Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle

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.

A Two-Stage Bayesian optimisation for Automatic Tuning of an Unscented Kalman Filter for Vehicle Sideslip Angle Estimation

“…The observation noise parameters are usually tuned through statistical analysis, obtained after sensor calibration tests [5]. The only exception is when the observation noise parameters are tuned together with the process noise parameters [9]. This rarely happens because the filter performance depends mainly on the ratio between the eigenvalues of the observation and process covariance matrices.…”

“…Metaheuristics are procedures that can provide an acceptable solution to an optimisation problem with incomplete information about the cost function. Examples of metaheuristic algorithms are: the already cited downhill simplex algorithm [12], GA [13] or the Multi-Objective GA [5], SA [9] and FFO [4]. A comparison of different metaheuristic algorithms for Kalman filter tuning [4] shows that GA and FFO found the best cost function optimum.…”

Vehicle state and parameter estimation based on adaptive robust unscented particle filter

“…is the detect residual values for sensors; the value range of the adjustment factor are 𝐾 0 ∈ (1,1.5) and 𝐾 𝑔 ∈ (3,8).…”

“…Alatorre et al [7] proposed an algorithm that merges the concepts of least squares method and sliding mode observer for the estimation of the vehicle mass. Heidfeld et al [8] proposed an Unscented KALMAN Filter (UKF) for simultaneous state and parameter estimation. Badini et al [9] presented a simple parameter independent speed estimation algorithm for vector-controlled permanent magnet synchronous motor (PMSM) drive.…”

Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

“…Electronic body stability systems, such as yaw moment control, use the quick response of four-wheel motors to improve the vehicle control system's performance. Distributed-drive electric vehicles' steering, driving, and braking signals are more accessible than those of conventional vehicles, effectively improving the vehicle's sensing capability and making real-time observation of vehicle dynamics parameters easier [2]. Accurate vehicle state estimation results directly improve the sensing capability of intelligence vehicles, which is important for path tracking, trajectory tracking, and active safety controller design of intelligence vehicles.…”