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Battery health degree detection method and device

A detection method and technology for health degree, applied in the field of battery health degree detection method and detection device, can solve the problems of inconvenience, non-linear influence of battery characteristics, low practical performance of model, etc., and achieve the effect of high detection accuracy

Pending Publication Date: 2019-04-16
GUANGDONG INST OF INTELLIGENT MFG
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing technology has the following defects: current, power, state of charge, temperature and other factors have nonlinear effects on battery characteristics, and the improved Thevenin battery equivalent model used in the scheme is a linear model, so the practical performance of the model is not high
And the acquisition of the initial state requires many setting conditions, such as open circuit voltage, battery static capacity identification, etc., which is not very convenient for practical application

Method used

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  • Battery health degree detection method and device
  • Battery health degree detection method and device
  • Battery health degree detection method and device

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Experimental program
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Effect test

Embodiment 1

[0035] combine Figure 1-4 , the present invention provides a battery health detection method, characterized in that it comprises the following steps:

[0036] Step S1: Detect the battery voltage and current, and obtain the voltage value and current value;

[0037] Step S2: Detect the AC impedance value of the battery under different frequency currents;

[0038] Step S3: Establish a first deep learning parameter optimization model, and input the voltage value and the current value obtained in step S1 and the AC impedance value obtained in step S2 to obtain an estimated value of battery health SOH 1 ;

[0039] Step S4: Obtain the charge and discharge efficiency value through the charge and discharge efficiency calculation formula;

[0040] Step S5: Establish a second deep learning parameter optimization model, and input the voltage value and the current value obtained in step S1 and the charge-discharge efficiency value obtained in step S4 to obtain an estimated value of bat...

Embodiment 2

[0055] combine Figure 5 , this embodiment provides a battery health detection device, including a battery module 1, a sampling module 2, an impedance detection module 5, a data processing module 4 and a control module 3, the battery module 1 is connected to the sampling module 2 , the sampling module 2 is used to detect the current and voltage of the battery in real time, the impedance detection module 5 is connected to the battery module 1, and the impedance detection module 5 is used to detect the impedance value of the battery at different frequencies , the data processing module 4 is used for data storage, transmission and updating; the control module 3 is connected to the sampling module 2 and the data processing module 4 in communication, and the control module 3 is used for managing the sampling module 2 , process the collected sampling data, and establish a deep learning parameter optimization model through the data processing module 4 to realize battery health detect...

Embodiment 3

[0070] This embodiment is a further optimization of Embodiment 2. The present invention provides a battery health detection device, which also includes:

[0071] A warning module, the warning module is electrically connected to the data processing module 4, and can analyze the battery health calibration value calculated by the data processing module 4, and give a warning to whether the battery is healthy or not.

[0072] The warning module includes a numerical comparator, an optical modulator and a speaker, and the numerical comparator is electrically connected to the output unit for comparing the battery health calibration value with the battery health health value;

[0073] The range value (a-b) of the battery health degree is input into the numerical comparator, the numerical comparator receives the calibration value c of the battery health degree transmitted by the output unit, and the numerical comparator logs the value c and the Compared with the battery health range val...

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Abstract

The invention discloses a battery health degree detection method and device, and belongs to the technical field of battery detection, and the method comprises the following steps: detecting the voltage and current of a battery, and obtaining a voltage value and a current value; detecting alternating current impedance values of the battery under different frequency currents; establishing a deep learning model, and obtaining a battery health degree estimation value SOH1; obtaining a charging and discharging efficiency estimation value through a charging and discharging efficiency calculation formula; according to the voltage value, the current value and the charging and discharging estimation value, establishing a deep learning mode, and obtaining a battery health degree estimation value SOH2; obtaining a battery health degree estimation comprehensive value SOH through an information fusion algorithm; and carrying out optimization calibration on the battery health degree estimation comprehensive value SOH to obtain a battery health degree calibration value. Compared with the prior art, the battery health degree detection method and device have the advantages that the self-repairing capability is realized, and the detection precision is high, and online learning optimization can be realized, various environments are adapted, and the influence of detection conditions is avoided.

Description

technical field [0001] The invention relates to the technical field of battery detection, in particular to a battery health detection method and detection device. technical background [0002] Studies have shown that the main mechanisms of lithium-ion battery capacity fading include: the occurrence of side reactions, the deposition of lithium metal, the anodic oxidation and cathodic reduction of the electrolyte, the formation of passivation films on the surface of positive and negative electrodes, the dissolution of electrode active materials, phase changes and Structural changes, corrosion of current collectors, etc. However, the mechanism of capacity fading of lithium-ion batteries is still not very clear. Li-ion batteries with different structural forms and different electrochemical systems have different mechanisms of capacity fading. [0003] The existing technology is the patent No. CN201210524782.X, and the patent name is "A Method for SOC and SOH Prediction of Lithi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367G01R31/385G01R31/389G01R31/392
Inventor 刘晓光蒋晓明唐朝阳王长华
Owner GUANGDONG INST OF INTELLIGENT MFG
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