检索结果-内蒙古大学图书馆

23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2024

作者： Lin, Yujui Kwon, Heungdong Chen, Hao Gupta, Man Prakash Degner, Michael Asheghi, Mehdi Mantooth, H. Alan Goodson, Kenneth E. Stanford University Department of Mechanical Engineering StanfordCA94305 United States University of Arkansas Department of Electrical Engineering FayettevilleAR72701 United States Ford Motor Company Research and Advanced Engineering DearbornMI48124 United States

ISBN: (纸本)9798350364330

关键词： Electronic cooling

来源：评论

学校读者我要写书评

暂无评论

A Transient Response Adjustable MPC for Following A Dynamic Object

A Transient Response Adjustable MPC for Following A Dynamic ...

引用

American control Conference (ACC)

作者： Sanghyun Hong zJustin Miller Jianbo Lu Research & Advanced Engineering Ford Motor Company Dearborn MI USA

As mobile robots increasingly interact with humans, vehicles, and other robots, the ability to follow these dynamic objects becomes a crucial capability. In this paper, a mobile robot motion controller based on a down`scaled Model Predictive control (MPC) is proposed for performing dynamic object following. The controller can reflect the dynamics of an object for an appropriate response speed while ensuring safety of the robot against predicted risks. In addition, an adaptive prediction time horizon is proposed to improve following robustness. The proposed motion controller is demonstrated through extensive simulations and real-time experiments on mobile robot hardware.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sensitivity Analysis of Support Vector Regression-Based Incremental Capacity Analysis for Battery State of Health Estimations

Sensitivity Analysis of Support Vector Regression-Based Incr...

引用

2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Zhou, Qinan Hellstrom, Erik Anderson, Dyche Sun, Jing University of Michigan Department of Mechanical Engineering Ann ArborMI48103 United States Ford Motor Company Research and Advanced Engineering DearbornMI48121 United States University of Michigan Department of Naval Architecture and Marine Engineering Ann ArborMI48103 United States

ISBN: (纸本)9798350335446

Lithium-ion battery state of health (SOH) estimation has become a critical challenge for battery management systems (BMS) in electrified vehicles and consumer electronics. While support vector regression (SVR)-based incremental capacity analysis (ICA) was established as an effective method for estimating SOH, the published work on this topic has been focused on a single set of charging conditions. This paper proposes an improved SVR-based ICA method that expands the working conditions of prior algorithms by incorporating the temperature and C rate sensitivities of ICA explicitly into SOH estimation models. The proposed algorithm is shown to work for different positive electrode chemistries. Moreover, range insensitivity is established for the proposed SVR-based ICA algorithm, which allows the resulting SOH estimation models to be used in various partial charging ranges encountered in practice. Less than 1% root-mean-square SOH estimation error is achieved when the proposed algorithm is assessed using publicly available datasets. © 2023 IEEE.

关键词： Battery management systems

来源：评论

学校读者我要写书评

暂无评论

The Impact of Liquid Supply Delivery methods on the Thermal Performance of a Capillary-based Two-phase Micro-cooler for the Power Electronics

The Impact of Liquid Supply Delivery Methods on the Thermal ...

引用

Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITHERM)

作者： Yujui Lin Heungdong Kwon Hao Chen Man Prakash Gupta Michael Degner Mehdi Asheghi H. Alan Mantooth Kenneth E. Goodson Department of Mechanical Engineering Stanford University Stanford CA Department of Electrical Engineering University of Arkansas Fayetteville AR Research and Advanced Engineering Ford Motor Company Dearborn MI

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364347

Conventional cooling solutions such as air-cooled pin-fin, liquid-cooled cold-plate, or even single-phase microchannel coolers are reaching their limit to meet both increased heat flux and power densities in power electronics applications. The two-phase conventional microchannel cooling could potentially address these challenges but suffers from large superheat, flow maldistribution and instabilities, and large pressure *** overcome these challenges, we have developed a high-performance microcooler that utilizes the potential of two-phase cooling by capillary-driven flow in microchannels with porous wicks to achieve low superheat (4-6 °C) and specific thermal resistance ~0.015 to 0.02 cm 2 -°C /W at two-phase heat flux ~ 300 W/cm 2 and a low liquid flow rate 1.5 g/min corresponding to vapor quality of x = 1. However, the thermal performance of the capillary-based microcooler is sensitive to the liquid supply flow rate and packaging solution for x < ***, we examine the impacts of liquid feed packaging configurations and flow rates on the two-phase cooling performance. The flooding at the microchannels caused by excess liquid supply leads to increased superheat and thermal resistance that can be partially prevented using porous tissue that serves as a buffer. We concluded that the optimal operation condition for the microchannel cooler is at vapor quality x ~ 1.0. The higher heat fluxes can be achieved by increasing the supply of liquid at the expense of increased thermal resistance. We have demonstrated the potential to improve the operation efficiency of the capillary-based microcooler to support the growing power density of the next-generation power electronics.

关键词： Power system measurements Liquids Cooling Thermal resistance Fluid flow Packaging Electronic packaging thermal management Power electronics Microchannels Resistance heating

来源：评论

学校读者我要写书评

暂无评论

Safe Data-Driven Predictive control

arXiv

引用

arXiv 2025年

作者： Vahidi-Moghaddam, Amin Chen, Kaian Zhang, Kaixiang Li, Zhaojian Wang, Yan Wu, Kai Department of Mechanical Engineering Michigan State University East LansingMI48824 United States The Research and Advanced Engineering Ford Motor Company DearbornMI48121 United States

In the realm of control systems, model predictive control (MPC) has exhibited remarkable potential;however, its reliance on accurate models and substantial computational resources has hindered its broader application, especially within real-time nonlinear systems. This study presents an innovative control framework to enhance the practical viability of the MPC. The developed safe data-driven predictive control aims to eliminate the requirement for precise models and alleviate computational burdens in the nonlinear MPC (NMPC). This is achieved by learning both the system dynamics and the control policy, enabling efficient data-driven predictive control while ensuring system safety. The methodology involves a spatial temporal filter (STF)-based concurrent learning for system identification, a robust control barrier function (RCBF) to ensure the system safety amid model uncertainties, and a RCBF-based NMPC policy approximation. An online policy correction mechanism is also introduced to counteract performance degradation caused by the existing model uncertainties. Demonstrated through simulations on two applications, the proposed approach offers comparable performance to existing benchmarks with significantly reduced computational costs. © 2025, CC0.

关键词： Robust control

来源：评论

学校读者我要写书评

暂无评论

Addressing Rare Outages in CV2X with Time-controlled One-shot Resource Scheduling

TechRxiv

引用

TechRxiv 2023年

作者： Saifuddin, Md. Zaman, Mahdi Fallah, Yaser P. Rao, Jayanthi University of Central Florida OrlandoFL United States Research and Advanced Engineering for Ford Motor Company DearbornMI United States

Cellular Vehicle-to-Everything (C-V2X) has become one of the highest anticipated technologies for vehicular safety network. In LTE C-V2X Basic Safety Messages (BSMs) are transmitted on radio resources that are allocated with a periodic resource reusability. This allocation is based on a semi persistent sensing-based scheduling scheme (SPS) algorithm. But, due to this reuse of periodic resources, the possibility of loss of consecutive packets between the same vehicle pair is significant. This study discusses different approaches proposed to solve this consecutive loss problem. Based on this investigation, this article suggests an efficient One-Shot based solution with a new control parameter, that performs superior to the state-of-the-art solution that is standardized in SAE J3161/1 which this article analyzes and shows to have limitation in case of high-density scenario. © 2023, CC BY.

关键词： Vehicle to Everything

来源：评论

学校读者我要写书评

暂无评论

Robust AI Driving Strategy for Autonomous Vehicles

arXiv

引用

arXiv 2022年

作者： Nageshrao, Subramanya Rahman, Yousaf Ivanovic, Vladimir Jankovic, Mrdjan Tseng, Eric Hafner, Michael Filev, Dimitar Ford Motor Company Research and Advanced Engineering DearbornMI48124 United States

There has been significant progress in sensing, perception, and localization for automated driving, However, due to the wide spectrum of traffic/road structure scenarios and the long tail distribution of human driver behavior, it has remained an open challenge for an intelligent vehicle to always know how to make and execute the best decision on road given available sensing / perception / localization information. In this chapter, we talk about how artificial intelligence and more specifically, reinforcement learning, can take advantage of operational knowledge and safety reex to make strategical and tactical decisions. We discuss some challenging problems related to the robustness of reinforcement learning solutions and their implications to the practical design of driving strategies for autonomous vehicles. We focus on automated driving on highway and the integration of reinforcement learning, vehicle motion control, and control barrier function, leading to a robust AI driving strategy that can learn and adapt safely. © 2022, CC BY-NC-ND.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Data-Enabled Neighboring Extremal Optimal control: A Computationally Efficient DeePC

Data-Enabled Neighboring Extremal Optimal Control: A Computa...

引用

IEEE Conference on Decision and control

作者： Amin Vahidi-Moghaddam Kaixiang Zhang Zhaojian Li Yan Wang Department of Mechanical Engineering Michigan State University East Lansing MI USA Research and Advanced Engineering Ford Motor Company Dearborn MI USA

Model-based optimal control strategies typically rely on accurate parametric representations of the underlying systems, which can be challenging to obtain, especially for nonlinear and complex systems. Therefore, data-driven optimal controllers have become increasingly attractive to both academics and industry practitioners. As a data-driven optimal control approach that can explicitly handle constraints, data-enabled predictive control (DeePC) makes a transition from the model-based optimal control strategies (e.g. model predictive control (MPC)) to a data-driven one such that it seeks an optimal control policy from raw input/output (I/O) data without requiring system identification prior to control deployment, achieving remarkable successes in various applications. However, this approach involves high computational cost due to the dimension of the decision variable, which is generally significantly higher than its MPC counterpart. Several approaches have been proposed to reduce the computational cost of the DeePC for linear time-invariant (LTI) systems. However, finding a computationally efficient method to implement the DeePC for the nonlinear systems is still an open challenge. In this paper, we propose a data-enabled neighboring extremal (DeeNE) to approximate the DeePC policy and reduce its computational cost for the constrained nonlinear systems. The DeeNE adapts a pre-computed nominal DeePC solution to the perturbations of the initial I/O trajectory and the reference trajectory from the nominal ones. We also develop a scheme to handle nominal non-optimal solutions so that we can use the DeeNE solution as the nominal solution during the control process. Promising simulation results on the cart inverted pendulum problem demonstrate the efficacy of the DeeNE framework.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Impact of Reference-Command Preview on Human-in-the-Loop control Behavior

arXiv

引用

arXiv 2023年

作者： Rabiee, Pedram Mousavi, S. Alireza Seyyed Sheffler, Amelia J.S. Hellström, Erik Jankovic, Mrdjan Santillo, Mario A. Seigler, T.M. Hoagg, Jesse B. The Department of Mechanical and Aerospace Engineering University of Kentucky LexingtonKY United States Research and Advanced Engineering Ford Motor Company DearbornMI United States

This article presents results from an experiment in which 44 human subjects interact with a dynamic system to perform 40 trials of a command-following task. The reference command is unpredictable and different on each trial, but all subjects have the same sequence of reference commands for the 40 trials. The subjects are divided into 4 groups of 11 subjects. One group performs the command-following task without preview of the reference command, and the other 3 groups are given preview of the reference command for different time lengths into the future (0.5 s, 1 s, 1.5 s). A subsystem identification algorithm is used to obtain best-fit models of each subject's control behavior on each trial. The time- and frequency-domain performance, as well as the identified models of the control behavior for the 4 groups are examined to investigate the effects of reference-command preview. The results suggest that preview tends to improve performance by allowing the subjects to compensate for sensory time delay and approximate the inverse dynamics in feedforward. However, too much preview may decrease performance by degrading the ability to use the correct phase lead in feedforward. Copyright © 2023, The Authors. All rights reserved.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Unsupervised 3D Object Detection Domain Adaptation based on Pseudo-label Variance Regularization

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Ke Zhou, Peng Hu, Man Lu, Jianbo Chongqing University College of Mechanical and Vehicle Engineering 400044 China Chongqing University Key Lab of Mechanical Transmission 400044 China Changan Auto Company Intelligent Vehicle R and D Institute Chongqing401120 China Ford Motor Company Research and Advanced Engineering DearbornMI48121 United States

Cross-domain detection frequently encounters a decline in detection accuracy, necessitating the application of domain adaptation techniques. One crucial approach to unsupervised domain adaptation is the pseudo label-based self-training method, which iteratively trains the model by treating the pseudo labels as ground truth. However, differences in distribution that can exist between the source and target domains can lead to potentially incorrect generated pseudo labels. This can result in the threshold-setting method failing to accurately select the pseudo labels. Therefore, to tackle the challenge of determining pseudo label thresholds in self-training, we propose an unsupervised 3D object detection domain adaptation method based on pseudo label regularization. Specifically, a self-training framework based on the fusion of two detection heads is used to obtain more accurate pseudo labels. The variance of the two detection heads is utilized as the noise information for the corresponding pseudo labels. Then, the noise information is incorporated as a regularization term to enhance the bounding box regression loss, thereby addressing the challenge of determining pseudo label thresholds in self-training. The experimental results demonstrate that the method proposed in this paper achieves higher cross-domain detection accuracy compared to existing domain adaptation methods for 3D object detection. © 1991-2012 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：