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; 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源存储从手机和卫星到车辆和电网都无处不在。所有这些应用都需要对可用功率和能源的可靠估计。消费者的担忧，例如电动汽车（电动汽车）的范围焦虑，阻碍市场渗透。通过改善关键电池状态的估计（例如能力和内部阻力）随着时间的推移而变化，可以通过改善估计值来缓解此类担忧。这项研究旨在通过改善电池健康诊断来解决这些问题。 这项进步将使所有电池供电的设备受益，对于第二寿命使用汽车电池在电网上存储的储能尤其至关重要，这有可能通过扩大现有电池技术的扩大利用来实现可再生资源的实质性渗透。电池机械响应的实验验证模型及其与电池健康的连接将填补公共域数据和模型的可用性。最先进的电池估计方法的状态取决于电池端子电压测量值，该计划是通过测量和解释电池电池的扩展来改善该项目中的电池健康诊断，这在电极层在充电和放电过程中用锂离子空的液体填充并空。 这项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，并分析测得的电气和机械信号以增强电池健康估计。石墨电极在充满锂离子时表现出不同的膨胀模式，这可以诊断出电极特异性降解。需要解决该方法的益处之前，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，才能在涉及动态充电和放电的相关用法周期中实现这种方法的好处。此外，关节状态和参数估计的准确性取决于工作区域以及当前的激发或使用概况。评估可识别性的系统技术将从基于物理的模型中受益，这些模型可用于研究各种降解机制的影响，跨敏感性，具有热肿胀，传感器位置和商业上相关的包装。这项奖项反映了NSF的法规任务，并认为通过基金会的知识优点和广泛的crietia criteria criteria criteria criteria criteria criteria criteria criteria criteria criteria criteria cripitia cripitia recectia cristia cripitia cristeria均值得一评论。