课题基金 / 基金详情

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

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
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

项目摘要

项目成果

Lin, Xianke的其他基金

相似基金

相关文献

中文摘要
翻译
不断增长的人口对能源的需求和气候变化的挑战为可再生能源和交通电气化提供了强大的推动力。锂离子电池作为最有前景的储能系统之一,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着许多问题。其中最重要的问题之一是电池在运行过程中的退化,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退化(退化分析);(2)如何设计更长寿命的锂离子电池(优化设计);(3)如何在不同的应用中管理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,并解决这些知识缺陷。由于其复杂性,长期的退化过程仍然知之甚少。初步结果表明,在长期循环过程中,会出现不同的降解阶段。将开发一个高保真的退化模型,并进行实验验证,以预测长期退化。通过退化分析提供的见解,将确定改善循环寿命的潜在机会。将开发优化设计技术来优化电池设计参数,以实现更长的循环寿命。拟议的计划还将基于退化模型开发最优电池形成协议、智能充电和健康意识管理。整个计划分为5个目标:1)调查主要的退化机制和建立退化模型;2)优化电池设计;3)设计最优的化成协议和智能充电;4)开发先进的电池监测系统;5)开发智能管理策略。*本研究的成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周期寿命的电池,并在运行期间保护电池健康。这将使相关产品和服务在加拿大广泛的重要应用中受益匪浅。该项目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。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Degradation Analysis, Optimal Design, and Intelligent Management of Lithium Ion Batteries
Degradation Analysis, Optimal Design, and Intelligent Management of Lithium Ion Batteries
AI-based early failure detection in 3D printing for better print quality, less material waste, and shorter trial and error process
Degradation Analysis, Optimal Design, and Intelligent Management of Lithium Ion Batteries
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Intelligent Patent Analysis for Optimized Technology Stack Selection:Blockchain BusinessRegistry Case Demonstration
  • 批准号:
    --
  • 项目类别:
    外国学者研究基金项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    USHARANI HAREESH GOVINDARA JAN
  • 依托单位:
基于Meta-analysis的新疆棉花灌水增产模型研究
  • 批准号:
    41601604
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    22.0万元
  • 批准年份:
    2016
  • 负责人:
    赵爱琴
  • 依托单位:
大规模微阵列数据组的meta-analysis方法研究
  • 批准号:
    31100958
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    20.0万元
  • 批准年份:
    2011
  • 负责人:
    赵洪雅
  • 依托单位: