Elman neural network using ant colony optimization algorithm for estimating of state of charge of lithium-ion battery

Abstract The state of charge (SOC) is a parameter to describe the remaining charge of lithium-ion batteries in electric vehicles. It is a key problem to be solved in the field of electric vehicles. In this paper, ant colony optimization (ACO) algorithm is creatively applied to improve Elman neural network to form ACO-Elman neural network model, and it is applied to lithium-ion battery SOC prediction for the first time. The ACO-Elman model is trained and tested under Dynamic Stress Test and Federal Urban Driving Schedule drive profiles. The SOC estimation results of ACO-Elman model are evaluated from three aspects: mean absolute error, root mean square error, and SOC error. The results show that the ACO-Elman model has high accuracy and robustness. It has a good application prospect.

[1]  Hicham Chaoui,et al.  State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks , 2017, IEEE Transactions on Vehicular Technology.

[2]  Krishna R. Pattipati,et al.  Open circuit voltage characterization of lithium-ion batteries , 2014 .

[3]  He Jiang,et al.  Model forecasting based on two-stage feature selection procedure using orthogonal greedy algorithm , 2018, Appl. Soft Comput..

[4]  Xiaosong Hu,et al.  Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for elec , 2011 .

[5]  Whei-Min Lin,et al.  A New Elman Neural Network-Based Control Algorithm for Adjustable-Pitch Variable-Speed Wind-Energy Conversion Systems , 2011, IEEE Transactions on Power Electronics.

[6]  Zhenwei Cao,et al.  A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles , 2014 .

[7]  Peng Zhang,et al.  Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles , 2016 .

[8]  Miguel A. Cristin Valdez,et al.  Estimating Soc in Lead-Acid Batteries Using Neural Networks in a Microcontroller-Based Charge-Controller , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[9]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[10]  A. Emadi,et al.  Electrochemical and Electrostatic Energy Storage and Management Systems for Electric Drive Vehicles: State-of-the-Art Review and Future Trends , 2016, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[11]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background , 2004 .

[12]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[13]  Michael Pecht,et al.  State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation , 2014 .

[14]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[15]  Hongwen He,et al.  Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles , 2012 .

[16]  Ali Emadi,et al.  Fast Model Predictive Control for Redistributive Lithium-Ion Battery Balancing , 2017, IEEE Transactions on Industrial Electronics.

[17]  A. Bonfitto,et al.  Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries , 2019, Batteries.

[18]  Guojun Li,et al.  State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting , 2015 .

[19]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

[20]  Yanqing Shen,et al.  Adaptive online state-of-charge determination based on neuro-controller and neural network , 2010 .

[21]  Jae Wan Park,et al.  Battery state of charge estimation using a load-classifying neural network , 2016 .

[22]  Xiaosong Hu,et al.  Robustness analysis of State-of-Charge estimation methods for two types of Li-ion batteries , 2012 .

[23]  Jian Ma,et al.  A new neural network model for the state-of-charge estimation in the battery degradation process , 2014 .

[24]  Chris Manzie,et al.  Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery , 2016 .

[25]  Seongjun Lee,et al.  State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .

[26]  Bor Yann Liaw,et al.  On state-of-charge determination for lithium-ion batteries , 2017 .

[27]  Shengbo Eben Li,et al.  Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles , 2016, IEEE Transactions on Transportation Electrification.

[28]  D. Sauer,et al.  Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries , 2011 .

[29]  Jae Sik Chung,et al.  A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation , 2010 .

[30]  Mohammad Farrokhi,et al.  State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF , 2010, IEEE Transactions on Industrial Electronics.

[31]  Chao Dong,et al.  Estimation of power battery SOC based on improved BP neural network , 2014, 2014 IEEE International Conference on Mechatronics and Automation.