CAREER: An Ecologically Inspired Approach to Battery Lifetime Analysis and Testing

职业生涯：一种受生态启发的电池寿命分析和测试方法

• 批准号: 1651256
• 负责人: Lucia Gauchia
• 金额: $ 50万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651256&HistoricalAwards=false
• 关键词: CAREER; Ecologically; Inspired; Approach; Battery

As transportation and grid applications increase their dependency on batteries, challenges related to battery operation and aging dependency on the individual context circumstances remain. This is a particularly relevant problem as batteries perform multiple tasks in each application (e.g. driving, recharging, grid services, etc.) which can contribute to its aging differently. Furthermore, batteries not only perform multiple tasks in a single application, but migrate to a second application as a second life battery. This CAREER proposal aims to understand battery aging dynamics as context-dependent and to provide a unified theory and modeling that can link context events and lives with cell and module aging events. This will benefit all battery applications and the emerging battery repurposing sector by providing tangible methods to improve battery testing, estimation and management. As educational components, this project will propose new hands-on distributed laboratory capabilities for undergraduate and graduate students to explore battery technologies in the context of grid and vehicle applications. Outreach includes hosting female Hispanic students through the Michigan College and University Partnership, and also participating in the Society for Hispanic Professional Engineering mentoring and conferences.Batteries are subjected to highly uncertain scenarios depending on their context, present cell to module and pack variations due to its space and function distribution and different monitoring capabilities at different scales. This CAREER proposal will consider that these conditions are comparable to ecological systems, such as fishery, forestry, etc. and that battery lifetime and aging should tackle the multi-scale and multi-life problem under ecological approaches and methods. For this, testing methods will include low-cost large-scale distributed testing that will experimentally probe individuals and populations of batteries across context variations and different lives. Data from these tests will be used to develop probabilistic reasoning networks to link causality for battery aging and will provide the ability to establish monitoring and data needs across lives. To formulate the battery aging and life modeling, the proposal will focus on studying intraspecific trait variations (variations inside a species) that arise from battery aging. For this purpose, populations of batteries will be identified through the establishment of a patch hierarchy to identify the structure and functional distribution of intraspecific trait per patch at the individual, sub-population and population level. The intraspecific traits will be modeled for each patch using individual-based, mixed models and integral projection models that are used in ecological systems to model population variations. These approaches will provide a probabilistic model across the population. However, as battery populations are monitored at different scales (pack and cells sparsely depending on the technology), the models will consider incomplete data availability and develop scaling ladders. These ladders will scale the intraspecific trait models from individuals to populations and vice versa to adapt to different data availability and mixed approaches. These models will be implemented in battery management systems to learn the traits models from scavenged data. Trait filters will also be formulated and deployed to identify and model internal and external factors that will determine the trait variations for each life. The models and ecology-based theory obtained will be experimentally validated through the large-scale population testing and real electric vehicle and grid-scale battery deployments.

随着运输和电网应用增加了对电池的依赖，与电池操作和对各个环境的依赖相关的挑战仍然存在。这是一个特别相关的问题，因为电池在每个应用程序中执行多个任务（例如，驾驶，充电，网格服务等），可以对其衰老有所不同。此外，电池不仅在单个应用程序中执行多个任务，还可以作为第二寿命电池迁移到第二个应用程序。该职业建议旨在将电池老化动态视为上下文依赖性，并提供统一的理论和建模，可以将上下文事件和生活与细胞和模块衰老事件联系起来。通过提供切实的方法来改善电池测试，估计和管理，这将使所有电池应用和新兴电池重新利用扇区受益。作为教育组件，该项目将为本科生和研究生提供新的动手分布式实验室功能，以在网格和车辆应用中探索电池技术。外展活动包括通过密歇根学院和大学合作伙伴关系接待女性西班牙裔学生，还参加了西班牙裔专业工程指导和会议协会。Batteries受到高度不确定的场景，取决于其上下文，由于其空间和功能分配以及不同尺度上的不同监控功能而导致的模块和包装变化。该职业建议将考虑这些条件与渔业，林业等生态系统相媲美，并且电池寿命和衰老应在生态方法和方法下解决多尺度和多生命问题。为此，测试方法将包括低成本的大规模分布测试，这些测试将在环境变化和不同生活中实验探测电池的个人和人群。这些测试的数据将用于开发概率推理网络，以链接电池老化的因果关系，并将提供在生活中建立监视和数据需求的能力。为了制定电池老化和寿命建模，该提案将集中于研究由电池老化引起的种内性状变化（物种内部的变化）。为此，将通过建立贴片层次结构来确定电池的种群，以确定每个贴片，子人口和人口水平的每个斑块内种内性状的结构和功能分布。种内性状将使用基于个体的混合模型和积分投影模型为每个贴片建模，这些模型用于生态系统中，以模拟人群变化。这些方法将在整个人群中提供概率模型。但是，由于电池数量以不同的尺度监测（包装和细胞稀少，具体取决于技术），因此模型将考虑不完整的数据可用性并开发缩放阶梯。这些梯子将将种内特征模型从个人扩展到人群，反之亦然，以适应不同的数据可用性和混合方法。这些模型将在电池管理系统中实现，以从清除数据中学习特质模型。还将制定和部署性状过滤器，以识别和建模内部和外部因素，以决定每个生命的特征变化。获得的模型和基于生态的理论将通过大规模的人口测试以及真实的电动汽车和网格尺度的电池部署来实验验证。