Efficient estimating and clustering lithium-ion batteries with a deep-learning approach.

Area of Science:

• Materials Science
• Electrochemistry
• Machine Learning

Background:

• Increasing demand for energy storage highlights the need for efficient lithium-ion battery (LIB) recycling.
• Accurate state-of-health (SOH) assessment is critical for sorting retired LIBs for secondary applications.
• Variations in service conditions lead to diverse capacity fading behaviors within battery packs.

Purpose of the Study:

• To develop a deep-learning framework for rapid and transferable SOH estimation and battery classification.
• To integrate electrochemical, mechanical, and thermal features for enhanced prediction accuracy.
• To enable accurate SOH prediction using minimal historical data and facilitate adaptation to new battery chemistries.

Main Methods:

• Development of a deep-learning framework utilizing deep neural networks.
• Integration of interconnected electrochemical, mechanical, and thermal features.
• Validation of the model's performance across diverse conditions and electrode systems.

Main Results:

• Achieved high accuracy in SOH estimation with a Mean Absolute Error (MAE) of 0.822% and Root Mean Square Error (RMSE) of 1.048% using combined features.
• Demonstrated robust performance across various operating conditions.
• Enabled accurate SOH prediction using data from just one previous cycle.
• Showcased adaptability to different electrode systems with minimal additional training data.

Conclusions:

• The developed deep-learning framework provides a rapid, transferable, and accurate method for SOH estimation and battery classification.
• Highlighting critical features for SOH assessment is key to efficient battery recycling and sustainable energy storage.
• The model's ability to generalize and perform with limited data supports its practical application in battery lifecycle management.