)₂PbIBr₃ crystal structure is presented that exhibits magnetoelectricity and piezoelectricity coupling properties at room temperature. The interaction between ferromagnetism and piezoelectricity was achieved by harmonizing their respective ferroic orders. 2D layered organic–inorganic hybrid perovskites demonstrated controlled multifunctional ferroic attributes, with each characteristic established. The magnetic and piezoresponse phase rotation was controlled by modulating the electric-field strength using the magnetoelectricity and piezoelectricity. The responsive and phase-controllable behavior of ferromagnetism and piezoelectricity in 2D hybrid perovskites was effectively managed by an external electric field induced by the coupling between ferroic orders at room temperature. This discovery offers a promising avenue for future research on hybrid perovskites with novel 2D layered-crystal structure. © 2025 The Author(s)

关键词： Hybrid perovskite Layered van der Waals Magnetoelectricity Multifunctional ferroic coupling Piezoelectricity

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Human-Like Robot Impedance Regulation Skill Learning from Human-Human Demonstrations

arXiv

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arXiv 2025年

作者： Li, Chenzui Wu, Xi Liu, Junjia Teng, Tao Chen, Yiming Calinon, Sylvain Caldwell, Darwin Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Idiap Research Institute Martigny Switzerland Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa16163 Italy

Humans are experts in collaborating with others physically by regulating compliance behaviors based on the perception of their partners’ states and the task requirements. Enabling robots to develop proficiency in human collaboration skills can facilitate more efficient human-robot collaboration (HRC). This paper introduces an innovative impedance regulation skill learning framework for achieving HRC in multiple physical collaborative tasks. The framework is designed to adjust the robot compliance to the human partner’s states while adhering to reference trajectories provided by human-human demonstrations. Specifically, electromyography (EMG) signals from human muscles are collected and analyzed to extract limb impedance, representing compliance behaviors during demonstrations. Human endpoint motions are captured and represented using a probabilistic learning method to create reference trajectories and corresponding impedance profiles. Meanwhile, an LSTM-based module is implemented to develop task-oriented impedance regulation policies by mapping the muscle synergistic contributions between two demonstrators. Finally, we propose a whole-body impedance controller for a human-like robot, coordinating joint outputs to achieve the desired impedance and reference trajectory during task execution. Experimental validation was conducted through a collaborative transportation task and two interactive Tai Chi pushing hands tasks, demonstrating superior performance from the perspective of interactive forces compared to a constant impedance control method. Copyright © 2025, The Authors. All rights reserved.

关键词： Electromyography

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Compliant actuators that mimic biological muscle performance with applications in a highly biomimetic robotic arm

Advanced Bionics

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Advanced Bionics 2025年第1期1卷 2-18页

作者： Haosen Yang Guowu Wei Lei Ren Lingyun Yan Darwin Caldwell Department of Mechanical Aerospace and Civil EngineeringUniversity of ManchesterOxford RdManchester M139PLUK School of Science Engineering and EnvironmentUniversity of Salford43 CrescentSalford M54WTUK Key Laboratory of Bionic Engineering Ministry of EducationJilin UniversityNo.2699Qianjin RoadChangchun 130025China Department of Robotics Engineering School of Electrical and Electronic EngineeringShanghai Institute of Technology100 Haiquan RoadShanghai 200235China Department of Advanced Robotics Italian Institute of TechnologyVia Morego30Genova 16163Italy

This paper endeavours to bridge the existing gap in muscular actuator design for ligament-skeletal-inspired robots,thereby fostering the evolution of these robotic *** introduce two novel compliant actuators,namely the Internal Torsion Spring Compliant Actuator(ICA)and the External Spring Compliant Actuator(ECA),and present a comparative analysis against the previously conceived Magnet Integrated Soft Actuator(MISA)through computational and experimental *** actuators,employing a motor-tendon system,emulate biological muscle-like forms,enhancing artificial muscle ***,applications of the proposed actuators in a robotic arm inspired by the human musculoskeletal system are *** demonstrate satisfactory power in tasks like lifting dumbbells(peak power:36 W),playing table tennis(end-effector speed:3.2 m/s),and door opening,without compromising biomimetic *** to other linear stiffness serial elastic actuators(SEAs),ECA and ICA exhibit high power-to-volume(361×10^(3)W/m^(3))and power-to-mass(111.6 W/kg)ratios respectively,endorsing the biomimetic design’s promise in robotic development.

关键词： Artificial muscle Compact compliant actuator Series elastic actuator Biomimetic robots

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