IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i1p60-d87080.html
   My bibliography  Save this article

Battery Internal Temperature Estimation for LiFePO 4 Battery Based on Impedance Phase Shift under Operating Conditions

Author

Listed:
  • Jiangong Zhu

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    School of Automotive Engineering, Tongji University, Shanghai 201804, China)

  • Zechang Sun

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    School of Automotive Engineering, Tongji University, Shanghai 201804, China)

  • Xuezhe Wei

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    School of Automotive Engineering, Tongji University, Shanghai 201804, China)

  • Haifeng Dai

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    School of Automotive Engineering, Tongji University, Shanghai 201804, China)

Abstract

An impedance-based temperature estimation method is investigated considering the electrochemical non-equilibrium with short-term relaxation time for facilitating the vehicular application. Generally, sufficient relaxation time is required for battery electrochemical equilibrium before the impedance measurement. A detailed experiment is performed to investigate the regularity of the battery impedance in short-term relaxation time after switch-off current excitation, which indicates that the impedance can be measured and also has systematical decrement with the relaxation time growth. Based on the discussion of impedance variation in electrochemical perspective, as well as the monotonic relationship between impedance phase shift and battery internal temperature in the electrochemical equilibrium state, an exponential equation that accounts for both measured phase shift and relaxation time is established to correct the measuring deviation caused by electrochemical non-equilibrium. Then, a multivariate linear equation coupled with ambient temperature is derived considering the temperature gradients between the active part and battery surface. Equations stated above are all identified with the embedded thermocouple experimentally. In conclusion, the temperature estimation method can be a valuable alternative for temperature monitoring during cell operating, and serve the functionality as an efficient implementation in battery thermal management system for electric vehicles (EVs) and hybrid electric vehicles (HEVs).

Suggested Citation

  • Jiangong Zhu & Zechang Sun & Xuezhe Wei & Haifeng Dai, 2017. "Battery Internal Temperature Estimation for LiFePO 4 Battery Based on Impedance Phase Shift under Operating Conditions," Energies, MDPI, vol. 10(1), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:60-:d:87080
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/1/60/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/1/60/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiong, Rui & Sun, Fengchun & Gong, Xianzhi & Gao, Chenchen, 2014. "A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 1421-1433.
    2. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    3. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    4. Andreas Melcher & Carlos Ziebert & Magnus Rohde & Hans Jürgen Seifert, 2016. "Modeling and Simulation of the Thermal Runaway Behavior of Cylindrical Li-Ion Cells—Computing of Critical Parameters," Energies, MDPI, vol. 9(4), pages 1-19, April.
    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. Jinlei Sun & Guo Wei & Lei Pei & Rengui Lu & Kai Song & Chao Wu & Chunbo Zhu, 2015. "Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter," Energies, MDPI, vol. 8(5), pages 1-16, May.
    7. Xiaobin Hong & Nianzhi Li & Jinheng Feng & Qingzhao Kong & Guixiong Liu, 2015. "Multi-Electrode Resistivity Probe for Investigation of Local Temperature Inside Metal Shell Battery Cells via Resistivity: Experiments and Evaluation of Electrical Resistance Tomography," Energies, MDPI, vol. 8(2), pages 1-23, January.
    8. Odile Capron & Ahmadou Samba & Noshin Omar & Peter Van Den Bossche & Joeri Van Mierlo, 2015. "Thermal Behaviour Investigation of a Large and High Power Lithium Iron Phosphate Cylindrical Cell," Energies, MDPI, vol. 8(9), pages 1-26, September.
    9. Jaeshin Yi & Boram Koo & Chee Burm Shin, 2014. "Three-Dimensional Modeling of the Thermal Behavior of a Lithium-Ion Battery Module for Hybrid Electric Vehicle Applications," Energies, MDPI, vol. 7(11), pages 1-16, November.
    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. Raijmakers, L.H.J. & Danilov, D.L. & Eichel, R.-A. & Notten, P.H.L., 2019. "A review on various temperature-indication methods for Li-ion batteries," Applied Energy, Elsevier, vol. 240(C), pages 918-945.
    2. Sumukh Surya & Akash Samanta & Vinicius Marcis & Sheldon Williamson, 2022. "Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison," Energies, MDPI, vol. 15(2), pages 1-21, January.
    3. Rui Xiong & Hailong Li & Xuan Zhou, 2017. "Advanced Energy Storage Technologies and Their Applications (AESA2017)," Energies, MDPI, vol. 10(9), pages 1-3, September.
    4. Xuezhe Wei & Xueyuan Wang & Haifeng Dai, 2018. "Practical On-Board Measurement of Lithium Ion Battery Impedance Based on Distributed Voltage and Current Sampling," Energies, MDPI, vol. 11(1), pages 1-15, January.
    5. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    6. Haifeng Dai & Bo Jiang & Xuezhe Wei, 2018. "Impedance Characterization and Modeling of Lithium-Ion Batteries Considering the Internal Temperature Gradient," Energies, MDPI, vol. 11(1), pages 1-18, January.

    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. 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.
    2. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    3. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    4. Tang, Xiaopeng & Liu, Boyang & Lv, Zhou & Gao, Furong, 2017. "Observer based battery SOC estimation: Using multi-gain-switching approach," Applied Energy, Elsevier, vol. 204(C), pages 1275-1283.
    5. Dafen Chen & Jiuchun Jiang & Xue Li & Zhanguo Wang & Weige Zhang, 2016. "Modeling of a Pouch Lithium Ion Battery Using a Distributed Parameter Equivalent Circuit for Internal Non-Uniformity Analysis," Energies, MDPI, vol. 9(11), pages 1-18, October.
    6. Xiaogang Wu & Zhe Chen & Zhiyang Wang, 2017. "Analysis of Low Temperature Preheating Effect Based on Battery Temperature-Rise Model," Energies, MDPI, vol. 10(8), pages 1-15, August.
    7. Zou, Yuan & Li, Shengbo Eben & Shao, Bing & Wang, Baojin, 2016. "State-space model with non-integer order derivatives for lithium-ion battery," Applied Energy, Elsevier, vol. 161(C), pages 330-336.
    8. Xiangyu Cui & Zhu Jing & Maji Luo & Yazhou Guo & Huimin Qiao, 2018. "A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-21, January.
    9. Wang, Chun & He, Hongwen & Zhang, Yongzhi & Mu, Hao, 2017. "A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures," Applied Energy, Elsevier, vol. 196(C), pages 268-278.
    10. Lin, Cheng & Mu, Hao & Xiong, Rui & Shen, Weixiang, 2016. "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, Elsevier, vol. 166(C), pages 76-83.
    11. Jichao Hong & Zhenpo Wang & Peng Liu, 2017. "Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-16, July.
    12. Yunlong Shang & Qi Zhang & Naxin Cui & Chenghui Zhang, 2017. "A Cell-to-Cell Equalizer Based on Three-Resonant-State Switched-Capacitor Converters for Series-Connected Battery Strings," Energies, MDPI, vol. 10(2), pages 1-15, February.
    13. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    14. Cuma, Mehmet Ugras & Koroglu, Tahsin, 2015. "A comprehensive review on estimation strategies used in hybrid and battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 517-531.
    15. Jiang, Yunfeng & Xia, Bing & Zhao, Xin & Nguyen, Truong & Mi, Chris & de Callafon, Raymond A., 2017. "Data-based fractional differential models for non-linear dynamic modeling of a lithium-ion battery," Energy, Elsevier, vol. 135(C), pages 171-181.
    16. Guo, Feng & Hu, Guangdi & Xiang, Shun & Zhou, Pengkai & Hong, Ru & Xiong, Neng, 2019. "A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters," Energy, Elsevier, vol. 178(C), pages 79-88.
    17. 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.
    18. Lin, Cheng & Gong, Xinle & Xiong, Rui & Cheng, Xingqun, 2017. "A novel H∞ and EKF joint estimation method for determining the center of gravity position of electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 609-616.
    19. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    20. Xiaogang Wu & Siyu Lv & Jizhong Chen, 2017. "Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles," Energies, MDPI, vol. 10(11), pages 1-17, October.

    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:10:y:2017:i:1:p:60-:d:87080. 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.