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- PostPropulsion development model for MiL/HiL/DiL purposes(2024) Lawrence, Abner Ankit; Artese, Gabriele; Gamaz Berral, Miguel; Hassan Ananda Kumar, Sanjana; Joy, Simran; Pai, Sumanth; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Vdovin, Alexey; Jacobson, Bengt; Gröndahl, AlbinVirtual simulations are increasingly critical for reducing vehicle development timelines, a prior- ity in the automotive industry. Driving simulators and vehicle simulation software are essential tools that enable manufacturers to test and validate vehicle subsystems and overall perfor- mance prior to constructing physical prototypes. In the case of electric vehicles, early virtual verification of subsystems is particularly important, given the modular nature of the driveline platform. Modular simulation models further enhance this process by allowing companies to de- velop custom models for specific subsystems, which can be exchanged with subsystem suppliers and vehicle manufacturers, facilitating the creation of virtual prototypes and streamlining the design workflow. This report details the implementation of a modular propulsion model specifically designed for electric vehicles, aimed at supporting feature verification across various simulation environments, including Model-in-the-Loop (MiL), Hardware-in-the-Loop (HiL), and Driver-in-the-Loop (DiL). The model’s primary goal is to accelerate development cycles, reduce costs, and improve the reliability and performance of electric vehicle drivetrains. Key features such as torque vectoring, slip control, and torque distribution are incorporated, making the model compatible with di- verse powertrain configurations, including rear-wheel drive (RWD) and all-wheel drive (AWD). Integration with CarMaker allows for realistic testing and validation of the model under various driving conditions. The results indicate that the model effectively simulates vehicle dynamics and control, respond- ing accurately to driver inputs, within its design domain. The model offers the opportunity to the customer to easily modify and calibrate each subsystem, which was the main objective of the project. However, some limitations and assumptions have to be considered, making future work on the model reasonable.
- PostVoltage vs. Horsepower: Comparing Heavy Vehicle Performance through Simulation(2024) Kamat, Ganapati Girish; Ahmadi, Hadi; Rajput, Maulik Rakesh; Adiga Nagaraj, Pavan Kumar; Yousefi, Wahid; Perfetti, Lorenzo; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Vdovin, Alexey; Jacobson, Bengt; Bruzelius, Fredrik; Emvin, Carl; Von Corswant, FredrikGreenhouse gas emissions from the transportation sector are a major global concern, with the sector being one of the largest contributors. Improving energy efficiency within transportation systems is critical to mitigating these emissions. One promising solution is the introduction of Long Combination Vehicles (LCVs), which enhance logistical efficiency by increasing load-carrying capacity. By reducing the number of vehicles required to transport the same amount of cargo, LCVs contribute to lower emissions and promote more sustainable transportation. This project investigates the energy consumption of a Battery Electric Vehicle (BEV) used for daily freight transport between Gothenburg Harbor and Viared. The LCV used for this operation is an A-double combination vehicle. The study employs a Forward Simulation modeling approach, accounting for environmental factors, driver behavior, and vehicle performance. It examines the energy cost and efficiency of this daily trip, as well as the vehicle’s long-term operation over the course of a year. The analysis also explores how many trips can be completed and the total energy required for this operation.
- PostMachine Learning-Based Optimization for Battery Pack Cooling(2024) Abukar, Abubakar; John, Pedro; Boudagh, Francisco; Verde, Salvatore; Wendel, Gabriel; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Vdovin, Alexey; Vdovin, Alexey; Vivek, Anthony; Koutsimanis, Dimitrios; Alatalo, ViktorThe thermal management of electric vehicle (EV) batteries is a critical factor in safety, performance and durability. Traditional design methods for cooling channels rely on manual adjustments, trial and error processes and intensive topology optimizations leading to time con suming approaches and significant limitations in efficiency. This study introduces an innovative framework that combines machine learning (ML) and computational fluid dynamics (CFD) to optimize cooling channel designs that circumvent the challenges faced by traditional methods. These improvements are achieved by using two different components: The first component is a surrogate model. It is a machine learning model trained on a large dataset produced through CFD simulations using Star-CCM+. This model significantly reduces computational costs and time by predicting pressure drops and temperature distributions based on input geometries and system parameters. The second component is a genetic algorithm for geometry optimization. This component generates an optimal geometry by balancing pressure drop and an effective thermal regulation by using a Non-Dominated Sorting Genetic Algorithm (NSGA-II). This algorithm creates a number of random solutions and iteratively improves on them to find a Pareto front, which represents the optimal trade-offs between competing objectives: minimizing pressure drop and maximizing thermal regulation. The role of the surrogate model is to provide instant feedback on potential solutions throughout the algorithm, which is vital as the algorithm itself is inherently time consuming. By combining these two components, the framework accelerates the design process ensuring the creation of advanced cooling solutions. The results demonstrate the potential to reduce computational time while achieving superior performance. This work provides a framework for future advancements in the thermal management of EV batteries, highlighting the importance of combining CFD with ML in modern engineering solutions.
- PostDrawbar Eye Identification and Guiding(2024) Garg, Mahin; Winbo, Andreas; Liu, Biying; Qui, Shiyi; Renberg, Felix; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Vdovin, Alexey; Von Corswant, Fredrik; Johansson, TobiasThe purpose of this research project is to study if a machine learning algorithm can utilize a video feed from a camera mounted on the back of a truck to detect a drawbar eye under varying environmental, instrumental and lightning conditions. In order to achieve this, lab based and real world testing data was collected from a setup which simulated connecting a truck and a trailer. With these setup cases, different video feeds were collected and later, images were extracted from this feed, which were labeled for the machine learning algorithm. In order to train the algorithm, YOLOv8n (You Only Look Once Version 8) was used, which is a real time object detection algorithm to identify objects in an image. This was used to classify the drawbar eye and its position in real time. In order to better guide the driver, distance between camera and drawbar eye was calculated using the pinhole camera principle, and here the angle of the camera was introduced. From the calculation, we had the horizontal distance (X), vertical distance (Y) and distance in depth (Z). Two additional tests were done in order to verify the accuracy of distance and height calculation, and take the distortions of a fisheye camera into account while calculating the said distances. The final detection accuracy (ability to detect position of drawbar eye with 50-95 percent accuracy of the labeled position) of the model came at around 75.4 percent.
- PostWheelset maintenance and monitoring: Current practices and strategies(2024) Cervenka, Jakub; Bergkvist, Herman; Wirdheim, Eskil; Chalmers tekniska högskola // Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Ekberg, Anders; Ekberg, AndersRailway wheelsets are a central component of railway vehicles. Maximizing the lifespan of wheelsets requires proper health monitoring and maintenance strategies. From a background in deterioration phenomena and the operational conditions of railway vehicles, this project gives an overview of maintenance and monitoring within the railway industry. This has been done through interviews with and visits to multiple industry actors. Practices within monitoring, maintenance, and possible business plans are highly variable, depending on operating conditions, involved actors, and the size and uniformity of the fleet. Potential benefits could be achieved through more cooperation and data sharing between actors, however this brings new challenges.