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Lithium-ion power battery health state evaluating system and method

A power battery and health state technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problems of difficult calculation accuracy, large calculation error, large change rate, etc., and achieve accurate calculation results and accurate calculation results Effect

Inactive Publication Date: 2013-10-09
广州赛益迪新能源科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] This method has the following problems: a. The SOH value evaluation step based on battery capacity involves measuring the SOC of the battery with the ampere-hour integration method. , the error will gradually increase; b. The SOH value evaluation step based on the internal resistance of the battery uses the method of measuring the AC impedance, and the AC impedance calculated at different current frequencies may have relatively large differences, and the AC impedance of the battery is only The rate of change is relatively large when the SOC of the battery is very low or high, and the rate of change is small when the SOC is in the middle. If the measurement is not accurate enough, the calculation error will be relatively large.
The actual dynamic resistance and the terminal voltage of the battery are affected by nonlinear factors such as temperature and charge and discharge state, so it is difficult to calculate accurately, thus affecting the estimation accuracy of SOH

Method used

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Examples

Experimental program
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Effect test

Embodiment 1

[0048] Such as figure 1 As shown, the lithium-ion power battery health status evaluation system of the present embodiment includes an MCU central control module 1, a voltage and current detection module 2, a GPES and FRA detection module 3, a display module 4 and a battery module 5, and the battery module 5 Contains a lithium-ion power battery; the battery module 5 is connected to the voltage and current detection module 2 and the GPES and FRA detection module 3 respectively, and the voltage and current detection module 2 uses an infrared transceiver or an optical fiber transceiver to communicate with the MCU central control module 1 data connection, the display module 4 is connected to the output end of the MCU central control module 1 through a circuit, and the MCU central control module 1 is connected to the GPES and FRA detection module 3, and the GPES and FRA detection module 3 are AutoLab instrument automatic with modules.

[0049] Such as figure 2 As shown, the MCU c...

Embodiment 2

[0063] Step 1) to step 3) are the same as in Example 1. In step 4), the lithium-ion power battery that has undergone 600 charge-discharge cycles is charged, and finally the SOH value calculated in step 7) is about 0.6. Since 0.5<0.6 <1, the lithium-ion power battery is still in a normal state.

Embodiment 3

[0065] Step 1) to step 3) are the same as in Example 1. In step 4), the lithium-ion power battery that has undergone 800 charge-discharge cycles is charged, and finally the SOH value calculated in step 7) is about 0.45. Since 0.45<0.5 , the lithium-ion power battery is scrapped.

[0066]From the above-mentioned Examples 1-3, it can be found that the mode cycle of "charging-discharging" is carried out to the lithium-ion power battery. The health status of the battery is more accurate than the evaluation method of the prior art. This is because the number of cycles is different (the charge and discharge current and voltage parameters are set the same), and then it is discharged at the same rate, and then it is left to stand, and the voltage rises immediately, and it is open. The voltage recovery rate has a monotonic relationship with the number of cycles, that is, the more cycles, the faster the open circuit voltage recovery rate, which is related to the polarization of the batt...

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Abstract

The invention discloses a lithium-ion power battery health state evaluating system and method. The system comprises an MCU central control module, a voltage and current detecting module, a GPES and FRA detecting module, a display module and a battery module with a lithium-ion power battery. According to the method, voltage changes after the lithium-ion power battery discharges and is cut off are recorded so as to obtain voltage recovery rate and further obtain a main factor judgment value through the voltage recovery rate; alternating-current impedance testing is conducted on the lithium-ion power battery so as to obtain alternating-current impedance spectra, fitting is conducted on the alternating-current impedance spectra to obtain an equivalent circuit diagram, the interior impedance of the lithium-ion power battery is worked out according to impedances of all parts inside the lithium-ion power battery, a secondary factor judgment value is obtained through the interior impedance, the state of health (SOH) value of the lithium-ion power battery is the total of the main factor judgment value and the secondary factor judgment value, and therefore calculation result is more accurate.

Description

technical field [0001] The invention relates to a battery health state evaluation system, in particular to a lithium-ion power battery health state evaluation system and method, belonging to the technical field of battery management. Background technique [0002] At present, there are mainly the following methods for evaluating the health status of lithium-ion power batteries: [0003] 1) Based on the current characteristics, voltage characteristics, temperature characteristics to obtain the safe state of the battery [0004] Decompose the time series of current value, voltage value and temperature value collected by wavelet packet algorithm and calculate the energy, local value-peak value, local maximum value, local minimum value, peak factor and skewness of each branch on the wavelet decomposition tree ; and calculate current characteristics, voltage characteristics and temperature characteristics by artificial neural network method, self-organizing map method and regress...

Claims

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Application Information

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IPC IPC(8): G01R31/36
Inventor 李小平黄伟昭李伟善邱显焕蔡燕凤
Owner 广州赛益迪新能源科技有限公司
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