Comparative Study of Different Vehicle Models with Respect to Their Dynamic Behavior

Htike

Lin

et al. 2022

Sensors

Automated vehicles are expected to push towards the evolution of the mobility environment in the near future by increasing vehicle stability and decreasing commute time and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS), which can put their wide impact at risk, as well as their acceptance by the public. In this direction, this paper presents the application of motion planning in order to minimise motion sickness in automated vehicles. Thus, an optimal control problem is formulated through which we seek the optimum velocity profile for a predefined road path for multiple fixed journey time (JT) solutions. In this way, a Pareto Front will be generated for the conflicting objectives of MS and JT. Despite the importance of optimising both of these, the optimum velocity profile should be selected after taking into consideration additional objectives. Therefore, as the optimal control is focused on the MS minimisation, a sorting algorithm is applied to seek the optimum solution among the pareto alternatives of the fixed time solutions. The aim is that this solution will correspond to the best velocity profile that also ensures the optimum compromise between motion comfort, safety and driving behaviour, energy efficiency, journey time and riding confidence.

“…So, the maximum value of the suspension travel is usually selected as an index to assess the vehicle handling based on Equation ( 17 ):

where

refers to four vehicle suspension systems, i.e., front right (

) and left (

), rear right (

) and left (

axle, as shown below, is used as a metric of vehicle handling:

where J is the front ( F ) and rear ( R ) axle.…”

Section: Performance Metricsmentioning

confidence: 99%

“…where i = FR, FL, RR and RL refers to four vehicle suspension systems, i.e., front right (FR) and left (FL), rear right (RR) and left (RL), respectively. The detailed equations for the suspension travel for various vehicle models can be found in Papaioannou et al [31].…”

Section: Vehicle Handlingmentioning

confidence: 99%

Multi-Criteria Evaluation for Sorting Motion Planner Alternatives

Htike

Lin

et al. 2022

Sensors

“…The half car model, shown in Figure 1, is selected for optimising the suspension systems, as it is the most efficient [9]. The model includes the front and rear axles of the vehicle allowing both pitch and bounce phenomena to be observed, but lateral motions are not considered.…”

Section: A Vehicle Modelingmentioning

confidence: 99%

Assessment of optimal passive suspensions regarding motion sickness mitigation in different road profiles and sitting conditions

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

Jerrelind

Drugge

et al. 2021

Automated vehicles (AVs) are expected to lead the evolution of mobility. Motion sickness, known as car sickness, is one of the main issues AVs will face, and could jeopardise their wide impact. However, a limited work has been done on how the optimisation of suspension dynamics could contribute. In this direction, this paper explores the mitigation of car sickness and the improvement of ride comfort through the optimisation of passive suspension systems. More specifically, a half car model, which represents a passenger vehicle from IPG/CarMaker, is used to optimise front suspension system for minimising comfort, but also maintaining vehicle handling while the vehicle is driving over two different road classes. The evaluation of comfort is conducted using the common standardised metric suggested by ISO-2631. After having obtained the optimum design solutions, the optimal solutions are simulated using IPG/CarMaker by assigning the road profiles on a 23 km long countryside road path. Then, vehicle accelerations are transferred to the occupant's head using appropriate models from the literature for both back-on and back-off sitting conditions. Afterwards, car sickness and ride comfort are further assessed to explore in detail how the tuning of the suspension systems through optimisation has minimised the first and enhanced the latter. For the assessment of car sickness, a three dimensional detailed biomechanical human model is used. The results imply that the pitch velocity seems more suitable, as a cost function for optimising the suspension systems with regards to motion sickness mitigation. Therefore, it should be considered either on its own or in combination with metric suggested by ISO-2631.

“…A quarter car model is used in this work (Figure 1), following Papaioannou et al 35 suggestions regarding vehicle models suitability in investigating semi-active suspensions. The body of the vehicle is modeled as a rigid body, equal to one quarter of the total mass of the vehicle (m s = 260 kg).…”

Section: Vehicle Modelmentioning

confidence: 99%

Skyhook control strategy for vehicle suspensions based on the distribution of the operational conditions

Proceedings of the Institution of Mechanical Engineers, Part D:

Koulocheris

Velenis

2021

In this work, a novel distribution-based control strategy of semi-active vehicle suspensions is tested under different conditions. The novelty lies in the use of an appropriate threshold in the operational condition of the control algorithm, with which the operational conditions severity is quantified and the state of the damper is controlled according to the magnitude of the operational conditions and not their sign. The value of the threshold depends on the vibrations induced to the sprung mass by the road profile. In order to be evaluated, the operational conditions of the algorithm are fitted to a t-student distribution. The cumulative distribution function of this distribution is used in order to decrease the fraction of the sample operating with the damper’s stiff state. The strategy is applied to traditional SH control algorithms and is tested using a quarter car model excited by different road excitations. A sensitivity analysis for various threshold values is performed, investigating the impact of adopting the cumulative distribution functioned (CDF) controller to various performance metrics. The results illustrate an increase of up to 13% in the ride comfort of the passengers and increase of 6% in the road holding of the vehicle. Both are achieved by minimizing the switches of the damping ratio up to 80%.