Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods
Abstract
Abstract The pseudo-two-dimensional (P2D) electrochemical model can give insight into the internal behavior of lithium-ion batteries, which is of great significance for intelligent battery management. However, the computational complexity of the P2D model greatly limits its onboard application. This paper devotes to develop a reduced-order electrochemical model (ROEM) with high fidelity yet low computational cost. First, for the simplification of the electrolyte diffusion process in batteries, domain decomposition technique is applied to divide the whole cell sandwich into two computation domains. The polynomial approximation method is adopted to describe the electrolyte concentration distribution in each domain, so that the electrolyte diffusion process is represented by two independent state variables. Next, combining the simplified electrolyte diffusion process and other dynamics in lithium-ion batteries, the complete ROEM is obtained as a five-state diagonal system. Finally, the prediction accuracy of the ROEM on electrolyte concentration, electrolyte diffusion overpotential and terminal voltage is verified by comparing with the P2D model and experimental results. Moreover, the proposed ROEM has an ideal computing speed for real-time application, which is at least 600 times faster than the rigorous P2D model.
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Embedding electrochemical parameters or state equations into the models offers prior knowledge, enabling more precise predictions when data is scarce.
“For example, embedding electrochemical parameters or state equations[128] into the models offers prior knowledge, enabling more precise predictions when data is scarce.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [128]
Electrochemical models are often combined with other models, such as ECMs, to maintain a good level of detail without demanding excessive time and resources.
“Consequently, electrochemical models are often combined with other models to maintain a good level of detail and at the same time, not demand excessive amounts of time and resources; for instance, with ECMs [36].”
From Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement (2024) · cites this paper as [36]
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References 45 · Cited by 143
- Authors
- Li, C., Cui, N., Wang, C., & Zhang, C.
- Affiliations
- Shandong University (CN)
- Corresponding author
- Cui, Naxin
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 221
- Pages
- 119662
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W3125460992
- Semantic Scholar
- 8d3109a569de072bd5f0c2cb4d08ead8d926b7da
- Cited by
- 143
- References
- 45
- Citations by year
- 2026: 20 · 2025: 36 · 2024: 27 · 2023: 25 · 2022: 24 · 2021: 11
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Key Technology Research and Development Program of Shandong (2019JZZY020814, U1764258)
- National Natural Science Foundation of China (U1764258, U1864202, 61633015, U1864205)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- lithium-ion batteries, reduced-order electrochemical model, pseudo-two-dimensional model, domain decomposition, polynomial approximation, electrolyte diffusion, overpotential, terminal voltage prediction, battery management systems
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