© 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.
All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Materials Science(all)
- Computer Science(all)
- Signal Processing
Hossain Lipu, M. S., Hannan, M. A., Hussain, A., & Saad, M. H. M. (2017). Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection. Journal of Renewable and Sustainable Energy. https://doi.org/10.1063/1.5008491