Machine learning based hybrid energy storage device coordinated control method and system

By using machine learning to predict load spectrum and adaptive control mode, and combining reinforcement learning to optimize power allocation, the lifespan loss problem of mobile energy storage and charging devices under tidal operation characteristics has been solved, achieving a balance between health and efficiency throughout the entire life cycle.

CN122159328APending Publication Date: 2026-06-05STATE GRID ANHUI ELECTRIC POWER CO LTD FEIXI POWER SUPPLY CO
View PDF 2 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
STATE GRID ANHUI ELECTRIC POWER CO LTD FEIXI POWER SUPPLY CO
Filing Date
2026-05-08
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing technologies cannot effectively adapt to the tidal and intermittent operation characteristics of mobile energy storage and charging devices, resulting in accelerated lifespan loss during peak periods and lack of health maintenance during idle periods, making it difficult to achieve the optimal balance between asset health and overall benefits throughout the entire life cycle.

Method used

The machine learning-based collaborative control method for hybrid energy storage devices adaptively selects performance-priority, lifespan balancing, maintenance, or economic benefit modes by predicting future load spectra and equipment health status. It also optimizes power allocation by combining reinforcement learning, monitors lifespan loss in real time, and updates control strategies accordingly.

Benefits of technology

It achieves a balance between performance and economic requirements under different operating scenarios, effectively suppresses the lifespan loss of energy storage components, and ensures the optimization of asset health and overall benefits throughout the entire life cycle of the hybrid energy storage system.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122159328A_ABST
    Figure CN122159328A_ABST
Patent Text Reader

Abstract

The present application relates to the technical field of energy storage control, in particular to a hybrid energy storage device cooperative control method and system based on machine learning. First, the expected workload spectrum is obtained according to historical operation data and service period, then the dominant control mode is determined and the mode health degree is generated according to the expected workload spectrum and the current health state of the system, the real-time state characteristics are extracted to input the reinforcement learning controller branch to obtain power distribution instructions, the life consumption increment and the comprehensive quality coefficient are calculated, and finally the effectiveness of the instructions is evaluated according to the quality coefficient threshold to realize control mode switching or controller parameter updating. By implementing the present application, the optimal balance of asset health and comprehensive benefit of the mobile storage and charging device hybrid energy storage system in the whole life cycle can be realized.
Need to check novelty before this filing date? Find Prior Art

Citation Information

Patent Citations

  • Hybrid energy storage system optimization method and device for stabilizing intermittent loads

    CN112260296A

  • Supply deployment space-time optimization method, device and equipment for mobile energy storage vehicle

    CN119944768A

  • Intelligent optimization method and system for revenue mode of commercial energy storage power station

    CN120601424A

  • Method and system for predicting and analyzing health state of cable in full life cycle

    CN120974284A

  • Robot noninvasive health physical examination and monitoring platform

    CN121545703A