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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Bibliographic Details
Published in:Journal of energy storage Vol. 132; p. 117717
Main Authors: Babazadeh, Mehrdad, Marco, James, Niri, Mona Faraji
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.10.2025
Subjects:
Equivalent circuit model
Lithium-ion battery
Lumped-parameter model
Real-time model identification
Robustness
Thermal model
Lithium-ion battery
Robustness
Real-time model identification
Lumped-parameter model
Equivalent circuit model
Thermal model
ISSN:2352-152X
Online Access:Get full text
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Summary: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.
ISSN:2352-152X
DOI:10.1016/j.est.2025.117717