IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v229y2021ics0360544221009476.html
   My bibliography  Save this article

Online estimation of battery model parameters and state of health in electric and hybrid aircraft application

Author

Listed:
  • Hashemi, Seyed Reza
  • Mahajan, Ajay Mohan
  • Farhad, Siamak

Abstract

The accuracy of state of health (SOH) estimation function in battery management systems is an essential factor for ensuring the safety and reliability of battery systems in electric aircraft. Most common SOH estimation approaches are model-based and work with constant model parameters. However, the model parameters vary by the change in operating temperature and state of charge (SOC). The variation of model parameters has adverse impact on the accuracy of battery state's estimation if they are not updated. In this paper, an accurate online parameter estimation method is proposed for lithium-ion batteries (LIBs) to increase the accuracy of the battery model. A more accurate model for the battery leads to a more accurate SOC and SOH estimation. An adaptive sliding observer is developed to estimate the SOC and capacity based on the proposed parameter estimator. An adaptive SOH estimation scheme is proposed to mitigate the temperature variation effect on the accuracy of the SOH estimation. The experimental results verify the effectiveness of the proposed parameter estimator along with the adaptive sliding observer on achieving accurate estimation of SOC and capacity. The proposed adaptive SOH shows good agreement with experiments by less than 1.3% estimation error at different operating temperature conditions.

Suggested Citation

  • Hashemi, Seyed Reza & Mahajan, Ajay Mohan & Farhad, Siamak, 2021. "Online estimation of battery model parameters and state of health in electric and hybrid aircraft application," Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:energy:v:229:y:2021:i:c:s0360544221009476
    DOI: 10.1016/j.energy.2021.120699
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221009476
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120699?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    2. Zheng, Yuejiu & Qin, Chao & Lai, Xin & Han, Xuebing & Xie, Yi, 2019. "A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    4. Zhengyu Liu & Jingjie Zhao & Hao Wang & Chao Yang, 2020. "A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs," Energies, MDPI, vol. 13(4), pages 1-17, February.
    5. Jiang, Cong & Wang, Shunli & Wu, Bin & Fernandez, Carlos & Xiong, Xin & Coffie-Ken, James, 2021. "A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter," Energy, Elsevier, vol. 219(C).
    6. Julian Hoelzen & Yaolong Liu & Boris Bensmann & Christopher Winnefeld & Ali Elham & Jens Friedrichs & Richard Hanke-Rauschenbach, 2018. "Conceptual Design of Operation Strategies for Hybrid Electric Aircraft," Energies, MDPI, vol. 11(1), pages 1-26, January.
    7. Zheng, Yuejiu & Wang, Jingjing & Qin, Chao & Lu, Languang & Han, Xuebing & Ouyang, Minggao, 2019. "A novel capacity estimation method based on charging curve sections for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 185(C), pages 361-371.
    8. Bravo, Guillem Moreno & Praliyev, Nurgeldy & Veress, Árpád, 2021. "Performance analysis of hybrid electric and distributed propulsion system applied on a light aircraft," Energy, Elsevier, vol. 214(C).
    9. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
    10. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    11. Sziroczak, David & Jankovics, Istvan & Gal, Istvan & Rohacs, Daniel, 2020. "Conceptual design of small aircraft with hybrid-electric propulsion systems," Energy, Elsevier, vol. 204(C).
    12. Shuxiang Song & Chen Fei & Haiying Xia, 2020. "Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction," Energies, MDPI, vol. 13(4), pages 1-13, February.
    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. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    2. Li, Renzheng & Hong, Jichao & Zhang, Huaqin & Chen, Xinbo, 2022. "Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles," Energy, Elsevier, vol. 257(C).
    3. Qiao, Jialu & Wang, Shunli & Yu, Chunmei & Yang, Xiao & Fernandez, Carlos, 2023. "A chaotic firefly - Particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance," Energy, Elsevier, vol. 263(PE).
    4. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    5. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    6. Wang, Bin & Wang, Chaohui & Wang, Zhiyu & Ni, Siliang & Yang, Yixin & Tian, Pengyu, 2023. "Adaptive state of energy evaluation for supercapacitor in emergency power system of more-electric aircraft," Energy, Elsevier, vol. 263(PA).
    7. Huang, Kai & Yao, Kaixin & Guo, Yongfang & Lv, Ziteng, 2023. "State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining," Energy, Elsevier, vol. 282(C).
    8. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    9. Tarhan, Burak & Yetik, Ozge & Karakoc, Tahir Hikmet, 2021. "Hybrid battery management system design for electric aircraft," Energy, Elsevier, vol. 234(C).

    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. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    3. Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    4. Mei Zhang & Wanli Chen & Jun Yin & Tao Feng, 2022. "Health Factor Extraction of Lithium-Ion Batteries Based on Discrete Wavelet Transform and SOH Prediction Based on CatBoost," Energies, MDPI, vol. 15(15), pages 1-17, July.
    5. Jinhyeong Park & Munsu Lee & Gunwoo Kim & Seongyun Park & Jonghoon Kim, 2020. "Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH," Energies, MDPI, vol. 13(9), pages 1-20, April.
    6. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    7. Wang, Tao & Zhang, Yu & Yin, Zhao & Qiu, Liang & Hua, Yang & Zhang, Xian-wen & Qian, Ye-jian, 2023. "Decoupling control scheme optimization and energy analysis for a triaxial gas turbine based on the variable power offtakes/inputs," Energy, Elsevier, vol. 262(PB).
    8. Zhang, Jinning & Roumeliotis, Ioannis & Zolotas, Argyrios, 2022. "Model-based fully coupled propulsion-aerodynamics optimization for hybrid electric aircraft energy management strategy," Energy, Elsevier, vol. 245(C).
    9. Qiao, Dongdong & Wang, Xueyuan & Lai, Xin & Zheng, Yuejiu & Wei, Xuezhe & Dai, Haifeng, 2022. "Online quantitative diagnosis of internal short circuit for lithium-ion batteries using incremental capacity method," Energy, Elsevier, vol. 243(C).
    10. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
    11. Zheng, Yuejiu & Cui, Yifan & Han, Xuebing & Ouyang, Minggao, 2021. "A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method," Energy, Elsevier, vol. 237(C).
    12. Sumukh Surya & Vidya Rao & Sheldon S. Williamson, 2021. "Comprehensive Review on Smart Techniques for Estimation of State of Health for Battery Management System Application," Energies, MDPI, vol. 14(15), pages 1-22, July.
    13. Li, Kuo & Gao, Xiao & Liu, Caixia & Chang, Chun & Li, Xiaoyu, 2023. "A novel Co-estimation framework of state-of-charge, state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method," Energy, Elsevier, vol. 269(C).
    14. Lai, Xin & Yi, Wei & Cui, Yifan & Qin, Chao & Han, Xuebing & Sun, Tao & Zhou, Long & Zheng, Yuejiu, 2021. "Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter," Energy, Elsevier, vol. 216(C).
    15. Ma, Jian & Shang, Pengchao & Zou, Xinyu & Ma, Ning & Ding, Yu & Sun, Jinwen & Cheng, Yujie & Tao, Laifa & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2021. "A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries," Applied Energy, Elsevier, vol. 282(PA).
    16. Lai, Xin & Huang, Yunfeng & Deng, Cong & Gu, Huanghui & Han, Xuebing & Zheng, Yuejiu & Ouyang, Minggao, 2021. "Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    17. Matthieu Pettes-Duler & Xavier Roboam & Bruno Sareni, 2022. "Integrated Optimal Design for Hybrid Electric Powertrain of Future Aircrafts," Energies, MDPI, vol. 15(18), pages 1-25, September.
    18. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    19. Hao Sun & Bo Jiang & Heze You & Bojian Yang & Xueyuan Wang & Xuezhe Wei & Haifeng Dai, 2021. "Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions," Energies, MDPI, vol. 14(2), pages 1-19, January.
    20. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).

    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:eee:energy:v:229:y:2021:i:c:s0360544221009476. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.