Please use this identifier to cite or link to this item:
|Title:||Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm||Authors:||Hossain Lipu, M.S.
|Issue Date:||2018||Abstract:||This paper aims to develop an accurate estimation technique for computing state of charge (SOC) of a lithium-ion battery using recurrent neural network algorithm. Nonlinear autoregressive with exogenous input (NARX) model is a well-known subclass of the recurrent neural network which has proven to be very effective and computationally rich for controlling dynamic system and predicting time series. However, the accuracy of recurrent NARX neural network depends on the amount of input and output order as well as a number of neurons in a hidden layer. Therefore, this study presents an improved recurrent NARX neural network based SOC estimation with particle swarm optimization (PSO) algorithm for finding the best value of input delays, feedback delays and a number of neurons in a hidden layer. The proposed model uses three most significant factor such as current, voltage and temperature without considering battery model. The model robustness is checked at low temperature (0°C), medium temperature (25°C), and high temperature (45°C). The US06 drive cycle is selected for model training and testing. The effectiveness of the proposed approach is compared with the back-propagation neural network (BPNN) optimized by PSO based on the SOC error, root mean square error (RMSE) and mean absolute error (MAE) and average execution time (AET). The results prove that the proposed model has higher estimation speed and achieves higher accuracy in reducing RMSE and MAE by 53% and 50% than BPNN based PSO model at 25°C. © 2018 IEEE.||URI:||http://dspace.uniten.edu.my/jspui/handle/123456789/10632||DOI:||10.1109/ISCAIE.2018.8405498|
|Appears in Collections:||CCI Scholarly Publication|
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.