Physics-Informed Machine Learning for Predicting Long-Term Lithium-Ion Battery Degradation

Temel Kavramlar

Physics-informed machine learning models can accurately predict long-term degradation of lithium-ion battery cells using only short-term aging data by leveraging physics-based principles.

Özet

The content discusses the use of physics-informed machine learning (PIML) techniques for predicting long-term degradation of lithium-ion battery cells. Key points:

Monitoring battery health over the lifetime is important for ensuring safety and reliability. Common health indicators like capacity and resistance can be estimated online, but they do not provide insight into the health of a cell's internal components.

Three main degradation modes in lithium-ion batteries are loss of active materials on positive and negative electrodes (LAMPE, LAMNE), and loss of lithium inventory (LLI). These can be quantified using a half-cell model, but this process is expensive and time-consuming.

The paper explores four PIML approaches to predict battery capacity and degradation modes using short-term aging data: physics-informed neural networks (PINN), data augmentation, delta learning with kriging, and delta learning with elastic net.

The methods are evaluated using long-term (>4 years) cycling data from 24 implantable-grade lithium-ion cells. The PIML techniques aim to leverage physics-based models and simulation data to improve prediction accuracy and extrapolation capabilities compared to purely data-driven approaches.

The comparative study provides insights into the strengths, limitations, and suitability of each PIML method for battery degradation diagnostics under different data availability scenarios.

İstatistikler

The capacity trajectories for the 24 implantable-grade lithium-ion cells tested under six different operating conditions are shown in Figure 1.
The half-cell model is used to quantify the true values of the three degradation modes (LAMPE, LAMNE, LLI) over the life of the cells. Cells were periodically removed from the aging tests and disassembled to fabricate aged half-cells, which were then cycled to experimentally estimate the true active mass in the electrodes.

Alıntılar

"Monitoring the health of lithium-ion batteries over their lifetime is important for ensuring the safety and reliability of the electric vehicles and portable electronics they power."
"Three commonly reported degradation modes help to elucidate the root cause of cell capacity loss and resistance increase: they are loss of active materials on the positive and negative electrodes, abbreviated as LAMPE and LAMNE, respectively, and loss of lithium inventory, LLI."

Önemli Bilgiler Şuradan Elde Edildi

Physics-Informed Machine Learning for Battery Degradation Diagnostics

by Sina Navidi,... : arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04429.pdf

Daha Derin Sorular

How can the PIML techniques be extended to handle more complex degradation phenomena, such as the coupling between different degradation modes or the impact of operating conditions on degradation

To extend the Physics-Informed Machine Learning (PIML) techniques to handle more complex degradation phenomena, such as the coupling between different degradation modes or the impact of operating conditions on degradation, several strategies can be implemented:

Multi-Physics Integration: Incorporating multiple physics-based models that represent different degradation modes and their interactions can provide a more comprehensive understanding of the battery degradation process. By integrating these models into the PIML framework, the machine learning algorithms can learn the complex relationships between various degradation phenomena.

Feature Engineering: Developing advanced feature engineering techniques that capture the interplay between different degradation modes and operating conditions can enhance the model's ability to predict complex degradation scenarios. By extracting relevant features from the data that reflect the coupling between degradation modes, the PIML models can better capture the underlying degradation mechanisms.

Dynamic Model Adaptation: Implementing adaptive models that can adjust their structure and parameters based on the observed degradation patterns and operating conditions can improve the model's adaptability to changing degradation scenarios. By continuously updating the model architecture and parameters, the PIML techniques can effectively handle the dynamic nature of battery degradation.

Ensemble Learning: Utilizing ensemble learning methods that combine multiple PIML models trained on different aspects of degradation phenomena can provide a more holistic view of the battery health status. By aggregating the predictions from diverse models, the ensemble approach can capture the complex interactions between degradation modes and operating conditions.

What are the potential challenges in deploying these PIML methods for real-time battery health monitoring in practical applications, and how can they be addressed

Deploying PIML methods for real-time battery health monitoring in practical applications may face several challenges, including:

Computational Complexity: PIML techniques often involve complex mathematical models and computations, which can be computationally intensive and time-consuming for real-time applications. Addressing this challenge requires optimizing the algorithms, leveraging parallel processing, and implementing efficient hardware solutions to reduce computational burden.

Data Quality and Quantity: Ensuring the availability of high-quality and sufficient data for training and validation is crucial for the effectiveness of PIML models. In real-time monitoring, data collection may be limited, noisy, or incomplete, leading to challenges in model accuracy. Implementing data preprocessing techniques, data augmentation strategies, and quality control measures can help mitigate these issues.

Model Interpretability: Interpreting the decisions made by complex PIML models can be challenging, especially in critical applications like battery health monitoring. Ensuring the transparency and explainability of the models is essential for building trust and confidence in their predictions. Techniques such as model explainability tools, feature importance analysis, and model visualization can enhance interpretability.

Real-time Implementation: Integrating PIML models into real-time monitoring systems requires efficient deployment strategies and seamless integration with existing hardware and software infrastructure. Developing lightweight models, optimizing inference speed, and ensuring compatibility with real-time data streams are essential for successful deployment.

Given the limited availability of late-life experimental data, how can the PIML models be further improved to enhance their extrapolation capabilities and robustness to unseen degradation scenarios

To enhance the extrapolation capabilities and robustness of PIML models in the absence of late-life experimental data, several approaches can be considered:

Transfer Learning: Leveraging transfer learning techniques to transfer knowledge from related tasks or domains with available data to the target degradation prediction task can improve model generalization. By pre-training the models on relevant data sources and fine-tuning them on the limited late-life experimental data, the models can adapt to unseen degradation scenarios more effectively.

Semi-Supervised Learning: Incorporating semi-supervised learning methods that utilize both labeled early-life experimental data and unlabeled late-life simulation data can enhance the model's ability to generalize to unseen degradation scenarios. By leveraging the information present in the unlabeled data through self-training or co-training approaches, the models can learn from a broader range of data sources.

Uncertainty Estimation: Integrating uncertainty estimation techniques, such as Bayesian inference or dropout regularization, can provide insights into the model's confidence in its predictions and improve robustness to unseen degradation scenarios. By quantifying prediction uncertainties and incorporating them into decision-making processes, the models can make more reliable predictions in real-world applications.

Domain Adaptation: Implementing domain adaptation strategies that align the distribution of early-life experimental data with that of late-life simulation data can help bridge the gap between different data sources. By minimizing the domain shift between the training and testing data distributions, the models can better generalize to unseen degradation scenarios.

By incorporating these strategies, the PIML models can be further improved to handle the challenges of limited late-life experimental data and enhance their extrapolation capabilities in real-time battery health monitoring applications.