Electrochemical Model Libraries for Secondary Battery Performance Prediction
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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 that utilize an electrochemical model library to predict the electrical performance of secondary batteries by receiving design conditions and experiment data, generating model parameters, and optimizing these parameters using algorithms like particle swarm optimization, genetic algorithms, or Bayesian algorithms, allowing for accurate prediction without repeated experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model parameters are fitted with design conditions for experiments, then prediction accuracy is improved, but time consumption increases when design conditions change frequently
Solution Approach 1:
The patent pre-establishes an electrochemical model library containing multiple model parameters corresponding to different design conditions before actual predictions are needed. When design conditions change, the system retrieves pre-computed model parameters from the library rather than performing new experiments, thereby maintaining prediction accuracy while significantly reducing time consumption.
Solution Approach 2:
The patent creates a virtual copy of the electrochemical model library that stores replicated model parameters for various design conditions. This digital copy allows the system to query and retrieve appropriate model parameters for different design scenarios without requiring physical re-experimentation, thus resolving the contradiction between accuracy and time efficiency.
2Measurement precision
If repeated experimental fitting is performed when design conditions change, then prediction accuracy is maintained, but productivity decreases
Solution Approach 1:
The system performs preliminary computation of model parameters for various design conditions and stores them in the electrochemical model library. When new design conditions arise, the system retrieves pre-computed parameters from the library, eliminating the need for repeated experiments and thereby maintaining both accuracy and high productivity.
Solution Approach 2:
The patent replaces the mechanical experimental fitting process with a computational approach. Instead of physically conducting experiments to fit model parameters for each design condition, the system uses computer-based retrieval from the electrochemical model library, significantly improving productivity while maintaining prediction accuracy.
3Productivity
If an electrochemical model library is generated with optimized model parameters, then prediction efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the electrochemical model into multiple discrete model parameters, each corresponding to specific design conditions (e.g., different electrode materials, electrolyte compositions, operating temperatures). These segmented parameters are stored separately in the electrochemical model library, allowing the system to retrieve only the relevant parameters needed for each specific prediction task, thereby improving efficiency while managing complexity through modular organization.
Data Source
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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.