access icon openaccess State of charge prediction for UAVs based on support vector machine

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 17/10/2018, Accepted 09/01/2019, Published 25/03/2019

Unmanned aerial vehicle (UAV) is a power-driven aircraft that is unmanned and reusable. The purpose of this study is to accurately estimate the state of charge (SOC) of lithium-ion batteries for UAVs. A support vector machine (SVM) method, SVM is a type of learning machine based on statistical learning, is used as the input variable of the battery charging discharge data (current, voltage and temperature). The kernel of the radial basis function is the best kernel of authors’ experiment, where the C, and g values are 1, 0.012 and 0.0125, respectively. The experimental results from the lithium-ion battery data at NASA Ames Prognostics Center of Excellence demonstrate the potential application of the proposed method as an effective tool for battery SOC prediction. The accuracy of the whole experiment is 98.42%. Mean-squared error is 1.783%. The experimental results show that the model has higher accuracy in predicting the discharge capacity of lithium battery SOC-training samples.

Inspec keywords: mean square error methods; secondary cells; learning (artificial intelligence); radial basis function networks; support vector machines; autonomous aerial vehicles; power engineering computing

Other keywords: NASA Ames Prognostics Center of Excellence demonstrate; UAV; SVM method; battery charging discharge data; lithium-ion batteries; statistical learning; state of charge prediction; radial basis function; mean-squared error; support vector machine method; machine learning; battery SOC prediction

Subjects: Interpolation and function approximation (numerical analysis); Secondary cells; Knowledge engineering techniques; Secondary cells; Power engineering computing; Neural computing techniques; Interpolation and function approximation (numerical analysis); Mobile robots; Numerical approximation and analysis; Aerospace control

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