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RRAM array-based neural network energy storage battery operation state monitoring method

A neural network and energy storage battery technology, which is applied in the field of RRAM array-based neural network energy storage battery operation status monitoring, can solve problems such as inability to fully reflect the health status of energy storage batteries, early warning lag, and energy storage power station management lag, etc., to achieve Improve the quality of power supply, fast response, and improve the effect of cumbersomeness

Pending Publication Date: 2022-05-20
XIAN UNIV OF TECH
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AI Technical Summary

Problems solved by technology

The current BMS (battery management system) generally has the problems of early warning lag and the inability to fully reflect the health status of energy storage batteries, resulting in hysteresis in the management of energy storage power stations

Method used

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  • RRAM array-based neural network energy storage battery operation state monitoring method
  • RRAM array-based neural network energy storage battery operation state monitoring method
  • RRAM array-based neural network energy storage battery operation state monitoring method

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Embodiment Construction

[0027] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0028] The present invention is based on the neural network energy storage battery operating state monitoring method of the RRAM array, such as figure 1 As shown, it includes measurement module E1, signal processing module E2, neural network module E3 and energy storage battery monitoring center E4.

[0029] The measurement module E1 represents all the measurement units in the energy storage battery cabinet, covering the characteristic quantity monitoring modules E11 to E1n of 1 to n energy storage batteries, where the detected physical quantity of each monitoring module includes the characteristic gas released by the energy storage battery, energy storage The temperature of the battery and the characteristic sound when the safety valve operates.

[0030] The signal processing module E2 processes the state characteristic quantities of each e...

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Abstract

The invention discloses a neural network energy storage battery operation state monitoring method based on an RRAM array, and the method specifically comprises the following steps: 1, collecting the characteristic quantity of an energy storage battery module through an energy storage battery characteristic quantity measurement module, the characteristic quantity comprising characteristic gas, battery temperature and characteristic sound generated by a safety valve; 2, converting the characteristic quantity acquired in the step 1 into an optical signal, converting the optical signal into an electric signal through an optical fiber demodulator, and transmitting the electric signal to a signal processing module; step 3, the signal processing module processes the collected characteristic quantity and transmits the processed characteristic quantity to a neural network module; 4, after the neural network module receives the signal processed by the signal processing module, the abnormal state of the energy storage battery can be distinguished according to the circuit triggering condition of the trained neural network, and information is sent to a monitoring center. By adopting the method, the health condition of the energy storage battery can be predicted, and the health condition of the energy storage battery can be quickly and accurately judged.

Description

technical field [0001] The invention belongs to the technical field of battery health status monitoring for energy storage power stations, and relates to a method for monitoring the running status of a neural network energy storage battery based on an RRAM array. Background technique [0002] With the proposal of the "double carbon" goal, the future will develop towards the direction of building a new power system with new energy as the main body. Due to the intermittency and volatility of new energy sources such as photovoltaics and wind power, it will affect the stability and reliability of power supply. The deployment of energy storage can effectively promote the consumption of new energy and become an important strategic support for the large-scale utilization of new energy in the future. New energy supporting energy storage has become the general trend. However, due to various reasons, the current commercialized energy storage battery modules may cause thermal runaway ...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/392
CPCG01R31/367G01R31/392Y02E70/30
Inventor 张嘉伟邓威航王倩李程秦司晨
Owner XIAN UNIV OF TECH
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