Physics-Based Probabilistic Prognostics for Battery Health Management

基于物理的电池健康管理概率预测

• 批准号: 2015710
• 负责人: Shan Hu
• 金额: $ 38.48万
• 依托单位: Iowa State University
• 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: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015710&HistoricalAwards=false
• 关键词: Physics; Based; Probabilistic; Prognostics; Battery

The objective of this project is to create a physics-based probabilistic prognostics platform for lithium-ion (Li-ion) batteries. Li-ion battery technology is playing an increasingly important role in realizing wide-scale adoption of hybrid and electric vehicles, and renewable energy sources. Successful development of the proposed platform will produce major advancements in extending battery life while ensuring battery safety. Advances in energy storage management could reduce the costs and promote the wide-scale adoption of hybrid and electric vehicles and renewable energy sources, which in turn will reduce the dependence of our nation on foreign sources of energy. The research findings will be disseminated to the battery industry, main stakeholders, and decision makers through collaboration with an industry leader in battery safety. An inclusive education and outreach plan will help (1) train a globally competitive workforce in battery reliability modeling by incorporating research findings into classroom teaching, (2) provide research experiences to undergraduate and K-12 students with an emphasis on increasing the participation of women and underrepresented minorities, and (3) raise public awareness about battery reliability and safety by giving talks to the local communities. The novelty of the proposed prognostics platform is its ability to integrate mechanistic degradation analysis into remaining useful life prediction using probabilistic models. More specifically, model-based smoothing and learning are adopted, in conjunction with bias-corrected half-cell models, to infer the degrees of degradation from noisy voltage and current measurements. This is followed by physics-based prognostics, where model-based tracking is used to predict future degradation trajectories and remaining useful life. The resulting prognostics approach leverages a combination of physical knowledge and sensor data to achieve gains in prediction accuracy and robustness. The proposed platform will advance the field of battery health management by furthering understanding on: (1) how to validate mechanistic half-cell models using relatively few expensive experiments; (2) how to predict the long-term degradation using only early-life data; (3) the role of physics in designing prognostics approaches; and (4) the coupling effects of degradation modes on capacity fade and failure.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.

该项目的目标是为锂离子（Li-ion）电池创建一个基于物理的概率动力学平台。锂离子电池技术在实现混合动力和电动汽车以及可再生能源的广泛采用方面发挥着越来越重要的作用。该平台的成功开发将在延长电池寿命的同时确保电池安全性方面取得重大进展。储能管理的进步可以降低成本，促进混合动力和电动汽车以及可再生能源的广泛采用，这反过来又会减少我国对外国能源的依赖。研究结果将通过与电池安全行业领导者的合作传播给电池行业，主要利益相关者和决策者。包容性教育和推广计划将有助于（1）通过将研究结果纳入课堂教学，培养具有全球竞争力的电池可靠性建模工作人员，（2）为本科生和K-12学生提供研究经验，重点是增加妇女和代表性不足的少数民族的参与，及（3）透过在当地社区举办讲座，提高公众对电池可靠性及安全性的认识。所提出的mistics平台的新奇在于它能够利用概率模型将机械退化分析集成到剩余使用寿命预测中。更具体地说，采用基于模型的平滑和学习，结合偏置校正的半电池模型，从噪声电压和电流测量推断退化的程度。其次是基于物理学的可预测性，其中基于模型的跟踪用于预测未来的退化轨迹和剩余使用寿命。由此产生的预测方法利用物理知识和传感器数据的组合，以实现预测精度和鲁棒性的增益。所提出的平台将推进电池健康管理领域，通过进一步理解：（1）如何使用相对较少的昂贵实验来验证机械半电池模型;（2）如何仅使用早期寿命数据来预测长期退化;（3）物理学在设计物理学方法中的作用;以及（4）降级模式对容量衰减和故障的耦合影响。该奖项反映了NSF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

Integrating Physics-Based Modeling and Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries

• DOI: 10.1016/j.ensm.2022.05.047
• 发表时间: 2022-05
• 期刊: Energy Storage Materials
• 影响因子: 20.4
• 作者: Adam Thelen;Y. Lui;Sheng Shen;S. Laflamme;Shan Hu;Hui Ye;Chao Hu

Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries

• DOI: 10.1115/detc2021-71407
• 发表时间: 2021-08
• 期刊: Volume 3A: 47th Design Automation Conference (DAC)
• 作者: Adam Thelen;Y. Lui;Sheng Shen;S. Laflamme;Shan Hu;Chao Hu