Battery Model Parameter Fitting Using Pulse and Rest Segments
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Solution Overview
Problem
Existing parameter identification methods for lithium-ion battery equivalent circuit models suffer from poor precision and accuracy due to reliance on either pulse or rest segments in open-circuit voltage-time curves, leading to significant estimation errors.
Innovation Solution
A method that combines fitting algorithms with fitting optimization using both pulse and rest segments to identify battery model parameters, ensuring the identified parameters satisfy voltage errors in both segments, thereby improving accuracy and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameter identification methods use only pulse segment or rest segment data for fitting, then the identification process is simple, but the precision and accuracy of identified parameters are poor
Solution Approach 1:
The patent divides the open-circuit voltage-time curve into distinct pulse segments and rest segments, analyzing each segment separately to extract different characteristics. The pulse segment provides information about battery response during current application, while the rest segment provides information about voltage relaxation, allowing comprehensive parameter identification without requiring complex unified models
Solution Approach 2:
The patent combines the analysis of both pulse segments and rest segments into a unified parameter identification process. By merging the advantages of both segment types, the method achieves high precision parameter identification that cannot be obtained by using either segment alone, balancing computational complexity with improved accuracy
2Reliability
If existing parameter identification methods are used, then the identification process is straightforward, but significant estimation errors occur
Solution Approach 1:
The patent implements a feedback mechanism where the identified parameters are validated against the original voltage-time curve data. The method calculates estimation errors and uses this feedback to refine the parameter identification process, ensuring that the identified parameters accurately represent the battery's electrochemical characteristics and minimizing estimation errors in subsequent applications
Data Source
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AI summary
A method includes: fitting, based on a battery parameter of a pulse segment, a target model parameter using a fitting algorithm, to obtain an initial fitted value of the target model parameter; inputting the initial fitted value into a fitting optimization algorithm, and performing, based on battery parameters of a rest segment and the pulse segment, fitting optimization on the target model parameter using the fitting optimization algorithm, to obtain an optimized value of an optimized target model parameter; calculating, based on the battery parameter of the rest segment and the optimized value, a first difference between a predicted value and an actual value of an open-circuit voltage; and using the optimized value as an identified target model parameter in a case that the first difference does not exceed a preset difference. The method aims to address an issue of poor parameter identification precision in related technologies.