Student for Master's thesis in the field of battery development - Validation of a prediction model for the degradation of HV batteries taking into account the uncertainty from the capacity estimation

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Number: MER0003YAU Motivation Automotive technology is currently undergoing the greatest change in history. With your final work in the field of battery development, you can actively contribute to this transformation. Li-ion high-voltage batteries in electric vehicles are intended to ensure safe, reliable and as long as possible operation. The capacity of the high-voltage battery is an essential characteristic of electrified vehicles. It correlates with the vehicle range and is therefore customer-ready. Furthermore, there is a direct relationship between the remaining capacitance and the residual value of the vehicle. If the capacity falls below a limit value, a potential battery exchange will be accompanied within the warranty period with corresponding costs for the vehicle manufacturer. In order to fulfil the interests of the customer and the vehicle manufacturer, it is therefore essential to analyse the capacity of field vehicles in such a way that forecast models can be trained for a defined viewing period. In particular, it is necessary to take into account the blur associated with the capacity estimate and to take this into account in accordance with the forecast. The systematic processing of diagnostic data from field vehicles and transfer to a robust forecast model for predicting the capacity of high-voltage batteries creates an efficient early warning system. On the other hand, guarantee and culmination costs can be minimized and customer satisfaction maximized. Tasks/work packages: Analysis and evaluation of diagnostic data from testing and field Analysis of existing ageing models Literature research for modeling the blur based on methods for SOH (State of Health) estimation Development of an advanced method of quantifying uncertainty taking into account statistics and probabilistics Implementation of the SOH uncertainty into the aging model based on field data Validation using a PoC The activity can begin from April 2026. The final topics will be discussed with the university, you and us.

Study programme in the field of data science, vehicle and motor engineering, mechanical engineering or a comparable study programme Knowledge in the field of machine learning (cloud-based big data application), knowledge in the field of stochastics, interest in product development of HV batteries, expertise in the construction and functioning of a modern automobile are advantageous Very good knowledge of MS-Excel and PowerPoint, Matlab or Python or SQL skills required German, good knowledge in English in word and writing Commitment Ready for use Quick conception of technical relationships Independent, structured and methodological procedures Communication Driver's license class B Additional information: The activity is limited to 6 months. We are looking forward to your online application with CV, lettering, certificates, current enrollment certificate with an indication of the semester and proof of the regular study period. Please do not forget to mark your documents as ''relevant for this application' in the online form and to observe the maximum file size of 5 MB. Further information on the setting criteria can be found here. Disabled and equalized applicants are welcome! The severely disabled representative (sbv-untertuerkheim@mercedes-benz.com) is happy to support you in the application process. HR Services will be happy to help you with questions about the application process. You can reach us by email via myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mo-Fr 10-12am & 13-15am). Questions about the job will be answered by Mr Göldenboth, marcel.goeldenboth@mercedes-benz.com.

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