Advanced state of charge estimation for lithium-sulfur batteries.

Lithium-sulfur (Li-S) batteries have a high theoretical energy density, which could outperform classic Li-ion technology in weight, manufacturing costs, safety and environmental impact. The aim of this study is to extend the research around Li-S through practical applications, specifically to develop a Li-S battery state of charge (SoC) estimation in the environment of electrical vehicles. This thesis is written in paper based form and is organised into three main areas. Part I introduces general topic of vehicle electrification, the framework of the research project REVB, mechanisms of Li-S cells and techniques for SoC estimation. The major scientific contribution is given in Part II within three studies in paper-based form. In Paper 1, a simple and fast running equivalent circuit network discharge model for Li-S cells over different temperature levels is presented. Paper 2 uses the model as an observer for Kalman filter (KF) based SoC estimation, employing and comparing the extended Kalman filter, the unscented Kalman filter and the Particle filter. Generally, a robust Li-S cell SoC estimator could be realized for realistic scenarios. To improve the robustness of the SoC estimation with different current densities, in Paper 3 a fast running online parameter identification method is applied, which could be used to improve the battery model as well as the SoC estimation precision. In Part III, the results are discussed and future directions are given to improve the SoC estimation accuracy for a wider range of applications and conditions. The final conclusion of this work is that a robust Li-S cell SoC estimation can be achieved with Kalman filter types of algorithms. Amongst the approaches of this study, the online parameter identification approach could deliver the best results and also contains most potential for further improvement.