Method for preventive detection and diagnosis of battery pack
A battery pack and preventive technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problems of battery pack failure, few application scenarios, and difficult actual operation, and achieve easy acquisition, effective calculation results, The effect of low computing power requirements
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example 1
[0032] The target voltage extreme value difference data comes from the WXL12S537300A power module produced by a certain company. The experimental procedure is to carry out constant current charging and charging cycles on the cascade batteries, and cycle at 300A and a constant temperature of 45°C until the battery pack fails to work normally. The results of the experiment took a total of 3 days, and the echelon battery pack was scrapped, and the battery was in a static state at a constant temperature.
[0033] From the cell voltage data of previous charging, the battery pack voltage extreme value difference under the condition of SOC=50% is extracted, and the voltage extreme value difference data set ΔV is obtained 50％· ={Δv 1 , Δv 2 ,... Δv n}, n is the number of cycles. The change of the charging voltage extreme value difference of the battery pack with the number of cycles is as follows: image 3 As shown, the specific time corresponding to the number of charging cycles i...
example 2
[0050] The analysis of the actual charging cycle data of the battery pack of an energy storage power station is the 4-month operation data from January to April, with 4 charges per month. There are as many as 240 battery cells in the battery pack. For the maximum voltage, the analysis and research of only two values is more convenient in data acquisition. The diagnostic results of the preventive detection and diagnosis method are shown in Table 5, and no failure has occurred.
[0051] Table 5 Diagnosis results of box plot
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[0054] And compared with the actual operation situation, it is consistent with the actual operation situation, which shows that the diagnosis result of this method is accurate, the probability of wrong diagnosis is low, and the cost of regular maintenance can be reduced and the maintenance cost of avoiding false alarms can be reduced.
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