Lithium ion batteries have become one of the most popular types of battery in consumer electronics as well as aerospace and automotive applications. The efficient use of Li-ion batteries in automotive applications requires well designed battery management systems. Low order Li-ion battery models that are fast and accurate are key to well- designed BMS. The control oriented low order physics based model developed previously cannot predict the temperature and predicts inaccurate voltage dynamics. This thesis focuses on two things: (1) the development of a thermal component to the isothermal model and (2) the development of a hybrid neural net and physics based battery model that corrects the output of the physics based model. A simple first law based thermal component to predict the temperature model is implemented. The thermal model offers a reasonable approximation of the temperature dynamics of the battery discharge over a wide operating range, for both a well-ventilated battery as well as an insulated battery. The model gives an accurate prediction of temperature at higher SOC, but the accuracy drops sharply at lower SOCs. This possibly is due to a local heat generation term that dominates heat generation at lower SOCs. A neural net based modeling approach is used to compensate for the lack of knowledge of material parameters of the battery cell in the existing physics based model. This model implements a neural net that corrects the voltage output of the model and adds a temperature prediction sub-network. Given the knowledge of the physics of the battery, sparse neural nets are used. Multiple types of standalone neural nets as well as hybrid neural net and physics based battery models are developed and tested to determine the appropriate configuration for optimal performance. The prediction of the neural nets in ventilated, insulated and stressed conditions was compared to the actual outputs of the batteries. The modeling approach presented here is able to accurately predict voltage output of the battery for multiple current profiles. The temperature prediction of the neural nets in the case of the ventilated batteries was harder to predict since the environment of the battery was not controlled. The temperature predictions in the insulated cases were quite accurate. The neural nets are trained, tested and validated using test data from a 4.4Ah Boston Power lithium ion battery cell. / text

Diese Arbeit befasst sich mit der Implementierung eines Energiespeichersystems in ein dieselelektrisches Schienenfahrzeug. Ziel der Arbeit ist es mit einem automatisierten Ansatz die besten Parameter für das Energiespeichersystem zu finden. Um die geeignetsten Parameter zu bestimmen, wurde eine Optimierung basierend auf genetischen Algorithmen verwendet. Neben der Charakteristik des Energiespeichersystems wird auch dessen Betriebsstrategie untersucht und optimiert. Der Fahrstil genau wie die Leistung des Dieselmotors werden ebenfalls variiert, um die Ergebnisse der Optimierung mit und ohne Energiespeichersystem zu vergleichen. / This work deals with an implementation of an energy storage system in a diesel electric driven rail vehicle. The aim of this work is an automatic approach to find the best parameters for an energy storage system. To find the best fitting parameters an optimization based on genetic algorithms is used. Beside the characteristics of the energy storage system as well the strategy to operate it is investigated and optimized. The driving style as well as the power of the internal combustion engine are varied in order to compare the solution of the optimization with and without energy storage systems.