Lifetime modeling and model-based lifetime optimization of Li-ion batteries for use in electric two-wheelers

This thesis deals with the lifetime prediction of Li-ion batteries and the applicability of such models to optimize the operation and charging strategy. A model-based optimal control is therefore designed. The focus is on typical application scenarios for electric two-wheelers (E2W) in Shanghai.
A realistic driving behavior is first derived to reflect the typical use of an E2W. A data acquisition campaign is conducted to capture the local conditions in Shanghai. Based on survey and fleet data, a typical driving cycle for E2W is designed in Shanghai.
Models of the electric motor and power control in combination with vehicle-to-street characteristics are used to convert the recorded typical driving cycles to equivalent power profiles. The main focus is to investigate and model a Li-ion battery from a system perspective. Different model representations are compared regarding their applicability in simulation and optimization. An aging campaign is initiated to investigate the characteristics of a lithium manganese oxide (LMO) cell. Results for calendar and cycle aging are presented and compared to further cell chemistries. A single particle model as well as a simplified electrical equivalent circuit model are introduced and adapted to the cell results.
The driving cycle and system model are incorporated into a model-based optimization strategy. Since the greatest optimization potential was identified during charging and storage, a combined optimal charging and operating strategy is described. The validity of the results is proven by further cell tests and compared with simulation results, which show that an extension of the lifetime is achieved. A heuristic operating strategy is then derived, which can also be implemented online on the E2W control unit.