Condition monitoring for electric and hybrid vehicle energy storage systems

电动和混合动力汽车储能系统的状态监测

• 批准号: 452271-2013
• 负责人: Habibi, Saeid
• 金额: $ 8.96万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618510
• 关键词: Condition; monitoring; electric; hybrid; vehicle

A key strategy in reducing transportation related greenhouse gas emissions as well as fossil fuel consumption is the electrification of automobiles in both electric and hybrid electric forms. This proposal is concerned with energy storage that continues to be a limiting factor in both cost and performance for electrified vehicles. The project aims to improve on current control and monitoring techniques used in the management of Lithium Ion batteries.Two important parameters for automotive batteries are the State Of Charge (SOC - the electric equivalent of a fuel gauge) and the State Of Health (SOH - the amount of energy storage capacity available relative to when the battery was new). For lithium Ion batteries, SOH and SOC are not directly measurable and must be estimated. Estimation methods generally rely on a mathematical model of the battery and physical measurements (voltage and current) to create the estimated SOH and SOC. These mathematical models can vary from relatively simple equivalent circuits to complex electro-chemical models; depending on the battery chemistry and available information, the accuracy of the models can vary considerably. In addition, measurement errors and noise also affect the accuracy of SOC and SOH determination.To minimize the effects of measurement errors and noise, advanced filtering algorithms are used. McMaster has developed a unique filter, called the Smooth Variable Structure Filter (SVSF) which has been shown to improve the accuracy of SOH and SOC estimation in practical applications involving uncertainties and noise. This project will apply the SVSF to batteries with a wide range of different models and, use experimental data to optimize and refine the parameterization of the models. Both the models and the SVSF estimation technique will be incorporated into a software condition monitoring tool that will be used by our industrial partners.

减少与运输有关的温室气体排放和化石燃料消耗的一个关键战略是电动和混合动力形式的汽车电气化。这项提议关注的是能源储存，这仍然是电动汽车成本和性能的限制因素。该项目旨在改进锂离子电池管理中使用的电流控制和监测技术。汽车电池的两个重要参数是荷电状态(SOC-燃料表的电当量)和健康状态(SOH-相对于新电池的可用储能容量)。对于锂离子电池，SOH和SOC不能直接测量，必须估计。估算方法通常依赖于电池的数学模型和物理测量(电压和电流)来产生估算的SOH和SOC。这些数学模型可以从相对简单的等效电路到复杂的电化学模型；根据电池化学和可获得的信息，模型的精度可能会有很大差异。此外，测量误差和噪声也会影响SOC和SOH的测定精度，为了最大限度地减少测量误差和噪声的影响，采用了先进的滤波算法。麦克马斯特开发了一种独特的滤波器，称为平滑变结构滤波器(SVSF)，在涉及不确定性和噪声的实际应用中，它已被证明可以提高SOH和SOC估计的精度。该项目将SVSF应用于具有广泛不同型号的电池，并使用实验数据来优化和改进模型的参数化。模型和SVSF估计技术都将被整合到我们的行业合作伙伴将使用的软件状况监控工具中。