实时动态粘附系数估计和BP神经网络优化了分布式驱动电动汽车的侧向稳定控制
Zhigang Zhou1, Ruili Yang2, Fang Xu3
1College of Vehicle and Traffic Engineering, Henan University of Science and technology, Luoyang, 410300, China.
Scientific reports
|December 5, 2025
概括
本研究引入了一种用于分布式驱动电动汽车 (DDEV) 的新型稳定性控制系统,该系统可以准确估计不断变化的道路状况. 改进后的系统改善了车辆的操控和在各种表面上的驾驶稳定性.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 分布式驱动电动汽车 (DDEV) 面临的道路上的不稳定性与波动的粘附.
- 准确估计实时粘附系数对于车辆的稳定性至关重要.
研究的目的:
- 为DDVEs开发一个强大的横向稳定性控制策略和扭矩分配方法.
- 为了提高车辆的稳定性和驾驶能力,在道路上与合系数的突然变化.
主要方法:
- 使用无气味卡尔曼波器 (UKF),实时检测粘合系数变化.
- 一个分层的控制策略使用了蝙蝠算法 (BA) 优化的反向传播 (BP) 神经网络来计算曲折时刻.
- 根据估计的附着系数,二次编程优化了基于估计的附着系数的轮子扭矩分布.
主要成果:
- UKF算法准确地估计了粘附系数,即使在极端,突然变化的情况下.
- 拟议的稳定性控制器在模拟中显著改善了驾驶和驾驶稳定性.
- 优化BA的BP神经网络提高了计算效率和准确性.
结论:
- 开发的控制策略有效地解决了DDEV的不稳定性,在不同的粘附的道路上.
- 先进估计和层次控制的综合方法为DDEV安全提供了一个有希望的解决方案.
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