Philipp Dechent

TL;DR: In this article, a multivariable analysis of a detailed series of accelerated lifetime experiments representing typical operating conditions in a hybrid electric vehicle is presented, where the impact of temperature and state of charge on impedance rise and capacity loss is quantified.

TL;DR: In this article, the parameters to fully parameterize a physico-chemical model for a 7.5 Ah cell produced by Kokam are determined and compared with existing literature values.

TL;DR: The scope of this work is the development of a data-driven capacity estimation model for cells under real-world working conditions with recurrent neural networks having long short-term memory capability, which achieves a best-case mean absolute percentage error and is extremely robust while handling input noise.

TL;DR: In this article , the authors review prior work on knee degradation in lithium-ion battery aging trajectories and identify key design and usage sensitivities for knees, and discuss challenges and opportunities for knee modeling and prediction.

TL;DR: A deep learning-based battery health prognostics approach to predict the future degradation trajectory in one shot without iteration or feature extraction, which shows an increase in accuracy as well as in computing speed by up to 15 times.