Method for forecasting service life of lead-acid storage battery

Abstract

The invention discloses a method for forecasting the service life of a lead-acid storage battery. The method comprises the steps of: periodically activating the storage battery to obtain an activated charging capacity thereof and thereby obtain a variable point (t, Sc) of each periodically activating time t and the corresponding charging capacity Sc; naturalizing the variable point (t, Sc) obtained after each activation to the corresponding normal capacity point (ti, Sci), wherein i is the number of corresponding periodically activating times; calling a least square method to fit a parabolic function Sc=(i)at( / i)2(i)+bt( / i)+c of the charging capacity Sc and the time t as the forecasting curve for the service life of the storage battery, wherein a, b and c are related coefficients of the parabola. The method for forecasting the service life of the lead-acid storage battery achieves improved forecasting accuracy, and is applicable to various storage batteries and low in forecasting cost; the forecasting method adopts a single parameter for forecasting, thus reducing computation complexity.

Description

technical field [0001] The invention relates to the field of distribution networks, in particular to a method for predicting the service life of a lead-acid battery pack. Background technique [0002] As an independent DC power supply for transmission, protection, control and communication, lead-acid batteries are widely used in distribution network automation systems. The opening and closing of the mechanism and the operation of the power distribution terminal equipment provide electric energy. Therefore, the stable, reliable and safe operation of batteries is of great significance to distribution network automation. [0003] Batteries in the distribution network are mostly installed outdoors, and the operating environment is harsh. In addition, improper use of batteries can easily lead to water loss inside the battery and corrosion of the plates, resulting in a decrease in battery capacity and performance. As the distribution of batteries becomes wider and larger, the nu...

AI Technical Summary

Problems solved by technology

[0015] Among them, the disadvantage of life prediction based on a single internal resistance parameter is that the internal resistance will not change significantly until the battery capacity drops by 25%~30%, and currently 80% of the failures are judged to be failures, so the accuracy of the internal resistance measurement method deviates very big

However, the multi-input parameter variable neural network class needs to carry out strict system identification on a specific battery model before it can be applied to practice. A large amount of sample data is required in advance to support it, and all possible life-influencing factors in practical applications are tested experimentally. also unrealistic

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