GOALI: Battery Health Dynamics and Its Management

方便、实惠和安全的电动汽车在推动汽车行业摆脱对单一能源的主要依赖方面发挥着重要作用。然而，作为现代电动汽车中占主导地位的能量存储技术的锂离子电池遭受长充电时间、高成本和热稳定性问题。该项目将对电池退化机制进行建模，并产生一个适用于分析、设计和控制电池管理功能（如充电和电池平衡）的数学框架。将在这一领域实现新功能的核心创新是在时间和长度尺度上集成微机械，电化学和电化学行为，以提供电池退化的准确预测。其结果可以帮助实现运输应用的储能系统的容量，功率和寿命的改善，从而大大改变美国的能源格局。除了促进对电池物理和控制的基本学术理解外，教育/研究综合活动将为研究生，本科生和K-12学生提供广泛的机会，以培养对前沿科学研究的兴趣和了解。研究计划解决未解决的问题，对锂离子电池的最佳电池管理至关重要;电池材料中的机械故障如何影响化学降解，并最终导致电池性能和容量衰减。将高保真退化机制集成到电解质颗粒模型中将用于控制电池系统，以最大限度地提高其性能和寿命。这项研究旨在：（1）获得对机械和化学降解机制的基本理解，并将该知识结合到完整的电池电化学模型中，（2）构建面向控制的模型，该模型定量预测由于机械和化学降解机制以及它们的耦合效应引起的容量衰减，以及（3）利用该动态模型来基于有限的信号估计操作期间的电池健康状态，并利用这些估计来优化电池管理系统功能，例如充电和电池单元平衡。如果成功实现，将为储能系统的基本挑战提供解决方案，即，理解微观和宏观材料行为之间的联系，以及故障和控制之间的联系，将得以实现。此外，关于微观层面上的电池材料故障的定量信息将对计算和建模社区特别有用。

Convenient, affordable, and safe electric vehicles have an important role in moving the automotive sector beyond primary reliance on a single energy source. However, the lithium-ion batteries that are the dominant energy storage technology in modern electric vehicles suffer from long charging times, high cost, and thermal stability concerns. This Grant Opportunity for Academic Liaison with Industry (GOALI) project will model battery degradation mechanisms and produce a mathematical framework suitable for analysis, design, and control of battery management functions such as charging and cell balancing. The central innovation that will enable new capabilities in this area is the integration of micromechanical, electrochemical, and electrothermal behaviors across time and length scales to provide accurate prediction of battery degradation. The result can help achieve capacity, power, and lifetime improvements in energy storage systems for transportation applications, thereby dramatically changing the energy landscape of the United States. Beyond advancing the fundamental academic understanding of battery physics and control, the education/research-integrated activity will provide a broad range of opportunities for graduate, undergraduate, and K-12 students to develop an interest in and learn about cutting-edge scientific research.The research plan addresses unsolved questions essential to optimum battery management for lithium-ion batteries; how mechanical failures in battery materials affect chemical degradation, and eventually battery performance and capacity fade. Integration of high fidelity degradation mechanisms into the electrolyte-particle model will be used for the control of battery systems to maximize their performance and lifetime. This research aims at: (1) gaining a fundamental understanding of mechanical and chemical degradation mechanisms and incorporating this knowledge into a complete battery electrochemical model, (2) constructing a control-oriented model that quantitatively predicts capacity fade due to mechanical and chemical degradation mechanisms, as well as their coupled effects, and (3) leveraging this dynamic model to estimate battery state-of-health during operation based on limited signals, and utilize these estimates to optimize battery management system functions such as charging and cell balancing. If successfully realized, the solution for the fundamental challenges in energy storage systems, i.e., understanding the linkages between microscopic and macroscopic material behavior, and between failure and its control, will be realized. Further, the quantitative information regarding battery material failure on the microscopic level will be of particular use to the computational and modeling community.