PROFESSIONAL DIAGNOSIS METHOD OF BATTERY PERFORMANCE ANALYSIS

• US 20110054815A1
• Filed: 04/17/2008
• Published: 03/03/2011
• Est. Priority Date: 04/20/2007
• Status: Active Grant

• Current Assignee: Hangzhou Gold Electronic Equipment Company Limited
• Original Assignee: Hangzhou Gold Electronic Equipment Company Limited
• Inventors: Zheng, Yi, Xu, Jianhong
• Granted Patent: US 8,306,781 B2
• Time in Patent Office: Days
• US Class Current: 702/63
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/382 Arrangements for monitoring battery or accumulator variables, e.g. SoC

Abstract

The present invention discloses a professional diagnosis method of battery performance analysis, Through the overall evaluation of experiential data library, several parameters about the battery are input into the artificial neural network, outputting capacity prediction and service life prediction of each battery, etc. and giving useful advices for each battery. Therefore the result is much more in conformity with the real condition of the battery. Besides, it designs an adaptive learning function of the abovementioned artificial neural network. This invention effectively avoids the defect of evaluating the VRLA battery performance at single moment, from single perspective and by single method, and it does the real-time monitoring and evaluating for the performance of the battery during VRLA battery working period, which is easy to operate, and avoids checking discharge test to the battery so that it doesn'"'"'t affect the cycle life of the VRLA battery.

Claims

1. A professional diagnosis method of battery performance analysis, using any two or more than two online gathered parameters, including maximum voltage of each battery in equalized charge period, minimum voltage of each battery in equalized charge period, float voltage dispersion ratio of single battery, float voltage dispersion ratio of battery set, battery IR at the end of the discharge r(i), voltage of each battery at the end of the short-term discharge, voltage difference and temperature of each battery during the short-term charge and discharge, as the input of the artificial neural network which calculates and outputs the predicted battery performance and gives relevant operating advices.

2. The professional diagnosis method of battery performance analysis according to claim 1, wherein said float voltage dispersion ratio of single battery can be calculated by equation (3):

3. The professional diagnosis method of battery performance analysis according to claim 1, wherein discharge battery IR at the end of the discharge can be calculated by equation (5):

r(i)=[U_ij−

U_ij+2]/I 

 

(5)

where, I is the discharge current, and U_ij is the voltage of the battery i at the moment j in the discharge process, U_ij+2 is the voltage of the battery i at the moment j+2 in the discharge process, which satisfy the condition;

(U_ij−

U_ij+2)>

0.002.

4. The professional diagnosis method of battery performance analysis according to claim 3, wherein uses the modifying IR of each battery as the input of the artificial neural network, modifying IR of each battery can be calculated by equation (6):

R(i)=40.404×

[r(i)−

r]²+0.5836371(6)

where, r is the average IR of battery set.

5. The professional diagnosis method of battery performance analysis according to claim 1, wherein the present system designs an adaptive learning function of the abovementioned artificial neural network, which the network will automatically change the learning set, restart learning and training, and then form a new evaluation system under special condition when the valve-regulated lead-acid battery goes through full or half capacity checking discharge testing.