{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-computationally-informed-realisation","title":"A Computationally Informed Realisation Algorithm for Lithium-Ion Batteries Implemented with LiiBRA.jl","arxiv_id":"2203.17105","date":"2022-03-31","proceeding":null,"authors":["Brady Planden","Katie Lukow","Paul Henshall","Gordana Collier","Denise Morrey"],"abstract":"Real-time battery modelling advancements have quickly become a requirement 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 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 models. 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 sensitivity analysis on the resultant computational time is completed as well as experimental validation of a worldwide harmonised light vehicle test procedure (WLTP) for a LG Chem. M50 21700 parameterisation. A performance comparison to the conventional Matlab implemented discrete realisation algorithm (DRA) is completed showcasing a mean computational time improvement of 88%. Finally, an ARM based compilation is investigated for in-vehicle model generation and shows a modest performance reduction of 43% when compared to the x86 implementation while still generating accurate models within 5.5 seconds.","url_abs":"https://arxiv.org/abs/2203.17105v1","url_pdf":"https://arxiv.org/pdf/2203.17105v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-computationally-informed-realisation","repo_url":"https://github.com/bradyplanden/liibra.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}