A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life

2026-03-20

AI Summary

This work addresses the challenge of effectively extracting salient features and modeling long-term temporal dependencies in deep learning–based prediction of lithium-ion battery state of health (SOH) and remaining useful life (RUL). To this end, the authors propose a multi-task directed learning framework that integrates multi-scale CNNs, an enhanced dilated LSTM, and a dual-stream attention mechanism—comprising polarization and sparse attention modules—to selectively capture task-specific critical information for SOH and RUL estimation. Coupled with Hyperopt-based hyperparameter optimization, the proposed method achieves substantial improvements on public battery aging datasets, reducing the average RMSE by 111.3% for SOH and 33.0% for RUL compared to existing approaches, thereby significantly enhancing both joint prediction accuracy and robustness.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Intelligent Robots: Learning & Optimization for ROB
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and efficient operation of electric vehicles while minimizing associated risks. However, current deep learning methods are limited in their ability to selectively extract features and model time dependencies for these two parameters. Moreover, most existing methods rely on traditional recurrent neural networks, which have inherent shortcomings in long-term time-series modeling. To address these issues, this paper proposes a multi-task targeted learning framework for SOH and RUL prediction, which integrates multiple neural networks, including a multi-scale feature extraction module, an improved extended LSTM, and a dual-stream attention module. First, a feature extraction module with multi-scale CNNs is designed to capture detailed local battery decline patterns. Secondly, an improved extended LSTM network is employed to enhance the model's ability to retain long-term temporal information, thus improving temporal relationship modeling. Building on this, the dual-stream attention module-comprising polarized attention and sparse attention to selectively focus on key information relevant to SOH and RUL, respectively, by assigning higher weights to important features. Finally, a many-to-two mapping is achieved through the dual-task layer. To optimize the model's performance and reduce the need for manual hyperparameter tuning, the Hyperopt optimization algorithm is used. Extensive comparative experiments on battery aging datasets demonstrate that the proposed method reduces the average RMSE for SOH and RUL predictions by 111.3\% and 33.0\%, respectively, compared to traditional and state-of-the-art methods.

Problem

• state-of-health
• remaining useful life
• lithium-ion battery
• time-series modeling
• feature extraction

Innovation

• multi-task learning
• multi-scale CNN
• extended LSTM
• dual-stream attention
• battery state-of-health