Actively Managed Battery Degradation of IoT Wireless Sensors for Group Replacement

主动管理物联网无线传感器的电池退化以进行成组更换

基本信息

  • 批准号:
    2027425
  • 负责人:
  • 金额:
    $ 47.9万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-09-01 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

This award will enhance the Nation's competitiveness by reducing life cycle costs of battery-powered wireless devices that constitute networked sensor systems. Networked sensor systems are integral to emerging Internet of Things (IoT) applications in such diverse domains as manufacturing systems and civil infrastructure systems. Battery life continues to limit network performance. This project will investigate system-level performance improvements through individual battery management and group replacement strategies. By monitoring and controlling individual battery usage, system level quality of service can be dynamically adjusted, leading to more uniform useful battery life. When these networked systems are located in remote locations, cost-effective group replacement policies can lead to lower costs to maintain the network. The project will involve graduate students and will include mentoring activities to attract students from underrepresented groups.The project is expected to advance the state of the art in the following aspects: (1) a prognostic algorithm that accurately predicts battery remaining useful life, using transfer learning to leverage condition monitoring data of all batteries in a connected environment; (2) a novel predictive maintenance policy designed for battery group replacement, enabled by active management of battery degradation processes; (3) optimal scheduling of battery maintenance activities under the presence of another independently managed maintenance program with higher priority. The project will advance knowledge on battery health management in connected environments and provide effective power management strategies for battery-powered wireless IoT devices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该合同将通过降低构成网络传感器系统的电池供电无线设备的生命周期成本来增强美国的竞争力。网络传感器系统是新兴的物联网(IoT)应用在制造系统和民用基础设施系统等不同领域不可或缺的一部分。电池寿命继续限制网络性能。该项目将通过单个电池管理和组更换策略研究系统级性能改进。通过监测和控制单个电池的使用情况,可以动态调整系统级服务质量,从而实现更均匀的有效电池寿命。当这些网络系统位于远程位置时,具有成本效益的组替换策略可以降低维护网络的成本。该项目将涉及研究生,并将包括指导活动,以吸引来自代表性不足群体的学生。该项目预计将在以下几个方面推进最新技术:(1)使用迁移学习来利用连接环境中所有电池的状态监测数据,准确预测电池剩余使用寿命的预测算法;(2)通过主动管理电池退化过程,设计了一种新的电池组更换预测性维护策略;(3)存在另一个具有更高优先级的独立管理的维修计划时,电池维修活动的最优调度。该项目将推进互联环境中电池健康管理方面的知识,并为电池供电的无线物联网设备提供有效的电源管理策略。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Actively managed battery degradation of wireless sensors for structural health monitoring
  • DOI:
    10.1117/12.2658497
  • 发表时间:
    2023-04
  • 期刊:
  • 影响因子:
    64.5
  • 作者:
    Tahsin Afroz Hoque Nishat;Jong-Hyun Jeong;H. Jo;Qiang Zhou;Jian Liu
  • 通讯作者:
    Tahsin Afroz Hoque Nishat;Jong-Hyun Jeong;H. Jo;Qiang Zhou;Jian Liu
Adaptive sequential predictive maintenance policy with nonperiodic inspection for hard failures
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Hongki Jo其他文献

Reinforcement learning for adaptive battery management of structural health monitoring IoT sensor network
用于结构健康监测物联网传感器网络自适应电池管理的强化学习
  • DOI:
    10.1016/j.apenergy.2025.125731
  • 发表时间:
    2025-07-15
  • 期刊:
  • 影响因子:
    11.000
  • 作者:
    Tahsin Afroz Hoque Nishat;Jong-Hyun Jeong;Hongki Jo;Shenghao Xia;Jian Liu
  • 通讯作者:
    Jian Liu
Toward Real-World Implementation of Deep Learning for Smartphone-Crowdsourced Pavement Condition Assessment
面向智能手机众包路面状况评估的深度学习的实际实施
  • DOI:
    10.1109/jiot.2023.3312353
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    10.6
  • 作者:
    Jong;Hongki Jo
  • 通讯作者:
    Hongki Jo

Hongki Jo的其他文献

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