Open-Circuit Voltage-Based State of Charge Estimation of Lithium-ion Battery Using Dual Neural Network Fusion Battery Model

Electrochimica Acta

Volumn 188, Issue , 2016, Pages 356-366

  Dang, Xuanju ^a   Yan, Li ^a   Xu, Kai ^a   Wu, Xiru ^a   Jiang, Hui ^a   Sun, Hanxu ^b  

^a GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Dual neural network fusion battery model; Lithium ion battery; Open circuit voltage; Second order battery model; State of Charge

Indexed keywords

BATTERY MANAGEMENT SYSTEMS; CHARGING (BATTERIES); ELECTRIC BATTERIES; LITHIUM ALLOYS; OPEN CIRCUIT VOLTAGE; SECONDARY BATTERIES;

BATTERY MODELING; DUAL NEURAL NETWORKS; DYNAMIC STRESS; ELECTROCHEMICAL MODELING; LINEAR NEURAL NETWORK; NEURAL NETWORK MODEL; STATE OF CHARGE; STATE-OF-CHARGE ESTIMATION;

LITHIUM-ION BATTERIES;

EID: 84949580066     PISSN: 00134686     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.electacta.2015.12.001     Document Type: Article

References (35)

1. K. Jonghoon, S. Jongwon, C. Changyoon, and B.-H. Cho Stable Configuration of a Li-Ion Series Battery Pack Based on a Screening Process for Improved Voltage/SOC Balancing Power Electronics IEEE Transactions on 27 1 2012 411 424
2. W. Waag, C. Fleischer, and D.-U. Sauer Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles Journal of Power Sources 258 14 2014 321 339
3. M. Corno, N. Bhatt, S.-M. Savaresi, and M. Verhaegen Electrochemical Model-Based State of Charge Estimation for Li-Ion Cells Control Systems Technology IEEE Transactions on 23 1 2015 117 127
4. Y.-J. Xing, W. He, M. Pecht, and K.-L. Tsui State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures Applied Energy 113 1 2014 106 115
5. Y.-H. Chiang, W.-Y. Sean, and J.-C. Ke Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles Journal of Power Sources 196 2011 3921 3932
6. N.-X. Yang, X. Zhang, and G. Li State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting Electrochimica Acta 2015 63 71
7. J.-X. Lv, H.-W. Yuan, and Y.-M. Lv Battery state-of-charge estimation based on fuzzy neural network and improved particle swarm optimization algorithm Instrumentation, Measurement, Computer, Communication and Control (IMCCC) 2012 Second International Conference on IEEE 2012 22 27
8. A.-A. Hussein Capacity fade estimation in electric vehicles Li-ion batteries using artificial neural networks Energy Conversion Congress and Exposition (ECCE), IEEE 2013 677 681
9. Y.-B. Huang, H.-D. Tang, H. Zhang, and G.-Q. Weng Prediction of lithium-ion battery SOC in EV based on genetic neural network Mechanical and Electrical Engineering 30 10 2013
10. Z.-Y. Liu, J.-B. Yang, and Q. Zhang Estimation for SOC of lithium battery based on QPSO-BP neural network Journal of Electronic Measurement & Instrument 27 3 2013 224 228
11. P. Spagnol, S. Rossi, and S.-M. Savaresi Kalman filter SOC estimation for Li-ion batteries Control Applications (CCA), IEEE International Conference on 2011 IEEE 2008 587 592
12. J.-P. Wang, J.-G. Guo, and L. Ding An adaptive Kalman filtering based State of Charge combined estimator for electric vehicle battery pack Energy Conversion and Management 50 12 2009 3182 3186
13. 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
14. W.-Y. Chang Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA Electrical Power and Energy Systems 53 2013 603 611
15. L.-W. Kang, X. Zhao, and J. Ma A new neural network model for the state-of-charge estimation, in the battery degradation process Applied Energy 121 2014 20 27
16. G.-Z. Dong, X. Zhang, C.-B. Zhang, and Z.-H. Chen A method for state of energy estimation of lithium-ion batteries based on neural network model Energy 2015 1 10
17. D. Andre, A. Nuhic, T.-S. Guth, and D.U. Sauer Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ionbatteries in hybrid electric vehicles Engineering Applications of Artificial Intelligence 26 2013 951 961
18. 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
19. W. He, N. Williard, C. Chen, and 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 783 791
20. S.-M. Zhang, L. Yang, X.-W. Zhao, and J.-X. Qiang A GA optimization for lithium-ion battery equalization based on SOC estimation by NN and FLC Electrical Power and Energy Systems 73 2015 318 328
21. H. Dai, P. Guo, X. Wei, Z. Sun, and J. Wang ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries Energy 80 2015 350 360
22. Y. Shen Adaptive online state-of-charge determination based on neuro-controller and neural network Energy Conversion and Management 51 2010 1093 1098
23. L. Xu, J. Wang, and Q. Chen Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model Energy Conversion and Management 53 2012 33 39
24. Z. Chen, S. Qiu, M.-A. Masrur, and Y.-L. Murphey Battery State of Charge Estimation Based on a Combined Model of Extended Kalman Filter and Neural Networks Proceedings if International Joint Conference on Neural Networks San Jose, California, USA, July 31-August 5 2011
25. C.-R. Gould, C.-M. Bingham, D.-A. Stone, and P. Bentley New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observer Techniques Vehicular Technology, IEEE Transactions on 58 8 2009 p. 3905 3916
26. B. Pattipati, B. Balasingam, G.-V. Avvari, K.-R. Pattipati, and Y. Bar-Shalom Open circuit voltage characterization of lithium-ion batteries Original Research Article Journal of Power Sources 269 10 2014 317 333
27. S. Lee, J. Kim, J. Lee, and B.-H. Cho State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge Journal of Power Sources 185 2008 1367 1373
28. H. He, X. Zhang, R. Xiong, Y. Xu, and H. Guo Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles Energy 39 2012 310 318
29. A. Baba, and S. Adachi State of charge estimation of HEV/EV battery with series Kalman filter 2012 Proceedings of. IEEE SICE Annual Conference (SICE) 2012 845 850
30. V.-H. Johnson Battery performance models in advisor Journal of power sources 110 2 2002 321 329
31. B. Chen, H.-D. Ma, H.-Z. Fang, H.-Z. Fan, K. Luo, and B. Fan An approach for state of charge estimation of Li-ion battery based on Thevenin equivalent circuit model Prognostics and System Health Management Conference (PHM-2014Hunan) IEEE 2014 647 652
32. A.-D. Yin, W.-X. Zhang, and H. Jiang Research on estimation for SOC of LiFePO-4 Li-ion battery based on neural network Journal of Electronic Measurement & Instrument 25 5 2011 433 437
33. J. Liu, Y.-K. Shi, X.-Q. Yuan, and Y. Cao SOC Estimation of Lithium Battery Based on RLS and EKF The 31st Chinese Control Conference 2012 6815 6819
34. H.-W. He, X.-W. Zhang, R. Xiong, Y. Xu, and H.-Q. Guo Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles Energy 39 1 2012 310 318
35. B. Wang, S.-E. Li, H. Peng, and Z. Liu Fractional-order modeling and parameter identification for lithium-ion batteries Journal of Power Sources 293 2015 151 161