DiffLiB: High-fidelity differentiable modeling of lithium-ion batteries and efficient gradient-based parameter identification
Journal:
arXiv
Published Date:
Apr 29, 2025
Abstract
The physics-based Doyle-Fuller-Newman (DFN) model, widely adopted for its
precise electrochemical modeling, stands out among various simulation models of
lithium-ion batteries (LIBs). Although the DFN model is powerful in forward
predictive analysis, the inverse identification of its model parameters has
remained a long-standing challenge. The numerous unknown parameters associated
with the nonlinear, time-dependent, and multi-scale DFN model are extremely
difficult to be determined accurately and efficiently, hindering the practical
use of such battery simulation models in industrial applications. To tackle
this challenge, we introduce DiffLiB, a high-fidelity finite-element-based LIB
simulation framework, equipped with advanced differentiable programming
techniques so that efficient gradient-based inverse parameter identification is
enabled. Customized automatic differentiation rules are defined by identifying
the VJP (vector-Jacobian product) structure in the chain rule and implemented
using adjoint-based implicit differentiation methods. Four numerical examples,
including both 2D and 3D forward predictions and inverse parameter
identification, are presented to validate the accuracy and computational
efficiency of DiffLiB. Benchmarking against COMSOL demonstrates excellent
agreement in forward predictions, with terminal voltage discrepancies
maintaining a root-mean-square error (RMSE) below 2 mV across all test
conditions. In parameter identification tasks using experimentally measured
voltage data, the proposed gradient-based optimization scheme achieves superior
computational performance, with 96% fewer forward predictions and 72% less
computational time compared with gradient-free approaches. These results
demonstrate that DiffLiB is a versatile and powerful computational framework
for the development of advanced LIBs.
