Ultrasound Non-destructive Evaluation for Battery Management Systems.

电池管理系统的超声波无损评估。

• 批准号: 2601814
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601814
• 关键词: Ultrasound; Non; destructive; Evaluation; Battery

Recent research has shown that ultrasound can detect the changes in material elasticity that occur as batteries undergo charging and discharging, enabling accurate measurement of the battery's state of charge. Our study aims to build on this approach by implementing it in automotive batteries, allowing for continuous monitoring of the battery's charge level and structural health during normal use.Lithium-ion battery cells are widely recognized as a crucial component of sustainable transportation solutions. Incorporating ultrasound for charge monitoring can enhance the performance of these cells. Traditional methods for measuring the battery state of charge (SOC) rely on tracking voltage and current, but such methods suffer from limited efficiency and accuracy. In contrast, ultrasound enables direct SOC measurement at any time, independent of charge history, thus eliminating errors that may accumulate in successive measurements. This technology can provide highly accurate SOC readings, improve battery range estimation, and enhance the structural integrity of the battery.Previously, successful demonstration of ultrasound charge monitoring on an individual battery cell was carried out in a laboratory environment. The changes in elastic properties and density of lithium-ion batteries affect the wave speed travelling through the test cell, and this wave speed is measured by determining the time taken by the longitudinal wave to traverse the cell. This information is then used to determine the battery SOC. However, automotive batteries consist of several cells stacked together, and hence, our research aims to investigate the application of various ultrasonic techniques to multiple cells within a battery module and its impact on module design.To achieve this, we will incorporate a built-in test system in a laboratory environment to collect data that will subsequently be analysed using signal processing and numerical modelling techniques in predictive machine learning. Initially, the equipment will include an ultrasonic pulse-generator, ultrasonic probes, a custom test bed, and an individual lithium-ion cell before moving on to multiple cells stacked in series. Therefore, we aim to address the challenges posed by offline, single-cell, history-dependent, complex, and costly SOC prediction techniques. Additionally, we aim to embed an ultrasonic network configuration relevant to the automotive industry.Our research aims to gain a comprehensive understanding of the physiochemical characteristics of lithium-ion batteries, with the goal of advancing the sustainable scalability of the automotive sector, particularly as electric vehicle deployment continues to increase. Specifically, we seek to provide new insights into the physiochemical changes of lithium-ion batteries, enabling more accurate determination of state of charge (SOC) and state of health (SOH) information. Our approach aims to extract this information in a cost-effective manner, without the need for expensive equipment, thus facilitating battery management systems that can provide accurate outputs under driving conditions. By offering a viable alternative to current SOC/SOH determination methods, our work can contribute to minimising manufacturing costs, reducing material requirements, and achieving a simple yet effective solution that moves the automotive industry towards sustainability.

最近的研究表明，超声波可以检测电池充电和放电时材料弹性的变化，从而能够准确测量电池的荷电状态。我们的研究旨在通过在汽车电池中实施这种方法来建立这种方法，从而在正常使用期间持续监测电池的充电水平和结构健康。锂离子电池被广泛认为是可持续交通解决方案的关键组成部分。将超声波用于电荷监测可以增强这些电池的性能。用于测量电池荷电状态（SOC）的传统方法依赖于跟踪电压和电流，但是这种方法的效率和准确性有限。相比之下，超声波可以在任何时候直接测量SOC，而不受充电历史的影响，从而消除了可能在连续测量中积累的误差。该技术可以提供高度准确的SOC读数，改善电池范围估计，并增强电池的结构完整性。此前，在实验室环境中成功演示了对单个电池单元的超声波充电监测。锂离子电池的弹性和密度的变化会影响穿过测试电池的波速，该波速通过确定纵波穿过电池所需的时间来测量。然而，汽车电池由多个电池堆叠在一起组成，因此，我们的研究旨在研究各种超声波技术在电池模块内的多个电池中的应用及其对模块设计的影响。为了实现这一点，我们将建造一个在实验室环境中的测试系统中收集数据，随后将使用信号处理和数字建模技术来分析数据，预测机器学习最初，该设备将包括一个超声波脉冲发生器，超声波探头，一个定制的测试台和一个单独的锂离子电池，然后再转移到串联堆叠的多个电池。因此，我们的目标是解决离线，单细胞，历史依赖，复杂和昂贵的SOC预测技术所带来的挑战。此外，我们的目标是嵌入与汽车行业相关的超声波网络配置。我们的研究旨在全面了解锂离子电池的物理化学特性，以推进汽车行业的可持续可扩展性，特别是随着电动汽车部署的不断增加。具体而言，我们寻求对锂离子电池的物理化学变化提供新的见解，从而能够更准确地确定充电状态（SOC）和健康状态（SOH）信息。我们的方法旨在以具有成本效益的方式提取这些信息，而不需要昂贵的设备，从而促进电池管理系统在驾驶条件下提供准确的输出。通过为当前的SOC/SOH确定方法提供可行的替代方案，我们的工作可以有助于最大限度地降低制造成本，降低材料要求，并实现简单而有效的解决方案，使汽车行业走向可持续发展。