Estimation of Critical Battery States via Strain and Stress Measurement

通过应变和应力测量估计关键电池状态

• 批准号: 1762247
• 负责人: Jason Siegel
• 金额: $ 33.77万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1762247&HistoricalAwards=false
• 关键词: Estimation; Critical; Battery; States; via

Energy storage utilizing lithium-ion batteries is ubiquitous from cell phones and satellites to vehicles and the electric grid. All these applications require reliable estimates of the available power and energy. Consumer concerns, such as range anxiety for Electric Vehicles (EVs), hamper market penetration. Such concerns can be alleviated by improving estimates of critical battery states such as capacity and internal resistance that change over time. This research seeks to address those concerns by improving battery health diagnostics. This advance will benefit all battery-powered devices and could be especially crucial for the second-life use of automotive batteries for energy storage on the electric grid which has the potential to enable more substantial penetration of renewable resources through expanded utilization of existing battery technology. Experimentally validated models of the battery mechanical response and its connection to battery health would fill a critical gap in the availability of public-domain data and models. The state of the art battery estimation methods relies on the cell terminal voltage measurements, and the plan is to improve the battery health diagnostics in this project by measuring and interpreting the battery cell expansion which happens when the electrode layers fill and empty with lithium-ions during charging and discharging. This research aims to create a multi-physics model and estimation techniques to harness the information in the deformation of the electrodes and to analyze the measured electrical and mechanical signals to enhance battery health estimation. The graphite electrode exhibits distinct expansion patterns as it fills with lithium-ions, which can enable diagnosis of electrode-specific degradation. Several fundamental gaps in the modeling of multi-scale inter-dependent thermal, electrochemical and mechanical responses of the battery need to be addressed before the benefits of this approach can be realized in relevant usage cycles involving dynamic charging and discharging. Moreover, the accuracy of joint state and parameter estimation depends on the operating region and the current excitation or usage profile. Systematic techniques to assess the identifiability would benefit from physics-based models that can be used to investigate the influence of various degradation mechanisms, cross-sensitivity with thermal swelling, sensor location, and commercially relevant packaging.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.

利用锂离子电池的能量存储无处不在，从手机和卫星到车辆和电网。所有这些应用都需要对可用功率和能量进行可靠的估计。消费者的担忧，如电动汽车（EV）的里程焦虑，阻碍了市场渗透。这种担忧可以通过改善对关键电池状态（如容量和随时间变化的内阻）的估计来缓解。这项研究旨在通过改善电池健康诊断来解决这些问题。 这一进展将使所有电池供电设备受益，对于汽车电池在电网上的二次使用尤其重要，这有可能通过扩大现有电池技术的利用来实现可再生资源的更大渗透。电池机械响应的实验验证模型及其与电池健康的联系将填补公共领域数据和模型可用性的关键空白。最先进的电池估计方法依赖于电池端电压测量，计划通过测量和解释电池膨胀来改善本项目中的电池健康诊断，电池膨胀发生在充电和放电期间，当电极层填充和排空锂离子时。 该研究旨在创建多物理模型和估计技术，以利用电极变形中的信息，并分析测量的电气和机械信号，以增强电池健康估计。石墨电极在填充锂离子时表现出不同的膨胀模式，这可以诊断电极特定的降解。在涉及动态充电和放电的相关使用周期中实现这种方法的益处之前，需要解决电池的多尺度相互依赖的热、电化学和机械响应的建模中的几个基本差距。此外，联合状态和参数估计的准确性取决于操作区域和当前激励或使用分布。评估可识别性的系统技术将受益于基于物理的模型，这些模型可用于调查各种降解机制的影响，热膨胀的交叉敏感性，传感器位置和商业相关的包装。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

An Algorithmic Safety VEST For Li-ion Batteries During Fast Charging

• DOI: 10.1016/j.ifacol.2021.11.225
• 发表时间: 2021-08
• 期刊: ArXiv
• 作者: Peyman Mohtat;Sravan Pannala;V. Sulzer;Jason B. Siegel;A. Stefanopoulou

Equivalent Circuit Model for High C-Rate Discharge with an External Short Circuit

• DOI: 10.23919/acc53348.2022.9867199
• 发表时间: 2022-06
• 期刊: 2022 American Control Conference (ACC)
• 作者: Vivian Tran;T. Cai;A. Stefanopoulou;Jason B. Siegel

Battery Internal Short Detection Methodology Using Cell Swelling Measurements

• DOI: 10.23919/acc45564.2020.9147956
• 发表时间: 2020-07
• 期刊: 2020 American Control Conference (ACC)
• 作者: T. Cai;Sravan Pannala;A. Stefanopoulou;Jason B. Siegel

Modeling Li-Ion Battery Temperature and Expansion Force during the Early Stages of Thermal Runaway Triggered by Internal Shorts

• DOI: 10.1149/2.1561910jes
• 发表时间: 2019-07-08
• 期刊: JOURNAL OF THE ELECTROCHEMICAL SOCIETY
• 影响因子: 3.9
• 作者: Cai, Ting;Stefanopoulou, Anna G.;Siegel, Jason B.
• 通讯作者: Siegel, Jason B.

Reversible and Irreversible Expansion of Lithium-Ion Batteries Under a Wide Range of Stress Factors

锂离子电池在各种应力​​因素下的可逆和不可逆膨胀

• DOI: 10.1149/1945-7111/ac2d3e
• 发表时间: 2021
• 期刊: Journal of The Electrochemical Society
• 影响因子: 3.9
• 作者: Mohtat, Peyman;Lee, Suhak;Siegel, Jason B.;Stefanopoulou, Anna G.
• 通讯作者: Stefanopoulou, Anna G.