Big data extraction and calculation method for capacity imbalance degree of lithium iron phosphate battery system

Abstract

The invention discloses a big data extraction and calculation method for the capacity imbalance degree of a lithium iron phosphate battery system. The method comprises the following specific steps: setting screening conditions, screening voltage and power values, obtaining a voltage-electric quantity curve, obtaining a broken line fitting curve of the voltage-electric quantity acquisition data, carrying out smooth fitting processing on the broken line fitting curve to obtain a voltage-electric quantity / voltage curve of the lithium iron phosphate battery, and carrying out calculation accordingto the voltage-electric quantity / voltage curve of the lithium iron phosphate battery to obtain imbalance degree delta SOC values of different batteries so that the calculation of the capacity imbalance degree is completed. According to the method, the M-shaped double-peak characteristic presented by the derivation curve can be stabilized, and the calculated amount of big data processing can be reduced on the premise that the precision of key characteristic parameters is not reduced.

Description

technical field [0001] The invention relates to the field of new energy passenger car battery systems, and more specifically to a large data extraction and calculation method for capacity imbalance of lithium iron phosphate battery systems. Background technique [0002] The power battery system of a new energy vehicle consists of several cells connected in series and parallel, and the performance of the system is affected by the consistency of the cells. After a new energy vehicle power battery system has been used for a long time, differences in temperature, self-discharge rate, capacity decay rate, and Coulomb efficiency between battery cells will cause inconsistencies in the SOC between battery cells, that is, capacity imbalance. This unbalanced phenomenon will reduce the available capacity of the battery system, reduce the utilization efficiency, and reduce the mileage of the vehicle. It is also the main reason for the battery undervoltage alarm to occur when the system ...

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Problems solved by technology

[0009] However, it is found that the above-mentioned disclosed technical solution still has deficiencies and shortcomings when it is put into use, that is, during the use of the above-mentioned solution, it directly performs filtering smooth fitting linear processing on the obtained voltage-electricity curve to obtain a derivation curve, In the smooth fitting process, not only because the curve needs to further eliminate the interference signal in the derivation process, but also needs to use multiple iterative convolution calculations, which is very uneconomical for large data operations with a huge amount of calculation, and because there is no The method artificially interferes with the batch processing operation, which will easily cause the final derivation calculation to fail, so that the derivation curve cannot present the voltage-∆power / ∆voltage curve that extracts the M-shaped characteristics of the lithium iron phosphate battery

On the other hand, in the smooth fitting process of filtering, because the obtained original curve is full of information burrs, there will be unfiltered information burrs in the process of linear processing, and the information burrs will appear when the curves are connected smoothly. Unpredictable chaotic lines will appear, which will affect the recognition of M-shaped characteristics of lithium iron phosphate batteries

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