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Integration of sampling based battery state of health estimation method in electric vehicles

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  • Ozkurt, Celil
  • Camci, Fatih
  • Atamuradov, Vepa
  • Odorry, Christopher

Abstract

Battery cost is one of the crucial parameters affecting high deployment of Electric Vehicles (EVs) negatively. Accurate State of Health (SoH) estimation plays an important role in reducing the total ownership cost, availability, and safety of the battery avoiding early disposal of the batteries and decreasing unexpected failures. A circuit design for SoH estimation in a battery system that bases on selected battery cells and its integration to EVs are presented in this paper. A prototype microcontroller has been developed and used for accelerated aging tests for a battery system. The data collected in the lab tests have been utilized to simulate a real EV battery system. Results of accelerated aging tests and simulation have been presented in the paper. The paper also discusses identification of the best number of battery cells to be selected for SoH estimation test. In addition, different application options of the presented approach for EV batteries have been discussed in the paper.

Suggested Citation

  • Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.
    • Handle: RePEc:eee:appene:v:175:y:2016:i:c:p:356-367
    DOI: 10.1016/j.apenergy.2016.05.037
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    1. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    2. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    3. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
    4. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2016. "Modelling the multilevel structure and mixed effects of the factors influencing the energy consumption of electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1351-1360.
    5. Maria Cieśla & Piotr Nowakowski & Mariusz Wala, 2024. "The Impact of Variable Ambient Temperatures on the Energy Efficiency and Performance of Electric Vehicles during Waste Collection," Energies, MDPI, vol. 17(17), pages 1-21, August.
    6. Rahbari, Omid & Omar, Noshin & Firouz, Yousef & Rosen, Marc A. & Goutam, Shovon & Van Den Bossche, Peter & Van Mierlo, Joeri, 2018. "A novel state of charge and capacity estimation technique for electric vehicles connected to a smart grid based on inverse theory and a metaheuristic algorithm," Energy, Elsevier, vol. 155(C), pages 1047-1058.
    7. Mingant, R. & Bernard, J. & Sauvant-Moynot, V., 2016. "Novel state-of-health diagnostic method for Li-ion battery in service," Applied Energy, Elsevier, vol. 183(C), pages 390-398.
    8. Gaizka Saldaña & José Ignacio San Martín & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Analysis of the Current Electric Battery Models for Electric Vehicle Simulation," Energies, MDPI, vol. 12(14), pages 1-27, July.
    9. Sun, Lishan & Huang, Yuchen & Liu, Shuli & Chen, Yanyan & Yao, Liya & Kashyap, Anil, 2017. "A completive survey study on the feasibility and adaptation of EVs in Beijing, China," Applied Energy, Elsevier, vol. 187(C), pages 128-139.
    10. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.

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