SBIR Phase I: Designing High Efficiency Small Scale Motors using Switched Reluctance Technology

该项目的更广泛的影响/商业潜力是，由于供暖和空调（HVAC）系统的普及，1至5马力的电机占美国安装电机总数的58%。这些系统的效率提高10-15%，预计在每个电机的10年使用寿命内节省超过1000美元的电力。因此，在1%的应用中安装这种电机将转化为每年节省5亿美元的电力，并导致仅在美国每年减少超过100万吨的二氧化碳排放量。另外，由于设备使用？物联网？微处理器可以连接到更广泛的网络，从而实现传统电机设计无法实现的效率监控和调整。这意味着可以在电机软件中进行更改，或者可以在特定行业设置中应用学习算法，以随着时间的推移进一步提高性能。这个小型企业创新研究第一阶段项目旨在解决一些尚未解决的研究问题，这些问题涉及小规模（1至5马力）的开关磁阻（SR）电动机。具体来说，该研究将开发一种新型高杆- HR-SR？采用低成本微处理器的电机？物联网？应用程序。这些算法将解决此类电机仍未解决的控制问题，包括转矩脉动、高速实时控制和非线性性能。这是一个未解决的问题，因为1 ？5 .大功率电机角动量小，需要高速感应和调整，需要实时优化才能实现高效率。（如今这种设计已经没有商业化生产了。）本研究还将使用有限元分析对HR-SR电机的物理设计进行分析，以优化电机的机械设计，降低噪音和热量水平并提高效率。所提出的方法将产生一种低成本的设计，具有比高成本ECM电机更高的效率，但在低成本低效率感应电机设计的价格点上。

The broader impact/commercial potential of this project are that 1 to 5 hp motors represent 58% of the total number of installed motors in the United States due to the prevalence of heating and air conditioning (HVAC) systems. A 10-15% improvement in efficiency in these systems is expected to save more than $1000 in electricity over the 10-year lifetime of each motor. As such, installing this motor in just 1% of applications would translate to $500M / year in electricity savings and result in a reduction of more than 1 million tons / year of carbon dioxide emissions in the U.S. alone. In addition, since the device uses ?internet of things? microprocessors it can potentially be connected to a broader network, allowing monitoring and tuning to efficiency to be done in a manner not possible with traditional motor designs. This means that changes can be made in motor software, or learning algorithms can be applied in industry-specific settings to further improve performance over time.This Small Business Innovation Research Phase I project will aim to address a number of unsolved research problems with switched reluctance (SR) electric motors at small scale (1 to 5 hp). Specifically, the proposed research will develop optimization and control algorithms for a novel high pole ?HR-SR? motor utilizing low-cost microprocessors of the type used in ?internet of things? applications. These algorithms will address control issues that remain unsolved for such motors including torque ripple, high speed real-time control, and non-linear performance. This is an unsolved issue since 1 ? 5 hp motors have low angular momentum and require high speed sensing and adjustment as well as real-time optimization to achieve high efficiency. (There are no such designs commercially manufactured today.) This research will also perform analysis of the physical design of HR-SR motors using finite element analysis to optimize the mechanical design of the motor, reduce noise and heat levels and increase efficiency. The proposed approach will produce a low-cost design that has better efficiency than high-cost ECM motors, but at the price point of low-cost low-efficiency induction motor designs.