通过人工神经网络提高电动汽车双感应电机的直接扭矩控制
Ahmed Chantoufi1, Aziz Derouich1, Said Mahfoud2,3
1Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
这项研究引入了基于人工神经网络的电动汽车推进系统的直接扭矩控制 (ANN-DTC). 与传统方法相比,ANN-DTC显著减少扭矩波动并改善动态响应.
科学领域:
- 电气工程
- 汽车工程
- 人工智能
背景情况:
- 电动汽车对于可持续能源转型至关重要,需要高效和高性能的推进系统.
- 电动汽车的常规直接扭矩控制 (DTC) 面临着扭矩波动和负载变化动态响应的挑战.
- 优化电动汽车推进控制对于清洁运输的广泛采用和有效性至关重要.
研究的目的:
- 开发电动汽车推进系统的智能控制策略,以克服传统DTC的局限性.
- 提高电动汽车动力系统的性能,效率和动态适应性.
- 减少扭矩波动,改善电动汽车在不同负载条件下的转速调节.
主要方法:
- 基于人工神经网络的直接扭矩控制 (ANN-DTC) 战略的实施.
- 用人工神经网络调节器取代传统的歇斯底里比较器和切换表.
- 使用Matlab/Simulink对拟议的ANN-DTC系统进行全面的模拟.
主要成果:
- ANN-DTC将扭矩波动从201.46Nm降低到87.31Nm,提高了56.66%.
- 建议的方法有效地消除了传统DTC观察到的超速.
- 模拟证实了电动推进系统的增强动态响应和适应性.
结论:
- 基于人工神经网络的直接扭矩控制 (ANN-DTC) 与传统的DTC对电动汽车推进提供了显著的改进.
- 通过最大限度地减少扭矩波动和改善动态响应来提高电动汽车动力总成的性能.
- 这种智能控制方法对于提高电动汽车技术的效率和可靠性至关重要.
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