Comparative analysis of electrical models of lith-ium-ion batteries in electric vehicles

Authors

DOI:

https://doi.org/10.30977/VEIT.2023.24.0.5

Keywords:

electric vehicle, lithium-ion battery, electric model, efficiency, state of charge, energy storage system, degradation

Abstract

Problem. This article addresses the challenge of enhancing the environmental friendliness and energy efficiency of vehicles. It does so by conducting a comparative analysis and identifying ways to improve the electrical models of lithium-ion batteries used in electric vehicles. The study includes an examination of well-known electrical models of lithium-ion rechargeable batteries, such as the Rint model, the RC model, the Thevenin model, and the PNGV model. It identifies key characteristics of lithium-ion batteries in electric vehicles, including state of charge, mass, actual voltage, energy required for recharging, among others. The study also explores models of battery degradation, focusing on capacity reduction and the increase in active resistance. It substantiates directions for improving electrical models of lithium-ion batteries in electric vehicles by considering changes in capacity, internal resistance, polarization resistance, and both calendar and cyclic degradation. Goal. The aim of this work is to enhance the environmental friendliness and energy efficiency of vehicles through a comparative analysis and by determining ways to improve the electrical models of lithium-ion batteries in electric vehicles. Methodology. Our approach to achieving this goal involves using electrical models of lithium-ion batteries in electric vehicles, which describe various parameters such as state of charge, actual voltage during charge/discharge processes, and energy required for recharging. The study encompasses an investigation into the degradation of electric vehicle batteries, including their use in Vehicle to Grid (V2G) technology. Results. The analysis of electrical models of lithium-ion batteries in electric vehicles, aiming to increase their accuracy, considers the following aspects: changes in internal resistance and polarization resistance; capacity variation; and battery degradation. The change in internal resistance and polarization resistance should be considered based on two factors: the state of charge of the battery and the degree of its degradation. While the first factor is relevant primarily when the battery is deeply discharged (SoC<30%), the second factor must be considered at any state of charge. Capacity changes should be accounted for based on calendar and cyclic degradation. It has been determined that the primary causes of degradation in electric vehicle batteries are calendar aging (service life) and aging due to charge/discharge cycles. Contrarily, it is argued that using Vehicle to Grid (V2G) technology can reduce battery degradation by 10%. Originality. The results of this study provide a comprehensive understanding of the electrical models of lithium-ion batteries in electric vehicles and contribute to the improvement of existing models. Practical value. This research enhances the accuracy of current electrical models of lithium-ion batteries in electric vehicles by considering the variable nature of internal resistance and capacity during vehicle operation. It may be valuable in assessing the residual parameters of electric vehicle batteries during their secondary use, such as in the residential sector for solar energy support. The findings can be recommended to scientific and technical professionals involved in developing energy storage systems for electric vehicles.

Author Biographies

Oleh Smyrnov , Kharkiv National Automobile and Highway University, 25, Yaroslava Mudrogo str., Kharkiv, 61002, Ukraine.

professor, Doct. of Science, Vehicle Electronics Department

Anna Borysenko, Kharkiv National Automobile and Highway University, 25, Yaroslava Mudrogo str., Kharkiv, 61002, Ukraine

Ph.D., Assoc. Prof., Vehicle Electronics Department

References

Hill, G., Heidrich, O., Creutzig, F., & Blythe, P. (2019). The role of electric vehicles in near-term mitigation pathways and achieving the UK’s carbon budget. Applied Energy, 251, 113111. https://doi.org/10.1016/j.apenergy.2019.04.107

Linn, J., & McConnell, V. (2019). Interactions between federal and state policies for reducing vehicle emissions. Energy Policy, 126, 507–517. https://doi.org/10.1016/j.enpol.2018.10.052

Dhar, S., Pathak, M., & Shukla, P. R. (2017). Electric vehicles and India's low carbon passenger transport: a long-term co-benefits assessment. Journal of Cleaner Production, 146, 139–148. https://doi.org/10.1016/j.jclepro.2016.05.111

Hao, H., Geng, Y., & Sarkis, J. (2016). Carbon footprint of global passenger cars: Scenarios through 2050. Energy, 101, 121–131. https://doi.org/10.1016/j.energy.2016.01.089

Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013). A review on the key issues for lithium-ion battery management in electric vehicles. Journal of Power Sources, 226, 272–288. https://doi.org/10.1016/j.jpowsour.2012.10.060

Eldeeb, H. H., Elsayed, A. T., Lashway, C. R., & Mohammed, O. (2019). Hybrid Energy Storage Sizing and Power Splitting Optimization for Plug-In Electric Vehicles. IEEE Transactions on Industry Applications, 55(3), 2252–2262. https://doi.org/10.1109/tia.2019.2898839

Farhadi, M., & Mohammed, O. (2016). Energy Storage Technologies for High-Power Applications. IEEE Transactions on Industry Applications, 52(3), 1953–1961. https://doi.org/10.1109/tia.2015.2511096

Bongartz, L., Shammugam, S., Gervais, E., & Schlegl, T. (2021). Multidimensional criticality assessment of metal requirements for lithium-ion batteries in electric vehicles and stationary storage applications in Germany by 2050. Journal of Cleaner Production, 292, 126056. https://doi.org/10.1016/j.jclepro.2021.126056

Urquizo, J., & Singh, P. (2023). A review of health estimation methods for Lithium-ion batteries in Electric Vehicles and their relevance for Battery Energy Storage Systems. Journal of Energy Storage, 73, 109194. https://doi.org/10.1016/j.est.2023.109194

Khan, F. M. N. U., Rasul, M. G., Sayem, A. S. M., & Mandal, N. (2023). Maximizing energy density of lithium-ion batteries for electric vehicles: A critical review. Energy Reports, 9, 11–21. https://doi.org/10.1016/j.egyr.2023.08.069

Hytowitz, A. N. (2023). Review of using the Dyop optotype for acuity and refractions per the article: https://www.sciencedirect.com/science/article/pii/S1888429622000656. Journal of Optometry. https://doi.org/10.1016/j.optom.2022.12.002

Selvaraj, V., & Vairavasundaram, I. (2023). A comprehensive review of state of charge estimation in lithium-ion batteries used in electric vehicles. Journal of Energy Storage, 72, 108777. https://doi.org/10.1016/j.est.2023.108777

Rauf, H., Khalid, M., & Arshad, N. (2023). A novel smart feature selection strategy of lithium-ion battery degradation modelling for electric vehicles based on modern machine learning algorithms. Journal of Energy Storage, 68, 107577. https://doi.org/10.1016/j.est.2023.107577

Wassiliadis, N., Kriegler, J., Gamra, K. A., & Lienkamp, M. (2023). Model-based health-aware fast charging to mitigate the risk of lithium plating and prolong the cycle life of lithium-ion batteries in electric vehicles. Journal of Power Sources, 561, 232586. https://doi.org/10.1016/j.jpowsour.2022.232586

Ando, K., Matsuda, T., & Imamura, D. (2018). Degradation diagnosis of lithium-ion batteries with a LiNi0.5Co0.2Mn0.3O2 and LiMn2O4 blended cathode using dV/dQ curve analysis. Journal of Power Sources, 390, 278–285. https://doi.org/10.1016/j.jpowsour.2018.04.043

Lewerenz, M., Münnix, J., Schmalstieg, J., Käbitz, S., Knips, M., & Sauer, D. U. (2017). Systematic aging of commercial LiFePO4 |Graphite cylindrical cells including a theory explaining rise of capacity during aging. Journal of Power Sources, 345, 254–263. https://doi.org/10.1016/j.jpowsour.2017.01.133

Li, P., Zhang, Z., Xiong, Q., Ding, B., Hou, J., Luo, D., Rong, Y., & Li, S. (2020b). State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network. Journal of Power Sources, 459, 228069. https://doi.org/10.1016/j.jpowsour.2020.228069

Hossain, E., Murtaugh, D., Mody, J., Faruque, H. M. R., Haque Sunny, M. S., & Mohammad, N. (2019). A Comprehensive Review on Second-Life Batteries: Current State, Manufacturing Considerations, Applications, Impacts, Barriers & Potential Solutions, Business Strategies, and Policies. IEEE Access, 7, 73215–73252. https://doi.org/10.1109/access.2019.2917859

He, H., Xiong, R., & Fan, J. (2011). Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach. Energies, 4(4), 582–598. https://doi.org/10.3390/en4040582

Avadikyan, A., & Larrue, P. (2003). The Partnership for a New Generation of Vehicles and the US DoE Transportation Fuel Cells Programme. У The Economic Dynamics of Fuel Cell Technologies (с. 133–158). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-24822-4_6

Electrically propelled road vehicles —Test specification for lithium-ion traction battery packs and systems — Part 4: Performance testing (ISO 12405-4:2018). (2018). https://www.iso.org/standard/55854.html

da Silva, S. F., Eckert, J. J., Corrêa, F. C., Silva, F. L., Silva, L. C. A., & Dedini, F. G. (2022b). Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle. Applied Energy, 324, 119723. https://doi.org/10.1016/j.apenergy.2022.119723

On the Ageing of High Energy Lithium-Ion Batteries—Comprehensive Electrochemical Diffusivity Studies of Harvested Nickel Manganese Cobalt Electrodes. (2018). Materials, 11(2), 176. https://doi.org/10.3390/ma11020176

Jiang, J., & Zhang, C. (2015). Fundamentals and Applications of Lithium-Ion Batteries in Electric Drive Vehicles. Wiley & Sons, Limited, John.

Assunção, A., Moura, P. S., & de Almeida, A. T. (2016). Technical and economic assessment of the secondary use of repurposed electric vehicle batteries in the residential sector to support solar energy. Applied Energy, 181, 120–131. https://doi.org/10.1016/j.apenergy.2016.08.056

Adnan Khan, M. S., Kadir, K. M., Mahmood, K. S., Ibne Alam, M. I., Kamal, A., & Al Bashir, M. M. (2019). Technical investigation on V2G, S2V, and V2I for next generation smart city planning. Journal of Electronic Science and Technology, 17(4), 100010. https://doi.org/10.1016/j.jnlest.2020.100010

İnci, M., Savrun, M. M., & Çelik, Ö. (2022). Integrating electric vehicles as virtual power plants: A comprehensive review on vehicle-to-grid (V2G) concepts, interface topologies, marketing and future prospects. Journal of Energy Storage, 55, 105579. https://doi.org/10.1016/j.est.2022.105579

Borge-Diez, D., Icaza, D., Açıkkalp, E., & Amaris, H. (2021b). Combined vehicle to building (V2B) and vehicle to home (V2H) strategy to increase electric vehicle market share. Energy, 237, 121608. https://doi.org/10.1016/j.energy.2021.121608

Uddin, K., Jackson, T., Widanage, W. D., Chouchelamane, G., Jennings, P. A., & Marco, J. (2017). On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system. Energy, 133, 710–722. https://doi.org/10.1016/j.energy.2017.04.116

Timilsina, L., Badr, P. R., Hoang, P. H., Ozkan, G., Papari, B., & Edrington, C. S. (2023). Battery Degradation in Electric and Hybrid Electric Vehicles: A Survey Study. IEEE Access, 1. https://doi.org/10.1109/access.2023.3271287

Meng, J., Cai, L., Stroe, D.-I., Luo, G., Sui, X., & Teodorescu, R. (2019). Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles. Energy, 185, 1054–1062. https://doi.org/10.1016/j.energy.2019.07.127

Jin, X. (2022). Aging-Aware optimal charging strategy for lithium-ion batteries: Considering aging status and electro-thermal-aging dynamics. Electrochimica Acta, 407, 139651. https://doi.org/10.1016/j.electacta.2021.139651

Saldana, G., Martin, J. I. S., Zamora, I., Asensio, F. J., Onederra, O., & Gonzalez, M. (2020). Empirical Electrical and Degradation Model for Electric Vehicle Batteries. IEEE Access, 8, 155576–155589. https://doi.org/10.1109/access.2020.3019477

Wang, D., Coignard, J., Zeng, T., Zhang, C., & Saxena, S. (2016). Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services. Journal of Power Sources, 332, 193–203. https://doi.org/10.1016/j.jpowsour.2016.09.116

Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009). Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy, 86(9), 1506–1511. https://doi.org/10.1016/j.apenergy.2008.11.021

Uddin, K., Dubarry, M., & Glick, M. B. (2018). The viability of vehicle-to-grid operations from a battery technology and policy perspective. Energy Policy, 113, 342–347. https://doi.org/10.1016/j.enpol.2017.11.015

EV Battery Health: What 6,000 EV Batteries Tell Us | Geotab. (2020). Geotab. https://www.geotab.com/blog/ev-battery-health/

Published

2023-12-25

How to Cite

Smyrnov , O., & Borysenko, A. (2023). Comparative analysis of electrical models of lith-ium-ion batteries in electric vehicles . Vehicle and Electronics. Innovative Technologies, (24), 50–61. https://doi.org/10.30977/VEIT.2023.24.0.5

Issue

Section

WAYS TO IMPROVE THE ECONOMIC AND ENVIRONMENTAL INDICATORS OF MOTOR VEHICLES. ENERGY SAVING TECHNOLOGIES