Energy storage battery health state evaluation method considering coupling effect of power distribution and micro-grid

By extracting global aging features using differential thermal voltammetry and incremental singular value decomposition, and combining Transformer and GRU models, along with ensemble learning using knowledge distillation and gradient boosting decision trees, the problem of insufficient accuracy in energy storage battery health status assessment was solved, achieving high-precision and robust assessment results.

CN121633853APending Publication Date: 2026-03-10STATE GRID ZHEJIANG ELECTRIC POWER CO LTD NINGBO POWER SUPPLY CO
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Patent Information

Application Number
CN202511883899.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-15
Publication Date
2026-03-10

AI Technical Summary

Technical Problem

Existing methods for assessing the health status of energy storage batteries have insufficient accuracy in microgrid coupling scenarios, making it difficult to meet practical application needs and affecting the operational stability and safety of energy storage systems.

Method used

Differential thermal voltammetry and incremental singular value decomposition were used to extract global aging features. A health status assessment model was constructed by combining the Transformer model and the GRU model. The student model was trained on the distribution network side and fine-tuned on the microgrid side using the knowledge distillation method. Gradient boosting decision tree was used for ensemble learning to improve the assessment accuracy.

Benefits of technology

It achieves high-precision health status assessment of energy storage batteries in microgrid scenarios, improves the accuracy and robustness of the assessment, reduces problems such as battery overcharging, over-discharging and accelerated aging, and ensures the safety and stability of the system.

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Abstract

The invention relates to the technical field of energy storage battery state-of-health assessment, in particular to an energy storage battery state-of-health assessment method considering a micro-grid coupling effect, which comprises the following steps: performing signal analysis and global aging feature extraction on obtained target micro-grid side energy storage battery circulation aging data based on a differential thermal voltammetry to obtain global aging features; constructing a health state evaluation model comprising a distribution network side teacher model and a micro-grid side student model; training the student model on the distribution network side based on a knowledge distillation method, and finely adjusting the student model on the micro-grid side by using local data; inputting the global aging features into a health state evaluation model for health state evaluation, and integrating a first health state evaluation result output by the teacher model and a second health state evaluation result output by the student model after fine adjustment updating based on an integrated learning method taking a gradient boosting decision tree as a core, therefore, the health state evaluation result of the energy storage battery is obtained, and the high-precision health state evaluation result is obtained.
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