IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p1234-d744429.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/1234/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/1234/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Angelo Bonfitto, 2020. "A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks," Energies, MDPI, vol. 13(10), pages 1-13, May.
    3. Bowen Jia & Yong Guan & Lifeng Wu, 2019. "A State of Health Estimation Framework for Lithium-Ion Batteries Using Transfer Components Analysis," Energies, MDPI, vol. 12(13), pages 1-14, June.
    4. Jianfang Jia & Jianyu Liang & Yuanhao Shi & Jie Wen & Xiaoqiong Pang & Jianchao Zeng, 2020. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators," Energies, MDPI, vol. 13(2), pages 1-20, January.
    5. Ethelbert Ezemobi & Andrea Tonoli & Mario Silvagni, 2021. "Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine," Energies, MDPI, vol. 14(8), pages 1-15, April.
    6. Prakash Venugopal & Vigneswaran T., 2019. "State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition," Energies, MDPI, vol. 12(22), pages 1-29, November.
    7. Wang, Kangkang & Gao, Fei & Zhu, Yanli & Liu, Hao & Qi, Chuang & Yang, Kai & Jiao, Qingjie, 2018. "Internal resistance and heat generation of soft package Li4Ti5O12 battery during charge and discharge," Energy, Elsevier, vol. 149(C), pages 364-374.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pablo Carrasco Ortega & Pablo Durán Gómez & Julio César Mérida Sánchez & Fernando Echevarría Camarero & Ángel Á. Pardiñas, 2023. "Battery Energy Storage Systems for the New Electricity Market Landscape: Modeling, State Diagnostics, Management, and Viability—A Review," Energies, MDPI, vol. 16(17), pages 1-51, August.
    2. Taysa Millena Banik Marques & João Lucas Ferreira dos Santos & Diego Solak Castanho & Mariane Bigarelli Ferreira & Sergio L. Stevan & Carlos Henrique Illa Font & Thiago Antonini Alves & Cassiano Moro , 2023. "An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles," Energies, MDPI, vol. 16(13), pages 1-18, June.
    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.
    5. Shantanu Pardhi & Sajib Chakraborty & Dai-Duong Tran & Mohamed El Baghdadi & Steven Wilkins & Omar Hegazy, 2022. "A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions," Energies, MDPI, vol. 15(24), pages 1-55, December.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    2. 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.
    3. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    4. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    5. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
    6. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.
    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.
    9. Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
    10. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    11. Balyogi Mohan Dash & Om Prakash & Arun Kumar Samantaray, 2023. "Failure prognosis of the components with unlike degradation trends: A data-driven approach," Journal of Risk and Reliability, , vol. 237(6), pages 1132-1149, December.
    12. Jun Peng & Zhiyong Zheng & Xiaoyong Zhang & Kunyuan Deng & Kai Gao & Heng Li & Bin Chen & Yingze Yang & Zhiwu Huang, 2020. "A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction," Energies, MDPI, vol. 13(3), pages 1-20, February.
    13. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization," Energy, Elsevier, vol. 204(C).
    14. Liu, Xinghua & Li, Siqi & Tian, Jiaqiang & Wei, Zhongbao & Wang, Peng, 2023. "Health estimation of lithium-ion batteries with voltage reconstruction and fusion model," Energy, Elsevier, vol. 282(C).
    15. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    16. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach," Sustainability, MDPI, vol. 13(23), pages 1-25, December.
    17. Wang, Zhe & Yang, Fangfang & Xu, Qiang & Wang, Yongjian & Yan, Hong & Xie, Min, 2023. "Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network," Applied Energy, Elsevier, vol. 336(C).
    18. Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).
    19. Jiahui Zhao & Yong Zhu & Bin Zhang & Mingyi Liu & Jianxing Wang & Chenghao Liu & Xiaowei Hao, 2023. "Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    20. Ouyang, Tiancheng & Xu, Peihang & Chen, Jingxian & Su, Zixiang & Huang, Guicong & Chen, Nan, 2021. "A novel state of charge estimation method for lithium-ion batteries based on bias compensation," Energy, Elsevier, vol. 226(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1234-:d:744429. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.