Bayesian Methods for Physics-Based Inverse Modeling of Battery Degradation: A Case Study on SEI Growth Mechanisms

Główne pojęcia

This research leverages Bayesian machine learning algorithms to analyze and parameterize physics-based models of battery degradation, specifically focusing on solid-electrolyte interphase (SEI) growth mechanisms, demonstrating the effectiveness of this approach in identifying dominant degradation processes and quantifying uncertainties.

Streszczenie

Bibliographic Information:

Philipp, M. C. J., Kuhn, Y., Latz, A., & Horstmann, B. (2024). Physics-based inverse modeling of battery degradation with Bayesian methods. arXiv preprint arXiv:2410.19478.

Research Objective:

This study aims to determine the dominant transport mechanisms responsible for continuous SEI growth in lithium-ion batteries and accurately parameterize corresponding physics-based degradation models using Bayesian machine learning methods.

Methodology:

The researchers employed a physics-informed battery model (Single Particle Model with electrolyte effects - SPMe) coupled with theoretical SEI growth mechanisms (electron diffusion, electron conduction, electron migration, and solvent diffusion). They utilized Bayesian optimization (EP-BOLFI) to inversely model synthetic and real storage data, quantifying parameter uncertainties and correlations. Additionally, they applied Bayesian model selection (BASQ) to identify the most probable SEI growth mechanism based on model evidence.

Key Findings:

• EP-BOLFI successfully parameterized the SEI growth models, accurately capturing the SoC-dependent capacity loss trends in both synthetic and real storage data.
• Parameter correlations obtained from EP-BOLFI provided insights into the interplay between different SEI growth mechanisms.
• BASQ identified electron diffusion as the most likely mechanism for SoC-dependent SEI growth during storage, supporting previous experimental findings.
• The study demonstrated the effectiveness of feature selection in EP-BOLFI for improving parameter identification, particularly under noisy conditions.

Main Conclusions:

This research highlights the power of Bayesian machine learning for analyzing complex battery degradation processes. By combining physics-based models with advanced statistical methods, the study provides a robust framework for identifying dominant degradation mechanisms, quantifying uncertainties, and ultimately guiding the development of longer-lasting batteries.

Significance:

This work contributes significantly to the field of battery degradation modeling by demonstrating a practical and efficient approach for analyzing complex, coupled physico-chemical processes. The findings have implications for battery management strategies and the development of more accurate lifetime prediction models.

Limitations and Future Research:

The study primarily focused on SEI growth during storage. Future research should investigate the interplay of multiple degradation mechanisms during cycling and explore the applicability of these methods to other battery chemistries and aging processes.

Statystyki

The study analyzed storage data of lithium-ion batteries with a nickel-cobalt-aluminium oxide (NCA) cathode, stored for 9.5 months with check-ups every two months.
Synthetic cycling data was generated for 500 full cycles with 1C CC-CV charge and 1C constant discharge, with voltage cut-offs of 2.5V and 4.2V.
The study investigated different noise levels applied to the synthetic cycling data, with variances of 8 · 10−6Ah2 and 8 · 10−5Ah2.

Cytaty

"The increasing capabilities of machine learning (ML) algorithms point to a possible way to tackle this problem."
"The most natural incorporation of uncertainty is achieved by Bayesian algorithms, favoring them over other ML algorithms for these purposes."
"Despite decades-long research efforts, the scientific community cannot fully explain the SEI growth, suggesting that the SEI is complex and possibly based on multiple coupled mechanisms."
"BASQ identifies electron diffusion as the best transport mechanism to describe the SoC-dependent characteristics in this storage data."

Kluczowe wnioski z

Physics-based inverse modeling of battery degradation with Bayesian methods

by Micha C. J. ... o arxiv.org 10-28-2024

https://arxiv.org/pdf/2410.19478.pdf

Głębsze pytania

How can these Bayesian methods be extended to incorporate other battery degradation mechanisms beyond SEI growth, such as lithium plating or particle cracking?

This study focuses on utilizing Bayesian methods, specifically EP-BOLFI and BASQ, to model Solid-Electrolyte Interphase (SEI) growth as a key degradation mechanism in lithium-ion batteries. The power of these methods lies in their ability to handle complex, coupled physico-chemical processes, making them suitable for investigating other degradation mechanisms beyond SEI growth. Here's how they can be extended:

Develop Physics-Informed Degradation Models: The first step is to formulate accurate physics-based models that describe the degradation mechanisms of interest.

Lithium Plating: Models should capture the transport of lithium ions in the electrolyte and their deposition on the anode surface. Factors like current density, temperature, and lithium concentration gradients need to be considered.
Particle Cracking: Models should account for the mechanical stress experienced by active material particles during lithiation and delithiation cycles. This involves considering factors like particle size, material properties, and volume changes.

Identify Relevant Parameters: Each degradation model will have a set of parameters that govern its behavior.

Lithium Plating: Key parameters might include the exchange current density for lithium deposition, diffusion coefficients, and reaction rate constants.
Particle Cracking: Important parameters could be the elastic modulus of the active material, fracture toughness, and particle size distribution.

Generate Synthetic and Experimental Data:

Synthetic Data: Use the physics-based models to generate synthetic datasets that simulate battery degradation under various operating conditions. This data is crucial for validating the models and understanding the parameter influences.
Experimental Data: Design experiments to isolate and measure the specific degradation mechanisms. Techniques like electrochemical impedance spectroscopy (EIS), electron microscopy, and capacity fade analysis can be employed.

Apply Bayesian Methods:

EP-BOLFI: Utilize EP-BOLFI to efficiently explore the parameter space of the degradation models and find the optimal parameter values that best fit the experimental data. Carefully select features that capture the unique characteristics of each degradation mechanism.
BASQ: Employ BASQ to compare different degradation models and identify the most probable mechanism or combination of mechanisms responsible for the observed degradation.

Uncertainty Quantification and Correlation Analysis: Analyze the posterior distributions and correlation coefficients obtained from EP-BOLFI to quantify the uncertainty in the parameterized models and understand the interplay between different degradation mechanisms.

By following this approach, researchers can leverage the power of Bayesian methods to gain a deeper understanding of various battery degradation mechanisms and develop strategies to mitigate them.

Could the identified dominance of electron diffusion in SEI growth be specific to the investigated battery chemistry and operating conditions, and might other mechanisms prevail under different scenarios?

Yes, the dominance of electron diffusion in SEI growth observed in this study could be specific to the investigated NCA/graphite battery chemistry and the storage conditions employed. Different scenarios might indeed favor other mechanisms:

Battery Chemistry:

Cathode Material: The choice of cathode material influences the electrolyte composition and the electrochemical potential at the anode, directly impacting SEI formation. For example, high-voltage cathodes can lead to more pronounced electrolyte decomposition and potentially favor solvent diffusion-limited SEI growth.
Electrolyte Composition: The type of solvent, salt, and additives used in the electrolyte significantly affects the SEI composition, morphology, and growth mechanisms. Some electrolytes might promote more compact SEI layers that hinder solvent diffusion, while others could lead to more porous structures that facilitate it.

Operating Conditions:

Temperature: Elevated temperatures can accelerate solvent diffusion rates, potentially making it the rate-limiting step in SEI growth. Conversely, at low temperatures, electron diffusion might become more dominant.
Current Density: High current densities during cycling can lead to significant concentration gradients in the electrolyte, potentially enhancing solvent diffusion. Additionally, high currents can induce mechanical stress in the SEI, potentially influencing its growth and evolution.
State of Charge (SoC): As demonstrated in the study, the SoC significantly affects the overpotential at the anode, influencing the driving force for both electron and solvent transport. Different SoC ranges might favor different mechanisms.
Therefore, it's crucial to investigate SEI growth mechanisms under a wide range of battery chemistries and operating conditions to develop a comprehensive understanding. The Bayesian methods presented in this study provide a powerful tool for such investigations.

How can the insights gained from this research be translated into practical strategies for optimizing battery design, operation, and charging protocols to mitigate degradation and extend battery lifespan?

The insights from this research, particularly the identification of electron diffusion as a dominant mechanism for SEI growth in the studied system, can be translated into practical strategies for enhancing battery lifespan:
1. Battery Design:

Electrolyte Engineering:

Solvent Selection: Choose solvents with high electrochemical stability windows to minimize unwanted side reactions and SEI formation.
Additive Incorporation: Introduce additives that can form stable and protective SEI layers, hindering further electron diffusion and electrolyte decomposition.

Anode Modification:

Surface Coatings: Apply thin protective coatings on the anode surface to physically block electron tunneling and reduce SEI growth.
Doping and Alloying: Modify the anode material properties through doping or alloying to create a more stable interface with the electrolyte and minimize side reactions.
2. Battery Operation:

Temperature Control: Operate batteries within a temperature range that minimizes both electron diffusion and solvent diffusion to limit SEI growth.
State of Charge (SoC) Management:

Avoid High SoCs: Limit the maximum SoC during storage and cycling to reduce the overpotential at the anode and minimize electron-driven SEI growth.
Partial State of Charge (PSoC) Storage: Store batteries at a PSoC to minimize the driving force for both electron and solvent transport, slowing down SEI formation.
3. Charging Protocols:

Optimized Charging Profiles:

Lower Current Rates: Employ lower charging currents, especially at high SoCs, to reduce concentration gradients and minimize solvent diffusion.
Pulse Charging: Implement pulse charging techniques to allow for relaxation of concentration gradients and potentially reduce SEI growth.

Voltage Control:

Reduced Cut-off Voltage: Lower the upper cut-off voltage during charging to minimize the overpotential at the anode and limit electron-driven SEI formation.
By implementing these strategies, informed by a deeper understanding of the underlying degradation mechanisms, battery manufacturers and users can effectively mitigate SEI growth, enhance battery lifespan, and improve the overall performance and reliability of lithium-ion batteries.