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Intelligent Power Assist Algorithms for Electric Bicycles.

机译:电动自行车的智能动力辅助算法。

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摘要

This dissertation considers intelligent power-assist algorithm designs for electric bicycles. Traditional electric power-assist bicycles (EPBs) employ proportional power-assist strategy. The ratio is usually set to 1:1, which means that the motor will provide the same amount of assistive torque as the amount of the human's pedaling torque. This strategy is too rigid and does not consider the interaction between the bicycle, the human and the environment. Intelligent power-assist algorithms are needed to address such issues. In this dissertation, we focus on the uphill riding scenario, since it is the situation where the cyclist faces the most difficulties. The dynamic properties of electric bicycles will be studied and an appropriate model will be developed for intelligent power-assist algorithm design purposes. Two types of intelligent power-assist algorithms will be introduced to help the human ride uphill more easily. One is the robust disturbance observer (DOB) based power-assist algorithm, which can observe and compensate for the environmental disturbance that the bicycle system is subjected to during uphill riding. The robust DOB provides flexibility to the power assistance and within the motor's capability, it can make riding uphill feel like riding on the level ground. The other intelligent power-assist algorithm is based on repetitive control technique. The human's pedaling torque is repetitive by nature of the crankset's mechanical design. The pedaling torque reaches it local minimum and maximum twice during one complete pedal cycle. During uphill riding, the difference between the maximum torque and minimum torque is so large as to cause severe fluctuation in the torque profile, and, in turn, result in fluctuations in the velocity and acceleration profiles. We call the fluctuant human torque input "nonuniform human input" and compensate for the fluctuation with a repetitive control based power-assist algorithm. Repetitive control designs in both the time domain and the pedal-angle domain are considered. An experimental EPB system was built to verify the effectiveness of these two types of algorithms. Details of the experimental setup will be introduced. Simulation and experimental results will be shown in this dissertation.
机译:本文考虑了电动自行车的智能助力算法设计。传统的电动助力自行车(EPB)采用比例电动助力策略。该比率通常设置为1:1,这意味着电动机将提供与人的踏板转矩相同的辅助转矩。这种策略过于僵化,没有考虑到自行车,人与环境之间的相互作用。需要智能功率辅助算法来解决此类问题。在本文中,我们将重点放在上坡骑行场景上,因为这是骑自行车者面临最大困难的情况。将研究电动自行车的动态特性,并为智能助力算法设计目的开发合适的模型。将介绍两种类型的智能助力算法,以帮助人们更轻松地上坡。一种是基于鲁棒干扰观察器(DOB)的功率辅助算法,该算法可以观察并补偿自行车系统在上坡骑行过程中遭受的环境干扰。坚固的DOB为动力辅助提供了灵活性,并且在电动机的能力范围内,它可以使上坡骑行感觉就像在水平地面上骑行一样。另一种智能功率辅助算法是基于重复控制技术的。人的踩踏扭矩由于曲柄组的机械设计而具有重复性。在一个完整的踏板循环中,踏板扭矩两次达到其局部最小值和最大值。在上坡行驶期间,最大扭矩和最小扭矩之间的差异很大,以至于引起扭矩曲线的严重波动,进而导致速度和加速度曲线的波动。我们将波动的人类转矩输入称为“非均匀的人类输入”,并使用基于重复控制的功率辅助算法来补偿波动。考虑了时域和踏板角度域的重复控制设计。建立了一个实验性的EPB系统来验证这两种算法的有效性。将介绍实验设置的详细信息。仿真和实验结果将在本文中给出。

著录项

  • 作者

    Fan, Xuan.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 98 p.
  • 总页数 98
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:36:59

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