Comparison of the performances of heuristic optimization algorithms PSO, ABC and GA for parameter estimation in the discharge processes of Li-NMC battery

Abstract

The effects of the studies performed for the development of cells, which are the fundamental components of electrochemical battery units are felt in many different areas such as electric rail transportation systems, battery-based energy storage systems, battery units in electric vehicles, and energy storage units for individual use. For this goal, studies conducted by other searchers in the similar field have been investigated. In this paper, optimization techniques are used to guess the model parameters with major righteousness using the electrical equivalent circuit model of the battery. The discharge processes of the 18650 cylindrical type 2000 mAh Li-NCM battery cell with 1 A pulsed constant current at 25 ºC have been investigated. The real parameter values obtained have been transferred to the electrical equivalent circuit model. The open circuit voltage is determined as a functional term depending on the state of current supply level by using the curve fitting method in the Matlab. Studies have been carried out on particle swarm optimization algorithm, artificial bee colony algorithm, and genetic algorithm to estimate the battery output terminal voltage by using the open circuit voltage. Comparisons have been made and differences have been analyzed between the technics by using different statistical methods of true error values, the correct prediction ability, and response speed. As a result, the optimization method that makes the most accurate estimation has been determined.

Keywords

• ABC
• GA
• Li-ion battery
• Parameter estimation
• PSO

References

1. [1] Dogan, A. Optimum sitting and sizing of WTs, PVs, ESSs and EVCSs using hybrid soccer league competition-pattern search algorithm. Engineering Science and Technology, an International Journal 2021; 24: 795-805. DOI: 10.1016/j.jestch.2020.12.007
2. [2] Moura, SJ, Stein, JL, Fathy, HK. Battery health conscious power management in plug in hybrid electric vehicles via electrochemical modeling and stochastic control. IEEE Transactionson Control Systems and Technology 2014; 1(3): 679-694. DOI: 10.1109/TCST.2012.2189773
3. [3] Panchal, C, Stegen, S, Lu, J. Review of static and dynamic wireless electric vehicle charging system. Engineering Science and Technology, an International Journal 2018; 21: 922-937. DOI: 10.1016/j.jestch.2018.06.015
4. [4] Michalczuk, M, Ufnalski, B, Grzesiak, LM, Rumniak, P. Power converter based electrochemical battery emulator. Przeglad Elektrotechniczny 2014; 90(7): 18-22. DOI: 10.12915/pe.2014.07.03
5. [5] Chin, CS, Gao, Z, Chiew, JH, Zhang, C. Nonlinear temperature dependent state model of cylindrical Li-FePO4 battery for open-circuit voltage, terminal voltage and state-of-charge estimation with extended kalman filter. Energies 2018; 11: 1-28. DOI: 10.3390/en11092467
6. [6] Pozzi, A, Ciaramella, G, Volkwein, S, Raimondo, DM. Optimal design of experiments for a lithium-ion cell: parameters identification of a single particle model with electrolyte dynamics. Industrial & Engineering Chemistry Research 2019; 58(3): 1286-1299. DOI: 10.1021/acs.iecr.8b04580
7. [7] Elmarghichi, M, Bouzi, M, Ettalabi, N. Online parameter estimation of a lithium-ion battery based on sunflower optimization algorithm. Bulletin of Electrical Engineering and Informatics 2021; 10(3): 1505-1513. DOI: 10.1 1591/eei.v10i3.2637
8. [8] Petzl, M, Danzer, MA. Advancements in OCV mesurement and analysis for lithium-ion batteries. IEEE Transactions on Energy Conversion 2013; 28(3): 675-681. DOI: 10.1109/TEC.2013.2259490

9. [9] Hu, X, Li, S, Peng, H. A comparative study of equivalent circuit models for li-ion batteries. Journal of Power Sources 2012; 198: 359-367. DOI: 10.1016/j.jpowsour.2011.10.013
10. [10] Mesbani, T, Khenfri, F, Rizoug, N, Chaaban, K, Bartholomeüs, P, Moigne, PL. Dynamic modeling of li-ion batteries for electric vehicle applications based on hybrid particle swarm nelder mead (PSO-NM) optimization algorithm. Electric Power Systems Research 2016; 131: 195-204. DOI: 10.1016/j.epsr.2015.10.018
11. [11] Kai, H, Fang, GY, Gang, LZ, Cheng, LH, Ling, LL. Development of accurate lithium-ion battery model based on adaptive random disturbance PSO algortithm. Mathematical Problems in Engineering 2018; 1-13. DOI: 10. 1155/2018/3793492
12. [12] Li, L, Hu, M, Xu, Y, Fu, C, Jin, G, Li, Z. State of charge estimation for lithium-ion power batery based on H-infibity filter algorithm. Applied Science 2020; 10(6371): 1-18. DOI: 10.3390/app10186371
13. [13] Sangwan V, Sharma A, Kumar R, Rathore AK. Equivalent circuit model parameters estimation of Li-ion battery: C-rate, SOC and Temperature effects. In: IEEE International Conference on Power Electronics, Drives and Energy Systems; 14-17 December 2016: IEEE, pp. 1-6. DOI: 10.1109/PEDES.2016.7914369
14. [14] Chen, WJ, Tan, XJ, Cai, M. Parameter identification of equivalent circuit models for li-ion batteries based on tree seeds algorithm. Earth and Environmental Science 2017; 73(1): 012024. DOI: 10.1088/1755-1315/73/1/0 12024
15. [15] Brondani, FM, Sausen, ATZR, Sausen, PS, Binelo, MO. Parameter estimation of lithium ion polymer battery mathematical model using genetic algorithm. Computational and Applied Mathematics 2018; 37(2): 296-313. DOI: 10.1007/s40314-017-0537-7
16. [16] Carmona, VP, Solis, SC, Carmona, MC, Ardanuy, JF, Bermejo, DJ. Optimization by means of genetic algorithm of the equivalent electrical circuit model of different order for li-ion battery pack. World Academy of Science, Engineering and Technology International Journal of Energy and Power Engineering 2020; 14(11): 343-348.
17. [17] Patil, MA, Tagade, P, Hariharan, KS, Kolake, SM, Song, T, Yeo, T, Doo, S. A novel multistage support vector machine based approach for li ion battery remaining useful life estimation. Applied Energy 2015; 159: 285-297. DOI: 10.1016/j.apenergy.2015.08.119
18. [18] Wang, Y, Ni, Y, Lu, S, Wang, J, Zhang, X. Remaining useful life prediction of lithium-ion batteries using support vector regression. IEEE Transactions on Vehicular Technology 2019; 68(10): 9543-9553. DOI: 10.11 09/TVT.2019.2932605
19. [19] Zhang, Y, Peng, Z, Guan, Y, Wu, L. Prognostics of battery cycle life in the early-cycle stage based on hybrid model. Energy 2021; 221: 119901. DOI: 10.1016/j.energy.2021.119901
20. [20] Yan, L, Peng, J, Gao, D, Wu, Y, Liu, Y, Li, H, Liu, W, Huang, Z. A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery. Energy 2022; 243: 123038. DOI: 10.101 6/j.energy.2021.123038
21. [21] Min, H, Sun, W, Li, X, Guo, D, Yu, Y, Zhu, T, Zhao, Z. Research on the optimal charging strategy for li-ion batteries based on multi objective optimizastion. Energies 2017; 10(5): 709. DOI: 10.3390/en10050709
22. [22] Ozturk, N, Celik, E. Solution of non-polynomial equations based on genetic algorithm. Erciyes University, Journal of Institute of Science and Technology 2012; 28(4): 322-328.
23. [23] Yuksel, R, Akkoc, S. Forecasting gold prices by using artificial neural network and an application. Doğuş University Journal 2016; 17(1): 39-50.
24. [24] Antanasijević, DZ, Pocajt, VV, Povrenović, DS, Ristić, MD, Perić-Grujić, AA. PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Science of the Total Environment 2013; 443: 511–519.
25. [25] Ozsahin, S, Singer, H. Utilizing an artificial neural network model in wood surface roughness prediction. Düzce University Journal of Science and Technology 2019; 7(3): 1764-1777.
26. [26] Köse, U. Development of Artificial Intelligence Based Optimization Algorithms. PhD Selçuk University, Graduate School of Natural and Applied Sciences, Konya, Turkey, 2017.
27. [27] Yadav, V, Nath, S. Forecasting of PM10 using autoregressive models and exponential smoothing technique. Asian Journal of Water, Environment and Pollution 2017; 14(4): 109-113. DOI: 10.3233/AJW-170041.
28. [28] Carkit, T, Alci M. Investigation of Voc and SoH on li-ion batteries with an electrical equivalent circuit model using optimization algorithms. Electrical Engineering 2022; DOI: 10.1007/s00202-021-01484-2.
29. [29] Çarkıt, T, Alçı, M. Investigation of electrical equivalent circuit model simulation data for li-ion battery by comparing with experimental discharge test results. In: ICMEPS2021 International Conference & Exposition on Modern Energy and Power Systems; 16-18 June 2021: Virtual, pp. 11-16.
30. [30] Singh, P, Khare, N, Chaturvedi, PK. Li-ion battery ageing model parameter: SEI layer analysis using magnetic field probing. Engineering Science and Technology, an International Journal 2018; 21(1): 35-42. DOI: 10.1016/j.jestch.2018.01.007.
31. [31] Nemes, R, Ciornei S, Ruba M, Hedesiu H, Martis C. Modeling and simulation of first-order li-ion battery cell with experimental validation. In: IEEE 8th International Conference on Modern Power Systems; 21-23 May 2019: IEEE, pp. 1-6. DOI: 10.1109/MPS.2019.8759769
32. [32] Shaheen, AM, Hamida, MA, El-Sehiemy, RA, Elattar, EE. Optimal parameter identification of linear and non-linear models for li-ion battery cells. Energy Reports 2021; 7: 7170-7185. DOI: 10.1016/j.egyr.2021.10.0 86
33. [33] Dogan, A. Application of Optimizastion Algorithms to Provide Optimum Power Flow on Power Systems. MSc, Erciyes University, Graduate School of Natural and Applied Sciences, Kayseri, Turkey, 2011.
34. [34] Karaboga, D. Artificial Intelligence Optimization Algorithms. Ankara, Turkey: Nobel Publishing House, 2006.
35. [35] Karaboga, D, Basturk, B. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization 2007; 39(3): 459-471. DOI: 10.1007/S10898-0 07-9149-X
36. [36] Zeybekoglu, U, Keskın, AU. Investigation of applicability of artificial bee colony algorithm on rainfall intensity duration frequency equations. Journal of Ecological Engineering 202; 21(7): 27-33. DOI: 10.12911/ 22998993/125458
37. [37] Kaya, B, Eke, I. Developments in artificial bee colony algorithm and the results. Journal of Productivity 2020; 1: 99-115, 2020.
38. [38] Yigitbasi, ED. Edge Detection with Artificial Bee Colony Optimization. MSc, Selçuk University, Graduate School of Natural and Applied Sciences, Konya, Turkey, 2014.
39. [39] Rahman, CM, Rashid, TA. Dragonfly algorithm and its aplications in applied science survey. Computational Intelligence and Neuroscience 2019; 2019:1-21. DOI: 10.1155/2019/9293617
40. [40] Ozsaglam, MY, Cunkas M. Particle swarm optimization algorithm for solving optimization problems. Journal of Polytechnic 2008; 11(4): 299-305.
41. [41] Coello, CA, Pulido, GT, Lechuga, MS. Handling multiple objectives with particle swarm optimization. IEEE Transactions of Evolutionary Computation 2004; 8(3): 256-279. DOI: 10.1109/TEVC.2004.826067
42. [42] Wang, SC, Liu, YH. A PSO based fuzzy controlled searching fort the optimal charge pattern of li-ion batteries. IEEE Transactions on Industrial Electronics 2015; 62(5): 2983-2993. DOI: 10.1109/TIE.2014.2363049
43. [43] Kumer, V, Minz, S. Multi objective particle swarm optimization: An introduction. Journal of Smart Computing Review 2014; 4(5): 335-353. DOI: 10.6029/smartcr.2014.05.001
44. [44] Huang, X, Zeng, X, Han, R, Wang, X. An enhanced hybridized artificial bee colony algorithm for optimization problems. IAES International Journal of Artificial Intelligence 2019; 8(1): 87-94. DOI: 10.11591/ijai.v8.i1.pp 87-94
45. [45] Chen, CL, Lin, YL, Fu, WY. Effects of battery energy storage system on the operating schedule of a renewable energy based TOU rate industrial user under competitive environment. Journal of Marine Science and Technology 2015; 23(4): 541-550. DOI: 10.6119/JMST-015-0521-1
46. [46] Eberhart, RC, Shi, Y. Comparing inertia weights and constriction factors in particle swarm optimization. In: IEEE Congress Evolutionary Computation; 16-19 July 2000: IEEE, pp. 84-88. DOI: 10.1109/CEC.2000.8702 79
47. [47] Saleh, IK. The Investigation and Development of Convergence Performance of Swarm-Based Multi Objective Optimizastion Methods. MSc, Süleyman Demirel University, Graduate School of Natural and Applied Sciences, Isparta, Turkey, 2017.
48. [48] Zhang, C, Jiang, J, Gao, Y, Zhang, W, Liu, Q, Hu, X. Charging optimization in lithium ion batteries based on temperature rise and charge time. Applied Energy 2017; 194: 569-577. DOI: 10.1016/j.apenergy.2016.10.059
49. [49] Liu, K, Li, K, Yang, Z, Zhang, C, Deng, J. An advanced lithium ion battery optimal charging strategy based on a couple thermoelectric model. Electrohimica Acta 2016; 225: 330-344. DOI: 10.1016/j.electacta.2016.12. 129
50. [50] Internet Web-Site: https://web.calce.umd.edu/batteries/data.htm, University of Marylan CALCE Battery Research Group. INR 18650-20R cylindrical cells data description. Published in 2018, 11 January 2021.
51. [51] Kallel, AY, Petrychenko, V, Kanoun, O. State of health of li-ion battery estimation based on the efficiency of the charge transfer extracted from impedance spectra. Applied Sciences 2022; 12(2): 885. DOI: 10.3390/app 12020885
52. [52] Strange, C, Li, S, Gilchrist, R, Reis, G. Elbows of internal resistance rise curves in li-ion cells. Energies 2021; 14(4): 1206. DOI: 10.3390/en14041206