State of charge prediction for UAVs based on support vector machine

Abstract

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, [inline-formula] 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.

Keywords

• learning (artificial intelligence)
• radial basis function networks
• power engineering computing
• support vector machines
• secondary cells
• autonomous aerial vehicles
• mean square error methods
• state of charge prediction
• machine learning
• battery charging discharge data
• nasa ames prognostics center of excellence demonstrate
• mean-squared error
• svm method
• uav
• statistical learning
• support vector machine method
• lithium-ion batteries
• battery soc prediction
• radial basis function