Self-Attention Battery Cycle Life Prediction With Physics Tuning
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Solution Overview
Problem
Existing learning models struggle to accurately predict the cycle life of batteries due to variations in battery types and physical characteristics, lack of interpretability, and the high cost of data acquisition for training, leading to inefficiencies and reduced model confidence.
Innovation Solution
A hybrid approach combining a physics model with a learning model, such as a self-attention model, is used to derive ground-truth parameters for a loss curve, physically tuning the model to improve accuracy and interpretability by comparing predicted cycles with actual cycles from the physics model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learning model is trained with diverse data representing physical characteristics and phenomena, then prediction accuracy improves, but data acquisition cost increases
Solution Approach 1:
The patent introduces a physics model as an intermediary to generate synthetic training data that represents physical characteristics and phenomena. This physics-based data generation approach provides diverse training samples without the high cost of acquiring real experimental data, thereby improving prediction accuracy while controlling data acquisition costs.
2Measurement precision
If a learning model is retrained due to end-of-life distortions and physical breakdown, then prediction accuracy is maintained, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by incorporating physical phenomena and degradation mechanisms into the training data generation process from the beginning. The physics model generates training data that includes end-of-life distortions and physical breakdown scenarios, enabling the learning model to learn these patterns during initial training rather than requiring retraining when such conditions occur.
3Adaptability or versatility
If a learning model lacks interpretability about internal mechanics, then model flexibility is maintained, but model confidence and transparency decrease
Solution Approach 1:
The patent uses the physics model as an intermediary that provides interpretability while the learning model maintains flexibility. The physics model component explains the internal mechanics and physical relationships, making the model's predictions interpretable, while the learning model component adapts to diverse battery types and conditions, maintaining flexibility. This hybrid approach resolves the contradiction between interpretability and flexibility.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to training a learning model to predict a cycling characteristic of a battery using a physics model. In one embodiment, a method includes deriving ground-truth parameters for a loss curve of an actual capacity using a physics model, the actual capacity associated with a battery type. The method also includes comparing the ground-truth parameters with curve parameters estimated by a self-attention model for physical tuning of the self-attention model, and the curve parameters being associated with a predicted capacity for the battery type. The method also includes adjusting the self-attention model by comparing predicted cycles from the self-attention model and actual cycles from the physics model for cycle life as additional tuning.