Early-Cycle Battery Performance Prediction for EOL Detection
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Solution Overview
Problem
Predicting battery lifespan and identifying catastrophic fade requires long-term data collection and knowledge of battery degradation mechanisms, which is inefficient and lacks universality across different battery chemistries.
Innovation Solution
Utilizing machine learning models trained on early-cycle battery test data, particularly DCIR tests, to predict end-of-life cycles and catastrophic fade in batteries, enabling accurate and cost-effective predictions without prior knowledge of battery chemistry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If long-term data collection is used to predict battery lifespan, then prediction accuracy is improved, but testing time and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by conducting battery tests at early cycles (e.g., cycles 1, 10, 100) to collect degradation data before the battery reaches end-of-life. This early data collection enables prediction models to estimate remaining lifespan without requiring the battery to be tested through its entire operational life, thus reducing testing time from thousands of cycles to just a few hundred cycles while maintaining prediction accuracy
Solution Approach 2:
The patent uses copying by creating virtual replicas of battery degradation patterns through machine learning models. These models are trained on early-cycle test data and generate predictions that replicate what would be obtained from complete long-term testing, effectively copying the outcome of extensive testing without performing the actual lengthy tests
2Loss of information
If battery tests are extended to capture degradation mechanisms, then understanding of battery behavior is improved, but device complexity and test requirements increase
Solution Approach 1:
The patent applies extraction by isolating and measuring specific key degradation indicators (such as capacity retention, internal resistance changes, and impedance spectroscopy parameters) at early test cycles. Instead of requiring complex comprehensive testing to understand all degradation mechanisms, the method extracts critical information from simplified early-cycle tests, reducing test complexity while capturing essential degradation knowledge
Solution Approach 2:
The patent uses parameter changes by transforming raw battery test data into meaningful degradation features through signal processing and feature extraction techniques. Early-cycle test parameters are transformed into predictive indicators that reveal degradation mechanisms, allowing complex battery behavior to be understood through changes in measurable parameters without requiring complex test setups
3Measurement precision
If prediction methods are customized for specific battery chemistries, then prediction accuracy is improved, but adaptability across different battery types decreases
Solution Approach 1:
The patent applies universality by developing a standardized early-cycle testing protocol and machine learning framework that can be applied across different battery chemistries (lithium-ion, lithium-polymer, nickel-metal hydride, etc.). The system uses universal features extracted from impedance spectroscopy and capacity measurements that are applicable to various battery types, enabling the same prediction methodology to work across diverse battery technologies while maintaining accuracy through chemistry-specific model training
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery performance prediction. One of the methods includes actions of receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property of at least two battery tests. Each battery test includes applying pulses on the battery cell during a battery cycle. The battery test data is provided as input to a machine learning system to predict battery cell performance. The machine learning system includes a machine learning model that has been trained using training data includes test data of battery cells that reached respective end of life (EOL) cycles. In response, a prediction result for the battery cell is automatically generated by the machine learning model. The prediction result indicates an EOL cycle of the battery cell. An action is taken based on the prediction result.