A computationally informed realisation algorithm for lithium-ion batteries implemented with LiiBRA.jl

Real-time battery modelling advancements have quickly become required as the adoption of battery electric vehicles (BEVs) has rapidly increased. In this paper an open-source, improved discrete realisation algorithm, implemented in Julia for the creation and simulation of reduced-order, real-time capable physics-based models is presented. This work reduces the Doyle-Fuller-Newman electrochemical model into continuous-form transfer functions and introduces a computationally informed discrete realisation algorithm (CI-DRA) to generate the reduced-order representation. Further improvements in conventional offline model creation are obtained as well as achieving in-vehicle capable model creation for ARM-based computing architectures. Furthermore, a parametric sensitivity analysis of the presented architecture is completed as well as experimental validation of a worldwide harmonised light vehicle test procedure (WLTP) for an LG Chem. M50 21700 parameterisation. A performance comparison to a MATLAB implementation is completed showcasing a mean computational time improvement of 3.51 times for LiiBRA.jl on x86 hardware. Finally, an ARM-based implementation showcases full system model generation within three minutes for potential in-vehicle updates.

Automated summary

This paper presents an improved algorithm for real-time battery modelling, which can create and simulate reduced-order physics-based models in Julia and implement in-vehicle model creation on ARM-based computing architectures. The paper also includes parametric sensitivity analysis, experimental validation, and performance comparison with MATLAB implementation.