Lithium-Ion Battery State of Charge Modelling based on Neural Networks

Lithium-ion (Li-ion) batteries have become a crucial factor in the recent electro-mobility trend. People's increased interest in electric vehicles (EVs) has motivated several automotive manufacturers and research organizations to develop suitable drivetrain designs involving batteries. Especially the development of the 48V Li-ion battery has been of great importance to reduce CO2 emissions and meet emission standards. However, accurately modeling Li-ion batteries is a difficult task since multiple factors have to be considered. Conservative Methods are using pyhsico-chemical models or electrical circuits in order to mimic the battery behavior. This thesis deals with developing a Li-ion battery model using artificial neural network (ANN) algorithms to predict the state of charge (SOC) as one of the key battery management system states. Due to the rising power of GPUs and the amount of available data, ANNs became popular in recent years. ANNs are also applicable to different areas of battery technology. Using battery data like the battery voltage, temperature, and current as input features, a neural network is trained that predicts battery SOC. A novel approach based on ANNs and one of the most commonly used SOC estimation methods are presented in this thesis to model the battery behavior. Furthermore, an approach for dealing with the highly unbalanced data by creating multidimensional bins and compare different neural network architectures for time series forecasting is introduced. By creating the model, our main priority is to reduce the model's errors in extreme operating areas of the battery. According to our results, long short-term memory (LSTM) architectures appear to be the best fit for this task. Finally, the developed ANN model can successfully learn battery behavior, however the model's accuracy under harsh operating circumstances is highly dependent on the data quality gathered