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Huber-based robust unscented kalman filter distributed drive electric vehicle state observation
W. Wan; J. Feng; B. Song and X. Li
2021
发表期刊Energies
ISSN19961073
卷号14期号:3
摘要Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
DOI10.3390/en14030750
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收录类别SCI ; EI
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文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/65306
专题中国科学院长春光学精密机械与物理研究所
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W. Wan,J. Feng,B. Song and X. Li. Huber-based robust unscented kalman filter distributed drive electric vehicle state observation[J]. Energies,2021,14(3).
APA W. Wan,J. Feng,&B. Song and X. Li.(2021).Huber-based robust unscented kalman filter distributed drive electric vehicle state observation.Energies,14(3).
MLA W. Wan,et al."Huber-based robust unscented kalman filter distributed drive electric vehicle state observation".Energies 14.3(2021).
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