TY - GEN
T1 - Prediction of discharge capacity of lithium battery based on cloud neural network
AU - Wan, Jing
AU - Li, Qingdong
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Cloud model
KW - Discharge capacity prediction
KW - Lithium battery
KW - Neural network
UR - https://www.scopus.com/pages/publications/84891911477
U2 - 10.1109/IHMSC.2013.86
DO - 10.1109/IHMSC.2013.86
M3 - 会议稿件
AN - SCOPUS:84891911477
SN - 9780769550114
T3 - Proceedings - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013
SP - 335
EP - 339
BT - Proceedings - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013
T2 - 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2013
Y2 - 26 August 2013 through 27 August 2013
ER -