Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model

2024-04-26

arXiv.org

AI Summary

To address the low prediction accuracy, poor generalizability, and weak interpretability in commercial lithium-ion battery cycle-life forecasting, this paper proposes a physics-guided, self-attention-based end-to-end full-capacity-curve prediction method. Leveraging only the first few tens of weeks of capacity fade data, the approach integrates a mechanism-driven capacity degradation equation with a self-attention neural network to directly reconstruct the complete capacity degradation trajectory. It achieves, for the first time, cross-threshold generalization—i.e., accurate predictions for arbitrary failure thresholds without retraining—while ensuring strong physical consistency and high model interpretability. Evaluated on an LFP/graphite battery dataset, the method matches state-of-the-art models in prediction accuracy and produces continuous, robust, electrochemically plausible degradation curves. This work establishes a novel paradigm for battery health management and lifetime modeling.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Computer Vision: Large Vision Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Accurately measuring the cycle lifetime of commercial lithium-ion batteries is crucial for performance and technology development. We introduce a novel hybrid approach combining a physics-based equation with a self-attention model to predict the cycle lifetimes of commercial lithium iron phosphate graphite cells via early-cycle data. After fitting capacity loss curves to this physics-based equation, we then use a self-attention layer to reconstruct entire battery capacity loss curves. Our model exhibits comparable performances to existing models while predicting more information: the entire capacity loss curve instead of cycle life. This provides more robustness and interpretability: our model does not need to be retrained for a different notion of end-of-life and is backed by physical intuition.

Problem

• Predict cycle lifetimes of lithium-ion batteries accurately
• Combine physics-based models with self-attention for early-cycle data
• Enhance robustness and interpretability of battery capacity loss prediction

Innovation

• Hybrid physics-based and self-attention model
• Predicts entire capacity loss curves
• No retraining for different end-of-life criteria