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

该项目更广泛的影响/商业潜力是，由于供暖和空调(HVAC)系统的普及，1至5马力的马达占美国已安装马达总数的58%。这些系统的效率提高10%-15%，预计每台电机在10年的使用寿命内将节省超过1000美元的电力。因此，仅在1%的应用中安装这种电机就可以每年节省5亿美元的电力，仅在美国就可以减少100多万吨/年的二氧化碳排放。此外，由于该设备使用了？物联网？微处理器它可以潜在地连接到更广泛的网络，允许以一种传统电机设计不可能完成的方式来监控和调整效率。这意味着可以在电机软件中进行更改，或者可以将学习算法应用于特定行业的环境中，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将致力于解决小规模(1至5马力)开关磁阻(SR)电机的一些尚未解决的研究问题。具体地说，提出的研究将为一种新型的高杆？HR-SR？采用物联网中使用的低成本微处理器的电机。申请。这些算法将解决此类电机尚未解决的控制问题，包括扭矩波动、高速实时控制和非线性性能。这是一个悬而未决的问题，因为1？5马力的马达角动量很小，需要高速传感和调整以及实时优化才能实现高效率。(如今还没有商业化生产的这种设计。)这项研究还将利用有限元分析对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，它比高成本的ECM电机具有更高的效率，但以低成本低效率的感应电机设计的价格点。

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.