Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection

M. S. Hossain Lipu, M. A. Hannan, Aini Hussain, M. H.M. Saad

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

© 2017 Author(s). The state of charge (SOC) is the residual capacity of a battery, which indicates the available charge left inside a battery to drive a vehicle. Accurate SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. This paper presents an improved SOC estimation strategy for a lithium-ion battery using the back-propagation neural network (BPNN). Two algorithms, principal component analysis (PCA) and particle swarm optimization (PSO), are used to enhance the accuracy and robustness. PCA is utilized to select the most significant input features. The PSO algorithm is developed to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal BPNN model. The proposed model is tested and evaluated by using three electric vehicle drive cycles. The performance of the proposed model is compared with common BPNN and radial basis function neural network (RBFNN) models and verified based on the root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The validation results are very effective in predicting SOC with very narrow SOC error which demonstrates the model robustness. The results indicate that the proposed model computes RMSE to be 0.58%, 0.72%, and 0.47% for the Beijing Dynamic Stress Test (BJDST), Federal Urban Drive Schedule (FUDS), and US06, cycle, respectively.
Original languageEnglish
JournalJournal of Renewable and Sustainable Energy
DOIs
Publication statusPublished - 01 Nov 2017

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Principal component analysis
Particle swarm optimization (PSO)
Feature extraction
Neural networks
Backpropagation
Mean square error
Electric vehicles
Neurons
Lithium-ion batteries

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Energy(all)
  • Energy Engineering and Power Technology
  • Materials Science(all)
  • Computer Science(all)
  • Signal Processing

Cite this

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title = "Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection",
abstract = "{\circledC} 2017 Author(s). The state of charge (SOC) is the residual capacity of a battery, which indicates the available charge left inside a battery to drive a vehicle. Accurate SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. This paper presents an improved SOC estimation strategy for a lithium-ion battery using the back-propagation neural network (BPNN). Two algorithms, principal component analysis (PCA) and particle swarm optimization (PSO), are used to enhance the accuracy and robustness. PCA is utilized to select the most significant input features. The PSO algorithm is developed to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal BPNN model. The proposed model is tested and evaluated by using three electric vehicle drive cycles. The performance of the proposed model is compared with common BPNN and radial basis function neural network (RBFNN) models and verified based on the root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The validation results are very effective in predicting SOC with very narrow SOC error which demonstrates the model robustness. The results indicate that the proposed model computes RMSE to be 0.58{\%}, 0.72{\%}, and 0.47{\%} for the Beijing Dynamic Stress Test (BJDST), Federal Urban Drive Schedule (FUDS), and US06, cycle, respectively.",
author = "{Hossain Lipu}, {M. S.} and Hannan, {M. A.} and Aini Hussain and Saad, {M. H.M.}",
year = "2017",
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AU - Hussain, Aini

AU - Saad, M. H.M.

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N2 - © 2017 Author(s). The state of charge (SOC) is the residual capacity of a battery, which indicates the available charge left inside a battery to drive a vehicle. Accurate SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. This paper presents an improved SOC estimation strategy for a lithium-ion battery using the back-propagation neural network (BPNN). Two algorithms, principal component analysis (PCA) and particle swarm optimization (PSO), are used to enhance the accuracy and robustness. PCA is utilized to select the most significant input features. The PSO algorithm is developed to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal BPNN model. The proposed model is tested and evaluated by using three electric vehicle drive cycles. The performance of the proposed model is compared with common BPNN and radial basis function neural network (RBFNN) models and verified based on the root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The validation results are very effective in predicting SOC with very narrow SOC error which demonstrates the model robustness. The results indicate that the proposed model computes RMSE to be 0.58%, 0.72%, and 0.47% for the Beijing Dynamic Stress Test (BJDST), Federal Urban Drive Schedule (FUDS), and US06, cycle, respectively.

AB - © 2017 Author(s). The state of charge (SOC) is the residual capacity of a battery, which indicates the available charge left inside a battery to drive a vehicle. Accurate SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. This paper presents an improved SOC estimation strategy for a lithium-ion battery using the back-propagation neural network (BPNN). Two algorithms, principal component analysis (PCA) and particle swarm optimization (PSO), are used to enhance the accuracy and robustness. PCA is utilized to select the most significant input features. The PSO algorithm is developed to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal BPNN model. The proposed model is tested and evaluated by using three electric vehicle drive cycles. The performance of the proposed model is compared with common BPNN and radial basis function neural network (RBFNN) models and verified based on the root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The validation results are very effective in predicting SOC with very narrow SOC error which demonstrates the model robustness. The results indicate that the proposed model computes RMSE to be 0.58%, 0.72%, and 0.47% for the Beijing Dynamic Stress Test (BJDST), Federal Urban Drive Schedule (FUDS), and US06, cycle, respectively.

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