Estimation of Critical Battery States via Strain and Stress Measurement

从手机和卫星到车辆和电网，利用锂离子电池的能量存储无处不在。所有这些应用都需要可靠的可用功率和能量估计。消费者的担忧，比如对电动汽车（ev）续航里程的焦虑，阻碍了市场渗透。这种担忧可以通过改进对电池关键状态（如容量和内阻随时间变化）的估计来缓解。这项研究试图通过改进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，对汽车电池在电网上的二次使用至关重要，通过扩大现有电池技术的利用，有可能使可再生资源得到更大的渗透。经过实验验证的电池机械响应模型及其与电池健康的关系将填补公共领域数据和模型可用性方面的一个关键空白。目前最先进的电池估计方法依赖于电池终端电压的测量，该计划是通过测量和解释电池在充电和放电过程中电极层充满和清空锂离子时发生的电池膨胀来改善电池健康诊断。本研究旨在建立一个多物理场模型和估计技术，以利用电极变形中的信息，并分析测量的电气和机械信号，以增强对电池健康的估计。石墨电极在充满锂离子时表现出明显的膨胀模式，这可以用于诊断电极特异性降解。在涉及动态充放电的相关使用周期中实现该方法的优势之前，需要解决电池多尺度相互依赖的热、电化学和机械响应建模中的几个基本空白。此外，联合状态和参数估计的准确性取决于工作区域和电流激励或使用情况。评估可识别性的系统技术将受益于基于物理的模型，该模型可用于研究各种降解机制、热膨胀交叉灵敏度、传感器位置和商业相关包装的影响。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。

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.