As an efficient energy storage technology, lithium-ion batteries play a key role in the ongoing electrification of the mobility sector. However, the required modelbased design process, including hardware in the loop solutions, demands precise battery models. In this work, an encoder-decoder model framework based on recurrent neural networks is developed and trained directly on unstructured battery data to replace time consuming characterisation tests and thus simplify the modelling process. A manifold pseudo-random bit stream dataset is used for model training and validation. A mean percentage error (MAPE) of 0.30% for the test dataset attests the proposed encoder-decoder model excellent generalisation capabilities. Instead of the recursive one-step prediction prevalent in the literature, the stage-wise trained encoder-decoder framework can instantaneously predict the battery voltage response for 2000 time steps and proves to be 120 times more time-efficient on the test dataset. Accuracy, generalisation capability and time efficiency of the developed battery model enable a potential online anomaly detection, power or range prediction. The fact that, apart from the initial voltage level, the battery model only relies on the current load as input and thus requires no estimated variables such as the state-of-charge (SOC) to predict the voltage response holds the potential of a battery ageing independent LIB modelling based on raw BMS signals. The intrinsically ageingindependent battery model is thus suitable to be used as a digital battery twin in virtual experiments to estimate the unknown battery SOH on purely BMS data basis.

<p dir="ltr">Detecting anomalies in spacecraft time-series data is a high priority, especially considering the harshness of the spacecraft operating environment. These anomalies often function as precursors for system failure. Traditionally, the time-series data channels are monitored manually by domain experts, which is time-consuming. Additionally, there are thousands of channels to monitor. Machine learning methods have proven to be useful for automatic anomaly detection, but a unique model must be trained from scratch for each time-series. This thesis proposes three approaches for reducing training costs. First, a transfer learning approach that finetunes a general pre-trained model to reduce training time and the number of unique models required for a given spacecraft. The second and third approaches both use online learning to reduce the amount of training data and time needed to identify anomalies. The second approach leverages an ensemble of extreme learning machines while the third approach uses deep learning models. All three approaches are shown to achieve reasonable anomaly detection performance with reduced training costs.</p><p dir="ltr">Measuring the phenotypes, or observable traits, of a plant enables plant scientists to understand the interaction between the growing environment and the genetic characteristics of a plant. Plant phenotyping is typically done manually, and often involves destructive sampling, making the entire process labor-intensive and difficult to replicate. In this thesis, we use image processing for characterizing two different disease progressions. Tar spot disease can be identified visually as it induces small black circular spots on the leaf surface. We propose using a Mask R-CNN to detect tar spots from RGB images of leaves, thus enabling rapid non-destructive phenotyping of afflicted plants. The second disease, bacteria-induced wilting, is measured using a visual assessment that is often subjective. We design several metrics that can be extracted from RGB images that can be used to generate consistent wilting measurements with a random forest. Both approaches ensure faster, replicable results, enabling accurate, high-throughput analysis to draw conclusions about effective disease treatments and plant breeds.</p>