Implementation of Hardware-in-the-Loop Based Platform for Real-time Battery State of Charge Estimation on Li-Ion Batteries of Electric Vehicles using Multilayer Perceptron

Keywords: Li-Ion batteries, hardware-in-the-loop, electric vehicles, multi-layer perceptron, artificial neural networks, state of charge estimation

Abstract

In this study, hardware-in-the-loop based real-time state of charge estimation was performed in Li-Ion batteries, which are widely used in hybrid and fully electric vehicles. The state of charge is estimated on the Li-Ion battery cell that forms the electric vehicle battery system. Multi-layer perceptron approach has been preferred as a method for estimating the battery state of charge. Discharge experiments under different electrical loads were applied to the Li-Ion battery cell to be used in multilayer perceptron learning processes. A special test setup has been prepared to perform the discharge process under different electrical loads. In each discharge experiment, battery open circuit voltage, battery discharge current and battery cell temperature parameters were measured and data were recorded. By using the data obtained from the experiments on the battery cell, a multi-layer perceptron was modeled in MATLAB environment. After creating the multi-layer perceptron model, the real-time charge state of the battery was estimated at different discharge currents in the experimental setup and the results obtained were evaluated.

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Author Biographies

Suleyman Ceven, Duzce University
Department of Electronics & Automation Technology, Düzce Vocational School, Düzce University, 81010- Turkey
Raif Bayir, Karabük University
Department of Mechatronics Engineering, Faculty of Technology, Karabük University, 78100- Turkey

References

J.-R. Riba, C. López-Torres, L. Romeral, and A. Garcia, “Rare-earth-free propulsion motors for electric vehicles: A technology review,” Renew. Sustain. Energy Rev., vol. 57, pp. 367–379, 2016.

A. Emadi, Y. J. Lee, and K. Rajashekara, “Power Electronics and Motor Drives in Electric, Hybrid Electric, and Plug-In Hybrid Electric Vehicles,” IEEE Trans. Ind. Electron., vol. 55, no. 6, pp. 2237–2245, 2008.

C. C. Chan, “The state of the art of electric and hybrid vehicles,” Proc. IEEE, vol. 90, no. 2, pp. 247–275, 2002.

J. A. P. Lopes, F. J. Soares, and P. M. R. Almeida, “Integration of Electric Vehicles in the Electric Power System,” Proc. IEEE, vol. 99, no. 1, pp. 168–183, 2011.

A. Poullikkas, “Sustainable options for electric vehicle technologies,” Renew. Sustain. Energy Rev., vol. 41, pp. 1277–1287, 2015.

S. Ould Amrouche, D. Rekioua, T. Rekioua, and S. Bacha, “Overview of energy storage in renewable energy systems,” Int. J. Hydrogen Energy, vol. 41, no. 45, pp. 20914–20927, 2016.

C. Capasso and O. Veneri, “Experimental analysis on the performance of lithium based batteries for road full electric and hybrid vehicles,” Appl. Energy, vol. 136, pp. 921–930, 2014.

A. F. Moghaddam, M. Mnati, H. Sun, and A. V d. Bossche, “Electric Vehicles Charging Concepts for Lithium Based Batteries,” in 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), 2018, pp. 397–401.

S. M. A. S. Bukhari, J. Maqsood, M. Q. Baig, S. Ashraf, and T. A. Khan, “Comparison of Characteristics -- Lead Acid, Nickel Based, Lead Crystal and Lithium Based Batteries,” in 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim), 2015, pp. 444–450.

V. Pop, H. J. Bergveld, D. Danilov, P. P. L. Regtien, and P. H. L. Notten, “State-of-the-art of battery state-of-charge determination,” Batter. Manag. Syst. Accurate State-of-Charge Indic. Batter. Appl., pp. 11–45, 2008.

S. Pang, J. Farrell, J. Du, and M. Barth, “Battery state-of-charge estimation,” in Proceedings of the 2001 American control conference.(Cat. No. 01CH37148), 2001, vol. 2, pp. 1644–1649.

M. A. Hannan, M. S. H. Lipu, A. Hussain, and A. Mohamed, “A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations,” Renew. Sustain. Energy Rev., vol. 78, pp. 834–854, 2017.

Z. Chen, L. Yang, X. Zhao, Y. Wang, and Z. He, “Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach,” Appl. Math. Model., vol. 70, pp. 532–544, 2019.

M. Mastali, J. Vazquez-Arenas, R. Fraser, M. Fowler, S. Afshar, and M. Stevens, “Battery state of the charge estimation using Kalman filtering,” J. Power Sources, vol. 239, pp. 294–307, 2013.

J. Sabatier, M. Aoun, A. Oustaloup, G. Grégoire, F. Ragot, and P. Roy, “Fractional system identification for lead acid battery state of charge estimation,” Signal Processing, vol. 86, no. 10, pp. 2645–2657, 2006.

R. Xiong, J. Cao, Q. Yu, H. He, and F. Sun, “Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles,” IEEE Access, vol. 6, pp. 1832–1843, 2018.

M. Charkhgard and M. Farrokhi, “State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF,” IEEE Trans. Ind. Electron., vol. 57, no. 12, pp. 4178–4187, 2010.

F. Sun, X. Hu, Y. Zou, and S. Li, “Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles,” Energy, vol. 36, no. 5, pp. 3531–3540, 2011.

I.-S. Kim, “The novel state of charge estimation method for lithium battery using sliding mode observer,” J. Power Sources, vol. 163, no. 1, pp. 584–590, 2006.

K. S. Ng, C.-S. Moo, Y.-P. Chen, and Y.-C. Hsieh, “Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries,” Appl. Energy, vol. 86, no. 9, pp. 1506–1511, 2009.

K. Ng, Y. Huang, C. Moo, and Y. Hsieh, “An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries,” in INTELEC 2009 - 31st International Telecommunications Energy Conference, 2009, pp. 1–5.

Y. Jeong, Y. Cho, J. Ahn, S. Ryu, and B. Lee, “Enhanced Coulomb counting method with adaptive SOC reset time for estimating OCV,” in 2014 IEEE Energy Conversion Congress and Exposition (ECCE), 2014, pp. 1313–1318.

M. A. Awadallah and B. Venkatesh, “Accuracy improvement of SOC estimation in lithium-ion batteries,” J. Energy Storage, vol. 6, pp. 95–104, 2016.

E. Leksono, I. N. Haq, M. Iqbal, F. X. N. Soelami, and I. G. N. Merthayasa, “State of charge (SoC) estimation on LiFePO4 battery module using Coulomb counting methods with modified Peukert,” in 2013 Joint International Conference on Rural Information Communication Technology and Electric-Vehicle Technology (rICT ICeV-T), 2013, pp. 1–4.

L. Zhao, M. Lin, and Y. Chen, “Least-squares based coulomb counting method and its application for state-of-charge (SOC) estimation in electric vehicles,” Int. J. Energy Res., vol. 40, no. 10, pp. 1389–1399, 2016.

Li Ran, Wu Junfeng, Wang Haiying, and Li Gechen, “Prediction of state of charge of Lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory,” in 2010 5th IEEE Conference on Industrial Electronics and Applications, 2010, pp. 684–688.

K. Bundy, M. Karlsson, G. Lindbergh, and A. Lundqvist, “An electrochemical impedance spectroscopy method for prediction of the state of charge of a nickel-metal hydride battery at open circuit and during discharge,” J. Power Sources, vol. 72, no. 2, pp. 118–125, 1998.

I. A. J. Gordon et al., “Electrochemical Impedance Spectroscopy response study of a commercial graphite-based negative electrode for Li-ion batteries as function of the cell state of charge and ageing,” Electrochim. Acta, vol. 223, pp. 63–73, 2017.

D. Andre, M. Meiler, K. Steiner, C. Wimmer, T. Soczka-Guth, and D. U. Sauer, “Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. I. Experimental investigation,” J. Power Sources, vol. 196, no. 12, pp. 5334–5341, 2011.

U. Westerhoff, T. Kroker, K. Kurbach, and M. Kurrat, “Electrochemical impedance spectroscopy based estimation of the state of charge of lithium-ion batteries,” J. Energy Storage, vol. 8, pp. 244–256, 2016.

Q. Wang, Y. He, J. Shen, X. Hu, and Z. Ma, “State of Charge-Dependent Polynomial Equivalent Circuit Modeling for Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries,” IEEE Trans. Power Electron., vol. 33, no. 10, pp. 8449–8460, 2018.

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,” J. Power Sources, vol. 185, no. 2, pp. 1367–1373, 2008.

Y. 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,” Appl. Energy, vol. 113, pp. 106–115, 2014.

R. Xiong, Q. Yu, L. Y. Wang, and C. Lin, “A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter,” Appl. Energy, vol. 207, pp. 346–353, 2017.

C. Weng, J. Sun, and H. Peng, “A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring,” J. Power Sources, vol. 258, pp. 228–237, 2014.

S. Tong, M. P. Klein, and J. W. Park, “On-line optimization of battery open circuit voltage for improved state-of-charge and state-of-health estimation,” J. Power Sources, vol. 293, pp. 416–428, 2015.

Y. Zheng, M. Ouyang, X. Han, L. Lu, and J. Li, “Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles,” J. Power Sources, vol. 377, pp. 161–188, 2018.

S. Piller, M. Perrin, and A. Jossen, “Methods for state-of-charge determination and their applications,” J. Power Sources, vol. 96, no. 1, pp. 113–120, 2001.

Y. Tian, B. Xia, W. Sun, Z. Xu, and W. Zheng, “A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter,” J. Power Sources, vol. 270, pp. 619–626, 2014.

M. Corno, N. Bhatt, S. M. Savaresi, and M. Verhaegen, “Electrochemical Model-Based State of Charge Estimation for Li-Ion Cells,” IEEE Trans. Control Syst. Technol., vol. 23, no. 1, pp. 117–127, 2015.

C. Zhang, K. Li, L. Pei, and C. Zhu, “An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries,” J. Power Sources, vol. 283, pp. 24–36, 2015.

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, vol. 39, no. 1, pp. 310–318, 2012.

L. Kang, X. Zhao, and J. Ma, “A new neural network model for the state-of-charge estimation in the battery degradation process,” Appl. Energy, vol. 121, pp. 20–27, 2014.

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,” Int. J. Electr. Power Energy Syst., vol. 62, pp. 783–791, 2014.

C. Bo, B. Zhifeng, and C. Binggang, “State of charge estimation based on evolutionary neural network,” Energy Convers. Manag., vol. 49, no. 10, pp. 2788–2794, 2008.

F. Yang, W. Li, C. Li, and Q. Miao, “State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network,” Energy, vol. 175, pp. 66–75, 2019.

M. A. Hannan, M. S. H. Lipu, A. Hussain, M. H. Saad, and A. Ayob, “Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm,” IEEE Access, vol. 6, pp. 10069–10079, 2018.

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,” Int. J. Electr. Power Energy Syst., vol. 53, pp. 603–611, 2013.

D. Liu, L. Li, Y. Song, L. Wu, and Y. Peng, “Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions,” Int. J. Electr. Power Energy Syst., vol. 110, pp. 48–61, 2019.

G. S. Misyris, D. I. Doukas, T. A. Papadopoulos, D. P. Labridis, and V. G. Agelidis, “State-of-Charge Estimation for Li-Ion Batteries: A More Accurate Hybrid Approach,” IEEE Trans. Energy Convers., vol. 34, no. 1, pp. 109–119, 2019.

J. P. Rivera-Barrera, N. Muñoz-Galeano, and H. O. Sarmiento-Maldonado, “SoC estimation for lithium-ion batteries: Review and future challenges,” Electronics, vol. 6, no. 4, p. 102, 2017.

J. Pablo Rivera-Barrera, N. Munoz-Galeano, and H. Omar Sarmiento-Maldonado, “SoC Estimation for Lithium-ion Batteries: Review and Future Challenges,” ELECTRONICS, vol. 6, no. 4, 2017.

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,” in The 2011 International Joint Conference on Neural Networks, 2011, pp. 2156–2163.

J. Gomez, R. Nelson, E. E. Kalu, M. H. Weatherspoon, and J. P. Zheng, “Equivalent circuit model parameters of a high-power Li-ion battery: Thermal and state of charge effects,” J. Power Sources, vol. 196, no. 10, pp. 4826–4831, 2011.

Y. Zhou, C. Bai, and J. Sun, “Application of Genetic Neural Network in Power Battery Charging State-of-Charge Estimation,” Int. J. Intell. Syst. Appl., vol. 3, no. 2, p. 24, 2011.

D. Jiménez-Bermejo, J. Fraile-Ardanuy, S. Castaño-Solis, J. Merino, and R. Álvaro-Hermana, “Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles,” Procedia Comput. Sci., vol. 130, pp. 533–540, 2018.

B. Yegnanarayana, Artificial neural networks. PHI Learning Pvt. Ltd., 2009.

J. A. K. Suykens, J. P. L. Vandewalle, and B. L. de Moor, Artificial neural networks for modelling and control of non-linear systems. Springer Science & Business Media, 2012.

M. M. Mijwel, “Artificial neural networks advantages and disadvantages,” Retrieved from LinkedIn https//www. linkedin. com/pulse/artificial-neuralnet Work., 2018.

Xinghuo Yu, M. O. Efe, and O. Kaynak, “A general backpropagation algorithm for feedforward neural networks learning,” IEEE Trans. Neural Networks, vol. 13, no. 1, pp. 251–254, 2002.

O. Plakhtii, V. Nerubatskyi, A. Mashura, and D. Hordiienko, “The Analysis of Mathematical Models of Charge-Discharge Characteristics in Lithium-Ion Batteries,” in 2020 IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO), 2020, pp. 635–640.

Published
2020-12-30
How to Cite
[1]
S. Ceven and R. Bayir, “Implementation of Hardware-in-the-Loop Based Platform for Real-time Battery State of Charge Estimation on Li-Ion Batteries of Electric Vehicles using Multilayer Perceptron”, IJISAE, vol. 8, no. 4, pp. 195-205, Dec. 2020.
Section
Research Article