An Integrated Framework for ARX Model Identification and Its Application to Lithium-Ion Battery

This article addresses the critical problem of real-time parameter estimation in dynamic systems using the autoregressive with exogenous input (ARX) model framework, with a particular focus on applications in lithium-ion batteries. The accurate and efficient parameter estimation is essential for opt...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 74; S. 1 - 14
Hauptverfasser: Chen, Mengting, Xie, Xiangpeng, He, Chaoyue, Ding, Jie, Xiao, Min
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Adaptation models
Batteries
Computational modeling
Dynamical systems
Equivalent circuits
Fractional-order derivative
gradient-based algorithm
Heuristic algorithms
Integrated circuit modeling
Kernel functions
Lithium
Lithium-ion batteries
Optimization
Parameter estimation
Parameter identification
Random errors
Real time
real-time identification
Real-time systems
Resistance
system identification
Technological innovation
ISSN:0018-9456, 1557-9662
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Zusammenfassung:This article addresses the critical problem of real-time parameter estimation in dynamic systems using the autoregressive with exogenous input (ARX) model framework, with a particular focus on applications in lithium-ion batteries. The accurate and efficient parameter estimation is essential for optimizing system performance in various contexts. However, traditional gradient-based methods often encounter limitations, such as poor adaptability and reduced reliability under dynamic conditions. To address these challenges, this study proposes a hybrid gradient-based approach that integrates fractional-order gradients and a convex combination mechanism. This approach enables a more broader exploration of the optimization objective. A kernel function is introduced to dynamically adjust the weights of multi-innovation variables, reducing the impact of random errors in parameter estimation. Furthermore, the proposed ARX-based identification framework is tailored to the second-order RC equivalent circuit model (ECM) for lithium-ion batteries. Experimental results suggest that the proposed identification framework possesses the potential to achieve a more satisfactory performance than traditional methods.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2025.3545698