A variable structure extended Kalman filter for vehicle sideslip angle estimation on a low friction road

Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers' safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for highprecision measurements of the vehicle's states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces.

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“…[9] and for sideslip angle estimation of low friction roads in Ref. [6]. The UKF for vehicle state estimation is presented in [7,8].…”

Section: Kalman Filters -Theory For Advanced Applicationsmentioning

confidence: 99%

Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics

Wielitzka¹,

Busch²,

Dagen³

et al. 2018

Kalman Filters - Theory for Advanced Applications

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“…According to the deformation of the Ackermann steering geometry, centroid steering radius can be deduced by equation (11) …”

Section: Four-wheel Trajectory Modelmentioning

confidence: 99%

“…Then calculate each wheel's side-slip angle and steering radius according to the vehicle geometry parameters (Li et al 2014 ABSTRACT: Conventional indirect tire pressure monitoring system (TPMS) is mainly designed according to the wheel speed signal of anti-lock brake system (ABS). Four wheels' velocity change because yaw rate exists when vehicles are under steering conditions.…”

Section: Niclas Persson From Department Of Electricalmentioning

confidence: 99%

Monitoring Method of Indirect TPMS Under Steering Situation

Wang¹,

Zhang²,

Yao³

et al. 2017

dtetr

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“…However, roll stability is also important for some vehicles with high centers of gravity and narrow wheel tracks. The rollover index and anti-rollover controller depend on the effect of state estimation [20]. In this paper, in considering roll dynamics, the joint estimation is extended to four states.…”

Section: Introductionmentioning

confidence: 99%

State Estimation of Vehicle’s Dynamic Stability Based on the Nonlinear Kalman Filter

Pei

Liu

et al. 2018

Automot. Innov.

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An accurate estimation of a vehicle's state of motion is the basis of dynamic stability control. Two different nonlinear Kalman filters are adopted for the estimation of the vehicle's lateral/rollover stability state. First, the overall structure of the state estimation with four inputs and four outputs is introduced. After determining tire-cornering stiffness using a recursive leastsquares (RLS) method, the equations of state and of observation for the nonlinear Kalman filter are established based on a vehicle model with four degrees of freedom including planar and rollover dynamics. Then, the specific steps of real-time state estimation using the extended Kalman filter (EKF) and unscented Kalman filter (UKF) are both given. In a co-simulation, we find that the RLS algorithm estimates tire-cornering stiffness accurately and quickly, and the UKF improves the effect of state estimation compared with EKF. In addition, the UKF is verified against data from vehicle tests. The results show the proposed method is reliable and practical in estimating vehicle states.

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A variable structure extended Kalman filter for vehicle sideslip angle estimation on a low friction road

Cited by 149 publications

References 11 publications

Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics

Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics

Monitoring Method of Indirect TPMS Under Steering Situation

State Estimation of Vehicle’s Dynamic Stability Based on the Nonlinear Kalman Filter

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