Mobility exists in a dynamic and constantly evolving environment. The shift towards electrified vehicles is accelerating, particularly within the passenger car and commercial vehicle segments. A significant driver of this change is the impending ban on new registrations of vehicles powered solely by combustion engines, set to take effect in Europe in 2035. However, partially and fully electrified vehicles remain highly price-sensitive due to the cost of traction batteries. The service life of these batteries diminishes over time and is largely dependent on the specific usage profile. Currently, it is not possible to determine key parameters of traction batteries, such as the state of health or service life, in an independent and standardized manner. This article presents a methodological approach for independently determining traction battery data. An online-based evaluation tool processes and analyzes batteries life date and historical data using an AI-based evaluation method to draw conclusions about battery longevity. Based on past and anticipated user profiles, this tool can forecast the primary service life of the battery in the dynamic environment of the vehicle, as well as its secondary service life in a static environment as an energy storage device.