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: $ 8.92万
• 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=575476
• 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.

减少交通相关温室气体排放和化石燃料消耗的关键战略是电动和混合动力汽车的电气化。该提案关注的是能量存储，这仍然是电动汽车成本和性能的限制因素。该项目旨在改进用于锂离子电池管理的电流控制和监测技术。