DiffLiB: high-fidelity differentiable modeling of lithium-ion batteries and efficient gradient-based parameter identification

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 , 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 . Benchmarking against demonstrates excellent agreement in forward predictions, with terminal voltage discrepancies maintaining a root-mean-square error (RMSE) below $$2\mathrm ~mV$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mspace/> <mml:mi>m</mml:mi> <mml:mi>V</mml:mi> </mml:mrow> </mml:math> across all test conditions. In parameter identification tasks using experimentally measured voltage data, the gradient-based optimization with reasonable initialization achieves superior computational performance, requiring $$96\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>96</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> fewer forward predictions and $$72\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>72</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> less computational time than gradient-free approaches. These results demonstrate that is a versatile and powerful computational framework for the development of advanced LIBs.