Predictive Active Cell Balancing for Li-Ion Batteries Using GRU-Based Voltage Estimation

One of the most important functions of a battery management system (BMS) is cell balancing. The limitations of active balancing systems arise from reactive control strategies that rely exclusively on instantaneous measurements of cell voltage or state of charge (SOC). Such strategies do not account for short-term voltage dynamics, which can lead to unnecessary energy transfers. This paper proposes a predictive cell balancing strategy based on cell voltage estimation, intended for active balancing systems, particularly those employing flyback converters. The proposed predictive model uses historical voltage and current measurements, as well as operating temperature information, to estimate the short-term evolution of the cell voltage. The model is trained using experimental datasets obtained from NCR18650B lithium-ion cells (Panasonic, Osaka, Japan) subjected to multiple current profiles and temperature conditions. The proposed strategy is implemented on the DC2100B-C module (Linear Technology, Milpitas, CA, USA), which employs the LTC3300-1 integrated circuit (Linear Technology, Milpitas, CA, USA), and is experimentally validated on a battery pack consisting of 12 NCR18650B cells connected in series. The experimental results demonstrate that the use of short-term voltage prediction improves the balancing process by reducing the voltage equalization time and the number of balancing command reconfigurations.