Title :
Prediction of Discharge Capacity of Lithium Battery Based on Cloud Neural Network
Author :
Jing Wan ; Qingdong Li
Author_Institution :
Dept. of Autom. Sci. & Electr. Eng., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China
Abstract :
The prediction of discharge capacity of lithium batteries was one of the main tasks of battery management system. The discharge capacity of lithium batteries was related with many parameters, including discharge current, voltage, temperature, and the past charge and discharge history. The prediction methods of existing lithium battery discharge capacity mostly have no learning capabilities and nonlinear prediction ability, in order to predict the discharge capacity of lithium battery more accurately, an algorithm Based on cloud neural network (CNN) was presented. On the basis of the analysis of the actual data of NASA, determine the related influence factors of discharge capacity, set up a corresponding CNN prediction model using cloud model, and use the cloud model for adaptive adjustment of the learning speed. Comparing with the traditional NN method, the simulation result demonstrates that the CNN prediction model has smaller prediction error.
Keywords :
battery management systems; discharges (electric); lithium; neural nets; power engineering computing; secondary cells; CNN; Li; NASA; adaptive adjustment; battery management system; cloud model; cloud neural network; discharge capacity prediction; learning speed; lithium battery; prediction error; Batteries; Clouds; Discharges (electric); Entropy; Lithium; Neural networks; Predictive models; cloud model; discharge capacity prediction; lithium battery; neural network;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on
Print_ISBN :
978-0-7695-5011-4
DOI :
10.1109/IHMSC.2013.86