@article{GregorBauderKirstenVidaldeOliveiraetal.2025, author = {Gregor, Markus and Bauder, Maximilian and Kirsten Vidal de Oliveira, Aline and Mast, Pascal and R{\"u}ther, Ricardo and Schweiger, Hans-Georg}, title = {Approaches for Lifetime Prediction of Vehicle Traction Battery Systems During a Technical Inspection: A Systematic Review}, volume = {17}, pages = {3}, journal = {World Electric Vehicle Journal}, number = {1}, publisher = {MDPI}, address = {Basel}, issn = {2032-6653}, doi = {https://doi.org/10.3390/wevj17010003}, year = {2025}, abstract = {Creating trust in society for new technologies, such as a new types of powertrains, and making them marketable requires transparent, neutral, and independent technical verification. This is crucial for the acceptance and success of electrified vehicles in the used car markets. A key component of electric vehicles is the traction battery, whose current and future condition, particularly regarding aging, determines its residual value and safe operation. This review aims to identify and evaluate methods for predicting the lifetime of onboard traction batteries, focusing on their applicability in technical inspections. A systematic literature and patent review was conducted using targeted keywords, yielding 22 patents and 633 publications. From these, 150 distinct lifetime prediction methods were extracted and categorized into a four-level mind map. These methods are summarized, cited, and structured in detailed tables. The relationships between approaches are explained to clarify the current research landscape. Long Short-Term Memory, Convolutional Neural Networks, and Particle Filters were identified as the most frequently used techniques. However, no methods were found suitable for predicting the lifetime of traction batteries during technical vehicle inspections, which operate under short test durations, limited data access, and diverse real-world operating conditions. Most studies focused on cell-level testing and did not address complete battery systems in operational vehicles. This gap highlights the need for applied research and the development of practical methods to support battery assessment in real-world conditions. Advancing this field is essential to foster confidence in battery systems and enable a sustainable transition to electromobility.}, language = {en} } @article{WalterDielRufinoJunioretal.2024, author = {Walter, Gero and Diel, Sergej and Rufino J{\´u}nior, Carlos Ant{\^o}nio and Schweiger, Hans-Georg and Valente de Bessa, Iury and Mallmann, Ma{\´i}ra and Kirsten Vidal de Oliveira, Aline and Odilio Dos Santos, Daniel and R{\"u}ther, Ricardo}, title = {Operating Behavior of a Photovoltaic-driven Electrolysis System}, volume = {15}, journal = {Revista Brasileira de Energia Solar}, number = {1}, publisher = {ABENS}, address = {S{\~a}o Paulo}, issn = {2526-2831}, doi = {https://doi.org/10.59627/rbens.2024v15i1.456}, pages = {82 -- 92}, year = {2024}, language = {en} }