CAREER: Collaborative Modeling for Distributed Sensing and Real-time Intelligent Control to Improve Battery Manufacturing Productivity and Efficiency

职业：分布式传感和实时智能控制的协作建模，以提高电池制造生产力和效率

• 批准号: 1935728
• 负责人: Qing Chang
• 金额: $ 10.88万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-11-19 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935728&HistoricalAwards=false
• 关键词: CAREER; Collaborative; Modeling; Distributed; Sensing

This Faculty Early Career Development (CAREER) Program award is an integrated research and education study centered on collaborative sensing information processing, and an intelligent online control framework for battery manufacturing. It aims to integrate decision theory with real-time control, which will allow the full potential of distributed sensor networks to improve production system responsiveness, productivity and overall efficiency. The methodology includes the following tasks: 1) establish a virtual multi-layer sensing framework and event-based modeling to directly model distributed-sensor systems, 2) develop a collaborative information processing framework to effectively identify system transient loss and root causes of system inefficiency, and create a system learning method to optimize sensor network design, 3) establish an intelligent online control method to integrate decision theory with control for adaptive resource allocation and distributed management, and 4) validate these methods using simulation and real data through collaboration with industrial partners. If successful, this CAREER project will advance the state of the art on distributed sensor networks by contributing new concepts, criteria, and algorithms to its system level real-time decision making capabilities, and create an enabling methodology for next-generation plant-wide sensing, information processing and control. Accomplishing these goals will lead to dramatic cost reductions as a result of reduced downtime, improved quality and system efficiency, boosting the competitiveness of U.S. industries and the nation's economy. The CAREER education program will make a positive contribution to workforce training through curriculum and lab development, real-world problem solving and other outreach activities, with a focus on broadening participation of underrepresented groups. Dissemination through conference/journal publications and international/industrial collaborations will lead to exposure of these research results to a wide range distributed sensor networks that are of vital importance to the nation's economic growth.

该学院早期职业发展（职业）计划奖是一项以协作传感信息处理和电池制造智能在线控制框架为中心的综合研究和教育研究。它旨在将决策理论与实时控制相结合，这将充分发挥分布式传感器网络的潜力，以提高生产系统的响应能力、生产率和整体效率。该方法包括以下任务：1）建立虚拟多层传感框架和基于事件的建模，以直接对分布式传感器系统进行建模，2）开发协作信息处理框架，以有效识别系统瞬态损耗和系统效率低下的根本原因，并创建一种系统学习方法来优化传感器网络设计，3）建立一种智能在线控制方法，将决策理论与控制相结合，实现自适应资源分配和分布式管理，4）通过与工业界合作，使用仿真和实际数据验证这些方法合作伙伴。 如果成功，这个职业项目将通过为其系统级实时决策能力贡献新的概念、标准和算法，推进分布式传感器网络的最先进水平，并为下一代全厂传感创建一种支持方法。 、信息处理和控制。实现这些目标将通过减少停机时间、提高质量和系统效率而大幅降低成本，从而提高美国工业和国家经济的竞争力。职业教育计划将通过课程和实验室开发、解决实际问题和其他外展活动，为劳动力培训做出积极贡献，重点是扩大代表性不足群体的参与。通过会议/期刊出版物和国际/工业合作的传播将使这些研究成果接触到广泛的分布式传感器网络，这对国家的经济增长至关重要。