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 Hybrid modeling of lithium-ion battery : physics-informed neural network for battery state estimation(2023) Singh, Soumya; Ebongue, Yvonne Eboumbou; Rezaei, Shahed; Birke, Kai PeterAccurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations.Item Open Access Sodium-based batteries : in search of the best compromise between sustainability and maximization of electric performance(2020) Karabelli, Duygu; Singh, Soumya; Kiemel, Steffen; Koller, Jan; Konarov, Aishuak; Stubhan, Frank; Miehe, Robert; Weeber, Max; Bakenov, Zhumabay; Birke, Kai PeterTill 2020 the predominant key success factors of battery development have been overwhelmingly energy density, power density, lifetime, safety, and costs per kWh. That is why there is a high expectation on energy storage systems such as lithium-air (Li-O2) and lithium-sulfur (Li-S) systems, especially for mobile applications. These systems have high theoretical specific energy densities compared to conventional Li-ion systems. If the challenges such as practical implementation, low energy efficiency, and cycle life are handled, these systems could provide an interesting energy source for EVs. However, various raw materials are increasingly under critical discussion. Though only 3 wt% of metallic lithium is present in a modern Li-ion cell, absolute high amounts of lithium demand will rise due to the fast-growing market for traction and stationary batteries. Moreover, many lithium sources are not available without compromising environmental aspects. Therefore, there is a growing focus on alternative technologies such as Na-ion and Zn-ion batteries. On a view of Na-ion batteries, especially the combination with carbons derived from food waste as negative electrodes may generate a promising overall cost structure, though energy densities are not as favorable as for Li-ion batteries. Within the scope of this work, the future potential of sodium-based batteries will be discussed in view of sustainability and abundance vs. maximization of electric performance. The major directions of cathode materials development are reviewed and the tendency towards designing high-performance systems is discussed. This paper provides an outlook on the potential of sodium-based batteries in the future battery market of mobile and stationary applications.Item Open Access Tackling xEV battery chemistry in view of raw material supply shortfalls(2020) Karabelli, Duygu; Kiemel, Steffen; Singh, Soumya; Koller, Jan; Ehrenberger, Simone; Miehe, Robert; Weeber, Max; Birke, Kai PeterThe growing number of Electric Vehicles poses a serious challenge at the end-of-life for battery manufacturers and recyclers. Manufacturers need access to strategic or critical materials for the production of a battery system. Recycling of end-of-life electric vehicle batteries may ensure a constant supply of critical materials, thereby closing the material cycle in the context of a circular economy. However, the resource-use per cell and thus its chemistry is constantly changing, due to supply disruption or sharply rising costs of certain raw materials along with higher performance expectations from electric vehicle-batteries. It is vital to further explore the nickel-rich cathodes, as they promise to overcome the resource and cost problems. With this study, we aim to analyze the expected development of dominant cell chemistries of Lithium-Ion Batteries until 2030, followed by an analysis of the raw materials availability. This is accomplished with the help of research studies and additional experts’ survey which defines the scenarios to estimate the battery chemistry evolution and the effect it has on a circular economy. In our results, we will discuss the annual demand for global e-mobility by 2030 and the impact of Nickel-Manganese-Cobalt based cathode chemistries on a sustainable economy. Estimations beyond 2030 are subject to high uncertainty due to the potential market penetration of innovative technologies that are currently under research (e.g. solid-state Lithium-Ion and/or sodium-based batteries).Item Open Access User-friendly, requirement-based assistance for production workforce using an asset administration shell design(2020) Al Assadi, Anwar; Fries, Christian; Fechter, Manuel; Maschler, Benjamin; Ewert, Daniel; Schnauffer, Hans-Georg; Zürn, Michael; Reichenbach, MatthiasFuture production methods like cyber physical production systems (CPPS), flexibly linked assembly structures and the matrix production are characterized by highly flexible and reconfigurable cyber physical work cells. This leads to frequent job changes and shifting work environments. The resulting complexity within production increases the risk of process failures and therefore requires longer job qualification times for workers, challenging the overall efficiency of production. During operation, cyber physical work cells generate data, which are specific to the individual process and worker. Based on the asset administration shell for Industry 4.0, this paper develops an administration shell for the production workforce, which contains personal data (e.g. qualification level, language skills, machine access, preferred display and interaction settings). Using worker and process specific data as well as personal data, allows supporting, training and instating workers according to their individual capabilities. This matching of machine requirements and worker skills serves to optimize the allocation of workers to workstations regarding the ergonomic workplace setup and the machine efficiency. This paper concludes with a user-friendly, intuitive design approach for a personalized machine user interface. The presented use-cases are developed and tested at the ARENA2036 (Active Research Environment for the Next Generation of Automobiles) research campus.