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Senast inlagda
Nonlinear Model Identification for Thermal Control in BEV: A Data-Driven Approach Using Sparse Identification of Nonlinear Dynamics
(2025) Boracic, Asija
This thesis investigates the use of data-driven system identification method to support control development for the thermal management system of a battery electric vehicle. The identification process is carried out using the Sparse Identification of Nonlinear Dynamics (SINDy) method combined with sequential thresholding as an optimizer. The goal is to obtain a control model suitable to use for the development of a nonlinear model predictive controller(NMPC). Several models of different complexity and accuracy are identified from recorded data and evaluated offline. To assess their ability to reach a set-point, each model is tested in a single-run optimal control problem using a direct multiple shooting approach.
Algorithms for Wind-Powered Cargo Ship Routing
(2024) Leskinen, Björn; Käll, Gabriel
This thesis explores a novel approach to long-distance ship weather routing by employing a quadtree data structure to represent the ocean surface, combined with an approach to interpolating graph weights used by a modified Dijkstra’s Algorithm. This approach allows access to a wider range of relative wind angles compared to using a uniform grid.
This study evaluates the performance of the interpolation technique, specifically examining how quadtree subdivision levels (or bounding box size) relate to interpolation accuracy. The findings indicate that the interpolation method performs similarly over a varying range of bounding box sizes if one assumes a relatively high engine-driven calm water speed. The auxillary electric engine is used when wind speeds can’t propel the vessel above the calm water speed. In some cases, larger bounding boxes yield better results, however it is possible this was due to the weather forecast deviating from the actual historical data, or approximation errors in the graph weight interpolation. The study highlights the importance of selecting an appropriate default calm-water speed, as it influences the accuracy of the interpolation method.
The interpolation algorithm introduces runtime overhead to the path planning algorithm, especially when the size of the bounding box is large. Memory savings are significant, even though the quadtree was only subdivided in the local area around the path. The best trade-off between memory and runtime savings is achieved with a bounding box size of 222-111 km.
Future work should focus on refining default speed selection, incorporating additional weather data, and further optimizing the quadtree framework to improve efficiency and robustness in real-world maritime applications.
Adding a Composable Extension for Custom Instructions to the MicroBlaze-V core
(2024) Prasannanpillai Sreevilasam, Aravind; Suresh Velloli, Shailesh
This report presents the design and implementation of a Composable Extension (CX) for custom instructions in the MicroBlaze-V core, which is a customisable RISC-V core offered by AMD. The implementation is done on a Field Programmable Gate Array (FPGA) and the performance is evaluated with accelerators against the current MicroBlaze-V design.
Integration of a new CX interface allows the designer to add any number of custom instructions and accelerators according to the requirement. The accelerators can be either freshly designed or by using the existing Xilinx Intellectual Property (IP) cores with additional parameters. In this project, existing IP cores have been used as accelerators as this demonstrates how easy it is to integrate the IP cores with the interface design.
The accelerator functions were also programmed in software using C to compare and analyze the performance of the CX extension in MicroBlaze-V. Different metrics like speedup, resource utilisation and power consumption were considered to evaluate the efficiency of the entire system. A significant performance improvement has been observed with the accelerators at the expense of higher resource utilisation.
Implementation of a Trace Encoder for NOEL-V RISC-V Processors
(2024) Hessman, Max; Stenvik, Oskar
This thesis proposes a solution to the problem of overwhelming trace data production compared to available bandwidth in systems-on-chip in space applications. The proposed solution revolves around compressing the trace data through instruction branch trace, which assumes sequential execution and reduces the trace to instructions and events that result in a non-sequential instruction flow. Given full compilation of a program at the receiving location, the instruction flow of the traced program can be fully reconstructed.
The thesis compares two known trace standards (Nexus and E-trace), and concludes that E-trace is the preferred standard for the stated problem, due to its higher potential compression rate. The thesis then presents an implementation of the trace data encoder in VHDL. The implemented encoder is fed by instruction trace from Spike RISC-V simulator running a test suite, and the correct encoding of the trace is proven through correct reconstruction of the instruction flow by a third-party decoder. The resulting compression rate in a worst case scenario indicates that a 12 core system could be traced simultaneously in a 100 MHz dual-issue system, given a high-speed link bandwidth of 6.25 Gbps. The encoder is then successfully integrated into Frontgrade Gaisler RISC-V implementation NOEL-V. Through synthesization, the thesis shows that the encoder does not introduce a new lower limit on the attainable clock frequency of the processor. Furthermore, the synthesis shows that the encoder falls within a reasonable boundary of the total available hardware in the chosen FPGA. The thesis concludes that the proposed solution to the trace data problem in space applications is valid and realizable.
Quality Monitoring in AM Metal Through Efficient Streaming Machine Learning
(2024) Palm, Hampus; Sai Dinesh Uddagiri, Venkata
Additive Manufacturing (AM), popularly known as 3D printing, has become a transformative technology with wide-ranging applications in a number of industries such as medical, aerospace, and automotive. The increasing adoption of AM in these critical sectors necessitates a focus on ensuring the quality of printed components. An emerging method to estimate the quality of an object is to process data coming out of the printer during printing. This data can then help detect defects within the object. Most of the current research focuses on the ability and accuracy of finding defects but fails to take into account the crucial time aspect associated with processing the data coming from the printer. This thesis proposes a method of quality monitoring in AM through the integration of machine learning and low-latency datastream processing. Monitoring live sensor data from inside the printer (in-situ) has been advocated to minimize time for defect detection, yet its successful implementation relies on establishing a quantitative relationship between sensing data and the defects in an object. This proves challenging given the multitude of variables involved with this specific type of AM method.
This thesis utilizes Optical Tomography (OT) images captured as the object is being printed. The OT images are used together with spatial algorithms and machine learning models to identify defects in objects. The entire process is implemented in a streaming fashion, enabling low-latency monitoring and assessment. Importantly, the proposed approach supports not only the detection and position of defects but also the size of the defects, addressing both aspects that are critical for assessing the quality of AM components. The proposed methodology contributes to the advancement of in-situ quality control in AM, addressing the critical need for timely defect detection and ensuring the production of high-quality components. The output includes the location of defects, and the geometric attributes of porous areas across the current layer, considering the adjacent layers in the object during the manufacturing process.
The proposed method for finding defects has a sub-quadratic processing overheadin the size of the input. In this work it was tested with data of multiple objectsand images for 250 layers; with a recall of up to 95%, its running time for finding overlapping clusters in two adjacent layers is a few milliseconds, hence allowing significant margin for closed-loop control.