Abstract
Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and more flexible generation of wheel individual propulsion and braking torques. Exact and robust estimation of vehicle state of motion in the presence of unknown disturbances, such as changes in road conditions, is crucial for realization of such control systems. This article shows the design, tuning, implementation, and test of a state estimator with individual tire model adaption for direct drive electric vehicles. The vehicle dynamics are modeled using a double-track model with an adaptive tire model. State-of-the-art sensors, an inertial measurement unit, steering angle, wheel speed, and motor current sensors are used as measurements. Due to the nonlinearity of the vehicle model, an Unscented Kalman Filter (UKF) is used for simultaneous state and parameter estimation. To simplify the difficult task of UKF tuning, an optimization-based method using real-vehicle data is utilized. The UKF is implemented on an electronic control unit and tested with real-vehicle data in a hardware-in-the-loop simulation. High precision even in severe driving maneuvers under various road conditions is achieved. Nonlinear state and parameter estimation for all wheel drive electric vehicles using UKF and optimization-based tuning is shown to provide high precision with minimal manual tuning effort.
Highlights
For realization of vehicle dynamic control and driver assistance systems, knowledge of the dynamic state variables of the vehicle motion is necessary
The Unscented KALMAN Filter (UKF) is implemented on an electronic control unit and tested with real-vehicle data in a hardware-in-the-loop simulation
This paper shows the design and validation of a UKF-based vehicle state estimator with wheel-individual tire model adaption
Summary
For realization of vehicle dynamic control and driver assistance systems, knowledge of the dynamic state variables of the vehicle motion is necessary. Observer-based approaches, which utilize different kinds of vehicle models, are the most common While these methods can offer highly accurate results, the computational complexity can be a problem in practical real-time applications. Two important questions are identified in [2], extending the range of estimation towards connected and automated driving vehicles and overcoming challenges due to nonlinear system behavior. The latter will be addressed in this article
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