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.


Electrical and Computer Engineering

Second Department

Mechanical and Aerospace Engineering

Third Department



National Science Foundation, Grant 1610396

Keywords and Phrases

Battery capacity fade modeling; Chebyshev spectral method

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


File Type





© 2023 Elsevier, All rights reserved.

Publication Date

15 Mar 2022