Intelligent Diagnostics and Prognostics of Electric Vehicle Powertrains

电动汽车动力系统的智能诊断和预测

• 批准号: RGPIN-2021-04272
• 负责人: Wang, Wilson
• 金额: $ 3.35万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742958
• 关键词: Intelligent; Diagnostics; Prognostics; Electric; Vehicle

Electric vehicle (EV) technology will soon revolutionize the transportation industry. EV performance depends mainly on the functionality of its powertrain, especially the battery pack and the drive induction motor (IM). The imperfections in the motor will generate EV malfunction, and degrade its power efficiency and drive safety. On the other hand, Lithium ion technology is still in its early stages. No matter how good a battery is, it will degrade over time with every charge/discharge cycle. Due to volatility, flammability and entropy changes, a battery could ignite by overheating. Battery failure will not only result in inconvenience and repair costs, but also risk catastrophic consequences especially in passenger vehicles. Accordingly, the long term objective of this research program is to develop new technology and tools to improve the reliability of intelligent diagnosis and prognosis (IDP) in EV powertrains. The IDP system can be used to monitor the health condition of the drive motors in real-time. It can also effectively estimate battery state of life, improve battery performance, efficiency and lifespan, and optimize vehicle operation. Over the next five years, the specific research themes include: (1) new hybrid techniques will be developed to improve the reliability of bearing fault detection in IMs; (2) An intelligent classifier will be developed for real-time health condition monitoring of drive motors in EVs; (3) A new diagnostic system will be proposed to examine battery state of health; and (4) a hybrid intelligent predictor will be developed to predict the remaining useful life of batteries. Appropriate machine learning algorithms will be proposed to improve the decision-making convergence and robustness of the related intelligent systems. This multidisciplinary research program will provide unique and leading-edge opportunities to train HQP in the related academic fields. Since IMs and batteries are also commonly used in other industrial and domestic applications, the developed IDP technology and tools can not only significantly benefit the EV and hybrid EV industrial sectors in Canada, but can also help with a wide range of industrial and domestic applications using IMs and batteries, boosting the economy, protecting the environment, and enhancing the competitiveness of Canadian companies in the global market.

电动汽车（EV）技术将很快彻底改变交通运输行业。电动汽车的性能主要取决于其动力系统的功能，特别是电池组和驱动感应电机（IM）。电动机的缺陷会导致电动汽车故障，降低电动汽车的功率效率和行驶安全性。另一方面，锂离子技术仍处于早期阶段。无论电池有多好，它都会随着每次充电/放电循环而退化。由于挥发性、易燃性和熵变，电池可能因过热而着火。电池故障不仅会导致不便和维修成本，而且还可能带来灾难性后果，特别是在乘用车中。因此，本研究计划的长期目标是开发新技术和工具，以提高电动汽车动力系统智能诊断和预测（IDP）的可靠性。IDP系统可用于实时监控驱动电机的健康状况。它还可以有效地估计电池的寿命状态，提高电池的性能，效率和寿命，并优化车辆运行。在未来五年，具体的研究主题包括：（1）将开发新的混合技术，以提高IM轴承故障检测的可靠性;（2）将开发智能分类器，用于电动汽车驱动电机的实时健康状态监测;（3）将提出一种新的诊断系统，以检查电池的健康状态;（4）将开发一种新的诊断系统，以检查电池的健康状态。（4）开发了一种混合智能预测器，用于预测电池的剩余使用寿命。适当的机器学习算法将被提出来改善相关智能系统的决策收敛性和鲁棒性。这个多学科的研究计划将提供独特的和领先的机会，以培训HQP在相关的学术领域。由于IM和电池也常用于其他工业和家庭应用，因此开发的IDP技术和工具不仅可以使加拿大的电动汽车和混合动力电动汽车工业部门受益匪浅，而且还可以帮助使用IM和电池的广泛工业和家庭应用，促进经济，保护环境，提高加拿大公司在全球市场上的竞争力。