Iterative Nonlinear Fuzzy Modeling of Lithium-Ion Batteries
Source abstract
Abstract
Electric vehicles (EVs), in their pure and hybrid variants, have become the main alternative to ensure the decarbonization of the current vehicle fleet. Due to its excellent performance, EV technology is closely linked to lithium-ion battery (LIB) technology. A LIB is a complex dynamic system with extraordinary nonlinear behavior defined by electrical, thermal and electrochemical dynamics. To ensure the proper management of a LIB in such demanding applications as EVs, it is crucial to have an accurate mathematical model that can adequately predict its dynamic behavior. Furthermore, this model must be able to iteratively adapt its parameters to accommodate system disturbances during its operation as well as performance loss in terms of efficiency and nominal capacity during its life cycle. To this end, a methodology that employs the extended Kalman filter to iteratively improve a fuzzy model applied to a real LIB is presented in this paper. This algorithm allows to improve the classical Takagi–Sugeno fuzzy model (TSFM) with each new set of data obtained, adapting the model to the variations of the battery characteristics throughout its operating cycle. Data for modeling and subsequent validation were collected during experimental tests on a real LIB under EVs driving cycle conditions according to the “worldwide harmonised light vehicle test procedure” (WLTP) standard. The TSFM results allow the creation of an accurate nonlinear dynamic model of the LIB, even under fluctuating operating conditions, demonstrating its suitability for modeling and design of model-based control systems for LIBs used in EVs applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Electric and Hybrid Vehicle Technologies · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Centre for Technology, Energy and Sustainability, La Rábida, Palos de la Frontera, 21071 Huelva, Spain
Funding
Spanish Government · Andalusian Regional Program of R+D+i · University of Huelva
References (94)
Show source-provided references
1. Philippot, M., Alvarez, G., Ayerbe, E., Mierlo, J.V., and Messagie, M. (2019). Eco-efficiency of a lithium-ion battery for electric vehicles: Influence of manufacturing country and commodity prices on ghg emissions and costs. Batteries, 5.10.3390/batteries5010023 ↗
2. Lowe, I. (2017). Proceedings of the The Routledge Handbook of Australian Urban and Regional Planning, Routledge.
3. Sajadi-Alamdari, S.A., Voos, H., and Darouach, M. (2016, January 21–24). Nonlinear model predictive extended eco-cruise control for battery electric vehicles. Proceedings of the 2016 24th Mediterranean Conference on Control and Automation (MED), Athens, Greece.10.1109/MED.2016.7535929 ↗
4. A smart cyber physical multi-source energy system for an electric vehicle prototype10.1016/j.sysarc.2020.101804 ↗
5. Overcharge cycling effect on the thermal behavior, structure, and material of lithium-ion batteries10.1016/j.applthermaleng.2019.114147 ↗
6. A comparative investigation of aging effects on thermal runaway behavior of lithium-ion batteries10.1016/j.etran.2019.100034 ↗
7. Tran, M.K., Dacosta, A., Mevawalla, A., Panchal, S., and Fowler, M. (2021). Comparative study of equivalent circuit models performance in four common lithium-ion batteries: LFP, NMC, LMO, NCA. Batteries, 7.10.3390/batteries7030051 ↗
8. Cittanti, D., Ferraris, A., Airale, A., Fiorot, S., Scavuzzo, S., and Carello, M. (2017, January 21–23). Modeling Li-ion batteries for automotive application: A trade-off between accuracy and complexity. Proceedings of the 2017 International Conference of Electrical and Electronic Technologies for Automotive, Antalya, Turkey.10.23919/EETA.2017.7993213 ↗
9. Review of simplified Pseudo-two-Dimensional models of lithium-ion batteries10.1016/j.jpowsour.2016.07.036 ↗
10. A technique for dynamic battery model identification in automotive applications using linear parameter varying structures10.1016/j.conengprac.2009.05.002 ↗
11. Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries10.1016/j.conengprac.2022.105176 ↗
12. Madani, S.S., Schaltz, E., and Kær, S.K. (2018). Review of parameter determination for thermal modeling of lithium ion batteries. Batteries, 4.10.3390/batteries4020020 ↗
13. Mathew, M., Mastali, M., Catton, J., Samadani, E., Janhunen, S., and Fowler, M. (2018). Development of an electro-thermal model for electric vehicles using a design of experiments approach. Batteries, 4.10.3390/batteries4020029 ↗
14. Model-Based Electrochemical Estimation and Constraint Management for Pulse Operation of Lithium Ion Batteries10.1109/TCST.2009.2027023 ↗
15. A Mathematical Model for a Lithium-Ion Battery/Electrochemical Capacitor Hybrid System10.1149/1.1940749 ↗
16. LIONSIMBA: A Matlab Framework Based on a Finite Volume Model Suitable for Li-Ion Battery Design, Simulation, and Control10.1149/2.0291607jes ↗
17. Electrochemical modeling of lithium polymer batteries10.1016/S0378-7753(02)00193-3 ↗
18. Shafiei, A., Momeni, A., and Williamson, S.S. (2011, January 6–9). Battery modeling approaches and management techniques for Plug-in Hybrid Electric Vehicles. Proceedings of the 2011 IEEE Vehicle Power and Propulsion Conference, Chicago, IL, USA.10.1109/VPPC.2011.6043191 ↗
19. A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects10.1109/TEC.2011.2167014 ↗
20. A control-oriented electrochemical model for lithium-ion battery. Part II: Parameter identification based on reference electrode10.1016/j.est.2019.101101 ↗
21. Electrochemical-Thermal Model of Lithium Polymer Batteries10.1149/1.1393490 ↗
22. A control oriented reduced order electrochemical model considering variable diffusivity of lithium ions in solid10.1016/j.jpowsour.2020.228322 ↗
23. Tran, N.T., Vilathgamuwa, M., Farrell, T., and Choi, S.S. (2016, January 5–8). Matlab simulation of lithium ion cell using electrochemical single particle model. Proceedings of the 2016 IEEE 2nd Annual Southern Power Electronics Conference (SPEC), Auckland, New Zealand.10.1109/SPEC.2016.7846195 ↗
24. Mathematical modeling of porous battery electrodes—Revisit of Newman’s model10.1016/j.electacta.2011.01.012 ↗
25. Sockeel, N., Shahverdi, M., Mazzola, M., and Meadows, W. (2017). High-fidelity battery model for model predictive control implemented into a plug-in hybrid electric vehicle. Batteries, 3.10.3390/batteries3020013 ↗
26. A mathematical model for lead-acid batteries10.1109/60.124547 ↗
27. Barletta, G., DiPrima, P., and Papurello, D. (2022). Thevenin’s Battery Model Parameter Estimation Based on Simulink. Energies, 15.10.3390/en15176207 ↗
28. Parameters identification of Thevenin model for lithium-ion batteries using self-adaptive Particle Swarm Optimization Differential Evolution algorithm to estimate state of charge10.1016/j.est.2021.103244 ↗
29. Baczyńska, A., Niewiadomski, W., Gonçalves, A., Almeida, P., and Luís, R. (2018). LI-NMC batteries model evaluation with experimental data for electric vehicle application. Batteries, 4.10.3390/batteries4010011 ↗
30. Zhou, W., Zheng, Y., Pan, Z., and Lu, Q. (2021). Review on the Battery Model and SOC Estimation Method. Processes, 9.10.3390/pr9091685 ↗
31. An improved Thevenin model of lithium-ion battery with high accuracy for electric vehicles10.1016/j.apenergy.2019.113615 ↗
32. Jiang, J., and Zhang, C. (2015). Fundamentals and Applications of Lithium-Ion Batteries in Electric Drive Vehicles, Wiley.10.1002/9781118414798 ↗
33. Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach10.3390/en4040582 ↗
34. Nonlinear Observers for Predicting State-of-Charge and State-of-Health of Lead-Acid Batteries for Hybrid-Electric Vehicles10.1109/TVT.2004.842461 ↗
35. Development of a universal modeling tool for rechargeable lithium batteries10.1016/j.jpowsour.2007.06.157 ↗
36. Danzer, M., Liebau, V., and Maglia, F. (2015). Advances in Battery Technologies for Electric Vehicles, Elsevier.10.1016/B978-1-78242-377-5.00014-5 ↗
37. Modeling capacity fade in lithium-ion cells10.1016/j.jpowsour.2004.08.017 ↗
38. Accurate electrical battery model capable of predicting runtime and I-V performance10.1109/TEC.2006.874229 ↗
39. Novel equivalent circuit model for high-energy lithium-ion batteries considering the effect of nonlinear solid-phase diffusion10.1016/j.jpowsour.2022.230993 ↗
40. Experimental Validation of a Battery Dynamic Model for EV Applications10.3390/wevj3020289 ↗
41. Über die Abhängigkeit der Kapazität von der Entladestromstärke bei Bleiakkumulatoren
42. Design of Primary and Secondary Cells10.1149/1.2423659 ↗
43. Improved lithium-ion battery model for photovoltaic applications based on comparative analysis and experimental tests10.1002/er.7899 ↗
44. Hussein, A.A.H., and Batarseh, I. (2011, January 24–29). An overview of generic battery models. Proceedings of the 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA.10.1109/PES.2011.6039674 ↗
45. Comparison study on the battery models used for the energy management of batteries in electric vehicles10.1016/j.enconman.2012.04.014 ↗
46. Modeling and control of non-linear systems using soft computing techniques10.1016/j.asoc.2005.12.005 ↗
47. ANFIS: adaptive–network-based fuzzy inference system10.1109/21.256541 ↗
48. Zhang, C.W., Chen, S.R., Gao, H.B., Xu, K.J., and Yang, M.Y. (2018). State of charge estimation of power battery using improved back propagation neural network. Batteries, 4.10.3390/batteries4040069 ↗
49. Neuro-fuzzy modeling and control10.1109/5.364486 ↗
50. Modeling for Lithium-Ion Battery used in Electric Vehicles10.1016/j.proeng.2011.08.540 ↗
51. Intelligent hybrid system for the prediction of the voltage-current characteristic curve of a hydrogen-based fuel cell
52. Fuel Cell Output Current Prediction with a Hybrid Intelligent System
53. Miao, J., Tong, Z., Tong, S., Zhang, J., and Mao, J. (2022). State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles under Extreme Operating Temperatures Based on an Adaptive Temporal Convolutional Network. Batteries, 8.10.3390/batteries8100145 ↗
54. State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation10.1016/j.ijepes.2014.04.059 ↗
55. State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review10.1109/ACCESS.2019.2942213 ↗
56. Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators10.1016/j.jpowsour.2005.04.039 ↗
57. Battery state of charge estimation using a load-classifying neural network10.1016/j.est.2016.07.002 ↗
58. A new approach to linear filtering and prediction problems10.1115/1.3662552 ↗
59. Fuzzy identification of systems and its applications to modeling and control10.1109/TSMC.1985.6313399 ↗
60. A general methodology for online TS fuzzy modeling by the extended Kalman filter10.1016/j.asoc.2013.09.005 ↗
61. Iterative Fuzzy Modeling Of Hydrogen Fuel Cells By The Extended Kalman Filter10.1109/ACCESS.2020.3013690 ↗
62. Babuška, R. (1995, January 20–24). Fuzzy modeling-A control engineering perspective. Proceedings of the 1995 IEEE International Conference on Fuzzy Systems, Yokohama, Japan.
63. Galichet, S., Montero, J., and Mauris, G. (2011). European Society for Fuzzy Logic and Technology (EUSFLAT-2011) and LFA-2011, Number 1 in Advances in Intelligent Systems Research.
64. A comparative study on sufficient conditions for Takagi–Sugeno fuzzy systems as universal approximators10.1109/91.890337 ↗
65. Fuzzy sets10.1016/S0019-9958(65)90241-X ↗
66. Fuzzy agents for ICa
67. A methodology to design stable nonlinear fuzzy control systems10.1016/j.fss.2005.03.006 ↗
68. A new approach to fuzzy estimation of Takagi–Sugeno model and its applications to optimal control for nonlinear systems10.1016/j.asoc.2011.08.044 ↗
69. Simulation and Experimental Validation for Takagi-Sugeno Fuzzy-Based Li-ion Battery Model
70. Size reduction by interpolation in fuzzy rule bases10.1109/3477.552182 ↗
71. Hybridization of fuzzy systems for modeling and control
72. Formal methodology for analyzing the dynamic behavior of nonlinear systems using fuzzy logic
73. Barragán, A.J., Enrique, J.M., Calderón, A.J., and Andújar, J.M. (2018). Discovering the dynamic behavior of unknown systems using fuzzy logic. Fuzzy Optim. Decis. Mak., 1–25.
74. A general and formal methodology for designing stable nonlinear fuzzy control systems10.1109/TFUZZ.2009.2021984 ↗
75. Al-Hadithi, B.M., Barragán, A.J., Andújar, J.M., and Jiménez, A. (2012, January 18–20). Fuzzy Optimal Control for Double Inverted Pendulum. Proceedings of the 7th IEEE Conference on Industrial Electronics and Applications (ICIEA 2012), Singapore.10.1109/ICIEA.2012.6360687 ↗
76. Wang, L.X. (1997). A Course in Fuzzy Systems and Control, Prentice Hall.
77. Wong, L., Leung, F., and Tam, P. (1997, January 5). Stability design of TS model based fuzzy systems. Proceedings of the IEEE International Conference on Fuzzy Systems, Barcelona, Spain.
78. Bogdanoff, J.L., and Kozin, F. (1963). Proceedings of the 1st Symposium On Engineering Applications of Random Function Theory and Probability, John Wiley and Sons.10.1063/1.3051150 ↗
79. Maybeck, P.S. (1979). Stochastic Models, Estimation, and Control, Academyc Press. Mathematics in Science and Engineering.
80. New Concepts for the Estimation of Takagi–Sugeno Model Based on Extended Kalman Filter
81. New Methods for the Estimation of Takagi–Sugeno Model Based Extended Kalman Filter and its Applications to Optimal Control for Nonlinear Systems10.1002/oca.1014 ↗
82. Fuzzy modelling using Kalman filter10.1049/iet-cta:20050268 ↗
83. Kalman filtering with state constraints: A survey of linear and nonlinear algorithms10.1049/iet-cta.2009.0032 ↗
84. TS Fuzzy Prediction-based SVC Compensation of Wind Farms Flicker: A Dual-UKF Approach
85. Training fuzzy systems with the extended Kalman filter10.1016/S0165-0114(01)00241-X ↗
86. Barragán, A.J., and Andújar, J.M. (2012). Fuzzy Logic Tools Reference Manual v1.0, University of Huelva.
87. Matía, F., Marichal, G.N., and Jiménez, E. (2014). Fuzzy Modeling and Control: Theory and Applications, Atlantis Press. Atlantis Computational Intelligence Systems.10.2991/978-94-6239-082-9 ↗
88. Economic and Social Council, United Nations Economic Commission for Europe (2013). Proposal for a New Global Technical Regulation on the Worldwide Harmonized Light Vehicles Test Procedure (WLTP), World Forum for Harmonization of Vehicle Regulations.
89. (UNECE), U.N.E.C.f.E. (2022, December 27). Parameter List for RLD-Validation (WLTP-DTP-10-08, WLTP-DTP). Available online: http://www.unece.org/fileadmin/DAM/trans/doc/2012/wp29grpe/WLTP-DHC-12-07e.xls.
90. A suitable state-space model for renewable source-based microgrids with hydrogen as backup for the design of energy management systems10.1016/j.enconman.2020.113053 ↗
91. Fuzzy model identification based on cluster estimation10.3233/IFS-1994-2306 ↗
92. On the interpretation and identification of dynamic Takagi–Sugeno fuzzy models10.1109/91.855918 ↗
93. A new methodology to improve interpretability in neuro-fuzzy TSK models10.1016/j.asoc.2009.08.027 ↗
94. A fuzzy clustering algorithm enhancing local model interpretability10.1007/s00500-006-0146-7 ↗