A hybrid energy storage system adaptive cycle life optimization system

Through a closed-loop optimization architecture that enables multi-module collaboration, the problem of heterogeneous degradation of batteries and supercapacitors in hybrid energy storage systems has been solved, resulting in extended system lifespan and improved operational performance, while reducing total lifecycle costs and carbon emissions.

CN122763535APending Publication Date: 2026-09-15HUAIYIN INSTITUTE OF TECHNOLOGY
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Patent Information

Application Number
CN202610886074.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-06-18
Publication Date
2026-09-15

AI Technical Summary

Technical Problem

The heterogeneous characteristics of batteries and supercapacitors in hybrid energy storage systems lead to differences in their degradation mechanisms. Traditional scheduling strategies lack precise control over the coordinated degradation process of the two, which can easily lead to problems such as overcharging and discharging, uneven stress distribution, and shorten the overall lifespan of the system.

Method used

The system adopts a highly efficient collaborative closed-loop optimization architecture integrating multiple modules, including a multi-source data synchronous acquisition and preprocessing module, a cross-domain coupling damage quantification and dynamic weight allocation module, a multi-criteria dynamic balance optimization decision module, a chaotic slime mold optimization algorithm module with dynamic topology evolution and experience replay, and an adaptive federated learning control module. This enables online learning of aging trajectories, accurate quantification of coupling damage, and dynamic adjustment of control strategies.

Benefits of technology

By accurately quantifying the degradation state of energy storage units and dynamically optimizing charging and discharging strategies, the aging rate can be slowed down, the overall lifespan of the system can be extended, operational stability and energy utilization efficiency can be improved, the total life cycle cost can be reduced, and carbon emissions can be reduced.

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Abstract

The application provides a kind of hybrid energy storage system adaptive cycle life optimization system, comprising: multi-source data synchronous acquisition and preprocessing module, acquisition preprocessing energy storage unit operating data and constructs standardized feature vector.Coupling damage quantification and dynamic weight distribution module across domains, double-channel quantification aging trend and fusion out of heterogeneous collaborative degradation index.Multi-criteria dynamic balance optimization decision module, construct loss function balance system life gain and operating stability.Dynamic topology evolution and experience replay of chaos mycoparasitism optimization algorithm module, output optimal charge and discharge power instruction adapted to system state and constraints.Adaptive federated learning control module, execute instruction and realize closed-loop feedback and operating condition disturbance suppression.The application learns operating degradation data with AI algorithm, dynamically adjusts charge and discharge strategy, delays system aging, improves operating stability and energy utilization efficiency.
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