Degradation Analysis, Optimal Design, and Intelligent Management of Lithium Ion Batteries

锂离子电池的劣化分析、优化设计与智能管理

• 批准号: RGPIN-2018-05471
• 负责人: Lin, Xianke
• 金额: $ 1.97万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713694
• 关键词: Degradation; Analysis; Optimal; Design; Intelligent

The increasing energy demands of a growing population and climate change challenges provide a strong driving force for renewable energy and transportation electrification. As one of the most promising energy storage systems, Li-ion batteries become widely used in the renewable energy systems and transportation electrification. However, there are still many issues facing Li-ion batteries. One of the most important issues is the degradation of the cells during operation, which becomes the limiting factor in battery cycle life. Longer cycle life is urgently needed to achieve the economic viability in electric vehicles and renewable energy infrastructure. In order to achieve longer cycle life, several key questions need to be addressed, including: (1) how do Li-ion batteries degrade over long-term cycling (degradation analysis); (2) how should we design Li-ion batteries that last longer (optimal design); (3) how should we manage Li-ion batteries in different applications to achieve longer cycle life (intelligent management). The proposed research will investigate the battery long term degradation and address these knowledge deficiencies. The long-term degradation process remains poorly understood due to its complexity. Preliminary results indicate different stages of degradation over long-term cycling. A high fidelity degradation model will be developed and experimentally validated to predict the long-term degradation. Through the insights provided by the degradation analysis, potential opportunities for cycle life improvement will be identified. Optimal design techniques will be developed to optimize the battery design parameters to achieve longer cycle life. The proposed program will also develop optimal battery formation protocol, intelligent charging and health conscious management based on the degradation model. The overall program is divided into 5 objectives: 1) Investigate the main degradation mechanisms and develop degradation models; 2) Optimize battery design; 3) Design optimal formation protocol and intelligent charging; 4) Develop advanced battery monitoring system; 5) Develop intelligent management strategies. The results of this research will contribute to strengthening Canadian leadership in the area of renewable energy and transportation electrification. It will provide a deep understanding of battery long term degradation, specific recommendations of battery optimal design, and optimized battery management strategies. This will enable the industry to produce longer cycle-life battery cells and protect battery health during operation. It will significantly benefit the relevant products and services in a wide range of important applications in Canada. This program will also provide tremendous opportunities for training HQP for the fast growing transportation and energy industry in Canada.

不断增长的人口和气候变化挑战对能源的需求不断增加，为可再生能源和交通电气化提供了强大的驱动力。锂离子电池作为最有前途的储能系统之一，在可再生能源系统和交通电气化中得到了广泛的应用。然而，锂离子电池仍然面临许多问题。最重要的问题之一是电池在运行期间的退化，这成为电池循环寿命的限制因素。 为了实现电动汽车和可再生能源基础设施的经济可行性，迫切需要更长的循环寿命。为了实现更长的循环寿命，需要解决几个关键问题，包括：（1）锂离子电池在长期循环中如何降解（降解分析）;（2）我们应该如何设计寿命更长的锂离子电池（优化设计）;（3）我们应该如何管理不同应用中的锂离子电池以实现更长的循环寿命（智能管理）。 拟议的研究将调查电池的长期退化，并解决这些知识的不足。由于其复杂性，长期降解过程仍然知之甚少。初步结果表明，在长期循环的不同阶段的退化。将开发一个高保真度的降解模型，并通过实验验证，以预测长期降解。通过降解分析提供的见解，将确定循环寿命改进的潜在机会。将开发优化设计技术以优化电池设计参数，从而实现更长的循环寿命。该计划还将根据退化模型开发最佳电池形成协议，智能充电和健康意识管理。整个计划分为5个目标：1）研究主要退化机制并开发退化模型; 2）优化电池设计; 3）设计最佳化成方案和智能充电; 4）开发先进的电池监测系统; 5）开发智能管理策略。 这项研究的结果将有助于加强加拿大在可再生能源和交通电气化领域的领导地位。它将提供对电池长期退化的深刻理解，电池优化设计的具体建议，以及优化的电池管理策略。这将使该行业能够生产更长循环寿命的电池，并在运行期间保护电池健康。它将在加拿大广泛的重要应用中显着受益于相关产品和服务。该计划还将为加拿大快速发展的交通和能源行业提供培训HQP的巨大机会。