05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Impedance based temperature estimation of lithium ion cells using artificial neural networks(2021) Ströbel, Marco; Pross-Brakhage, Julia; Kopp, Mike; Birke, Kai PeterItem Open Access Artificial feature extraction for estimating state-of-temperature in lithium-ion-cells using various long short-term memory architectures(2022) Kopp, Mike; Ströbel, Marco; Fill, Alexander; Pross-Brakhage, Julia; Birke, Kai PeterThe temperature in each cell of a battery system should be monitored to correctly track aging behavior and ensure safety requirements. To eliminate the need for additional hardware components, a software based prediction model is needed to track the temperature behavior. This study looks at machine learning algorithms that learn physical behavior of non-linear systems based on sample data. Here, it is shown how to improve the prediction accuracy using a new method called “artificial feature extraction” compared to classical time series approaches. We show its effectiveness on tracking the temperature behavior of a Li-ion cell with limited training data at one defined ambient temperature. A custom measuring system was created capable of tracking the cell temperature, by installing a temperature sensor into the cell wrap instead of attaching it to the cell housing. Additionally, a custom early stopping algorithm was developed to eliminate the need for further hyperparameters. This study manifests that artificially training sub models that extract features with high accuracy aids models in predicting more complex physical behavior. On average, the prediction accuracy has been improved by ΔTcell=0.01 °C for the training data and by ΔTcell=0.007 °C for the validation data compared to the base model. In the field of electrical energy storage systems, this could reduce costs, increase safety and improve knowledge about the aging progress in an individual cell to sort out for second life applications.Item Open Access A novel long short-term memory approach for online state-of-health identification in lithium-ion battery cells(2024) Kopp, Mike; Fill, Alexander; Ströbel, Marco; Birke, Kai PeterRevolutionary and cost-effective state estimation techniques are crucial for advancing lithium-ion battery technology, especially in mobile applications. Accurate prediction of battery state-of-health (SoH) enhances state-of-charge estimation while providing valuable insights into performance, second-life utility, and safety. While recent machine learning developments show promise in SoH estimation, this paper addresses two challenges. First, many existing approaches depend on predefined charge/discharge cycles with constant current/constant voltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery’s lifespan in order to formulate predictions within the time series. Our novel hybrid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing current pulses filtered from authentic drive cycles. Our innovative solution employs a Long Short-Term Memory-based neural network for SoH prediction based on residual capacity, making it well suited for online electric vehicle applications. By overcoming these challenges, our hybrid approach emerges as a reliable alternative for precise SoH estimation in electric vehicle batteries, marking a significant advancement in machine learning-based SoH estimation.Item Open Access Artificial neural network architectures for state estimation in lithium-ion batteries(2025) Kopp, Mike; Birke, Kai Peter (Prof. Dr.-Ing.)This dissertation investigates the application of artificial neural networks for predicting the state of charge, state of health, and temperature of lithium-ion battery cells. The study evaluates several model architectures, including encoder-based models, informer-based models, and transformer-based approaches (collectively referred to as attention-based models), as well as long short-term memory networks. The evaluation considers key aspects such as model size, complexity, and reproducibility. For state of charge and temperature predictions, the training data consists of charge cycles and worldwide harmonized light-duty vehicle test procedure cycles. A novel training algorithm was developed and consistently applied across all models to ensure comparability. Encoder-based models are systematically analyzed, focusing on architectural features such as positional encodings, normalization layers, and input scaling strategies, as well as autoregressive methods like artificial feature extraction and artificial recurrence. Similarly, both stateful and stateless long short-term memory models are evaluated for their robustness and predictive power. Among the attention-based models, the encoder-only architecture, incorporating positional encodings, post-normalization layers, and zero-importance scaling for current data, achieved notable results. However, long short-term memory models, particularly in stateless configurations, consistently outperformed encoder-based models in state of charge predictions, with the best-performing long short-term memory model achieving a root mean square error as low as 0.744 percent. This demonstrates that long short-term memory networks are more robust and effective for time series forecasting in the context of state estimation for lithium-ion battery cells. The challenges of temperature prediction are highlighted by limitations in the quality of the training data, which significantly impacted model performance. While increasing the number of trainable parameters improved model accuracy to some extent, these improvements eventually plateaued, emphasizing that data quality plays a more critical role than model complexity. Long short-term memory models exhibited more consistent performance, whereas encoder models were more variable, underscoring the importance of high-quality data in real-world applications. For state of health predictions, a novel classification model was developed by filtering out current pulses during complex operations, such as drive cycles, which were then analyzed by the artificial neural network to estimate the state of health. Long short-term memory models once again demonstrated superior performance and stability compared to encoder-based models. The findings reveal that long short-term memory networks remain a highly effective approach for state estimation, outperforming attention-based models in both state of charge and state of health predictions. These results underscore the potential of artificial neural networks in battery management systems while identifying key factors, such as data quality and architectural decisions, that significantly influence model performance.