Battery Model Parameter Optimization via Multi-Stage Algorithm Switching
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Solution Overview
Problem
The existing methods for battery state estimation, particularly using electrochemical models, face challenges due to the complexity and requirement for accurate parameter identification, which is time-consuming and costly, limiting their effectiveness in simulating battery behavior accurately.
Innovation Solution
A processor-implemented method that performs parameter optimization of a battery model using multiple optimization techniques, switching between them based on performance criteria and neural network evaluations to determine the most effective parameter combinations, thereby improving the accuracy and efficiency of battery state estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electrochemical model is used to simulate battery behavior, then simulation accuracy is improved, but parameter identification complexity and time consumption increase
Solution Approach 1:
The patent divides the parameter optimization process into multiple stages with different optimization techniques. Each stage focuses on specific parameters or uses different algorithms (e.g., gradient-based methods, genetic algorithms, particle swarm optimization) to handle different aspects of the electrochemical model parameters, thereby managing complexity while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary parameter identification using equivalent circuit models or simplified approaches before applying full electrochemical model optimization. This preliminary action provides initial parameter estimates that reduce the search space and computational burden of subsequent detailed optimization, saving time while achieving accurate results.
2Measurement precision
If traditional parameter identification methods like electrochemical impedance spectroscopy are used, then parameter accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent replaces traditional experimental methods like electrochemical impedance spectroscopy with computational optimization approaches. Instead of performing time-consuming physical measurements, the system uses numerical optimization algorithms to identify parameters by fitting model predictions to available data, significantly reducing time and cost while maintaining accuracy.
Solution Approach 2:
The patent creates virtual copies of the battery system through computational models that replicate battery behavior. By optimizing parameters in this virtual environment using available measurement data, the system achieves accurate parameter identification without requiring extensive physical testing, thereby reducing time and resource consumption.
3Productivity
If multiple optimization techniques are switched between, then optimization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a dynamic optimization system that automatically switches between different optimization techniques based on real-time performance metrics and problem characteristics. The system monitors convergence rates, solution quality, and computational cost to determine when to transition between algorithms, adapting the optimization strategy dynamically to improve efficiency while managing complexity through automated decision-making.
Data Source
AI summary
A processor-implemented method includes performing first parameter optimization of a battery model through a first predetermined optimization technique; switching, based on a count accumulated while performing the first parameter optimization indicating that a switching criterion has been met, from the first optimization technique to a second predetermined optimization technique; performing second parameter optimization of the battery model through the second predetermined optimization technique; and determining a final parameter combination as an optimized parameter of the battery model, in response to an occurrence of an optimization end event during the performance of the second parameter optimization.