A cycle-aware and physics-informed framework for battery remaining useful life prediction.

Area of Science:

• Battery Health Monitoring
• Machine Learning for Energy Systems

Background:

• Accurate Remaining Useful Life (RUL) prediction for Li-ion batteries is vital for system safety and efficiency.
• Current deep learning methods often lack domain specificity, hindering accurate degradation modeling due to irregularly sampled, multivariate sensor data.
• Generic patching techniques in existing models like T-PATCHGNN can fragment charge-discharge cycles, weakening signals and limiting the learning of true battery degradation patterns.

Purpose of the Study:

• To develop an advanced deep learning model, Bat-T-GNN, that integrates domain knowledge for enhanced Li-ion battery RUL prediction.
• To address the limitations of domain-agnostic methods by incorporating physically meaningful inputs and objectives.
• To establish a new state-of-the-art in battery RUL prediction through improved degradation modeling.

Main Methods:

• Cycle-Aware Patching: Segmenting time-series data based on actual charge-discharge cycles to provide coherent, physically meaningful inputs.
• Physics-Informed Consistency Loss (PINN-RUL): Regularizing model training by ensuring RUL predictions align with a physically plausible degradation curve derived from data.
• Integrating domain knowledge at both the input and objective levels of the deep learning architecture.

Main Results:

• The proposed Bat-T-GNN model significantly outperforms prior state-of-the-art methods, including T-PATCHGNN, on public battery degradation benchmarks.
• Ablation studies confirm that both Cycle-Aware Patching and PINN-RUL are critical components driving the performance improvements.
• The method demonstrates superior accuracy in predicting the Remaining Useful Life of Li-ion batteries.

Conclusions:

• Bat-T-GNN establishes a new state-of-the-art in battery RUL prediction by effectively injecting domain knowledge.
• The integration of cycle-aware segmentation and physics-informed loss functions leads to more robust and accurate battery health prognostics.
• This domain-informed approach offers a promising direction for advancing the safety and efficiency of energy storage systems.