Gaussian process regression for forecasting battery state of health (1703.05687v2)

Abstract: Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.

• The paper introduces a Gaussian Process-based method that forecasts battery degradation by optimizing kernel functions.
• It employs explicit mean functions and multi-output models to enhance long-term accuracy by capturing inter-cell correlations.
• Experimental results on lithium-ion datasets validate improved prediction accuracy for both short- and long-term battery health forecasting.

Gaussian Process Regression for Forecasting Battery State of Health

The paper "Gaussian Process Regression for Forecasting Battery State of Health" presents an approach utilizing Gaussian Processes (GPs) to predict the degradation and remaining useful life (RUL) of lithium-ion batteries. Addressing the complex nature of battery degradation, the researchers propose a data-driven method that leverages the availability of extensive battery data collected from various applications through cloud-connected devices.

Core Methodology

Gaussian Processes offer a Bayesian non-parametric model advantageous for capturing the intricacies of battery behavior while managing uncertainty. The key elements of the proposed method include:

• Kernel Function Selection: The appropriate choice of kernel functions in GPs is crucial for capturing the degradation behavior of batteries. The paper evaluates various compound kernels to model the disparate timescales and characteristics observed in capacity degradation.
• Explicit Mean Functions: By incorporating explicit mean functions inspired by known battery degradation models, the researchers enhance the model’s ability to predict long-term battery behavior accurately.
• Multi-output GPs: The use of multi-output GPs allows exploiting correlations in data from multiple cells undergoing similar load profiles, enhancing the prognostic performance.

Experimental Evaluation and Results

The paper evaluates its approach using different lithium-ion battery datasets derived from both real-world applications and controlled experiments, demonstrating predictive capability over both short-term and long-term horizons:

• Kernel Function Evaluation: Various compound kernels were tested, revealing that a combination of Matérn kernels and a periodic component offered robust predictions by accurately modeling both short-term variations and long-term degradation trends.
• Exponential Degradation Modeling: By using explicit mean functions reflecting exponential degradation models, the prediction accuracy was significantly improved compared to generic GP models, especially in long-term forecasting scenarios.
• Multi-Output Predictions: The multi-output GP approach showcased superior predictive capabilities over single-output models by effectively capturing inter-cell correlations, particularly beneficial when similar operational conditions were applied across battery cells.

Implications and Future Directions

This paper underscores the potential for GPs in enhancing battery management systems by providing accurate prognostics of battery health. The theoretical implications point towards the flexibility of non-parametric Bayesian methods in handling real-world uncertainty and noise in degradation data. Practically, adopting such techniques could lead to improved cost-efficiency and reliability in battery-dependent systems, crucial for applications in electric vehicles and energy storage technologies.

Future advancements could focus on extending the model to incorporate external factors such as temperature and load variability, offering more comprehensive forecasts. Additionally, exploring efficient computational techniques like sparse approximation methods could enable scaling to larger datasets, making the approach more applicable to real-time monitoring of battery systems across different domains.

This paper presents a significant step in leveraging advanced machine learning methods for practical engineering problems, marking a shift towards more data-driven solutions in battery health management.