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  • Post
    Brake load characterization of heavy-duty vehicles
    (2024) Achmad Munthahar, Sayid; Dhamane, Bhumika; Randby, David; Suresh, Nandu; Wurzinger, Jakob; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Sjöblom, Jonas; Srivastava, Suraj; Petersson, Martin
    In the fast-changing automotive world, more vehicles have zero tail-pipe emissions due to electrification. Global and local air quality has increased in recent times due to the implementation of legislation regarding tail-pipe emissions. Regulators are now looking at other sources of emissions, like particulate matter from brakes and tires, which have severe health effects. The particulate matter can enter the airways and if it is small enough, it can travel down to the lungs and even further into the bloodstream. The particle sizes can be divided into PM10, PM2.5, and Ultra-fine particles. Legislation for PM10 will be implemented regarding brake wear in the new Euro 7 legislation for light-duty vehicles in 2025 and something similar to this is expected for heavy-duty vehicles. The stakeholder for this project is Volvo Group, which has been involved during the whole project. This project mainly focuses on the emissions caused by disc brakes on Volvo’s Internal Combustion Engine (ICE) and Battery Electric Vehicle (BEV) heavy-duty vehicles. Field-test datasets from several ICE and BEV trucks, along with Volvo’s brake test rig data are used to estimate brake wear based on brake temperature and braking energy. Data analysis was performed on the dataset and brake wear estimation was done to characterize the braking behaviors of ICE and BEV trucks and determine which affects the brake wear the most. An in-depth analysis was performed by doing a multivariate analysis. The in-depth analysis resulted in the identification of four different clusters that were characterized by their unique driving behavior. From this exploration, it becomes evident that several factors influence brake wear, with temperature emerging as a prominent indicator linked to driving behavior.
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    Using machine learning to estimate road-user kinematics from video data
    (2024) Fang, Luhan; Malm, Oskar; Wu, Yahui; Xiao, Tianshuo; Zhao, Minxiang; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Sedarsky, David; Dozza, Marco; Rajendra Pai, Rahul; Rasch, Alexander
    Each year, there are over one million people who sustain fatal injuries in traffic-related crashes, with vulnerable road users, often abbreviated as VRUs, being involved in more than half of the crashes. In the context of road safety, VRUs are mainly pedestrians, cyclists, motorcyclists, and e-scooterists. To mitigate the crashes, it is essential to understand the causation mechanisms. Naturalistic data has been recognised as a good tool to understand the trafficant’s behaviour and address safety concerns within the field of traffic safety research. Traditionally, critical events are identified from naturalistic data using the kinematic information from the sensors onboard the vehicles. Video footage for the trip corresponding to the critical events is then used to validate and annotate the events. While this is a reliable method when it comes to identification of crashes, near-crashes may not display any anomalies in the sensor readings thereby going unidentified. These near-crash events would be visible in the video footage, but the manual identification of these by watching videos is not feasible because the amount of videos is too large for the human eyes. Therefore, developing tools that can identify and estimate the position of different road users using the video footage is essential and will enable automation of process of identifying critical events. This report describes such models and also delves into the application of machine learning to allow identify the severity of imminent critical interactions among road users in the future. This project investigates how models can be developed to estimate and predict the position and kinematics of various road users from video data from a camera mounted on an e-scooter. The initial generation of bounding boxes and categories for road users utilized You Only Look Once (YOLOv7) algorithms. The detection for cyclists was achieved by a simple rule-based model calculating the overlap area between the pedestrian and bicycle detected by YOLOv7. The e-scooterists detection model was implemented by combining YOLOv7 and MobileNetV2 models. Different machine learning models were trained to estimate distance for the four different road users: pedestrians, cyclists, e-scooterists, and cars separately using LiDAR and GPS data as the position ground truth. The input for these models was derived from bounding box data extracted from videos. Furthermore, a DBSCAN-based noise remover was used to remove the outlier point of the distance estimation model to filter out points with excessive errors. Finally, a Rauch-Tung-Striebel smoother was applied to the output of the noise remover to improve the distance estimation accuracy and generate both the relative position and velocity of the target road user. It was concluded that the object detection model could achieve an accuracy over 90%, and the distance estimation achieved the highest accuracy when using polar coordinates for all road users, compared with using the cartesian system. The highest R2 score for distance estimation was obtained with a k-nearest neighbors regression model (with n = 2) using the pixel position in x and y direction of center point of the bottom of bounding box, height, and width of the bounding box as input. As a consequence, the e-scooterist model achieved a R2 score of 0.978, while the cyclist and car models attained commendable scores of 0.92 and 0.96 respectively. This means that the distances predicted from the model are highly accurate. These models can now be used to detect critical interactions among road users using the naturalistic data collected using e-scooters.
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    Investigation of bus users’ motion comfort due to wind and bridge motion excitations
    (2024) Blakqori, Albijon; Hermansson, Harald; Kotur, Mille; Ramesh, Shreekara; Tj¨arnlund Lepp¨am¨aki, Joakim; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Sedarsky, David; Sekulic, Dragan; Jacobson, Bengt J H; Vdovin, Alexey
    This paper is based on a project in the 2023 ”Automotive Engineering Project” course atChalmers University of Technology. As a part of a large-scale project to reduce travel times,a floating bridge is planned to be built over Bjørnafjorden on the west coast of Norway. The planned floating bridge is prone to be sensitive to environmental factors (e.g. wind andwaves). Previous papers have studied the lateral stability of a bus riding over the bridge, and the Norwegian Public Roads Administration now sought an analysis of how riding over the bridge would affect bus passengers in terms of comfort and motion sickness. An existing 8 DOF bus model was extended to 13 DOF by adding a bus driver, and three passengers and also accounting for pitch motion. The model was simulated in Simulink. Accelerations from 8 axes for each bus occupant were retrieved and then weighted according to weighting filters from the ISO 2631-1. By using the mentioned ISO standard, bus users’ comfort could be assessed by comparison of bus occupants’ acceleration limits. Motion sickness was assessed numerically by the Motion Sickness Dose Value equation given in ISO 2631-1, however, it is a highly subjective illness. With the 1- and 2-year storm conditions and the bus model, ride comfort and motion sickness levels were assessed. According to the received results from the simulations, it could be seen that the key contributing factors to the ride comfort were vertical and lateral accelerations. It could also be concluded that the wind forces acting on the vehicle affected the ride comfort to a great extent. The seat position and the travelling speed were also big contributors to the ride comfort. Increasing speed and a seat position away from the bus’ centre of gravity had a negative impact on comfort. From a motion sickness perspective, the motions in the lower frequency range and accumulative travelling time were the main contributors. Travelling at a slower speed would negatively affect the Motion Sickness Dose Value, due to increased travelling time with motions at the low-frequency responses from the bus and bridge (e.g. wave load, current, wind excitations).
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    Method To Improve a Wheel Suspension Design Using VI-CarRealTime and Reinforcement Learning
    (2024) Denneler, Manuel; Heilig, Christoph; Bangalore Venkatesh Prasad , Vinayanand; Madhuravasal Narasimhan, Vivekanandan; Kolekar, Abhishek Amit; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Sedarsky, David; Boerboom, Max; Ekström, Kenneth; Huang, Yansong; Jacobson, Bengt
    The project focuses on the enhancement of wheel suspension design through the utilization of VI-CarRealTime and Reinforcement Learning techniques. The primary objective of the study is to improve vehicle dynamics and autonomous systems, thereby contributing to the advancement of automotive engineering. The development of vehicle suspension systems is a complex and iterative process, involving the adjustment of various parameters to meet quantitative and qualitative metrics. The report emphasizes the significance of simulating different suspension setups to achieve optimal design solutions. It highlights the essential collaboration between simulation engineers and design engineers to ensure the successful development of suspension systems. The project group aimed to use optimisation techniques and artificial intelligence to streamline the process of developing an optimal suspension in a time-saving manner. The use of the VI-CarRealTime simulation tool facilitated the analysis and synthesis loops in the suspension design development process and enabled the evaluation of kinematic properties and system requirements. Furthermore, this report deals with the application of machine learning theory, in particular with concepts of reinforcement learning. A comprehensive overview of reinforcement learning, its elements, workflows and classification is provided, highlighting its potential for suspension design optimisation. A detailed comparison of reinforcement learning with other optimisation methods is also presented, highlighting its benefits in the context of suspension development. The development and description of a MATLAB script for the project is presented, highlighting the technical aspects of implementing reinforcement learning techniques in the context of suspension design. This report concludes with a discussion of the potential impact of the research on the automotive industry, emphasising the importance of the results for the advancement of vehicle dynamics and automotive engineering as a whole. To summarise, the project represents a contribution to improving suspension design through the integration of VI-CarRealTime and reinforcement learning techniques. The findings and insights presented in this report have the potential to significantly impact the automotive industry by contributing to the development of more efficient and optimised vehicle suspension systems.
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    Phase-field modelling of fatigue crack propagation
    (2023) Degwekar, Sharvil; Purantagi, Ankeet Mohan; Tzanetou, Afroditi; Zetterlund, Gustav; Åkesson, Louise; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Johansson, Håkan; Larsson, Fredrik; Bharali, Ritukesh; Nezhad., Mohammad Salahi
    Fracture prediction and modeling are crucial in studying the behavior of materials under stress. This research focuses on utilizing the phase field method for accurate fracture prediction, which offers distinct advantages over traditional methods by representing fractures implicitly as smooth fields. The phase field method was implemented and analyzed using Matlab and COMSOL as tools, aiming to investigate and gain insights into the ease and feasibility of phase-field modelling for fatigue fracture problems. This was conducted mainly by introducing a fatigue degradation function, with the purpose of simulating the degrading process of the material after repeated cyclic loading. Through comprehensive analysis, quantities of interest such as the history variable, accumulated strain energy, and damage variable were examined. The obtained trends and results were found to align with existing literature, although neither calibration nor validation was conducted due to time limitations. Suggestions for future work include implementing a force-controlled load, calibration of the fatigue degradation function for a larger amount of load cycles, and validation with experimental data. Nevertheless, the results obtained from the fatigue implementation can provide a solid foundation for continued research. In conclusion, the progress during the project highlighted the potential of the phase-field model to predict fatigue fracture by modelling the crack through a damage field. Hence, fatigue prediction using phase-field modelling has the potential to make significant progress and thus contribute to less computationally expensive simulations for more complex fatigue fracture problems.