Lithium iron phosphate battery state-of-charge monitoring and early warning method based on neural network

Abstract

The invention discloses a lithium iron phosphate battery state-of-charge monitoring and early warning method based on a neural network, and the method comprises the steps: carrying out the training ofa T-S fuzzy neural network model through a training data set, and obtaining a trained T-S fuzzy neural network model; inputting actual battery voltage, battery current and battery temperature data atthe moment T into the trained T-S fuzzy neural network model, and outputting estimated battery SOC; when the SOC decline rate of the battery is higher than a set value, sending an early warning signal that the electric quantity declines too fast and the battery is abnormal; and when the SOC of the battery is lower than 10%, emitting an early warning signal of the low electric quantity of the battery. According to the lithium iron phosphate battery state-of-charge monitoring and early warning method based on the neural network provided by the invention, the SOC estimation precision can be effectively improved, the battery state-of-charge is monitored and early warned, and the reliability and safety of the battery are improved.

Description

technical field [0001] The invention relates to a method for monitoring and early warning of the state of charge of a lithium iron phosphate battery based on a neural network, and belongs to the technical field of state of charge monitoring and early warning of batteries. Background technique [0002] At present, in order to cope with the energy crisis and reduce global warming, many countries have begun to attach importance to reducing emissions, saving energy and developing a low-carbon economy. Because electric vehicles can reduce carbon dioxide emissions and even achieve zero emissions by using electric power, they have been paid attention to and developed rapidly by various countries. With the development of electric vehicles, more and more electric vehicles use lithium batteries as power sources to cut off, but the cost of batteries is still high, and the performance and price of power batteries have become the main "bottleneck" in the development of electric vehicles....

AI Technical Summary

Problems solved by technology

[0005] In the previous strategy, due to the inaccurate current measurement, there are cumulative errors, and the open circuit voltage method can accurately estimate the SOC of the battery, but the open circuit voltage can only be measured for a long time after the battery stops working, which is not suitable for online estimation

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