TY - THES A1 - Qin, Jialiang T1 - Estimation of the state of charge (SoC) and/or state of health (SOH) in real driving cycles N2 - Electric vehicles (EVs) are gaining popularity as sustainable transportation solutions due to increasing environmental concerns and stringent regulations. Efcient battery management is a crucial aspect of EVs, which includes predicting the State of Charge (SoC) and State of Health (SoH) of the battery. This study aims to develop a data-driven model to accurately anticipate these parameters, promoting a proactive approach toward battery maintenance and replacement. Real-world driving data from a BMW i3 electric vehicle was used to examine variables such as speed, acceleration, temperature, and altitude. The data was used to fne-tune a model to forecast the SoC and battery life, which is unique in its potential for anticipatory vehicle maintenance. The model can gauge battery capacity for diferent routes, aiding the prediction of battery replacement requirements. The study consists of two stages. Data collection, preprocessing, and feature analysis are performed in the frst stage. The second stage involves developing a predictive model for battery status and analyzing the importance of diferent features. This study represents a signifcant step towards improving EV technology, specifcally battery manage ment systems. The results are expected to enhance the performance and lifespan of EV batteries, thus paving the way for a more sustainable and eco-friendly transportation ecosystem. The addressed problem, employed methodology, and signifcant fndings will provide valuable insights and tools for the broader EV industry. Y1 - 2023 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38775 CY - Ingolstadt ER - TY - THES A1 - Fischer, Thomas T1 - Erstellung eines Simulationstools zur Ermittlung des Energierückgewinnungsvermögens elektrischer Kleinstfahrzeuge Y1 - 2023 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39255 CY - Ingolstadt ER -