Design and Simulation of an Accurate Neural Network State-of-Charge Estimator for Lithium Ion Battery Pack
State of charge (SOC) estimation is an important parameter that has to be determined by the battery management system, especially in electric vehicle and smart grid applications. SOC is an unmeasured parameter. Therefore, it must be inferred from other measured quantities from each battery such as discharge current, battery terminal voltage and temperature. There are various methods to estimate the battery’s SOC: Modeless approaches, Data driven nonlinear models and Model based observers. In this work, we adopted the second approach. An optimal feed forward-neural-network (FFNN) based battery model was suggested to simulate the complete dynamic electrical features of the battery and estimate accurately its SOC. Different charge/discharge current profiles are taken into account during training steps to improve the neural network model robustness and estimation accuracy. The obtained results show that FFNN, which is trained with an importance sampling data, is an accurate estimator for SOC.
Copyright © 2017 Praise Worthy Prize - All rights reserved.
Scrosati, B. and Garche, J. "Lithium batteries: Status, prospects and future," J. Power Sources, 195(9): 2419-2430, 2010.
Lu, Languang, HAN, Xuebing, LI, Jianqiu, et al. A review on the key issues for lithium-ion battery management in electric vehicles. Journal of power sources, 2013, vol. 226, p. 272-288.
D. Linden, T. Redyy, Handbook of Batteries, third ed., McGraw-Hill, New York.
G. Marangoni, Battery Management System for Li-Ion Batteries in Hybrid Electric Vehicles, University of Padova, 2010.
N.-X. Yang, X. Zhang, G. Li. State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting. Electrochimica Acta (2015), pp. 63–71.
Y.-B. Huang, H.-D. Tang, H. Zhang, G.-Q. Weng. Prediction of lithium-ion battery SOC in EV based on genetic neural network, Mechanical and Electrical Engineering, 30 (10) (2013).
Shijie Tong, Joseph H. Lacap, Jae Wan Park, Battery state of charge estimation using a load-classifying neural network, Journal of Energy Storage, Volume 7, August 2016, Pages 236-243.
Yujie Wang, Chenbin Zhang, Zonghai Chen, A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter, Journal of Power Sources, Volume 279, 1 April 2015, Pages 306-311.
W. He, N. Williard, C. Chen, M. Pecht, State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation, Electrical Power and Energy Systems, 62 (2014), pp. 783–791.
A. Baba, S. Adachi,State of charge estimation of HEV/EV battery with series Kalman filter, Proceedings of. IEEE, SICE Annual Conference (SICE) (2012), pp. 845–850.
Yegnanarayana, B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd.
Dai, Haifeng, Guo, Pingjing, Wei, Xuezhe, et al. ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries. Energy, 2015, vol. 80, p. 350-360.
Dang, Xuanju, Yan, Li, Xu, Kai, et al. Open-circuit voltage-based state of charge estimation of lithium-ion battery using dual neural network fusion battery model. Electrochimica Acta, 2016, vol. 188, p. 356-366.
A.-A. Hussein, Capacity fade estimation in electric vehicles Li-ion batteries using artificial neural networks. Energy Conversion Congress and Exposition (ECCE), IEEE, 2013, p. 677 - 681.
S. Sepasi, R. Ghorbani, and B.-Y. Liaw, A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter. Journal of Power Sources 245 (2014) 337-344.
A.-A. Hussein, Derivation and Comparison of Open loop and Closed-loop Neural Network Battery State-of-Charge Estimators, Energy Procedia 75 (2015) 1856 – 1861.
I.W. Sandberg, Nonlinear dynamical systems: feedforward neural network perspectives, John Wiley & Sons (2001), pp. 1–15.
Wu, Ji, Zhang, Chenbin, et Chen, Zonghai. An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Applied Energy, 2016, vol. 173, p. 134-140.
Chen, Yuehui, Yang, Bo, et Dong, Jiwen. Time-series prediction using a local linear wavelet neural network. Neurocomputing, 2006, vol. 69, no 4, p. 449-465.
Roscher, Michael A. et Sauer, Dirk Uwe. Dynamic electric behavior and open-circuit-voltage modeling of LiFePO 4-based lithium ion secondary batteries. Journal of Power Sources, 2011, vol. 196, no 1, p. 331-336.
Dong, G., Zhang, X., Zhang, C., & Chen, Z. (2015). A method for state of energy estimation of lithium-ion batteries based on neural network model. Energy, 90, 879-888.
Sadowsky JS. A new method for Viterbi decoder simulation using importance sampling. IEEE Trans Commun 1990.38:1341–51.
Biondini G. An introduction to rare event simulation and importance sampling. In: Venu Govindaraju VVR,Rao CR, editors. Handbook of Statistics, 2 (Elsevier: 2015. p. 29–68 )
Hartman, Eric J., Keeler, James D., et Kowalski, Jacek M. Layered neural networks with Gaussian hidden units as universal approximations. Neural computation, 1990, vol. 2, no 2, p. 210-215..
Firat, Mahmut et Gungor, Mahmud. Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers. Advances in Engineering Software, 2009, vol. 40, no 8, p. 731-737.
P. Spagnol, S. Rossi, S.-M. Savaresi, Kalman filter SOC estimation for Li-ion batteries, Control Applications (CCA), IEEE International Conference on, IEEE, 2008, pp. 587–592.
Cheddadi, Youssef, GAGA, Ahmed, Errahimi, Fatima, et al. Design of an energy management system for an autonomous hybrid micro-grid based on Labview IDE. 3rd International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2015. p. 1-6.
Di Domenico, Domenico, Fiengo, Giovanni, et Stefanopoulou, Anna. Lithium-ion battery state of charge estimation with a Kalman filter based on an electrochemical model, IEEE International Conference on Control Applications, 2008. p. 702-707.
Li, X., Koseki, H., Thermal Analysis on Lithium Primary Batteries, (2014) International Journal on Energy Conversion (IRECON), 2 (4), pp. 133-136.
Malik, F., Lehtonen, M., Saarijärvi, E., Safdarian, A., A Feasibility Study of Fast Charging Infrastructure for EVs on Highways, (2014) International Review of Electrical Engineering (IREE), 9 (2), pp. 341-350.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2020 Praise Worthy Prize