Data-Driven Feature Design Framework for Extreme Early Cycle Life Prediction for Lithium-Ion Batteries
Authors: Rhyu Jinwook , Schaeffer Joachim , Cui Xiao , Bazant Martin Z. , Chueh William C. , Braatz Richard D. 
Published: December 2024

Numerous publications describe the construction of data-driven models for cycle life prediction for lithium-ion batteries. The models with the highest predictive capability have been constructed using user-defined features, which are transformations of the raw input data, before applying feature selection and regression. The performance and interpretability of the results depend on the design of the features, especially when data are limited. For example, Severson et al. [1] designed the Var(ΔQ
100-10) feature, which was very successful for cycle life prediction for lithium-ion phosphate batteries. A linear regression model constructed from only that one nonlinear feature, computed from the raw voltage-capacity data collected during discharge, was able to predict cycle life from the first 100 cycles. The same feature was able to classify cells into short and long-lived using only the first 5 cycles. A variation of that feature, the ΔV feature, was later shown to apply to other battery chemistries (e.g., [2]), although not with the same degree of accuracy.

Following this work, there was much research focused on using various transformations of electrochemical and thermal input data for performing data-driven lithium-ion battery lifetime prognostics. These works show the interplay between mechanistic understanding and data-driven approach: mechanistic understanding guides the design of promising features, which results in more accurate and interpretable data-driven models.

However, it is challenging to design effective features for applications where less physics is known, such as performing lifetime prognostics right after the formation step of the lithium-ion battery (i.e., extreme early cycle life estimation) [3]. Performing extreme early cycle life estimation is important to (1) pre-screen battery cells with poor quality SEI layers before the product is shipped to the customer, (2) speed up optimization of the formation protocol without the need for extensive tests of aging cycles, and (3) reveal uninvestigated physics of formation. To achieve these goals, we have developed a systematic data-driven framework that guides the design of such promising features without requiring in-depth physics-based models and understanding.

In this presentation, a data-driven feature design framework is described which is composed of (1) downselection of input data segments from fused LASSO regression coefficients [4], (2) design of feature candidates based on learned coefficients and similar applications, and (3) evaluation of each feature candidate based on the predictive performance and the shape of the coefficients [4]. This framework is applied to the NMC532-graphite dataset with 62 different formation protocols and identical aging protocols which captures the sole impact of formation protocol on the cycle life. The data-driven model using the features designed from our framework is compared with the agnostic model using formation protocol parameters and an automated machine learning (aka autoML) model using the features generated from the ‘tsfresh’ package.

References

[1] K. A. Severson, P. M. Attia, N. Jin, N. Perkins, B. Jiang, Z. Yang, M. H. Chen, M. Aykol, P. K. Herring, D. Fraggedakis, M. Z. Bazant, S. J. Harris, W. C. Chueh, R. D. Braatz. "Data-driven prediction of battery cycle life before capacity degradation." Nature Energy 4.5 (2019): 383-391.

[2] B. Jiang, W. E. Gent, F. Mohr, S. Das, M. D. Berliner, M. Forsuelo, H. Zhao, P. M. Attia, A. Grover, P. K. Herring, M. Z. Bazant, S. J. Harris, S. Ermon, W. C. Chueh, and R. D. Braatz. "Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols." Joule 5.12 (2021): 3187-3203.

[3] A. Weng, E. Olide, I. Kovalchuk, J. B. Siegel, A. Stefanopoulou. "Modeling battery formation: Boosted SEI growth, multi-species reactions, and irreversible expansion." Journal of The Electrochemical Society 170.9 (2023): 090523.

[4] J. Schaeffer, E. Lenz, W. C. Chueh, M. Z. Bazant, R. Findeisen, R. D. Braatz. "Interpretation of high-dimensional linear regression: Effects of nullspace and regularization demonstrated on battery data." Computers & Chemical Engineering 180 (2024): 108471.