Model simplifications and their impact on computational complexity for an electrochemistry-based battery modeling toolbox

Abstract

Using electrochemistry-based battery models in battery management systems remains challenging due to their computational complexity. In this paper, we study for the first time the impact of several types of model simplifications on the trade-off between model accuracy and computation time for the Doyle–Fuller–Newman (DFN) model. As a basis for comparison, we consider, to what we refer as, the complete DFN (CDFN) model, which is a DFN model without any simplifications, and includes the concentration-dependency of parameters that have been studied in previous literature. Furthermore, we propose a highly efficient implementation of the CDFN model that leads to a considerable decrease in computation time, and is developed into a freely downloadable toolbox. This toolbox allows the user to easily toggle between the studied simplifications to make the desired trade-off between model accuracy and computation time. We compare several simplified DFN models to the single-particle-model and the CDFN model. Here, we show that with the proposed implementation, and by selectively making the proposed simplifications, as well as selectively choosing the grid parameters, a model can be obtained that has a minor impact on model accuracy, achieving a simulation time of over 5000 times faster than real-time.