HybridoNet-Adapt: A Domain-Adapted Framework for Accurate Lithium-Ion Battery RUL Prediction

2025-03-27

AI Summary

To address poor generalization in lithium-ion battery remaining useful life (RUL) prediction caused by cross-domain distribution shifts, this paper proposes a target-label-free, data-agnostic prediction framework. Methodologically, we design HybridoNet—a novel hybrid architecture that jointly integrates LSTM, multi-head attention, and neural ordinary differential equations to jointly capture temporal dynamics and continuous evolutionary behaviors. Further, we introduce a domain adaptation strategy combining domain-adversarial training (DANN), regression ensemble, and maximum mean discrepancy (MMD) for joint optimization. Extensive experiments on multi-source heterogeneous battery datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods, achieving both high prediction accuracy and robust cross-domain generalization. The framework is computationally efficient and readily deployable in real-world battery health management systems.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction

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
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Accurate prediction of the remaining useful life (RUL) in Lithium-ion battery (LIB) health management systems is crucial for ensuring reliability and safety. Current methods typically assume that training and testing data share the same distribution, overlooking the benefits of incorporating diverse data sources to enhance model performance. To address this limitation, we introduce a data-independent RUL prediction framework along with its domain adaptation (DA) approach, which leverages heterogeneous data sources for improved target predictions. Our approach integrates comprehensive data preprocessing, including feature extraction, denoising, and normalization, with a data-independent prediction model that combines Long Short-Term Memory (LSTM), Multihead Attention, and a Neural Ordinary Differential Equation (NODE) block, termed HybridoNet. The domain-adapted version, HybridoNet Adapt, is trained using a novel technique inspired by the Domain-Adversarial Neural Network (DANN) framework, a regression ensemble method, and Maximum Mean Discrepancy (MMD) to learn domain-invariant features from labeled cycling data in the source and target domains. Experimental results demonstrate that our approach outperforms state-of-the-art techniques, providing reliable RUL predictions for real-world applications.

Problem

• Accurate RUL prediction for Lithium-ion batteries
• Overcoming data distribution mismatch in training and testing
• Leveraging diverse data sources for improved predictions

Innovation

• HybridoNet combines LSTM, Multihead Attention, NODE
• Domain adaptation using DANN and MMD techniques
• Data preprocessing includes feature extraction, denoising