ISCF Wave 1: Translational Energy Storage Diagnostics (TRENDs)

锂电池单元的退化是在多个时间和空间域上发生的复杂过程。提高对电池健康的理解是在许多具有挑战性的应用中扩大锂离子电池技术使用的先决条件。早期检测关键参数的变化将能够进行性能评估和退化预测，并提供一种预测最可能的最终故障模式的途径。参数检测需要能够测量阐明电池系统状态的各种静态和动态特性。为了实现高效和安全的电池运行，诊断方案需要快速、准确和可靠，接近实时工作，并尽早检测潜在故障;为了实现广泛的实际应用，参数检测必须以最小的附加成本实现。同时，运行准确的在役电池模型的需求至关重要，并将实现基于模型的控制。充电状态（SOC）估计是电池管理系统（BMS）中仅次于电压和温度安全监测的最重要功能。更好的BMS SOC可以帮助最大限度地提高电池性能和寿命，但通常只能精确到+/- 10%，而且目前还没有提高这种精度的简单方法。能够在适度条件下预测锂离子电池性能的模型非常先进。但是，仍然需要显着的进展耦合的操作模型，适用于诊断和预测的退化和故障的退化机制的模型。通常故障和由此产生的退化表现为容量或功率衰减，往往是国家的最先进的技术，如X射线CT，开路电压测量，和热测量来消除退化。该提案汇集了一个世界级的团队，以解决领先的锂离子电池化学品的退化和健康评估的关键问题。我们特别关注转化诊断，我们将其定义为跨长度尺度、跨不同领域、跨学术研究转化为行业实践的诊断方法。我们工作的关键成果将是一套新的、经过验证的诊断工具，该工具与领先和新兴的锂离子和钠离子电池化学的电池模型相集成。我们的目标是确保这些诊断工具能够在小型和大型电池系统上具有成本效益地部署，并且能够以足够的准确性和可靠性在真实的时间内运行，从而可以实现更安全、更耐用和更低成本的电化学能量存储系统

Degradation of lithium battery cells is a complex process occurring over multiple temporal and spatial domains. Improved understanding of cell health is a prerequisite for expanded use of Li-ion battery technology in many challenging applications. Early detection of changes in critical parameters would enable performance assessment and degradation forecasting, as well as providing a route to predict the most likely eventual failure modes. Parameter detection requires the ability to measure a diverse set of static and dynamic properties that elucidate the state of a battery system. To enable efficient and safe battery operation, diagnostic schemes need to be fast, accurate, and reliable, work in near real-time, and detect potential faults as early as possible; to enable widespread practical adoption, parameter detection must be achieved with minimal added cost. In tandem, the need to run accurate in-service battery models is critical, and would enable model-based control. Second only to safety monitoring of voltage and temperature, state-of-charge (SOC) estimation is the most important function of a battery management system (BMS). Better BMS SOC could help maximize battery performance and lifetime, but is often accurate to only +/- 10% - and simple methods to improve this accuracy do not currently exist. Models capable of predicting Li-ion performance under modest conditions are highly advanced. But significant progress is still needed to couple operational models suitable for the diagnosis and prognosis of degradation and failure with models of degradation mechanisms.Generally faults and the resulting degradation manifest as capacity or power fade and often state-of-the-art techniques such as X-ray CT, open circuit voltage measurements, and thermal measurements are used to characterise the degradation. This proposal brings together a world-class team to address the critical issue of degradation and health estimation for leading lithium-ion-battery chemistries. We place particular focus on Translational Diagnostics, which we define as diagnostic methods that translate across length scales, across different domains, and across academic research into industry practice.Key outputs from our work will be a suite of new and validated diagnostic tools integrated with battery models for both leading and emerging lithium-ion and sodium- ion battery chemistries. We aim to ensure that these diagnostic tools are capable of cost-effective deployment on both small and large battery systems, and able to run in real time with sufficient accuracy and reliability, such that safer, more durable and lower cost electrochemical energy storage systems can be achieved