Igo Igo Besselink

Application of nonlinear tyre models to analyse shimmy

This paper focuses on the application of different tyre models to analyse the shimmy phenomenon. Tyre models with the Magic Formula and a non-constant relaxation length are introduced. The energy flow method is applied to compare these tyre models. A trailing wheel suspension is used to analyse shimmy stability and to evaluate the differences between tyre models. Linearisation and nonlinear techniques, including bifurcation analysis, are applied to analyse this system. Extending the suspension model with lateral flexibility and structural damping reveals more information on shimmy stability. Although the nonlinear tyre models do not change the stability of equilibria, they determine the magnitude of the oscillation. It is concluded that the non-constant relaxation length should be included in the shimmy analysis for more accurate results at large amplitude.

Controlling active cabin suspensions in commercial vehicles

The field of automotive suspensions is changing. Semi-active and active suspensions are starting to become viable options for vehicle designers. Suspension design for commercial vehicles is especially interesting given its potential. An active cabin suspension for a heavy-duty truck is considered, consisting of four ideal actuators with parallel springs, one acting on each corner of the cabin. The main question is how to control this suspension such that it gives optimal comfort when driving in a straight line, but still follows a specified compensation strategy when cornering, braking or accelerating. The proposed controller uses modal input-output-decoupling. Each of the modes has a separate controller including: a skyhook part for enhanced comfort; and an event part for attitude control. The proposed control strategy is tested in simulation using a validated tractor semi-trailer model with idealized actuators. It is shown that driver comfort can be greatly enhanced, without impairing the attitude behavior of the cabin. Furthermore, in contrast to what is known from quarter car analysis, it is shown that adding passive damping is highly desirable.

Validation of Longer and Heavier Vehicle Combination Simulation Models

This paper discusses the development and subsequent validation process of generic multi-body models for commercial vehicle combinations. The model is intended for performance assessment and improving of current and future combinations for the European road network. A second goal is to employ the model for the development of driver support systems and active steering strategies for both low speed manoeuvrability and high speed stability. The model is developed in SimMechanics, which is part of the MATLAB/Simulink software. Due to its modularity, one can quickly modify the model to the desired configuration and dimensions; therefore various multi articulation vehicle models can be created. The paper further illustrates the simplified and generic modelling methods used to build particular components such as chassis, tyres or suspension in the multibody domain. A stepwise approach of the model validation is presented, followed by a sensitivity analysis illustrating the effect of the selected vehicle parameters on particular vehicle states. To conclude, the approach is applied to the validation of the simulation model using measurement data obtained from experimental testing of two different vehicle combinations.

Vehicle state estimator based regenerative braking implementation on an electric vehicle to improve lateral vehicle stability

The driving range of electric vehicles can be extended using regenerative braking. Regenerative braking uses the electric drive system, and therefore only the driven wheels, for decelerating the vehicle. Braking on one axle affects the stability of the vehicle, especially for road conditions with reduced friction. This paper discusses three control strategies for preventing loss of stability while applying regenerative braking, two of which are using a state estimation algorithm developed by TNO. Experiments have been conducted with a front wheel driven vehicle on a low friction test track. The conclusions concerning the control concepts are however based on simulation results, due to unexpected system behaviour of the test vehicle. The results also indicate that the effectiveness of regenerative braking can be improved in cornering situations by using the vehicle yaw rate as a control signal. Due to hardware limitations, it has not been possible to rank the performance of the individual regenerative braking controllers in practise. It is recommended to further study the control concepts using an improved hardware setup.

Indirect drive in-wheel system for HEV/EV traction

In-wheel traction allows simplicity and freedom for the design of hybrid-electric/electric vehicle (HEV/EV). However, the in-wheel motor increases the unsprung mass and hence deteriorates ride comfort and reduces the road holding capability of the car. To solve these problems, this paper proposes an indirect drive in-wheel traction system including a light-weight in-wheel drive and a specially designed rear suspension. A prototype of the in-wheel drive is designed based on the vehicle model of Volkswagen (VW) Lupo 3L. The prototype analysis shows that the proposed in-wheel system gives a balanced distribution of the vehicle weight and thus improves ride comfort. Design principles of the electrical motor used for this in-wheel system is further investigated. By reducing the permanent magnet height, the field weakening capability of a 24-slot 8-pole surface permanent magnet synchronous motor is visibly improved.

On the achievable performance using variable geometry active secondary suspension systems in commercial vehicles

There is a need to further improve driver comfort in commercial vehicles. The variable geometry active suspension offers an interesting option to achieve this in an energy efficient way. However, the optimal control strategy and the overal performance potential remains unclear. The aim of this paper is to quantify the level of performance improvement that can theoretically be obtained by replacing a conventional air sprung cabin suspension design with a variable geometry active suspension. Furthermore, the difference between the use of a linear quadratic (LQ) optimal controller and a classic skyhook controller is investigated. Hereto, an elementary variable geometry actuator model and experimentally validated four degrees of freedom quarter truck model are adopted. The results show that the classic skyhook controller gives a relatively poor performance while a comfort increase of 17–28% can be obtained with the LQ optimal controller, depending on the chosen energy weighting. Furthermore, an additional 75% comfort increase and 77% energy cost reduction can be obtained, with respect to the fixed gain energy optimal controller, using condition-dependent control gains. So, it is concluded that the performance potential using condition-dependent controllers is huge, and that the use of the classic skyhook control strategy should, in general, be avoided when designing active secondary suspensions for commercial vehicles.

Evaluating the TU/e Lupo EL BEV performance

The TU/e has developed a battery electric vehicle (BEV) using a VW Lupo 3L as donor platform. The differences between the initial design calculations and actual vehicle performance are analysed. Battery charging and discharging efficiency, acceleration performance and top speed are as expected. The range at low, constant speeds is less than expected, due to a higher rolling resistance and lower power train efficiency at reduced power levels. The Lupo EL can nevertheless compete with electric vehicles offered by different car manufacturers today and has an attractive set of specifications. Both energy consumption and range appear to be quite good in comparison, due to the combination of low vehicle mass, good aerodynamic properties and large battery capacity. The donor vehicle, a VW Lupo 3L diesel, can be considered as one of the most fuel efficient vehicles being mass produced and is indicative for internal combustion cars of the future. The low fuel consumption has been confirmed by tests. In a direct comparison, the electric variant still has 30 to 50% lower CO2 emissions when using electricity from the grid in the Netherlands. These advantages disappear when including the CO2 emissions of battery production. Depending on the electricity price and driving conditions, the energy costs per kilometre are 25% to 70% lower compared to the Lupo 3L diesel.

Implementation and validation of a three degrees of freedom steering-system model in a full vehicle model

ABSTRACT This paper describes the coupling between a three degrees of freedom steering-system model and a multi-body truck model. The steering-system model includes the king-pin geometry to provide the correct feedback torque from the road to the steering-system. The steering-system model is combined with a validated tractor semi-trailer model. An instrumented tractor semi-trailer has been tested on a proving ground and the steering-wheel torque, pitman-arm angle, king-pin angles and drag-link force have been measured during steady-state cornering, a step steer input and a sinusoidal steering input. It is shown that the steering-system model is able to accurately predict the steering-wheel torque for all tests and the vehicle model is accurate for vehicle motions up to a frequency where the lateral acceleration gain is minimum. Even though the vehicle response is not accurate above this frequency, the steering-wheel torque is still represented accurately.

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

Drivers 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%.

Online Prediction of Battery Electric Vehicle Energy Consumption

The energy consumption of battery electric vehicles (BEVs) depends on a number of factors, such as vehicle characteristics, driving behavior, route information, traffic states and weather conditions. The variance of these factors and the correlation among each other make the energy consumption prediction of BEVs difficult. This paper presents an online algorithm to adjust the energy consumption prediction during driving. It includes a vehicle parameter estimation algorithm and a driving behavior correction algorithm. The vehicle parameter estimation algorithm can assess the vehicle mass and rolling resistance during driving. The driving behavior correction algorithm can adjust the energy consumption prediction based on the current driving behavior, and considers the influence of wind and road slope. The online energy consumption prediction algorithm is verified by 21 driving tests, including highway, city, rural and hilly area tests. The comparison shows that the mean absolute percentage error between the actual energy consumption value and online prediction result is within 5% for every test.