Secondary Battery Performance Prediction Using Model Libraries
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Solution Overview
Problem
Existing methods for predicting the performance of secondary batteries are inefficient when design conditions change frequently, as they require repeated experimental fitting of model parameters, which is time-consuming and impractical.
Innovation Solution
A method and system for predicting secondary battery performance using an electrochemical model library, where model parameters are obtained from experiment data and optimized through algorithms like particle swarm optimization, genetic algorithms, or Bayesian algorithms, and stored in an electrochemical model library for accurate prediction under varying design conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model parameters are fitted with design conditions through experiments, then prediction accuracy is improved, but time consumption and efficiency deteriorate when design conditions change frequently
Solution Approach 1:
The patent pre-fits model parameters for multiple design conditions in advance and stores them in a database. When a prediction is needed, the system directly retrieves the pre-fitted parameters corresponding to the given design conditions, eliminating the need for time-consuming real-time fitting experiments while maintaining high prediction accuracy.
2Reliability
If repeated experimental fitting is performed when design conditions change, then model accuracy is maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The system performs model parameter fitting for various design conditions in advance and stores the results in a database. When design conditions change, the system quickly retrieves the appropriate pre-fitted parameters from the database, maintaining model accuracy without requiring repeated time-consuming experiments, thus significantly improving productivity.
Solution Approach 2:
The patent creates a database that stores copies of model parameters fitted for different design conditions. Instead of performing new experiments each time conditions change, the system copies and uses the appropriate pre-fitted parameters from the database, maintaining reliability while enhancing efficiency.
3Reliability
If new experiments are conducted for each design condition change, then prediction reliability is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system pre-performs experiments for multiple design conditions and stores the results in a database. When a prediction is needed, the system automatically retrieves the appropriate pre-fitted parameters based on the given design conditions, ensuring prediction reliability while greatly simplifying the operational process and reducing the complexity of conducting new experiments.
Data Source
AI summary
A method of predicting an electrical performance of a secondary battery. The method includes receiving design conditions of the secondary battery, receiving experiment data of the secondary battery, obtaining model parameters based on the experiment data and an electrochemical model, generating an electrochemical model library including the model parameters, and predicting the electrical performance of the secondary battery, having the design conditions, based on the electrochemical model library. The design conditions of the secondary battery include at least one of an electrode condition or an active material condition.