Abstract: In battery circuit model parameter estimation, the bilinear transformation method combined with least squares has attracted significant attention owing to its superior performance. However, in digital energy storage systems centered around dynamic reconfigurable battery networks (DRBN), batteries exhibit coupling with power electronic switches, causing each battery's voltage and current responses to depend not only on its own state but also on the network topology, states of other batteries, and network output current. Conventional parameter identification methods solely employ individual battery terminal voltage and current information while neglecting network output current and topology variation data, making them unsuitable for DRBN battery parameter identification. To address this limitation, this paper develops a DRBN-compatible battery transfer function discretization method based on DRBN's current observation equation, establishes the corresponding battery circuit model parameter estimation model, and obtains its optimal solution by solving the model's augmented Lagrangian equation through the Newton-Raphson method. Experimental results and numerical simulations ultimately verify the proposed method's effectiveness.