Lohmann, N., Fischnaller, M., Melbert, J., Musch, T. et al., "Cycle Life Investigations on Different Li-Ion Cell Chemistries for PHEV Applications Based on Real Life Conditions," SAE Technical Paper 2012-01-0656, 2012, doi:10.4271/2012-01-0656.
Plug-In Hybrid Electric Vehicles (PHEV) are becoming increasingly important as an intermediate step on the roadmap to Battery Electric Vehicles (BEV). Li-Ion is the most important battery technology for future hybrid and electrical vehicles. Cycle life of batteries for automotive applications is a major concern of design and development on vehicles with electrified powertrain. Cell manufacturers present various cell chemistries based on Li-Ion technology. For choosing cells with the best cycle life performance appropriate test methods and criteria must be obtained. Cells must be stressed with accelerated aging methods, which correlate with real life conditions. There is always a conflict between high accelerating factors for fast results on the one hand and best accordance with reality on the other hand.Investigations are done on three different Li-Ion cell types which are applicable in the use of PHEVs. In order to obtain results independent of manufacturing tolerance, several cells of each type are used. Aging and characterization are done by using single cell testers. Each cell is stressed with a power profile which is derived from real life driving cycles. All cells are placed in a climate chamber and exposed to a temperature profile in order to simulate ambient conditions. The temperature profile results from typical ambient conditions in Central Europe.Capacity and internal impedance for charging and discharging are obtained periodically. Degeneration of both parameters is evaluated over cycle life. The results show a high dependence on cell chemistries in terms of capacity fade and increase of internal impedance.Three different indicators of aging are presented, which allow evaluating the state of health without additional characterization but can be derived directly from driving and charging data.