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Battery module safety state evaluation method and device based on convolutional neural network

A technology of convolutional neural network and safety state assessment, applied in biological neural network models, neural learning methods, measurement devices, etc., can solve the problem of not being able to identify thermal runaway or thermal runaway battery cells, and not being able to evaluate the safety state of battery cells at the same time and health status, the failure to detect the overall risk of the energy storage battery module in time, etc., to achieve the effect of low misjudgment rate and high accuracy

Pending Publication Date: 2022-06-03
ELECTRIC POWER RES INST OF STATE GRID ANHUI ELECTRIC POWER +3
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0010] The technical problem to be solved by the present invention is that the prior art cannot evaluate the safety state and health state of the battery unit at the same time, and cannot identify the battery unit that is about to experience thermal runaway or is undergoing thermal runaway, so that the overall risk of the energy storage battery module cannot be discovered in time

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  • Battery module safety state evaluation method and device based on convolutional neural network
  • Battery module safety state evaluation method and device based on convolutional neural network
  • Battery module safety state evaluation method and device based on convolutional neural network

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Embodiment 1

[0054] like figure 1 As shown, a method for evaluating the safety status of a battery module based on a convolutional neural network, the method includes:

[0055] S1: Extract the output voltage, output current and surface temperature at the center of the battery cell under different decay states and different working states, form a three-parameter collaborative evaluation database, and divide the samples in the database into a training set and a test set; the specific process is as follows:

[0056] The state of health (SOH) of a battery cell is equal to the current power after the battery is cycled and the battery capacity before the battery cell is cycled. For multiple groups of lithium-ion batteries, cycle between the discharge cut-off voltage and the charge cut-off voltage for many times by means of constant current and constant voltage charging or constant current discharge through standard charging or discharging rates, until different health states (100% SOH, 85% SOH,...

Embodiment 2

[0081] The invention also provides a battery module safety state assessment device based on a convolutional neural network, comprising a data acquisition module, a data processing module and a display module connected in sequence,

[0082] The data acquisition module is used to collect the output voltage signal, the output current signal and the temperature signal at the center wall of the battery unit, and send the collection result to the data processing module;

[0083] a data processing module, configured to receive the data of the data acquisition module, and execute the method described in any of the above;

[0084] The display module is used for displaying the processing and classification results of the data processing module on the front end of the computer.

[0085] Specifically, the data acquisition module includes a SMD thermistor and a BMS battery management system. The SMD thermistor is attached to the central wall of the battery unit of the battery module, and i...

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Abstract

The invention discloses a battery module safety state evaluation method and device based on a convolutional neural network, and the method comprises the steps: extracting the output voltage and output current of a battery unit and the surface temperature of a central position in different recession states and different working states, and forming a three-parameter collaborative evaluation database, dividing samples in the database into a training set and a test set; inputting the training set into a convolutional neural network; inputting the test set into the convolutional neural network, and if the output precision does not meet the requirement, returning to the above steps until the output result precision meets the requirement; inputting battery module data collected in real time into the final convolutional neural network model to obtain safety state information and health state information of the battery unit; the method has the advantages that the safety state and the health state of the battery unit are evaluated at the same time, and the battery unit about to be subjected to thermal runaway or being subjected to thermal runaway is identified, so that the overall risk of the energy storage battery module is found in time.

Description

technical field [0001] The invention relates to the field of safety warning of energy storage battery modules, and more particularly to a method and device for evaluating the safety state of battery modules based on a convolutional neural network. Background technique [0002] Under the dual pressures of energy shortage and ecological environment deterioration, electrochemical energy storage systems with lithium-ion batteries as the main storage medium have developed rapidly. However, due to its high energy density and flammable and explosive material system, lithium batteries, which are the core components of electrochemical energy storage systems, are prone to thermal runaway under abuse conditions such as thermal, electrical, and mechanical damage. Due to the large number of lithium batteries, large installed capacity and poor heat dissipation conditions in the energy storage system, once a single battery has thermal runaway, it will cause a chain thermal runaway of the e...

Claims

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Application Information

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IPC IPC(8): G01R31/392G01R31/367G01R31/3842G06N3/04G06N3/08G06K9/62G06F16/901G06Q10/06G06Q50/06
CPCG01R31/392G01R31/367G01R31/3842G06N3/084G06F16/901G06Q10/0635G06Q50/06G06N3/047G06N3/048G06N3/045G06F18/2415G06F18/214Y02E60/10
Inventor 汪书苹祝现礼王海超刘辉李昌豪高飞王青松张佳庆程宜风
Owner ELECTRIC POWER RES INST OF STATE GRID ANHUI ELECTRIC POWER
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