Thomas Soczka-Guth

TL;DR: In this paper, the influence of the operation conditions temperature and state of charge (SOC) on the performance of a commercial high-power lithium-ion cell is investigated by electrochemical impedance spectroscopy.

TL;DR: In this paper, two different equivalent circuit (EC) models are built up and parameterized for a commercial 6.5 Ah high-power lithium-ion cell and measured impedance spectroscopy data depending on temperature and state of charge (SOC) are used for parameter estimation.

TL;DR: In this article, a new data-driven approach for embedding diagnosis and prognostics of battery health in alternative power trains is proposed, where the support vector machine (SVM) as a well-known machine learning method is used.

TL;DR: In this paper, a dual filter consisting of an interaction of a standard Kalman filter and an Unscented Kalman Filter is proposed to predict internal battery states and a support vector machine (SVM) algorithm is implemented and coupled with the dual filter.

TL;DR: This work deduces two different parameter estimation methods to identify the SOH of battery resistance and investigates the feasibility of an application in HEVs.