Tire characteristics and modeling

\u3cp\u3eTires are the interface between vehicle and road. The forces and moments generated by the tires determine the motion of a road vehicle. Dedicated tire tests provide insight in these forces and moments and their dependency on slip, inclination angle and vertical force. The brush tire model can explain the measured characteristics qualitatively, the Magic Formula is a semi-empirical tire model to quantitatively describe them. To account for tire dynamic behavior, relaxation effects are discussed and modeled.\u3c/p\u3

Rear suspension design for an in-wheel-drive electric car

\u3cp\u3eThe in-wheel motor configuration can provide more flexibility to electric car design, making the car more compact and lightweight. However, current suspension systems are not designed to incorporate an in-wheel powertrain, and studies have shown deterioration in ride comfort and handling when more unsprung mass is added to a vehicle. In order to develop a battery electric vehicle with an in-wheel powertrain, a new rear-suspension design is proposed after considering several designs that are currently used in cars. The suspension is designed specifically for an in-wheel powertrain and, by efficiently using the available space, all the components of the powertrain and the battery pack can be fitted compactly within the limited space available in a subcompact (B-segment) car. The design also mitigates some issues of the high unsprung mass as simulations were conducted to determine the ride comfort and the handling characteristics of the vehicle. The in-wheel module is currently being tested at the Eindhoven University of Technology.\u3c/p\u3

Evaluation of MF-swift for durability simulations

Daimler AG uses simulation tools to predict how the car will perform before testing takes place. The simulations are important because it reduces costs and time. One of the important topics is durability. A customer wants a car which is reliable. Therefore several test are carried out like for example driving over cleats or through potholes. These test have a major impact to the suspension and chassis components. During the design of the suspension it’s important to simulate these tests. To predict the outcome of the simulation accurately the forces generated by the tyre need to be known. Therefore it is crucial to have a correct simulation model of the tyre. Several tyre models exist in the market like MF-Swift and Ftire. Because there are multiple tyre models available, it is necessary for Daimler to know how these models perform and make a comparison. The objective of this report is the evaluation of MF-Swift for durability load simulations. Various road obstacles are used to evaluate the performance of MF-Swift. Some of these simulations are compared with measurement data and other simulations with a different tyre model, Ftire. The resulting tyre forces are analyzed in the time domain and using damage calculations. The first step is to generate a tyre property file. This file contains all the required parameters of the tyre model. Daimler has two measurement sets available of tests executed in Aachen and Karlsruhe. With these measurement sets the required parameters for MF-Swift can be identified. The in-plane dynamics are parameterized with the Aachen measurements and the out-of-plane dynamics with the Karlsruhe measurements. Combining two measurement sets is not an ideal situation. The reason for this was that the out-of-plane dynamics were not excited in the Aachen measurements. The first part of this report evaluates the low speed enveloping behaviour and some dynamic cleat experiments. Simulations show that MF-Swift has a better match with the measurements for the enveloping behaviour compared to Ftire. However, if only the peak-to-peak values are analyzed it is visible that the general trend for MF-Swift is a higher vertical force while Ftire has a lower vertical force when they are compared with the measurements. The longitudinal forces of both models matches rather well with the measurements. The cleat experiments used for the parametrization process are simulated with the two tyre models. Both models are able to match the vertical force reasonably well, but some differences exist for the longitudinal force. MF-Swift is able to describe the peak-to-peak value for the out-of-plane measurement rather well, for the in-plane case MF-Swift is only able to match the negative peak. Ftire isn’t able to match the peak-to-peak values for both in-plane and out-of-plane. The difference for the out-of-plane is probably caused by the fact that it was not possible to simulate Ftire on a drum. The second part contains simulations for rough road conditions. The obstacles which are used are a cleat, oblique cleat, pothole and belgian blocks. The simulations show that MF-Swift has a higher vertical force compared to Ftire for all the simulations except for the cleat oblique manoeuvre. The trend for the longitudinal force is that Ftire always has a higher force. The differences were also shown by the damage sums. Those damage sums are evaluated for different x-positions of the cleat. MF-Swift has a very consistent behaviour, while Ftire sometimes shows a certain position dependent behaviour which originates from the discretisation of the tyre

Modeling of energy losses during cornering for electric city buses

Accurate energy consumption prediction is essential for optimal operation of battery electric buses. Conventional prediction algorithms do not consider energy losses during turning of the vehicle, which is especially relevant for city buses driving curvy routes. This paper presents a model describing steady-state cornering of such buses and analyses the additional energy consumption. The model includes multiple nonlinear effects, such as large steer angles, double rear wheels, and lateral load transfer. The resulting four nonlinear equilibrium equations are solved iteratively to obtain steady-state solutions. These reveal that both cornering resistance at the front wheels and tire scrub of the double rear wheels cause energy losses, varying as function of vehicle velocity and corner radius. Combination of the results with a recorded city trip of a battery electric bus reveals that these effects combined may account for 2.3% of the driveline energy consumption

Energy analysis of the Von Schlippe tyre model with application to shimmy

Shimmy is an engineering example of self-excited vibrations. Much research on shimmy has considered the tyre as a positive feedback or negative damping to introduce instability of the entire system. In this context, we focus on the behaviour of the tyre under periodic excitations. The Von Schlippe tyre model is selected and the energy flow method is applied to illustrate the energy transfer by the tyre during shimmy. The energy flow method evaluates the tyre performance with a prescribed sinusoidal motion and provides a novel evaluation method for tyre models. With the help of straight contact line assumption in the Von Schlippe tyre model, the relative motion between the contact line and the wheel centre is studied to understand the path dependency of the energy transfer. It turns out that the tyre is extracting energy from the forward motion to induce unstable lateral and yaw vibrations when the motion or orientation of the contact line has a phase lead with respect to the wheel centre

Battery electric vehicle energy consumption prediction for a trip based on route information

\u3cp\u3eDrivers of battery electric vehicles (BEVs) require an accurate and reliable energy consumption prediction along a chosen route to reduce range anxiety. The energy consumption for a future trip depends on a number of factors such as driving behavior, road topography information, weather conditions and traffic situation. This paper discusses an algorithm to predict the energy consumption for a future trip considering these influencing factors. The route information is obtained from OpenStreetMap and Shuttle Radar Topography Mission. The algorithm consists of an offline algorithm and an online algorithm. The offline algorithm is designed to provide information for the driver to make future driving plans, which provides a nominal energy consumption value and an energy consumption range before a trip begins. The online algorithm is designed to adjust the energy consumption prediction result based on current driving, which includes a vehicle parameter estimation algorithm and a driving behavior correction algorithm. The energy consumption prediction algorithm is verified by 30 driving tests, including city, rural, highway and hilly driving. A comparison shows that the measured energy consumption of all trips is within the energy consumption range provided by the offline algorithm and most of the differences between the measurement and nominal prediction are smaller than 10%. The offline prediction is used as a starting point and is corrected by the online algorithm during driving. The mean absolute percentage error between the measured energy consumption value and online prediction result of all trips is within 5%.\u3c/p\u3

In vehicle truck steering-system modeling and validation

In this paper a multi-body 44-DOF tractor semi-trailer model is coupled to a 4-DOF steeringsystem\u3cbr/\u3ewhich includes friction and hydraulic power-steering. An extended wheel hub geometry is used to provide the correct feedback torque from the wheels. A tie-rod with stiffness has been included to connect left and right. An instrumented tractor semi-trailer is used to verify the steering-system model predictions during driving. The focus lies on the prediction of the steering-wheel torque and the vehicle velocity and steering-wheel angle are prescribed as an input for the simulation. Two tests are discussed in this paper, a J-turn at 80 km/h and sinusoidal steering-wheel input with a frequency of 0.4 Hz at 65 km/h. The comparison of the measured signals and the predicted values shows that the steering-system model is accurate. The non-linearities caused by friction and hydraulic assistance system can clearly be seen in both the measurement and the simulation