使用混合方法估计总线质量:将忘记因子递归最小方程与扩展卡尔曼波器集成在一起
Jingyang Du1, Qian Wang1, Xiaolei Yuan1
1School of Automobile, Chang'an University, Xi'an 710064, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
准确的车辆质量估计对于安全驾驶至关重要. 使用现有传感器的新混合算法可以在没有新硬件的情况下提高准确性,这对于负载变化的公共汽车至关重要.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 信号处理 信号处理
背景情况:
- 车辆质量显著影响驾驶安全和效率.
- 现有的质量估计方法受到传感器要求和操作条件的限制.
- 由于乘客负载,公共汽车经历了显著的质量变化,使估计变得复杂.
研究的目的:
- 开发一种新,准确和方便的车辆质量估计算法.
- 为了应对像公共汽车这样具有可变载荷的车辆中质量估计的挑战.
- 使用现有的车辆传感器 (CAN-bus,IMU) 来进行质量估计,避免额外的成本.
主要方法:
- 一种混合算法,集成了强大的忘记因子递归最小方程 (强大的FFRLS) 和扩展卡尔曼波器 (EKF).
- 连续的双阶段质量估计与比例的协调因子来平衡FFRLS和EKF输出.
- 使用MATLAB-TruckSim模拟在空,半负荷和满负荷条件下的验证.
主要成果:
- 拟议的混合算法在所有测试的负载条件下实现了低于10%的错误率.
- 与单一方法方法相比,该方法显示出更高的性能.
- 该算法有效地估计了仅使用现有的CAN总线和IMU传感器数据的车辆质量.
结论:
- 混合FFRLS-EKF算法提供了准确和稳定的车辆质量估计.
- 该方法具有成本效益,依赖于先前存在的传感器,因此适合大规模生产.
- 该算法符合车辆质量估计的严格安全和稳定性要求.
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