Synthetic Degradation Dataset of 12 LG M50 Batteries

Description

A synthetic degradation dataset of 12 LG M50 cells was generated using physics-based models. Battery models: Underlying battery states are simulated using a Doyle-Fuller-Newman (DFN) model, and four degradation mechanisms (i.e., solid electrolyte interphase growth, particle cracking, lithium plating, and stress-driven loss of active material) are coupled with the DFN model in Python Battery Mathematical Modeling (PyBaMM) library. Model parameters: The DFN model parameters (i.e., electrode parameters, electrolyte parameters, and separator parameters) are taken from Chen et al. [1] for a commercial NMC 811/graphite-SiOx cylindrical cell manufactured by LG Chem (INR21700 M50, 5 Ah). The parameters of the degradation models in PyBaMM are taken from multiple sources and can be found in the supplementary information of Ref. [2]. Three degradation parameters (i.e., cracking rate in Paris' law, decay rate for dead lithium formation, and loss of active anode material proportional term) were intentionally varied. Cycling protocols: The cells are charged with a 1C constant current-constant voltage (CC-CV) step to 4.2 V and a current cut-off of C/100 (50 mA) followed by a rest for 5 minutes. Subsequently, the cells are discharged at 1C to 2.5 V with a current cut-off of C/100 (50 mA) and then at rest for 5 minutes. The ambient temperature is set to be constant at 25°C. References [1] Chen CH, Planella FB, O’regan K, Gastol D, Widanage WD, Kendrick E. Development of experimental techniques for parameterization of multi-scale lithium-ion battery models. Journal of The Electrochemical Society. 2020 May 15;167(8):080534. [2] O'Kane SE, Ai W, Madabattula G, Alonso-Alvarez D, Timms R, Sulzer V, Edge JS, Wu B, Offer GJ, Marinescu M. Lithium-ion battery degradation: how to model it. Physical Chemistry Chemical Physics. 2022;24(13):7909-22.

Steps to reproduce

The main packages and versions used for generating this synthetic data are as follows: 1. python (3.9.13) 2. pybamm (22.9) 3. casadi (3.5.5) 4. scipy (1.9.1)

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