György Istenes

This study presents a preliminary time- and frequency-domain analysis of road vehicle lateral dynamics based on experimental measurements with pseudo-random binary sequence excitation. The measurements were performed under controlled conditions, where the steering system was externally excited while the vehicle traveled in a straight line. Steering input and lateral acceleration signals were analyzed in both the time and frequency domains. Auto power spectral density analysis was used to examine the frequency content of the measured signals. Most of the signal energy is concentrated in the low-frequency range, while the applied excitation covers the frequency range relevant for system identification. The measurements can be used for later identification and modeling of the vehicle’s lateral dynamics.

This paper investigates the influence of weak but structured high-frequency disturbances on the linear system identification of lateral vehicle dynamics using experimental measurement data. The analyzed dataset originates from previously conducted driver-in-the-loop experiments involving free-driving and slalom maneuvers. Frequency-domain analysis confirms that the dominant vehicle dynamics are concentrated below approximately 2–3 Hz, while a weak but persistent narrow-band disturbance around 10 Hz is consistently present in the steering signal. To investigate the influence of this disturbance on identification, different disturbance-handling strategies are compared, including notch filtering, low-pass filtering, ARX, IV-ARX, and ARMAX model structures. The comparison considers prediction performance, model complexity, identified dynamics, and robustness under different measurement conditions. The results show that increasing the deterministic model order is generally less effective than either targeted preprocessing or explicit noise modeling. When the disturbance is spectrally well separated from the relevant vehicle dynamics, notch filtering combined with a low-order ARX model provides the most effective solution. If preprocessing is not possible, ARMAX models achieve comparable performance by representing part of the disturbance through the noise model. IV-ARX models are used as a benchmark to verify that the main conclusions remain valid under possible closed-loop bias. Based on these findings, a practical engineering workflow is proposed for selecting appropriate disturbance-handling strategies according to the spectral characteristics of the measured signals. The proposed methodology provides guidance for robust control-oriented identification of lateral vehicle dynamics using realistic measurement data.

In this paper, a time domain and a spectral analysis of the lateral dynamics of a Lexus passenger car are presented. Measurements were made of the vehicle’s lateral acceleration and steering angle. The aim of the measurements is to understand the vehicle’s lateral dynamics during different cornering maneuvers. For this purpose, part of the measurements is performed with a driver and the other part with autonomous control. The data processed and analyzed in this research can be used to determine the nature of the lateral dynamics, which is essential to establishing a mathematical relationship between the measured signals. This will allow the identification and modeling of vehicle dynamics, which is key to the development and optimization of autonomous vehicle control systems.

In this paper, the nonlinear identification of the lateral dynamics of a road vehicle and the velocity dependence of the dynamics are presented. One of the most useful methods to define the mathematical model is system identification based on measured data. A test vehicle for autonomous driving was constrained to move in a straight line while the vehicle’s steering servo was artificially excited. The input of the system is therefore the sum of the artificial excitation and the control signal of the autonomous function, and the output is the lateral acceleration of the vehicle. The measurements are used to identify Wiener and Hammerstein models of the lateral dynamics at different speeds using nonlinear methods. The aim is to investigate the velocity dependence of the dynamics.

In this paper, the nonlinear identification (ID) of the lateral dynamics of a road vehicle is presented. The mathematical description of lateral dynamics is crucial for developing various self-driving functions. One method of describing dynamics is system identification from measured data. During the measurements, the steering servo of a test vehicle kept in straight-line motion by a self-driving function was artificially excited. A Hammerstein–Wiener model was successfully applied for the identification of these measurements. A nonlinear estimator was used during the fitting, which needed high computing power. For the Hammerstein–Wiener model, we used the two-stage algorithm (TSA) with a bilinear estimation method, which makes it possible to apply linear regression. We compared these methods during simulations and real data.

On-road driving studies are essential for comprehending real-world driver behavior. This study investigates the use of eye-tracking (ET) technology in research on driver behavior and attention during Controlled Driving Studies (CDS). One significant challenge in these studies is accurately detecting when drivers divert their attention from crucial driving tasks. To tackle this issue, we present an improved method for analyzing raw gaze data, using a new algorithm for identifying ID tags called Binarized Area of Interest Tracking (BAIT). This technique improves the detection of incidents where the driver’s eyes are off the road through binarizing frames under different conditions and iteratively recognizing markers. It represents a significant improvement over traditional methods. The study shows that BAIT performs better than other software in identifying a driver’s focus on the windscreen and dashboard with higher accuracy. This study highlights the potential of our method to enhance the analysis of driver attention in real-world conditions, paving the way for future developments for application in naturalistic driving studies.

In this paper, a software framework is presented through an application that is able to jointly optimize an electric motor and a gearbox for the design of a drive system for electric vehicles. The framework employs a global optimization method and uses both analytical and finite element method (FEM) models to evaluate the objective functions. The optimization process is supported by a statistical surrogate model, which allows a large reduction of runtime. An earlier version of this framework was only suitable for electric motor optimization. In the application presented in a previous paper, the motor of a belt-driven electric drive system was optimized. In this paper, the optimization of the same drive system is shown, but now with a combined optimization of a gear drive and motor. The objective functions of optimization are minimizing the total loss energy and the weight of the drive system. The optimization results are compared with previous results to demonstrate the further potential of joint optimization.

In this paper, a multi-objective optimization framework for electric motors and its validation is presented. This framework is suitable for the optimization of design variables of electric motors based on a predetermined driving strategy using MATLAB R2019b and Ansys Maxwell 2019 R3 software. The framework is capable of managing a wide range of objective functions due to its modular structure. The optimization can also be easily parallelized and enhanced with surrogate models to reduce the runtime. The framework is validated by manufacturing and measuring the optimized electric motor. The method’s applicability for solving electric motor design problems is demonstrated via the validation process. A test application is also presented, in which the operating points of a predetermined driving strategy provide the input for the optimization. The kriging surrogate model is used in the framework to reduce the runtime. The results of the optimization and the framework’s benefits and drawbacks are discussed through the provided examples, in addition to displaying the properly applicable design processes. The optimization framework provides a ready-to-use tool for optimizing electric motors based on the driving strategy for single- or multi-objective purposes. The applicability of the framework is demonstrated by optimizing the electric motor of a world recorder energy-efficient race vehicle. In this application, the optimization framework achieved a 2% improvement in energy consumption and a 9% increase in speed at a rated DC voltage, allowing the motor to operate at desired working points even with low battery voltage.