Improving the convergence rate of Newman’s battery model using 2nd order finite element method

While battery cycling experiments last for years, battery modelling can save time and is environment friendly, to study the degradation mechanisms of lithium-ion batteries. However, battery models are being challenged by issues of long-time calculation, poor accuracy and bad convergence. In this work, a Julia-based framework (Jubat) has been developed for Newman's battery model. This framework benefits from the high execution efficiency of Julia language, so the mean computational time is lower than its counterpart PyBaMM written in Python. Governing equations are solved by the finite element method, and good accuracy is obtained. The convergence rate is improved by 2nd order elements, which capture better flux variation within elements than the finite volume method and the finite element method using linear elements, with slightly higher computation cost. Numerical examples show easy use of this framework and ability to solve practical problems like drive cycles. This work provides a compact, readable and fast framework for battery modelling and is useful for the rapid development of advanced battery management system. The code is open-source at https://github.com/weilongai/JuBat.