A method and system for screening the results of parameter identification of an electrochemical model based on statistical convergence characteristics constraints

Through statistical analysis and iterative screening of multiple parameter identification results, a parameter screening method guided by median and standard deviation was introduced, which solved the problem of unstable parameter identification results of electrochemical models, achieved unique and stable parameter identification, and improved the consistency and reliability of model application.

CN122201477APending Publication Date: 2026-06-12NANJING SCHMERSON NEW ENERGY TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANJING SCHMERSON NEW ENERGY TECHNOLOGY CO LTD
Filing Date
2026-03-04
Publication Date
2026-06-12

AI Technical Summary

⚠Technical Problem

The existing electrochemical model parameter identification results are unstable, resulting in low consistency and reliability of the model in subsequent applications, and lack of systematic constraints and evaluation of parameter distribution stability.

⚗Method used

Through statistical analysis and iterative screening of multiple parameter identification results, a parameter screening method guided by median and standard deviation is introduced to gradually converge to unique and stable electrochemical model parameters. Particle swarm optimization algorithm, genetic algorithm or differential evolution algorithm are used for multiple independent parameter identifications, and the parameter stability is improved by normalization processing and distribution entropy threshold criterion.

🎯Benefits of technology

It significantly reduces parameter discreteness, avoids the problem of multiple solutions and instability, ensures the uniqueness of the method and process and its engineering applicability, and improves the reliability and stability of parameter identification results.

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Abstract

The application provides a statistical convergence characteristic constraint-based electrochemical model parameter identification result screening method, including the following steps: S1, based on a single complete battery charging or discharging process data, combining a preset electrochemical model and a random optimization algorithm, performing multiple independent parameter identification processes, and obtaining not less than N groups of parameter solutions; S2, pre-processing the N groups of parameter solutions to obtain an initial effective parameter set; S3, uniformly normalizing the parameter set; S4, performing space contraction on the parameter dimension with the most stable distribution based on the median and standard deviation guided parameter screening; when the number of parameter samples is reduced to a preset threshold or only one sample is left, the screening process is terminated, and only one group of parameter solutions or a set of parameter dimension value intervals are obtained as the final parameter output of the electrochemical model. Through the statistical stability constraint, the parameter discreteness caused by the random optimization algorithm is significantly reduced.
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