By Topic

Neural network-based residual capacity indicator for nickel-metal hydride batteries in electric vehicles

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
W. X. Shen ; Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, China ; K. T. Chau ; C. C. Chan ; E. W. C. Lo

This paper presents a new estimation approach for the battery residual capacity (BRC) indicator in electric vehicles (EVs). The key of this approach is to model the EV battery by using a neural network (NN) with a newly defined output and newly proposed inputs. The inputs are the discharged and regenerative capacity distribution and the temperature. The output is the state of available capacity (SOAC) which represents the BRC. Various SOACs of the nickel-metal hydride (Ni-MH) battery are experimentally investigated under different EV discharge current profiles and temperatures. The corresponding data are recorded to train and verify the proposed NN. The results indicate that the NN can provide an accurate and effective estimation of the BRC. Moreover, this NN can be easily implemented as the BRC indicator or estimator for EVs by using a low-cost microcontroller.

Published in:

IEEE Transactions on Vehicular Technology  (Volume:54 ,  Issue: 5 )