Lossless Information-Based Dimensionality Reduction of Comprehensive Features With a Deep Variational Autoencoder Enables Early-Life Prediction of Lithium-Ion Batteries

Abstract

Lithium-ion batteries are widely used in various fields, including electric vehicles and energy storage systems. Accurate battery life prediction is essential for effective safety management. However, acquiring sufficient aging information from limited cycle data for accurate life prediction often results in increased feature dimensionality and model complexity. To solve this problem, this paper proposes a method to achieve lossless information dimensionality reduction through the deep variational autoencoder. Based on the lithium iron phosphate battery dataset, only a limited number of cycles are utilized. A comprehensive feature set with 1519 features is constructed to capture more detailed aging characteristics from limited data. After correlation analysis, 76 high-quality features are preliminarily screened. To balance the preservation of aging information with the complexity of the subsequent network, we propose a dimensionality reduction approach that minimizes feature redundancy while retaining essential information. This method reduces the feature set to 10 key features while preserving the original aging information with minimal loss. The maximum mean square error before and after dimension reduction is 0.02139. The proposed method enables life prediction only with the support of simple machine learning method, with only a few parameters required. The adopted dimensionality reduction method offers useful guidance for high-dimensional feature processing in similar scenarios.

Keywords

deep variational autoencoder / dimensionality reduction / early life prediction / feature extraction / lithium-ion batteries

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2025 The Author(s). Carbon Neutralization published by Wenzhou University and John Wiley & Sons Australia, Ltd.