A computationally efficient implementation of a full and reduced-order electrochemistry-based model for Li-ion batteries. (15th December 2017) - Xia, L.; Najafi, E.; Li, Z.; Bergveld, H.J.; Donkers, M.C.F.
Highlights: A computationally efficient implementation of the DFN model is proposed. Nonlinear model reduction is applied for the first time to the full nonlinear model. The implementation uses a particular numerical scheme based on Newton's method. This implementation is a step towards using the DFN model in real-time applications. Abstract: Lithium-ion batteries are commonly employed in various applications owing to high energy density and long service life. Lithium-ion battery models are used for analysing batteries and enabling power control in applications. The Doyle-Fuller-Newman (DFN) model is a popular electrochemistry-based lithium-ion battery model which represents solid-state and electrolyte diffusion dynamics and accurately predicts the current/voltage response using a set of nonlinear partial differential equations. However, implementation of the full DFN model requires significant computation time. This paper proposes a computationally efficient implementation of the full DFN battery model, which is convenient for real-time applications. The proposed implementation is based on applying model order reduction to a spatial and temporal discretisation of the governing model equations. For model order reduction, we apply proper orthogonal decomposition and discrete empirical interpolation method, which leads to a set of reduced order nonlinear algebraic equations. These equations are solved using a particular numerical scheme, based on a damped Newton's method. In a simulation study, the computational efficiency of the proposed implementation is shown and the resulting accuracy is presented.