Abstract
State of charge (SOC) is an important indicator for assessing the remaining capacity of the battery. An accurate SOC estimation is crucial for ensuring the safe operation of lithium batteries and preventing from over-charging or over-discharging in electric vehicle (EV) industry. However, to estimate an accurate capacity of SOC of the lithium batteries has become a major concern for the EV industry. In this paper, a recurrent nonlinear autoregressive external input neural network(NARXNN) model optimized by genetic algorithm(GA) is proposed to improve accuracy of SOC of lithium battery by finding the optimal value of input delays, feedback delays, and hidden layer neurons. The NARXNN based GA model is compared with the NARXNN in performance using statistical error values of mean absolute error and root mean square error are used to check the performance of the SOC estimation. The results show that the NARXNN based genetic algorithm outperforms NARXNN in estimating SOC with high accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
