A large-scale battery system consists of hundreds even thousands battery cells, which have different characteristics even when they are new, and change with time and operating conditions due to aging, operational conditions, and chemical property variations. SOC (state of charge), battery health, remaining life, charge and discharge resistance and capacitance demonstrate nonlinear and time-varying dynamics. Consequently, for enhanced battery management, system diagnosis, and optimal power efficiency, it is necessary to capture battery cell models in real time. This research aims to develop a real-time automated battery characterizer that will capture individualized characteristics of each battery cell and produce updated models in real time. The core of such a system is advanced system identification techniques that provide fast tracking capability to update each battery cell’s individual model when it is plugged into the battery system and to track battery aging and health conditions during operation.