Augmented real-time lumped-parameter model for enhanced reliability in battery management systems
Accurate and computationally efficient battery management systems (BMSs) rely heavily on lumped-parameter models for real-time monitoring and control. However, these models often fail to maintain precision under rapidly fluctuating operating conditions due to limitations in conventional parameter id...
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| Vydáno v: | Journal of energy storage Ročník 132; s. 117717 |
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| Jazyk: | angličtina |
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Elsevier Ltd
15.10.2025
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| ISSN: | 2352-152X |
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| Abstract | Accurate and computationally efficient battery management systems (BMSs) rely heavily on lumped-parameter models for real-time monitoring and control. However, these models often fail to maintain precision under rapidly fluctuating operating conditions due to limitations in conventional parameter identification methods. This paper presents a new framework that augments a modified equivalent circuit model (ECM) with a lumped-parameter thermal representation of a cell (LPTM) and a reliable real-time parameter estimation algorithm. The core novelty of the framework is the new representation of the cell and the Modified Recursive Least Squares (ModRLS) algorithm, which addresses challenges of data saturation, parameter sensitivity, and initial condition uncertainty. Simulations demonstrate a significant improvement in parameter tracking accuracy, with root mean square errors as low as 3% not only for key electrical parameters but also thermal ones. The proposed framework minimises the reliance on extensive sensor networks, offering a cost-effective and scalable solution for dynamic applications such as electric vehicles. This work lays the foundation for more reliable and longer-lasting energy storage systems through advanced monitoring.
•Unified model integrates electrical & thermal dynamics for real-time estimation.•Modified RLS improves parameter tracking with errors below 3%.•Dynamic framework adapts to ageing & varying battery conditions.•Eliminates sensor dependence, reducing BMS costs and complexity.•Enhanced safety and efficiency for EV and grid applications. |
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| AbstractList | Accurate and computationally efficient battery management systems (BMSs) rely heavily on lumped-parameter models for real-time monitoring and control. However, these models often fail to maintain precision under rapidly fluctuating operating conditions due to limitations in conventional parameter identification methods. This paper presents a new framework that augments a modified equivalent circuit model (ECM) with a lumped-parameter thermal representation of a cell (LPTM) and a reliable real-time parameter estimation algorithm. The core novelty of the framework is the new representation of the cell and the Modified Recursive Least Squares (ModRLS) algorithm, which addresses challenges of data saturation, parameter sensitivity, and initial condition uncertainty. Simulations demonstrate a significant improvement in parameter tracking accuracy, with root mean square errors as low as 3% not only for key electrical parameters but also thermal ones. The proposed framework minimises the reliance on extensive sensor networks, offering a cost-effective and scalable solution for dynamic applications such as electric vehicles. This work lays the foundation for more reliable and longer-lasting energy storage systems through advanced monitoring.
•Unified model integrates electrical & thermal dynamics for real-time estimation.•Modified RLS improves parameter tracking with errors below 3%.•Dynamic framework adapts to ageing & varying battery conditions.•Eliminates sensor dependence, reducing BMS costs and complexity.•Enhanced safety and efficiency for EV and grid applications. |
| ArticleNumber | 117717 |
| Author | Niri, Mona Faraji Marco, James Babazadeh, Mehrdad |
| Author_xml | – sequence: 1 givenname: Mehrdad orcidid: 0000-0001-9929-4157 surname: Babazadeh fullname: Babazadeh, Mehrdad email: mehrdad.babazadeh@warwick.ac.uk – sequence: 2 givenname: James surname: Marco fullname: Marco, James – sequence: 3 givenname: Mona Faraji surname: Niri fullname: Niri, Mona Faraji |
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| Cites_doi | 10.1016/j.jpowsour.2016.03.042 10.1016/j.measurement.2020.108567 10.1049/iet-pel.2019.1589 10.1016/j.est.2024.114555 10.1016/j.est.2021.103362 10.1016/j.jpowsour.2014.01.097 10.1016/j.applthermaleng.2018.07.105 10.1109/TCST.2012.2217143 10.1109/ITEC-India48457.2019.ITECINDIA2019-108 10.1109/TCST.2018.2885681 10.1016/j.apenergy.2016.10.020 10.1016/j.est.2020.101758 10.1016/j.est.2022.105293 10.1016/j.apenergy.2017.07.030 10.1016/j.geits.2024.100207 10.1109/CoDIT62066.2024.10708587 10.1016/j.jpowsour.2024.235594 10.1016/j.ifacol.2020.12.1752 10.1155/2017/9543781 10.1016/j.enconman.2014.12.076 10.23919/PCMP.2023.000257 10.1186/s10033-023-00846-0 10.1016/j.electacta.2018.12.167 10.3390/en15031150 10.1016/j.electacta.2018.04.098 10.1016/j.pnsc.2018.11.002 |
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| Keywords | Lithium-ion battery Robustness Real-time model identification Lumped-parameter model Equivalent circuit model Thermal model |
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Phys. – volume: 15 start-page: 1150 year: 2022 ident: 10.1016/j.est.2025.117717_b32 article-title: Polarization voltage characterization of lithium-ion batteries based on a lumped diffusion model and joint parameter estimation algorithm publication-title: Energies doi: 10.3390/en15031150 – volume: 275 start-page: 50 year: 2018 ident: 10.1016/j.est.2025.117717_b8 article-title: A parameter estimation method for a simplified electrochemical model for li-ion batteries publication-title: Electrochim. Acta doi: 10.1016/j.electacta.2018.04.098 – volume: 28 start-page: 653 year: 2018 ident: 10.1016/j.est.2025.117717_b3 article-title: Temperature effect and thermal impact in lithium-ion batteries: A review publication-title: Prog. Nat. Sci.: Mater. Int. doi: 10.1016/j.pnsc.2018.11.002 |
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| SubjectTerms | Equivalent circuit model Lithium-ion battery Lumped-parameter model Real-time model identification Robustness Thermal model |
| Title | Augmented real-time lumped-parameter model for enhanced reliability in battery management systems |
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