Lithium-ion battery physics and statistics-based state of health model

JOURNAL OF POWER SOURCES

卷:501

Article

Crawford, Alasdair J.¹  Choi, Daiwon¹  Balducci, Patrick J.²  Subramanian, Venkat R.³  Viswanathan, Vilayanur V.¹ 

[1] Pacific Northwest Natl Lab, Div Energy & Environm, POB 999, Richland, WA 99352 USA

[2] Argonne Natl Lab, Div Energy Syst, Lemont, IL 60439 USA

[3] Univ Texas Austin, Texas Mat Inst, Walker Dept Mech Engn & Mat Sci Engn, Austin, TX 78712 USA

关键词: Battery;    Health;    Degradation;    Solid electrolyte interphase;    Diffusion;    Dissolution;   

DOI  :  10.1016/j.jpowsour.2021.230032

来源: Elsevier

【 摘 要 】

A pseudo-2d model using COMSOL Multiphysics (R) software simulates performance degradation of Li-ion batteries when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown, cathode dissolution and its effect on SEI formation. The model is validated by simulating commercial cell performance. We develop a global model simulating performance across all chemistries, along with a model treating chemistries individually. There is good agreement between these two models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed, which fits data well. Good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. A top-down, statistics-based model using current, voltage, and anode expansion rate as degradation predictors is developed using insights from the physics-based model. This model predicts degradation for multiple grid services and electric vehicle drive cycles with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from computationally intensive physics-based algorithms.

【 授权许可】

Free