Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-ion Batteries (2101.00035v1)

Abstract: This paper presents the development of machine learning-enabled data-driven models for effective capacity predictions for lithium-ion batteries under different cyclic conditions. To achieve this, a model structure is first proposed with the considerations of battery ageing tendency and the corresponding operational temperature and depth-of-discharge. Then based on a systematic understanding of covariance functions within the Gaussian process regression, two related data-driven models are developed. Specifically, by modifying the isotropic squared exponential kernel with an automatic relevance determination structure, 'Model A' could extract the highly relevant input features for capacity predictions. Through coupling the Arrhenius law and a polynomial equation into a compositional kernel, 'Model B' is capable of considering the electrochemical and empirical knowledge of battery degradation. The developed models are validated and compared on the Nickel Manganese Cobalt Oxide (NMC) lithium-ion batteries with various cycling patterns. Experimental results demonstrate that the modified Gaussian process regression model considering the battery electrochemical and empirical ageing signature outperforms other counterparts and is able to achieve satisfactory results for both one-step and multi-step predictions. The proposed technique is promising for battery capacity predictions under various cycling cases.

• The paper introduces two GPR-based models that integrate empirical battery data and degradation mechanisms for enhanced cyclic capacity prediction.
• It shows that Model B reduces RMSE by over 70% compared to traditional SE-based models, achieving a maximum error of 1.2% in predictions.
• The enhanced GPR models improve SOH estimation, offering more reliable battery performance forecasting, crucial for safe and efficient EV operations.

Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-ion Batteries

The research titled "Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-ion Batteries" explores the application of machine learning techniques, specifically Gaussian Process Regression (GPR), to predict the capacity degradation of lithium-ion (Li-ion) batteries under varying cyclic conditions. Given the complexities involved in the nonlinear degradation process of Li-ion batteries, the paper focuses on improving prediction accuracy by incorporating electrochemical and empirical knowledge into the covariance functions of GPR models.

Overview of the Study

Two innovative GPR-based models are introduced in this paper, labeled 'Model A' and 'Model B.' These models aim to predict battery capacity degradation by accounting for critical factors such as temperature and depth-of-discharge (DOD).

• Model A leverages an Automatic Relevance Determination structure with a squared exponential (SE) kernel. This configuration is designed to effectively filter out irrelevant inputs, thereby enhancing feature extraction capabilities.
• Model B builds upon Model A's framework by integrating knowledge of battery degradation mechanisms directly into its kernel function. Specifically, it incorporates the Arrhenius law and a polynomial equation, which allows for the inclusion of the temperature-dependent reaction rates and the effects of varying DOD levels.

Key Findings

The application of these modified GPR models demonstrated notable improvements in accuracy for both training and testing datasets when compared to traditional SE kernel models. Notably, Model B consistently outperformed Model A and a standard SE-based GPR model (SEGM) by achieving lower prediction errors across several metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

Performance Indicators:

• Model B reduced the RMSE by over 70% in multi-step predictions compared to Model A and SEGM, indicating superior capability to predict future capacity values with minimal errors.
• Model B achieved a maximum error of 1.2% in multi-step predictions relative to the total battery capacity, underscoring its efficacy in real-world applications.

Implications and Speculation on Future Development

The incorporation of battery-specific degradation phenomena into the GPR framework presents significant advancements in predictive modeling for battery health management. These developments have practical implications for improving the reliability and safety of Li-ion batteries in electric vehicles (EVs), where accurate state of health (SOH) estimation is crucial.

Theoretically, this approach extends the utility of GPR models by demonstrating how mechanistic insights can be leveraged to refine data-driven predictions. The paper implicitly advocates for further exploration into combining physical degradation models with advanced statistical approaches to enhance the robustness and applicability of battery prognostics.

Future Developments

Further research could delve into diverse charging and discharging scenarios, as well as explore the implications of integrating additional stress factors such as charging rate and ambient environmental conditions into predictive models. Expanding the dataset beyond high-temperature regimes could also strengthen model generalization, especially in scenarios involving lower ambient temperatures common in various geographical regions.

In conclusion, the paper exemplifies an effective interdisciplinary approach by combining machine learning techniques with domain-specific knowledge to advance the predictive capabilities of Li-ion battery systems. Such methodologies could pave the way for breakthroughs in battery management systems, contributing to enhanced performance and longevity of energy storage solutions critical in modern technology applications.