Lithium-Ion Battery Lifetime Prediction With Probabilistic Cycle-Life Output
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Solution Overview
Problem
Existing methods for predicting the cycle life of lithium ion batteries are inefficient due to the need for retraining data with material and design modifications and lack of probability distribution outputs, leading to significant errors in long-term predictions.
Innovation Solution
A method involving data shaping, feature extraction, nonlinear conversion, and regression to generate a probability distribution of battery lifetime using a computer-based model, independent of material and design changes, by learning from cycle measurement data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning methods use measurement data from initial charge and discharge cycle tests as training data, then the training data becomes independent of material and design changes (satisfying requirement 1), but the prediction output is only a single value without probability distribution (failing requirement 2)
Solution Approach 1:
The patent applies dynamics by transitioning from a static single-value prediction to a dynamic probability distribution prediction. The neural network model is enhanced to output not just a predicted cycle life value but also uncertainty metrics and probability distributions, allowing the system to adapt its output format based on the complexity and variability of the input data patterns.
Solution Approach 2:
The patent introduces an intermediary layer between the neural network output and the final prediction result. This intermediary processing layer transforms the raw neural network outputs into probability distributions and uncertainty metrics, mediating between the simple single-value output and the complex reliability requirements for long-term predictions.
2Device complexity
If machine learning methods output only a single value for cycle life prediction, then the model structure remains simple, but the prediction reliability for long-term cycle life (several thousand cycles) deteriorates due to large error effects
Solution Approach 1:
The patent applies dimensionality change by expanding the prediction output from a one-dimensional single value to a multi-dimensional probability distribution. Instead of predicting only the mean cycle life, the model now outputs multiple dimensions including mean, variance, confidence intervals, and probability density functions, providing a more comprehensive view of prediction reliability.
3Measurement precision
If charge and discharge cycle tests are performed to measure cycle life, then accurate lifetime data is obtained, but the development time is excessively long as most time is spent on measurement
Solution Approach 1:
The patent applies preliminary action by using early-stage charge and discharge cycle test data (conducted for only a few cycles) to train the neural network model. The model then performs the prediction work in advance, eliminating the need to conduct thousands of cycles for actual measurement. The preliminary test data serves as the foundation for the machine learning model to make accurate long-term predictions without requiring the full lifetime test to be completed.
Data Source
AI summary
A lithium ion battery lifetime prediction method executes, by a computer, acquiring training data including cycle measurement data and lifetime data of a battery, learning a lifetime prediction model using the training data with respect to one or more cycle numbers at which a prediction is made, to acquire a set of learned lifetime prediction models corresponding to the cycle numbers at which the prediction is made, respectively, successively acquiring cycle measurement data for prediction of a battery that is a prediction target, up to the cycle numbers at which the prediction is made, respectively, and inputting the cycle measurement data for prediction acquired up to the cycle numbers at which the prediction is made, to the learned lifetime prediction models of the corresponding cycle numbers at which the prediction is made, and acquiring a probability distribution of a lifetime at the cycle numbers at which the prediction is made, respectively, as an output.