IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v187y2019ics036054421931552x.html
   My bibliography  Save this article

A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter

Author

Listed:
  • Zhu, Qiao
  • Xu, Mengen
  • Liu, Weiqun
  • Zheng, Mengqian

Abstract

This paper focuses on the state of charge (SOC) estimation of a lithium-ion battery based on a fractional-order adaptive extended Kalman filter (FO-AEKF). First, a fractional order model (FOM) is introduced to describe the physical behavior of the battery which is superior than the integral-order model (IOM), because there are diffused and decentralized characteristics in battery inner parameters. Then, the parameters of the FOM are identified by a genetic algorithm which can realize optimal parameter identification. After that, the FO-AEKF algorithm is developed, which combines the advantages of the FOM and the adaptive strategy. Consequently, the FO-AEKF can quickly track the unknown and time-invariant (or slow time-varying) noise variance and then improve the accuracy of SOC estimation. Finally, two types of lithium-ion batteries and two dynamic operation conditions are given to show the efficiency of the FO-AEKF by comparing with the extended Kalman filter (EKF) for FOM and the adaptive extended Kalman filter (AEKF) for IOM.

Suggested Citation

  • Zhu, Qiao & Xu, Mengen & Liu, Weiqun & Zheng, Mengqian, 2019. "A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter," Energy, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:energy:v:187:y:2019:i:c:s036054421931552x
    DOI: 10.1016/j.energy.2019.115880
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421931552X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.115880?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. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2017. "On-line battery state-of-charge estimation based on an integrated estimator," Applied Energy, Elsevier, vol. 185(P2), pages 2026-2032.
    2. Mu, Hao & Xiong, Rui & Zheng, Hongfei & Chang, Yuhua & Chen, Zeyu, 2017. "A novel fractional order model based state-of-charge estimation method for lithium-ion battery," Applied Energy, Elsevier, vol. 207(C), pages 384-393.
    3. Renxin Xiao & Jiangwei Shen & Xiaoyu Li & Wensheng Yan & Erdong Pan & Zheng Chen, 2016. "Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods," Energies, MDPI, vol. 9(3), pages 1-15, March.
    4. Yixing Chen & Deqing Huang & Qiao Zhu & Weiqun Liu & Congzhi Liu & Neng Xiong, 2017. "A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter," Energies, MDPI, vol. 10(9), pages 1-19, September.
    5. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    6. Ghedamsi, K. & Aouzellag, D. & Berkouk, E.M., 2008. "Control of wind generator associated to a flywheel energy storage system," Renewable Energy, Elsevier, vol. 33(9), pages 2145-2156.
    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. 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.
    2. Chen, Liping & Wu, Xiaobo & Lopes, António M. & Yin, Lisheng & Li, Penghua, 2022. "Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter," Energy, Elsevier, vol. 252(C).
    3. 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).
    4. 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.
    5. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    6. Fan, Guodong & Li, Xiaoyu & Zhang, Ruigang, 2021. "Global Sensitivity Analysis on Temperature-Dependent Parameters of A Reduced-Order Electrochemical Model And Robust State-of-Charge Estimation at Different Temperatures," Energy, Elsevier, vol. 223(C).
    7. He, Lin & Wang, Yangyang & Wei, Yujiang & Wang, Mingwei & Hu, Xiaosong & Shi, Qin, 2022. "An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery," Energy, Elsevier, vol. 244(PA).
    8. Jiandong Duan & Peng Wang & Wentao Ma & Xinyu Qiu & Xuan Tian & Shuai Fang, 2020. "State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter," Energies, MDPI, vol. 13(16), pages 1-18, August.
    9. Deng Ma & Kai Gao & Yutao Mu & Ziqi Wei & Ronghua Du, 2022. "An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error," Energies, MDPI, vol. 15(10), pages 1-18, May.
    10. Vichard, L. & Ravey, A. & Venet, P. & Harel, F. & Pelissier, S. & Hissel, D., 2021. "A method to estimate battery SOH indicators based on vehicle operating data only," Energy, Elsevier, vol. 225(C).
    11. Abdelghani Djeddi & Djalel Dib & Ahmad Taher Azar & Salem Abdelmalek, 2019. "Fractional Order Unknown Inputs Fuzzy Observer for Takagi–Sugeno Systems with Unmeasurable Premise Variables," Mathematics, MDPI, vol. 7(10), pages 1-16, October.
    12. Qi Wang & Tian Gao & Xingcan Li, 2022. "SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters," Energies, MDPI, vol. 15(16), pages 1-15, August.
    13. Lei Pei & Cheng Yu & Tiansi Wang & Jiawei Yang & Wanlin Wang, 2024. "A Training-Free Estimation Method for the State of Charge and State of Health of Series Battery Packs under Various Load Profiles," Energies, MDPI, vol. 17(8), pages 1-20, April.
    14. Zhao, Xinze & Sun, Bingxiang & Zhang, Weige & He, Xitian & Ma, Shichang & Zhang, Junwei & Liu, Xiaopeng, 2024. "Error theory study on EKF-based SOC and effective error estimation strategy for Li-ion batteries," Applied Energy, Elsevier, vol. 353(PA).
    15. Edgar D. Silva-Vera & Jesus E. Valdez-Resendiz & Gerardo Escobar & Daniel Guillen & Julio C. Rosas-Caro & Jose M. Sosa, 2024. "Data-Driven Modeling and Open-Circuit Voltage Estimation of Lithium-Ion Batteries," Mathematics, MDPI, vol. 12(18), pages 1-16, September.
    16. Hou, Jie & Liu, Jiawei & Chen, Fengwei & Li, Penghua & Zhang, Tao & Jiang, Jincheng & Chen, Xiaolei, 2023. "Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter," Energy, Elsevier, vol. 271(C).
    17. Renxin, Xiao & Yi, Yang & Xianguang, Jia & Nan, Pan, 2023. "Collaborative estimations of state of energy and maximum available energy of lithium-ion batteries with optimized time windows considering instantaneous energy efficiencies," Energy, Elsevier, vol. 274(C).
    18. 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).
    19. Yunjin Ao & Yong-Chao Liu & Salah Laghrouche & Denis Candusso, 2024. "Dynamic Fractional-Order Model of Proton Exchange Membrane Fuel Cell System for Sustainability Improvement," Sustainability, MDPI, vol. 16(7), pages 1-16, April.
    20. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    21. Fan Zhang & Lele Yin & Jianqiang Kang, 2021. "Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms," Energies, MDPI, vol. 14(19), pages 1-18, October.
    22. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).
    23. Jun Yuan & Zhili Qin & Haikun Huang & Xingdong Gan & Shuguang Li & Baihai Li, 2023. "State of Health Estimation and Remaining Useful Life Prediction for a Lithium-Ion Battery with a Two-Layer Stacking Regressor," Energies, MDPI, vol. 16(5), pages 1-15, February.

    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. Zheng Chen & Xiaoyu Li & Jiangwei Shen & Wensheng Yan & Renxin Xiao, 2016. "A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles," Energies, MDPI, vol. 9(9), pages 1-15, September.
    2. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    3. Lin Su & Guangxu Zhou & Dairong Hu & Yuan Liu & Yunhai Zhu, 2021. "Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model," Energies, MDPI, vol. 14(19), pages 1-23, October.
    4. Zhongwei Zhang & Dan Zhou & Neng Xiong & Qiao Zhu, 2021. "Non-Fragile H ∞ Nonlinear Observer for State of Charge Estimation of Lithium-Ion Battery Based on a Fractional-Order Model," Energies, MDPI, vol. 14(16), pages 1-17, August.
    5. Ruifeng Zhang & Bizhong Xia & Baohua Li & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "Study on the Characteristics of a High Capacity Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion Battery—An Experimental Investigation," Energies, MDPI, vol. 11(9), pages 1-20, August.
    6. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    7. Mu, Hao & Xiong, Rui & Zheng, Hongfei & Chang, Yuhua & Chen, Zeyu, 2017. "A novel fractional order model based state-of-charge estimation method for lithium-ion battery," Applied Energy, Elsevier, vol. 207(C), pages 384-393.
    8. Mehta, Rohit & Gupta, Amit, 2024. "Mathematical modelling of electrochemical, thermal and degradation processes in lithium-ion cells—A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    9. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    10. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    11. 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.
    12. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
    13. Ming Cai & Weijie Chen & Xiaojun Tan, 2017. "Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model," Energies, MDPI, vol. 10(10), pages 1-16, October.
    14. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    15. Li, Yifeng & Bao, Jie & Skyllas-Kazacos, Maria & Akter, Md Parvez & Zhang, Xinan & Fletcher, John, 2019. "Studies on dynamic responses and impedance of the vanadium redox flow battery," Applied Energy, Elsevier, vol. 237(C), pages 91-102.
    16. Li, Xiaoyu & Xu, Jianhua & Hong, Jianxun & Tian, Jindong & Tian, Yong, 2021. "State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy," Energy, Elsevier, vol. 214(C).
    17. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    18. Ragab El-Sehiemy & Mohamed A. Hamida & Ehab Elattar & Abdullah Shaheen & Ahmed Ginidi, 2022. "Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm," Energies, MDPI, vol. 15(13), pages 1-20, June.
    19. Ma, Zeyu & Yang, Ruixin & Wang, Zhenpo, 2019. "A novel data-model fusion state-of-health estimation approach for lithium-ion batteries," Applied Energy, Elsevier, vol. 237(C), pages 836-847.
    20. Li Zhai & Liwen Lin & Xinyu Zhang & Chao Song, 2016. "The Effect of Distributed Parameters on Conducted EMI from DC-Fed Motor Drive Systems in Electric Vehicles," Energies, MDPI, vol. 10(1), pages 1-17, December.

    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:187:y:2019:i:c:s036054421931552x. 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.