The characteristics of vehicle steering perception are decisive factors concerning vehicle safety and overall pleasure behind the wheel. It is a challenge for vehicle manufacturers to achieve these features and qualities, because usually vehicle tuning almost only relies on subjective evaluation of test drivers, which is costly and time consuming. In order to optimize suspension design and develop a tool that can be used to evaluate steering with objective metrics instead of subjective assessment, links between them must be confirmed. In this master thesis, both objective and subjective testing data of over 20 vehicles across four different segments are introduced in linear and nonlinear analysis. Linear regression analysis is applied to investigate simply positive or negative correlation between a pair of subjective-objective parameters. However, even if certain linear correlations are obtained, it is still hard to define the optimal value for objective metrics. Considering that the general shape of a correlation function can reveal which objective range give higher subjective rating, it is possible to define these preferred ranges with Neural Network (NN). The best data available is adopted from three drivers who tested 15 sedans, and some interesting results are found. The initial results demonstrate that NN is a powerful tool to uncover and graphically illustrate the links between objective metrics and subjective assessments, i.e., the specific range leading to better steering feel. Given a larger sample size, more reliable and optimal links can be defined by following the same method. These confirmed links would enable vehicle dynamics engineers to more effectively develop new vehicles with nearly perfect steering feel.

Vehicle dynamics development is strongly based on subjective assessments (SA) of vehicle prototypes, which is expensive and time consuming. Consequently, in the age of computer- aided engineering (CAE), there is a drive towards reducing this dependency on physical test- ing. However, computers are known for their remarkable processing capacity, not for their feelings. Therefore, before SA can be computed, it is required to properly understand the cor- relation between SA and objective metrics (OM), which can be calculated by simulations, and to understand how this knowledge can enable a more efficient and effective development process. The approach to this research was firstly to identify key OM and SA in vehicle dynamics, based on the multicollinearity of OM and of SA, and on interviews with expert drivers. Sec- ondly, linear regressions and artificial neural network (ANN) were used to identify the ranges of preferred OM that lead to good SA-ratings. This result is the base for objective require- ments, a must in effective vehicle dynamics development and verification. The main result of this doctoral thesis is the development of a method capable of predicting SA from combinations of key OM. Firstly, this method generates a classification map of ve- hicles solely based on their OM, which allows for a qualitative prediction of the steering feel of a new vehicle based on its position, and that of its neighbours, in the map. This prediction is enhanced with descriptive word-clouds, which summarizes in a few words the comments of expert test drivers to each vehicle in the map. Then, a second superimposed ANN displays the evolution of SA-ratings in the map, and therefore, allows one to forecast the SA-rating for the new vehicle. Moreover, this method has been used to analyse the effect of the tolerances of OM requirements, as well as to verify the previously identified preferred range of OM. This thesis focused on OM-SA correlations in summer conditions, but it also aimed to in- crease the effectiveness of vehicle dynamics development in general. For winter conditions, where objective testing is not yet mature, this research initiates the definition and identifica- tion of robust objective manoeuvres and OM. Experimental data were used together with CAE optimisations and ANOVA-analysis to optimise the manoeuvres, which were verified in a second experiment. To improve the quality and efficiency of SA, Volvo’s Moving Base Driving Simulator (MBDS) was validated for vehicle dynamics SA-ratings. Furthermore, a tablet-app to aid vehicle dynamics SA was developed and validated. Combined this research encompasses a comprehensive method for a more effective and ob- jective development process for vehicle dynamics. This has been done by increasing the un- derstanding of OM, SA and their relations, which enables more effective SA (key SA, MBDS, SA-app), facilitates objective requirements and therefore CAE development, identi- fies key OM and their preferred ranges, and which allow to predict SA solely based on OM. / <p>QC 20170223</p> / iCOMSA

When driving a vehicle on the road, the driver has to compensate continuously for small directional deviations from the desired course due to disturbances such as crosswinds, road irregularities and unintended driver inputs. These types of deviations have a tiring effect on the driver and should therefore be minimised. When the magnitude of these disturbances increases, especially with crosswind, the directional deviation might become so large that the driver will have difficulties in compensating for it, and will thereby affect the traffic safety. The objective of this research work is to increase the understanding of the crosswind sensitivity of buses and to find solutions to the problem of improving the safety of buses with respect to crosswind performance. The work presented in this thesis contributes to increased knowledge about the directional stability of buses under the influence of crosswind gusts through parameter studies using detailed vehicle simulation models, through full-scale experiments and through studies of the effect of steering feel on the subjective and objective evaluation of crosswind performance. A natural crosswind gust model has been derived from wind tunnel measurements and implemented in a multi-body dynamics simulation tool. The aerodynamic loads of the crosswind gust model have been applied on a detailed vehicle model and the behaviour of the vehicle model has been studied for various vehicle configurations in both open- and closed-loop manoeuvres. The vehicle model, with parameters corresponding to real vehicle data, has been validated and the agreement with measurements is good. A method for estimating the aerodynamic loads on a bus due to crosswind on a road section is also presented. Aerodynamic loads under real conditions were estimated using this method and these data were thereafter used in a study where the effect of steering feel on the subjective and objective evaluation of crosswind performance was investigated using a moving-base driving simulator, with the aim of finding a relationship between steering feel and crosswind sensitivity. The thesis covers the influence of changing chassis-related parameters and aerodynamics-related parameters on the crosswind sensitivity, as well as the influence of the setting of the steering system on the crosswind performance of the driver-vehicle system. The results identify areas of high potential for improving the crosswind sensitivity of buses, such as the centre of gravity location and the yaw moment overshoot at gust entry. Furthermore, the study shows the importance of having a vehicle that facilitates prompt driver corrections for reducing the lateral deviation under crosswind excitation; i.e. it is shown that a steering system with the possibility of changing the yaw rate gradient referencing the steering-wheel input when the vehicle is subjected to a sudden crosswind has a good potential for improving the crosswind performance of the driver-vehicle system. / QC 20100722

Road feedback is an essential part of the driving experience, representing a connection betweenthe driver and the vehicle motion. Road feedback in a steer-by-wire system must berecreated and transferred to the driver through a feedback motor mounted on the steeringwheel. This project aimed to implement a function in the feedback motor control with thepurpose of giving the driver road feedback. The function should enhance the drivers trustand confidence in the steering system. Specifically, the function should provide the driverwith a feeling of being connected to the vehicle. A pre-study on the subject of steering feel was made as well as an interview study, whichresulted in a list of hypotheses. The hypotheses became a basis for generating conceptideas, together with measurement data of vehicle network signals for different drivingscenarios. Two different functions were then modelled and implemented in MathWorksSimulink. Function 1 models force components acting on the front road wheels in longitudinal,lateral and vertical direction. The forces result in a torque contribution fromeach dimension that acts around the steering axis and represents a reaction in the steeringsystem due to road disturbances. The torque is then translated to a steering wheel torque.Function 2 strives to capture road surface roughness through the high frequency informationin the steering rack motor torque. Three different road surfaces were studied; smoothasphalt, rough asphalt and gravel road. A test rig was used in order to verify the behaviour of the functions. The final step of theproject was to implement the functions in a test vehicle, where they could be further tunedand evaluated. The force component models of Function 1 captured different types of roadfeedback which were evaluated separately by timing, authenticity and desirability. Theresult of this evaluation was positive considering them separately. When the models werecombined it resulted in an unwanted behaviour. Function 2 gave torque feedback that feltauthentic and natural, especially for the gravel road case. However, it was more difficult todistinguish different asphalt types. The conclusion was that neither Function 1 nor 2 couldbe approved as finished functions, however both are considered as interesting concepts forfurther development. Fully steer-by-wire steering transmissions have now been approved for usage. This meansthat one of the remaining challenges for implementing steer-by-wire cars on today’s marketis the subjective views from the customers. Function 1 and 2 could be a way to providethe drivers with trust towards the steering, and at the same time, enhance the drivingexperience. / <p>The presentation was held at ACAS at Linköping University. The presentation included a summary and learnings from the project. A short demonstration was performed using a test-rig consisting of a steering wheel, a feedback motor and a control unit. </p>