CAREER: : Advanced Thermal Management of Lithium-Ion Battery Packs: Combining Physics-Based and Machine Learning Models toward High Thermal Safety

职业：：锂离子电池组的先进热管理：结合基于物理的模型和机器学习模型以实现高热安全性

• 批准号: 1847651
• 负责人: Huazhen Fang
• 金额: $ 50万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1847651&HistoricalAwards=false
• 关键词: CAREER; Advanced; Thermal; Management; Lithium

Lithium-ion battery (LiB) packs play an essential role in electrified transportation, grid, renewables and energy-aware buildings. While the demand on the use of LiB is increasing everyday there are some safety issues that remain unresolved. These battery packs are susceptible to catching fire which pose a great danger to human life and property. The fire hazard in these batteries is caused by a continuous rise of temperature phenomenon (called "thermal runaway") that is not well understood. This Faculty Early Career Development Program (CAREER) project aims at developing reliable high-fidelity mathematical models that can help understand this phenomenon and help predict the thermal behavior more accurately. The success of this project will have huge impact on electrification happening in various industry sectors including transportation, grid, energy, and several others. This project will integrate research into diverse education and outreach activities, including curriculum improvement, community outreach, and research mentorship, to engage K-12, undergraduate, and graduate students. Despite its importance, LiB thermal management practices are largely empirical or coarse-grained with poor scientific rigor, thus inadequate for meeting the safety demands. To change this situation, this research will develop a foundational framework for characterizing and monitoring LiB packs' spatially and temporally distributed thermal behavior, which will build on a multi-disciplinary synthesis of ideas from first-principles modeling, machine learning, distributed estimation, and network systems. This will drive new knowledge advancement in: 1) a hybrid modeling methodology that integrates first-principles-based and data-driven machine learning models, 2) optimal estimation and machine learning theory based on hybrid models, and 3) hybrid-model-based principles, algorithms and tools for temperature field reconstruction and thermal runaway detection. The models and algorithms will be rigorously evaluated through a mix of theoretical analysis, software-based simulation, and experimental validation using a fully instrumented PEC SBT4050 battery tester. The results will open a new research avenue for LiB packs' thermal management while advancing the modeling, estimation and learning theories for complex spatio-temporal systems, with potential application to many other engineering fields.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.

锂离子电池（LiB）组在电气化交通、电网、可再生能源和节能建筑中发挥着至关重要的作用。虽然LiB的使用需求每天都在增加，但仍有一些安全问题尚未解决。这些电池组容易着火，对人类生命和财产构成极大的危险。这些电池中的火灾危险是由温度持续上升现象（称为“热失控”）引起的，这种现象尚未得到很好的理解。这个教师早期职业发展计划（CAREER）项目旨在开发可靠的高保真数学模型，可以帮助理解这种现象，并帮助更准确地预测热行为。该项目的成功将对包括交通、电网、能源等多个行业的电气化产生巨大影响。该项目将研究整合到不同的教育和推广活动中，包括课程改进，社区推广和研究导师，以吸引K-12，本科生和研究生。尽管其重要性，LiB热管理实践在很大程度上是经验性的或粗粒度的，科学严谨性差，因此不足以满足安全需求。为了改变这种情况，这项研究将开发一个基础框架，用于表征和监测LiB包的空间和时间分布的热行为，这将建立在第一原理建模，机器学习，分布式估计和网络系统的多学科综合思想的基础上。这将推动以下方面的新知识进步：1）集成基于第一原理和数据驱动的机器学习模型的混合建模方法，2）基于混合模型的最佳估计和机器学习理论，以及3）基于混合模型的温度场重建和热失控检测的原理，算法和工具。这些模型和算法将通过理论分析、基于软件的模拟和使用全仪表化PEC SBT 4050电池测试仪的实验验证进行严格评估。该研究成果将为LiB电池组的热管理开辟一条新的研究途径，同时推进复杂时空系统的建模、估计和学习理论，并有可能应用于许多其他工程领域。该奖项反映了NSF的法定使命，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

High‐order leader‐follower tracking control under limited information availability

• DOI: 10.1002/rnc.6251
• 发表时间: 2022-07
• 期刊: International Journal of Robust and Nonlinear Control
• 影响因子: 3.9
• 作者: Chuan Yan;Tao Yang;H. Fang

Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries

• DOI: 10.23919/acc50511.2021.9482997
• 发表时间: 2021-03
• 期刊: 2021 American Control Conference (ACC)
• 作者: H. Tu;S. Moura;H. Fang

Tensor Network-Based MIMO Volterra Model for Lithium-Ion Batteries

• DOI: 10.1109/tcst.2022.3232894
• 发表时间: 2023-07
• 期刊: IEEE Transactions on Control Systems Technology
• 影响因子: 4.8
• 作者: Yangsheng Hu;R. D. de Callafon;Ning Tian;H. Fang

Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification, and Validation

• DOI: 10.1109/tcst.2020.2976036
• 发表时间: 2019-06
• 期刊: IEEE Transactions on Control Systems Technology
• 影响因子: 4.8
• 作者: Ning Tian;H. Fang;Jian Chen;Yebin Wang

Spatio-Temporal Thermal Monitoring for Lithium-Ion Batteries via Kriged Kalman Filtering

• DOI: 10.1109/cdc51059.2022.9992543
• 发表时间: 2022-12
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC)
• 作者: H. Tu;Yebin Wang;Xianglin Li;H. Fang