Uncertainty Management and Proactive Maintenance for Lithium-ion Batteries in Electrified Vehicles

电动汽车锂离子电池的不确定性管理和主动维护

• 批准号: 1726706
• 负责人: Zhimin Xi
• 金额: $ 30.34万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1726706&HistoricalAwards=false
• 关键词: Uncertainty; Management; Proactive; Maintenance; Lithium

The objective of this project is to optimize performance, improve safety, minimize over-design, and reduce cost of battery systems in electrified vehicles. If successful, higher adoption of electrified vehicles is foreseeable in the near future to revolutionize the future transportation systems so that environmental challenges can be addressed for conserving energy and cutting back on carbon emissions and pollution. This research will also have significant impact on other applications such as aircraft electric systems, military portable devices, and aerospace battery applications, where lithium ion batteries are used for energy storage. These impacts will result in economic and social benefit and help the U.S. stay competitive globally. The theory and methodology will be integrated into education through modifications in related courses in order to engage graduate and undergraduate students. This project studies the uncertainty management and proactive maintenance of lithium ion batteries in electrified vehicles. The scope of the proposed work is to: i) increase accuracy of a baseline battery model; ii) develop an intelligent uncertainty management system; and iii) facilitate proactive maintenance decisions for lithium ion batteries. The investigators will address four issues including: i) characterization of battery model uncertainty under various battery operating conditions; ii) quantification of various uncertainties and their coupling effects in the battery system; iii) prediction of battery remaining useful life (RUL) under various battery operating conditions; and iv) validation of the proposed approaches on an electrified vehicle. Traditionally, researchers employ electrochemical models to improve battery model accuracy. However, a full order electrochemical model is not feasible for the battery management system (BMS) in electrified vehicles, and a significant level of model simplification must be established to meet the computational efficiency requirement which will cause unwanted model error. The first contribution of the research project will be to study an effective model uncertainty characterization approach to improve model prediction accuracy of a low-fidelity model (i.e., an equivalent circuit model) so that its accuracy is comparable to that of a high fidelity model (i.e., an electrochemical model) but with much higher computational efficiency. The second contribution of the project will be to develop an intelligent uncertainty management system for more effective battery performance estimation so that battery safety can be improved and lifetime risk minimized. Finally, the challenge of battery proactive maintenance stems from the complexity of battery devices. Even the best models cannot predict the complex degradation and failure mechanisms. As a result, accurate prediction of the battery RUL based on the underlying degradation theory is almost impossible. The third contribution of the research will be to study proactive maintenance decisions for batteries through accurate RUL prediction using innovative data-driven prognostics and health management (PHM) technologies.

该项目的目标是优化性能，提高安全性，最大限度地减少过度设计，并降低电动汽车电池系统的成本。如果成功的话，可以预见在不久的将来，电动汽车的采用率会更高，从而彻底改变未来的交通系统，从而解决环境挑战，节约能源，减少碳排放和污染。这项研究也将对其他应用产生重大影响，如飞机电气系统，军用便携式设备和航空航天电池应用，其中锂离子电池用于储能。这些影响将产生经济和社会效益，并有助于美国保持全球竞争力。理论和方法将通过修改相关课程纳入教育，以吸引研究生和本科生。本项目研究电动汽车锂离子电池的不确定性管理和主动维护。拟议工作的范围是：i）提高基准电池模型的准确性; ii）开发智能不确定性管理系统; iii）促进锂离子电池的主动维护决策。研究人员将解决四个问题，包括：i）各种电池工作条件下电池模型不确定性的表征; ii）电池系统中各种不确定性及其耦合效应的量化; iii）各种电池工作条件下电池剩余使用寿命（RUL）的预测; iv）在电动汽车上验证所提出的方法。传统上，研究人员采用电化学模型来提高电池模型的准确性。然而，全阶电化学模型是不可行的，在电动汽车的电池管理系统（BMS），和一个显着水平的模型简化必须建立，以满足计算效率的要求，这将导致不必要的模型误差。该研究项目的第一个贡献将是研究一种有效的模型不确定性表征方法，以提高低保真度模型（即，等效电路模型）以使其精度与高保真度模型的精度相当（即，电化学模型），但具有更高的计算效率。该项目的第二个贡献将是开发一个智能不确定性管理系统，以更有效地评估电池性能，从而提高电池安全性并最大限度地减少寿命风险。最后，电池主动维护的挑战源于电池设备的复杂性。即使是最好的模型也无法预测复杂的退化和故障机制。因此，基于潜在的劣化理论的电池RUL的准确预测几乎是不可能的。该研究的第三个贡献将是通过使用创新的数据驱动的生命周期和健康管理（PHM）技术进行准确的RUL预测，研究电池的主动维护决策。