In order to study the state of health (SOH) of unbalanced battery packs in real life, a thorough analysis is carried out using only data available and standard charging material. The possible relationships between the different parameters and how they affect aging are studied, leading to the identification of five key parameters to indicate aging, as well as parameters influencing aging. Based on the measurement results, a simple black box model using evolutionary genetic algorithm is presented, which is used as end-of-life prediction model of the battery pack, successfully providing an approximate estimation of aging. This approach might thus be used for the supervision of battery systems during real-life use.