Semi-empirical aging model for predicting the capacity loss of lithium-ion batteries in stationary storage systems

Abstract

Predicting battery lifetime is essential for the safe, sustainable, and profitable operation of battery systems. Reducing the measurement and parameterization effort and increasing model accuracy and extrapolation capability are current challenges for efficient battery aging modeling. This thesis presents an innovative semi-empirical battery aging model and applies it to optimize the operation of a hybrid system providing frequency containment reserve. The system consists of a lithium-ion battery coupled with a power-to-heat module. The defined holistic model includes physically based calendar and cyclic model equations describing selected aging mechanisms at the graphite anode. A focus was set on improving the parameterization of the model equations to increase the model extrapolation capability. First, the influence of the characterization measurement was quantified and considered. Second, a multi-step parameterization method is introduced for the cyclic aging model based on identifying aging modes via incremental capacity analysis. The success of the targeted parameterization was approved, for example, in identifying various stress factor dependencies of individual aging mechanisms. The models applicability to frequency containment reserve operation is validated on a dynamically aged cell with an application-related power profile. The economic analysis shows that operating the system at medium or high target state of charge and small to medium system sizes achieves the highest net present values. Furthermore, the battery lifetime is confirmed as the most critical factor influencing the system revenues at high FCR prices, which underlines the importance of optimizing operating strategies considering battery aging.