An Accurate and Computationally Efficient Method for Battery Capacity Fade Modeling
The industry demand for accurate and fast algorithms that model vital battery parameters, e.g., state-of-health, state-of-charge, pulse-power capability, is substantial. One of the most critical models is battery capacity fade. The key challenge with physics-based battery capacity fade modeling is the high numerical cost in solving complex models. In this study, an efficient and fast model is presented to capture capacity fade in lithium-ion batteries. Here, the high-order Chebyshev spectral method is employed to address the associated complexity with physics-based capacity fade models. Its many advantages, such as low computational memory, high accuracy, exponential convergence, and ease of implementation, allow us to efficiently model a comprehensive array of degradation physics such as solid electrolyte interface film formation, hydrogen evolution, manganese deposition, salt decomposition, manganese dissolution, and electrolyte oxidation. In this work, we developed a modeling framework that accurately and efficiently predicted degradation in a lithium-ion battery over extended cycles. For example, in long cycle battery operation, the implemented Chebyshev spectral method algorithm was found to be within 0.1358% - 0.28% of a high-fidelity model, while simulation times were reduced by an average of 91%. The developed Chebyshev spectral method algorithm shows great potential in advanced battery management systems, where maintaining accuracy and achieving a fast response is critical.
D. M. Ajiboye et al., "An Accurate and Computationally Efficient Method for Battery Capacity Fade Modeling," Chemical Engineering Journal, vol. 432, article no. 134342, Elsevier, Mar 2022.
The definitive version is available at https://doi.org/10.1016/j.cej.2021.134342
Electrical and Computer Engineering
Mechanical and Aerospace Engineering
Keywords and Phrases
Battery capacity fade modeling; Chebyshev spectral method
International Standard Serial Number (ISSN)
Article - Journal
© 2022 Elsevier, All rights reserved.
15 Mar 2022
The authors gratefully acknowledge financial support from the National Science Foundation (Award Nos. 1610396 and 1917055).