Charging method and device of unmanned aerial vehicle battery, medium and equipment

Abstract

The invention discloses a charging method and device of an unmanned aerial vehicle battery, a medium and equipment. The charging method comprises the steps of collecting the charging current I, battery temperature T and cycle number A of the unmanned aerial vehicle battery in the charging process; acquiring an integral correction coefficient K of the battery residual power according to I, T, A inthe step S01; obtaining the residual power of the battery according to the integral correction coefficient of the battery residual power; and acquiring the optimum charging rate according to the residual power of the battery, and adjusting the charging current so as to achieve quick charge for the unmanned aerial vehicle. The charging method and device of the unmanned aerial vehicle, the medium and the equipment all have the advantages of accurate estimation of the residual power, high charging speed, simple calculation and the like.

Description

technical field [0001] The present invention mainly relates to the technical field of drone batteries, in particular to a charging method, device, medium and equipment for drone batteries. Background technique [0002] The fast charging of the drone needs to fully charge the lithium battery in a short time, which requires charging in stages, and adopts different charging strategies at different SOC (remaining power) values, so the SOC needs to be measured accurately, and the commonly used SOC algorithm has open circuit voltage. method, current integration method, Kalman filter algorithm, neural network algorithm and ampere-hour integration method. The ampere-hour integration method is an open-loop prediction, which can accurately estimate the SOC in a short time, but there are problems that the initial SOC cannot be determined and the cumulative error is getting larger and larger. The open circuit voltage method is simple and easy, but it requires the battery to stand for a...

AI Technical Summary

Problems solved by technology

The ampere-hour integration method is an open-loop prediction, which can accurately estimate the SOC in a short time, but there are problems that the initial SOC cannot be determined and the cumulative error is getting larger and larger

The open circuit voltage method is simple and easy, but it requires the battery to stand for a long time, which cannot meet the requirements of online detection

Under the premise of building a good network model, the neural network method can obtain better accuracy by relying on a large number of samples for data training, but this method is very dependent on the training method and training data, and has not been well used yet.

The Kalman filter method can obtain the minimum variance estimation of SOC by a recursive algorithm based on the collected voltage and current, and solve the problems of inaccurate initial SOC estimation and cumulative errors. higher speed requirements

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