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Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

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  • Ines Baccouche

    (LATIS-Laboratory of Advanced technology and Intelligent Systems, ENISo, Sousse University, BP 526, 4002 Sousse, Tunisia
    ENIM, Monastir University, Ibn El Jazzar 5019, 5035 Monastir, Tunisia)

  • Sabeur Jemmali

    (LATIS-Laboratory of Advanced technology and Intelligent Systems, ENISo, Sousse University, BP 526, 4002 Sousse, Tunisia)

  • Bilal Manai

    (IntelliBatteries Company, SoftTech Firm Incubator, Technopole of Sousse, BP 24 Sousse Corniche 4059, 4002 Sousse, Tunisia)

  • Noshin Omar

    (MOBI-Mobility, Logistics and Automotive Technology Research Center, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium)

  • Najoua Essoukri Ben Amara

    (LATIS-Laboratory of Advanced technology and Intelligent Systems, ENISo, Sousse University, BP 526, 4002 Sousse, Tunisia)

Abstract

Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.

Suggested Citation

  • Ines Baccouche & Sabeur Jemmali & Bilal Manai & Noshin Omar & Najoua Essoukri Ben Amara, 2017. "Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(6), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:6:p:764-:d:100070
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    References listed on IDEAS

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    16. Thomas Märzinger & David Wöss & Petra Steinmetz & Werner Müller & Tobias Pröll, 2021. "Novel Modelling Approach for the Calculation of the Loading Performance of Charging Stations for E-Trucks to Represent Fleet Consumption," Energies, MDPI, vol. 14(12), pages 1-15, June.
    17. Park, Jinhyeong & Kim, Kunwoo & Park, Seongyun & Baek, Jongbok & Kim, Jonghoon, 2021. "Complementary cooperative SOC/capacity estimator based on the discrete variational derivative combined with the DEKF for electric power applications," Energy, Elsevier, vol. 232(C).
    18. Prarthana Pillai & Sneha Sundaresan & Pradeep Kumar & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Open-Circuit Voltage Models for Battery Management Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-25, September.
    19. Md Ohirul Qays & Yonis Buswig & Md Liton Hossain & Ahmed Abu-Siada, 2020. "Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System," Energies, MDPI, vol. 13(13), pages 1-16, July.
    20. Babaeiyazdi, Iman & Rezaei-Zare, Afshin & Shokrzadeh, Shahab, 2021. "State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach," Energy, Elsevier, vol. 223(C).

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