Online estimation of vehicle driving resistance parameters with recursive least squares and recursive total least squares

Contribution: The contribution of this paper is a recursive generalized total least-squares (RGTLS) estimator that offers exponential forgetting and treats data with unequally sized and correlated noise. Application: RGTLS is used for estimation of vehicle driving resistance parameters. A vehicle longitudinal dynamics model and available control area network (CAN) signals form appropriate estimator inputs and outputs. Results: We present parameter estimates for the vehicle mass, two coefficients of rolling resistance, and drag coefficient of one test run on public road. Moreover, we compare the results of the proposed RGTLS estimator with two kinds of recursive least-squares (RLS) estimators. Discussion: While RGTLS outperforms RLS with simulation data, the recursive least squares with multiple forgetting (RLSMF) estimator [1] provides superior accuracy and sufficient robustness through orthogonal parameter projection with experimental data

Acoustic effects of the coolant mass flow of an electric machine of a hybrid drive train

In this application paper, the influence of the coolant mantle on the acoustic radiation behaviour of a hybrid drive train is investigated. This was done on an electric machine on an acoustic component test bench. The coolant mass flow around the electric machine stator was varied and then completely drained. The electrical machine remained mechanically unchanged; any variations were made to the feed pumps on the test bench side. Triaxial acceleration sensors are glued to the machine housing and reviewed as evaluation criteria. For the evaluation, the square mean value of all three spatial directions of the glued acceleration sensors was calculated. The evaluation shows that there is no significant acoustic difference between an active stator cooling jacket and a stationary stator cooling jacket. If the stator cooling jacket is pumped out empty so that air remains in it, there is a strong reduction in surface acceleration. The observations are confirmed by analytical literature values. The results presented serve as a basis for further work and developments

Vehicle Impact on Tire Road Noise and Validation of an Algorithm to Virtually Change Tires

Especially for electric vehicles, the tire impact on car noise is becoming more and more important. The requirement of meeting certification criteria makes estimating the sound pressure level essential for vehicle manufacturers. Most recent research on tire road noise is conducted on component test benches. Little research exists into tires mounted on vehicles, and even less into the impact of acceleration on the generated noise. The literature mainly considers some vehicle shape differences, tire load, and inflation pressure. This article investigates the impact of different vehicles on tire noise through a series of measurements on a standardized test track. The rolling noise as well as accelerated noise of three different tires and five different vehicles are compared. The impact of the drive axle on accelerated noise as well as a weight variation is investigated. Additionally to the absolute measured differences between the vehicles, statistical methods are used to separate measurement dispersion from actual systematic differences. This research therefore validates older research on the vehicles’ impact on tire noise, which is necessary since the general tire structure, thread, and rubber composition have changed in the time period between the publication of previous research from the literature and this paper. This allows us to approximate the emitted noise on different vehicles. Furthermore, we validate an algorithm to virtually change tires on test benches. The algorithm is standardized and implemented in common measurement software

Robust Speed Control of a Multi-Mass System: Analytical Tuning and Sensitivity Analysis

The regeneration of highly dynamic driving maneuvers on vehicle test benches is challenging due to several influences, such as power losses, vibrations in the overall system that involves the vehicle with the test bench, uncertainties in the model parameterization, and time delays from both the test bench and the measurement systems. In order to improve the dynamic response of the vehicle test bench and to overcome system disturbances, we employed different types of control algorithms for a mechanical multi-mass model. First, those controllers are extensively investigated in the frequency domain to analyze their stability and evaluate the noise rejection quality. Then, the expectations from the frequency analysis are confirmed in a time-domain simulation. Furthermore, sensitivity analysis tests were conducted to evaluate each controller’s robustness against the modeling parameters’ uncertainty. The linear quadratic controller with integral action demonstrated the best compromise between performance and robustness

Human Response to Vehicle Vibrations and Acoustics during Transient Road Excitations

Driving over rising and falling edges on roads and pavements, rails, manhole covers, or transverse joints can influence the driving impression regarding the driver’s perception of vibrations and acoustics. To be able to describe this, objective parameters are used to make the subjective ride comfort measurable and scalable. Previous studies have already contributed to the investigation of the subjective perception regarding the interaction of vibrations and acoustics. However, the results were individual. Aimed at improving the quality of objective analysis methods, driving maneuvers were performed in a real vehicle to investigate the interaction of vibrations and acoustics due to transient road excitations. For this purpose, a sound reproduction system was used, which could provide the acoustic environment for the driver to adapt to while driving. With this method, subjective ratings by varying vibrations and acoustics were collected and with reference to objective parameters statistically evaluated. The results showed that both tactile and audible vibrations under transient influences had no significant interactive effects on the driver’s perception

Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay

The Stanley controller is a proven approach for path tracking control in automated vehicles. If time delays occur, for example, in signal processing and steering angle control, precision and stability decrease. In this article, enhancements for the Stanley controller are proposed to achieve stable behavior with improved tracking accuracy. The approach uses the curvature of the path as feedforward, whereby the reference point for the feedforward input differs from that of the controller setpoints. By choosing a point further along the path, the negative effects of system delay are reduced. First, the parameters of the Stanley controller are calibrated using a straight line and circle maneuver. Then, the newly introduced feedforward parameter is optimized on a dynamic circuit. The approach was evaluated in simulation and validated on a demonstrator vehicle. The validation tests with the demonstrator vehicle on the dynamic circuit revealed a reduction of the root-mean-square cross-track error from 0.11 m to 0.03 m compared to the Stanley controller. We proved that the proposed approach optimizes the Stanley controller in terms of compensating for the negative effects of system delay. This allows it to be used in a wider range of applications that would otherwise require a more complex control approach

Probabilistic Predictions with Federated L3earning

Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end devices. Federated learning can be applied in this setting in a communication-efficient and privacy-preserving manner but does not include predictive uncertainty. To represent predictive uncertainty in federated learning, our suggestion is to introduce uncertainty in the aggregation step of the algorithm by treating the set of local weights as a posterior distribution for the weights of the global model. We compare our approach to state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms. By applying proper scoring rules to evaluate the predictive distributions, we show that our approach can achieve similar performance as the benchmark would achieve in a non-distributed setting

An Analytical Method for Generating Determined Torque Ripple in Synchronous Machine with Interior Magnets by Harmonic Current Injection

In this paper, we present an extension for an analytical method of calculating the required amplitudes and phase angles of the injected harmonic currents, to generate a determined torque ripple for synchronous machines. With the consideration of reluctance torque in the system equations, this method is valid not only for synchronous machines with surface magnets, but also for those with interior magnets. First, we describe the machine equations as a function of the phase current and the back electromotive force. We then introduce an analytical way to calculate the reluctance torque. After combining the equations, we establish a linear system of equations. The solution of the equation system yields the amplitudes and phase angles of the harmonic currents to be injected. Finally, we validate the equations for calculating the reluctance Torque and the method to generate the determined torque ripple with several finite element method simulations. This allowed us to generate the desired torque fluctuations even for synchronous machines with interior magnets

A Novel Approach for a Predictive Online ECMS Applied in Electrified Vehicles Using Real Driving Data

To increase the efficiency of electrified vehicles, many energy management strategies (driving strategies) have been proposed. These include both offline optimization techniques to identify a system’s theoretical optimum and online optimization techniques created for onboard use in the vehicle. In the field of online optimization, predictive approaches can achieve additional savings. However, predictions are challenging, and robust usability in all driving situations of the vehicle is not guaranteed. In this study, a new approach for a predictive energy management strategy is presented. It is demonstrated how this so-called predictive Online Equivalent Consumption Minimization Strategy (ECMS) can achieve additional fuel savings compared to a non-predictive Online ECMS by predicting recuperation events using map data. As long as the route is known, map data are available, and the current position of the global navigation satellite system (GNSS) is given, the predictive Online ECMS can be applied. If these requirements are not met, the non-predictive basic implementation can still be used to ensure robust functionality. The methodology is investigated using a backward simulation model of a D-segment vehicle powered by a 48 V hybrid electric system in a P2 topology. A dataset including real driving cycles including map data from Open Street Map (OSM) is used. However, the investigations are limited to the consideration of traffic signal (TS) positions on the upcoming route. Simulation results focus on the interaction between the energy management strategy (EMS) and usable battery energy. More than 1 % average saving potentials compared to a non-predictive implementation are shown. The highest saving potentials are found with a usable battery energy of 100 Wh

Evaluation of Different Fault Diagnosis Methods and Their Applications in Vehicle Systems

A high level of automation in vehicles is accompanied by a variety of sensors and actuators, whose malfunctions must be dealt with caution because they might cause serious driving safety hazards. Therefore, a robust and highly accurate fault detection and diagnosis system to monitor the operational states of vehicle systems is an indispensable prerequisite. In the area of fault diagnosis, numerous techniques have been studied, and each one has pros and cons. Selecting the best approach based on the requirements or usage scenarios will save much needless work. In this article, the authors examine some of the most common fault diagnosis methods for their applicability to automated vehicle systems: the traditional binary logic method, the fuzzy logic method, the fuzzy neural method, and two neural network methods (the feedforward neural network and the convolutional neural network). For each approach, the diagnosis algorithms for vehicle systems were modeled differently. The analysis of the detection capabilities and the suitable application scenarios of each fault diagnosis approach for vehicle systems, as well as recommendations for selecting different methods for various diagnosis needs, are also provided. In the future, this can serve as an effective guide for the selection of a suitable fault diagnosis approach based on the application scenarios for vehicle systems