Top

Published in:

2024 | OriginalPaper | Chapter

Prediction of Lithium-Ion Batteries Output Voltage in Electric Vehicles

Authors : Mohamed Louzazni, Brahim Belmahdi, Rachid Herbazi, Manoharan Madhiarasan

Published in: The 17th International Conference Interdisciplinarity in Engineering

Publisher: Springer Nature Switzerland

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Electric vehicles (EVs) are becoming increasingly popular due to their capacity to reduce reliance on oil and reduce greenhouse gas emissions. Among the electric components of an electric vehicle (EV), the battery is considered as the biggest bottleneck. Among the several battery alternatives, lithium-ion batteries are widely used to power electric vehicles. The condition of a battery encompasses several aspects, including the state-of-charge (SoC), state-of-health (SoH), state-of-power (SoP), and state-of-life (SoL). A battery’s state-of-charge (SoC) refers to the proportion of its capacity that is still useable. Changes in the operating circumstances of the electric vehicle (EV) in which the battery is placed have the greatest impact on the SoC. The output voltage of a battery indicates its state of charge. When a battery’s output voltage goes below a set cut-off value, the state-of-charge (SoC) is deemed to be zero. In other words, the SoC is decided by whether the output voltage of the battery exceeds or falls below a certain threshold. In this paper, the neural network (NN) time series analysis is applied to predict the output voltage of a battery is introduced. The proposed approach is base in the past values to predict the actual measurement using Levenberg-Marquardt algorithm to optimize the input values. The objective is to estimate and forecast the output voltage of a battery during the operation of an electric vehicle (EV). The results show that utilizing the NN based Levenberg-Marquardt algorithm for forecasting yields a lower individual absolute error (IAE).

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Analysis of the CO2 Emissions Due to Charging of Battery Electric Vehicles Considering the Hourly Power Mix

next chapter Improved State of Charge Estimation of a Lithium-Ion Battery Output: Application to Conventional Neural Network

Baloch, M.A., Ozturk, I., Bekun, F.V., Khan, D.: Modeling the dynamic linkage between financial development, energy innovation, and environmental quality: does globalization matter? Bus. Strateg. Environ. 30(1), 176–184 (2021). https://doi.org/10.1002/bse.2615CrossRef

A global snapshot of the air pollution-related health impacts of transportation sector emissions in 2010 and 2015. In: International Council on Clean Transportation. https://theicct.org/publication/a-global-snapshot-of-the-air-pollution-related-health-impacts-of-transportation-sector-emissions-in-2010-and-2015/. Accessed 16 Jun 2023

Liu, Z., et al.: Carbon monitor, a near-real-time daily dataset of global CO₂ emission from fossil fuel and cement production. Sci. Data 7(1), 1–12 (2020). https://doi.org/10.1038/s41597-020-00708-7

Graham, J.D.: The Global Rise of the Modern Plug-In Electric Vehicle: Public Policy, Innovation and Strategy. Edward Elgar Publishing (2021)

Wu, Z., Wang, M., Zheng, J., Sun, X., Zhao, M., Wang, X.: Life cycle greenhouse gas emission reduction potential of battery electric vehicle. J. Clean. Prod. 190, 462–470 (2018). https://doi.org/10.1016/j.jclepro.2018.04.036CrossRef

Salgado, R.M., Danzi, F., Oliveira, J.E., El-Azab, A., Camanho, P.P., Braga, M.H.: The latest trends in electric vehicles batteries. Molecules 26(11), 3188 (2021). https://doi.org/10.3390/molecules26113188

Emadi, A., (ed.): Advanced Electric Drive Vehicles. CRC Press, Boca Raton (2014). https://doi.org/10.1201/9781315215570

Hu, X., Zou, C., Zhang, C., Li, Y.: Technological developments in batteries: a survey of principal roles, types, and management needs. IEEE Power Energ. Mag. 15(5), 20–31 (2017). https://doi.org/10.1109/MPE.2017.2708812CrossRef

Chemali, E., Preindl, M., Malysz, P., Emadi, A.: Electrochemical and electrostatic energy storage and management systems for electric drive vehicles: state-of-the-art review and future trends. IEEE J. Emerg. Sel. Top. Power Electron. 4(3), 1117–1134 (2016). https://doi.org/10.1109/JESTPE.2016.2566583CrossRef

10.

Shrivastava, P., Naidu, P.A., Sharma, S., Panigrahi, B.K., Garg, A.: Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications. J. Energ. Storage 64, 107159 (2023). https://doi.org/10.1016/j.est.2023.107159CrossRef

11.

Niri, M.F., Bui, T.M.N., Dinh, T.Q., Hosseinzadeh, E., Yu, T.F., Marco, J.: Remaining energy estimation for lithium-ion batteries via Gaussian mixture and Markov models for future load prediction. J. Energ. Storage 28, 101271 (2020). https://doi.org/10.1016/j.est.2020.101271CrossRef

12.

Sánchez-Sánchez, P.A., García-González, J.R., Coronell, L.H.P., Sánchez-Sánchez, P.A., García-González, J.R., Coronell, L.H.P.: Encountered problems of time series with neural networks: models and architectures. In: Recent Trends in Artificial Neural Networks - from Training to Prediction. IntechOpen (2019). https://doi.org/10.5772/intechopen.88901

13.

Ji, Y., Chen, Z., Shen, Y., Yang, K., Wang, Y., Cui, J.: An RUL prediction approach for lithium-ion battery based on SADE-MESN. Appl. Soft Comput. 104, 107195 (2021). https://doi.org/10.1016/j.asoc.2021.107195CrossRef

14.

Rong, P., Pedram, M.: An analytical model for predicting the remaining battery capacity of lithium-ion batteries. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 14(5), 441–451 (2006). https://doi.org/10.1109/TVLSI.2006.876094

15.

Wang, Y., Liu, C., Pan, R., Chen, Z.: Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator. Energy 121, 739–750 (2017). https://doi.org/10.1016/j.energy.2017.01.044CrossRef

16.

Pan, R., Wang, Y., Zhang, X., Yang, D., Chen, Z.: Power capability prediction for lithium-ion batteries based on multiple constraints analysis. Electrochim. Acta 238, 120–133 (2017). https://doi.org/10.1016/j.electacta.2017.04.004CrossRef

17.

Bian, C., Yang, S., Miao, Q.: Cross-domain state-of-charge estimation of Li-ion batteries based on deep transfer neural network with multiscale distribution adaptation. IEEE Trans. Transp. Electrif. 7(3), 1260–1270 (2021). https://doi.org/10.1109/TTE.2020.3041604CrossRef

18.

Chemali, E., Kollmeyer, P.J., Preindl, M., Ahmed, R., Emadi, A.: Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries. IEEE Trans. Industr. Electron. 65(8), 6730–6739 (2018). https://doi.org/10.1109/TIE.2017.2787586CrossRef

19.

Planella, F.B., et al.: A continuum of physics-based lithium-ion battery models reviewed. Prog. Energy 4(4), 042003 (2022). https://doi.org/10.1088/2516-1083/ac7d31CrossRef

20.

Zheng, Y., Shi, Z., Guo, D., Dai, H., Han, X.: A simplification of the time-domain equivalent circuit model for lithium-ion batteries based on low-frequency electrochemical impedance spectra. J. Power. Sources 489, 229505 (2021). https://doi.org/10.1016/j.jpowsour.2021.229505CrossRef

21.

Le Houx, J., Kramer, D.: Physics based modelling of porous lithium ion battery electrodes—a review. Energy Rep. 6, 1–9 (2020). https://doi.org/10.1016/j.egyr.2020.02.021CrossRef

22.

Vanem, E., Salucci, C.B., Bakdi, A., Sheim Alnes, Ø.Å.: Data-driven state of health modelling—a review of state of the art and reflections on applications for maritime battery systems. J. Energy Storage 43, 103158 (2021). https://doi.org/10.1016/j.est.2021.103158

23.

Li, W., et al.: Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries. J. Power. Sources 506, 230034 (2021). https://doi.org/10.1016/j.jpowsour.2021.230034CrossRef

24.

Kaur, J., Parmar, K.S., Singh, S.: Autoregressive models in environmental forecasting time series: a theoretical and application review. Environ. Sci. Pollut. Res. 30(8), 19617–19641 (2023). https://doi.org/10.1007/s11356-023-25148-9CrossRef

25.

Razavi, S.: Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling. Environ Model Softw. 144, 105159 (2021). https://doi.org/10.1016/j.envsoft.2021.105159CrossRef

26.

Ma, Y., Chang, Q., Lu, H., Liu, J.: Reconstruct recurrent neural networks via flexible sub-models for time series classification. Appl. Sci. 8(4), 630 (2018). https://doi.org/10.3390/app8040630CrossRef

27.

Wibawa, A.P., Utama, A.B.P., Elmunsyah, H., Pujianto, U., Dwiyanto, F.A., Hernandez, L.: Time-series analysis with smoothed convolutional neural network. J. Big Data 9(1), 44 (2022). https://doi.org/10.1186/s40537-022-00599-yCrossRef

28.

Ma, Z., Mei, G.: A hybrid attention-based deep learning approach for wind power prediction. Appl. Energy 323, 119608 (2022). https://doi.org/10.1016/j.apenergy.2022.119608CrossRef

29.

Choi, K., Yi, J., Park, C., Yoon, S.: Deep learning for anomaly detection in time-series data: review, analysis, and guidelines. IEEE Access 9, 120043–120065 (2021). https://doi.org/10.1109/ACCESS.2021.3107975CrossRef

30.

Alzaman, C.: Forecasting and optimization stock predictions: varying asset profile, time window, and hyperparameter factors. Syst. Soft Comput. 5, 200052 (2023). https://doi.org/10.1016/j.sasc.2023.200052CrossRef

31.

Alsumaiei, A.A.: A nonlinear autoregressive modeling approach for forecasting groundwater level fluctuation in urban aquifers. Water 12(3), 820 (2020). https://doi.org/10.3390/w12030820CrossRef

32.

Hu, J., Zheng, W.: A deep learning model to effectively capture mutation information in multivariate time series prediction. Knowl. Based Syst. 203, 106139 (2020). https://doi.org/10.1016/j.knosys.2020.106139CrossRef

Title: Prediction of Lithium-Ion Batteries Output Voltage in Electric Vehicles
Authors: Mohamed Louzazni
Brahim Belmahdi
Rachid Herbazi
Manoharan Madhiarasan
Publisher: Springer Nature Switzerland
Book: The 17th International Conference Interdisciplinarity in Engineering
Print ISBN: 978-3-031-54673-0

Electronic ISBN: 978-3-031-54674-7

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-54674-7_7

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partners