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State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions

Author

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  • Ethelbert Ezemobi

    (Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
    Mechanical and Mechatronics Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Mario Silvagni

    (Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy)

  • Ahmad Mozaffari

    (Mechanical and Mechatronics Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Andrea Tonoli

    (Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy)

  • Amir Khajepour

    (Mechanical and Mechatronics Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

Abstract

Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium-ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium-ion batteries. The ANN-based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large-scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real-time execution speed of 8.34 µs is possible with a negligible memory occupation.

Suggested Citation

  • Ethelbert Ezemobi & Mario Silvagni & Ahmad Mozaffari & Andrea Tonoli & Amir Khajepour, 2022. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions," Energies, MDPI, vol. 15(3), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1234-:d:744429
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    References listed on IDEAS

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    Cited by:

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    3. Vongdala Noudeng & Nguyen Van Quan & Tran Dang Xuan, 2022. "A Future Perspective on Waste Management of Lithium-Ion Batteries for Electric Vehicles in Lao PDR: Current Status and Challenges," IJERPH, MDPI, vol. 19(23), pages 1-22, December.
    4. Tuhibur Rahman & Ahmed Al Mansur & Molla Shahadat Hossain Lipu & Md. Siddikur Rahman & Ratil H. Ashique & Mohamad Abou Houran & Rajvikram Madurai Elavarasan & Eklas Hossain, 2023. "Investigation of Degradation of Solar Photovoltaics: A Review of Aging Factors, Impacts, and Future Directions toward Sustainable Energy Management," Energies, MDPI, vol. 16(9), pages 1-30, April.
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    6. Jikai Bi & Jae-Cheon Lee & Hao Liu, 2022. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics," Energies, MDPI, vol. 15(7), pages 1-24, March.
    7. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    8. Edoardo Lelli & Alessia Musa & Emilio Batista & Daniela Anna Misul & Giovanni Belingardi, 2023. "On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles," Energies, MDPI, vol. 16(12), pages 1-21, June.

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