基于 AVOA-MCSCKF 算法的分布式驱动电动汽车侧滑角度估计

IF 2.3 4区 工程技术 Q2 ENGINEERING, MECHANICAL
Qiping Chen, Binghao Yu, Hongyu Pang, Chengping Zhong, Daoliang You, Zhiqiang Jiang
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引用次数: 0

摘要

准确获取有关车辆行驶状态的信息对于实施车辆主动安全控制措施至关重要。为解决分布式电动汽车侧滑角精确测量的难题,本研究提出了一种基于非洲秃鹫优化算法的优化最大熵平方根立方卡尔曼滤波器(AVOA-MCSCKF)。该方法旨在提供侧滑角的精确估计。通过应用遗忘因子递归最小二乘法对车辆总质量进行实时估算。此外,还利用非洲秃鹫算法对 MCSCKF 进行自适应调整。这种调整的目的是减轻由于噪声协方差矩阵的不确定性而产生的估计误差,最终实现更精确的侧滑角估计。在 Carsim/Simulink 协同仿真环境中,该算法的准确性和鲁棒性在各种运行场景中得到了验证。研究结果表明,与标准协方差卡尔曼滤波器和平方根立方卡尔曼滤波器相比,AVOA-MCSCKF 算法至少提高了 51.8% 的侧滑角估计精度。这种方法有效地解决了分布式驱动电动汽车在复杂条件下运行时侧滑角估计的难题,从而提高了车辆的主动安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm
The accurate acquisition of information regarding the state of a vehicle's driving is essential for the implementation of active safety control measures in vehicles. To tackle the challenge of accurately measuring the sideslip angle in distributed electric vehicles, this study proposes an optimized maximum correntropy square-root cubature Kalman filter based on African vulture optimization algorithm (AVOA-MCSCKF). This method aims to provide accurate estimation of the sideslip angle. The real-time estimation of the total vehicle mass is conducted through the application of forgetting factor recursive least squares method. Additionally, the African vulture algorithm is utilized to adaptively adjust MCSCKF. This adjustment aims to mitigate estimation inaccuracies stemming from the uncertain nature of the noise covariance matrix, ultimately leading to a more accurate estimation of the sideslip angle. In the collaborative simulation environment of Carsim/Simulink, the algorithm's accuracy and robustness are validated across various operational scenarios. The research findings indicate that AVOA-MCSCKF algorithm enhances the accuracy of sideslip angle estimation by a minimum of 51.8% when compared to both the standard covariance Kalman filter and square-root cubature Kalman filter filter. This approach effectively addresses the challenging estimation issue of the sideslip angle in distributed drive electric vehicles operating under complex conditions, thereby improving the vehicle's active safety.
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来源期刊
CiteScore
3.80
自引率
16.70%
发文量
370
审稿时长
6 months
期刊介绍: The Journal of Process Mechanical Engineering publishes high-quality, peer-reviewed papers covering a broad area of mechanical engineering activities associated with the design and operation of process equipment.
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