IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v138y2017icp118-132.html
   My bibliography  Save this article

A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach

Author

Listed:
  • Wang, Qian-Kun
  • He, Yi-Jun
  • Shen, Jia-Ni
  • Ma, Zi-Feng
  • Zhong, Guo-Bin

Abstract

The thermal coupled equivalent circuit model provides a vital role not only in accurate and reliable state monitoring, but also in effective thermal management of lithium-ion batteries. However, it lacks appropriate modeling strategies for including both the temperature and state of charge effects into the thermal coupled equivalent circuit model. In this study, a unified artificial neural network based thermal coupled equivalent circuit model approach is proposed to accurately and reliably capture the electrical and thermal dynamics of lithium-ion batteries. Both reversible and irreversible heat generation mechanisms are introduced in the thermal model. The quantitative relationship between circuit parameters and temperature/state of charge in equivalent circuit model is modeled by artificial neural network. Both electrical and thermal related parameters are simultaneously identified by means of least square strategy with l1-norm penalty on output weights in artificial neural network and positive constraints on circuit parameters. The effectiveness of the proposed artificial neural network based thermal coupled equivalent circuit model approach is validated by the experimental constant current discharge, pulse current discharge test and hybrid pulse power characterization test of a commercial large-format pouch-type lithium-ion battery. It implies that the proposed hybrid modeling strategy can provide a general framework for the inclusion of other effects such as health state and current into battery models and can be easily extended to more complicated models such as first-principle electrochemical-thermal model.

Suggested Citation

  • Wang, Qian-Kun & He, Yi-Jun & Shen, Jia-Ni & Ma, Zi-Feng & Zhong, Guo-Bin, 2017. "A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach," Energy, Elsevier, vol. 138(C), pages 118-132.
    • Handle: RePEc:eee:energy:v:138:y:2017:i:c:p:118-132
    DOI: 10.1016/j.energy.2017.07.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217312100
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.07.035?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. Feng, Xuning & Lu, Languang & Ouyang, Minggao & Li, Jiangqiu & He, Xiangming, 2016. "A 3D thermal runaway propagation model for a large format lithium ion battery module," Energy, Elsevier, vol. 115(P1), pages 194-208.
    2. Miranda, Á.G. & Hong, C.W., 2013. "Integrated modeling for the cyclic behavior of high power Li-ion batteries under extended operating conditions," Applied Energy, Elsevier, vol. 111(C), pages 681-689.
    3. Rao, Zhonghao & Wang, Shuangfeng, 2011. "A review of power battery thermal energy management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4554-4571.
    4. Ximing Cheng & Liguang Yao & Yinjiao Xing & Michael Pecht, 2016. "Novel Parametric Circuit Modeling for Li-Ion Batteries," Energies, MDPI, vol. 9(7), pages 1-15, July.
    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. Dong, Xiao-Jian & Shen, Jia-Ni & He, Guo-Xin & Ma, Zi-Feng & He, Yi-Jun, 2021. "A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction," Energy, Elsevier, vol. 234(C).
    2. Dong, Xiao-Jian & Shen, Jia-Ni & Ma, Zi-Feng & He, Yi-Jun, 2022. "Simultaneous operating temperature and output power prediction method for photovoltaic modules," Energy, Elsevier, vol. 260(C).
    3. Ling, Ziye & Lin, Wenzhu & Zhang, Zhengguo & Fang, Xiaoming, 2020. "Computationally efficient thermal network model and its application in optimization of battery thermal management system with phase change materials and long-term performance assessment," Applied Energy, Elsevier, vol. 259(C).
    4. 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.
    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. Ardani, M.I. & Patel, Y. & Siddiq, A. & Offer, G.J. & Martinez-Botas, R.F., 2018. "Combined experimental and numerical evaluation of the differences between convective and conductive thermal control on the performance of a lithium ion cell," Energy, Elsevier, vol. 144(C), pages 81-97.
    7. Gao, Xinlei & Li, Yalun & Wang, Huizhi & Liu, Xinhua & Wu, Yu & Yang, Shichun & Zhao, Zhengming & Ouyang, Minggao, 2023. "Probing inhomogeneity of electrical-thermal distribution on electrode during fast charging for lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
    8. 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).
    9. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    10. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    11. Mina Naguib & Aashit Rathore & Nathan Emery & Shiva Ghasemi & Ryan Ahmed, 2023. "Robust Electro-Thermal Modeling of Lithium-Ion Batteries for Electrified Vehicles Applications," Energies, MDPI, vol. 16(16), pages 1-20, August.
    12. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    13. Adrian Chmielewski & Jakub Możaryn & Piotr Piórkowski & Krzysztof Bogdziński, 2018. "Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle," Energies, MDPI, vol. 11(11), pages 1-28, November.
    14. 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).
    15. 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).
    16. Dan Dan & Yihang Zhao & Mingshan Wei & Xuehui Wang, 2023. "Review of Thermal Management Technology for Electric Vehicles," Energies, MDPI, vol. 16(12), pages 1-38, June.
    17. Cai, Fengyang & Chang, Huawei & Yang, Zhengbo & Tu, Zhengkai, 2024. "Experimental study on self-heating strategy of lithium-ion battery at low temperatures based on bidirectional pulse current," Applied Energy, Elsevier, vol. 354(PB).
    18. Berrueta, Alberto & Urtasun, Andoni & Ursúa, Alfredo & Sanchis, Pablo, 2018. "A comprehensive model for lithium-ion batteries: From the physical principles to an electrical model," Energy, Elsevier, vol. 144(C), pages 286-300.
    19. Cui, Yingzhi & Zuo, Pengjian & Du, Chunyu & Gao, Yunzhi & Yang, Jie & Cheng, Xinqun & Ma, Yulin & Yin, Geping, 2018. "State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method," Energy, Elsevier, vol. 144(C), pages 647-656.
    20. Fan, Kesen & Wan, Yiming & Wang, Zhuo & Jiang, Kai, 2023. "Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process," Energy, Elsevier, vol. 268(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. 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. Wang, Tao & Tseng, K.J. & Zhao, Jiyun & Wei, Zhongbao, 2014. "Thermal investigation of lithium-ion battery module with different cell arrangement structures and forced air-cooling strategies," Applied Energy, Elsevier, vol. 134(C), pages 229-238.
    3. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    4. Situ, Wenfu & Zhang, Guoqing & Li, Xinxi & Yang, Xiaoqing & Wei, Chao & Rao, Mumin & Wang, Ziyuan & Wang, Cong & Wu, Weixiong, 2017. "A thermal management system for rectangular LiFePO4 battery module using novel double copper mesh-enhanced phase change material plates," Energy, Elsevier, vol. 141(C), pages 613-623.
    5. Mohammed, Abubakar Gambo & Elfeky, Karem Elsayed & Wang, Qiuwang, 2022. "Recent advancement and enhanced battery performance using phase change materials based hybrid battery thermal management for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    6. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    7. Ostanek, Jason K. & Li, Weisi & Mukherjee, Partha P. & Crompton, K.R. & Hacker, Christopher, 2020. "Simulating onset and evolution of thermal runaway in Li-ion cells using a coupled thermal and venting model," Applied Energy, Elsevier, vol. 268(C).
    8. Ling, Ziye & Wang, Fangxian & Fang, Xiaoming & Gao, Xuenong & Zhang, Zhengguo, 2015. "A hybrid thermal management system for lithium ion batteries combining phase change materials with forced-air cooling," Applied Energy, Elsevier, vol. 148(C), pages 403-409.
    9. Chen, Ruihua & Zhao, Ruikai & Deng, Shuai & Zhao, Li & Xu, Weicong, 2021. "A cycle research methodology for thermo-chemical engines: From ideal cycle to case study," Energy, Elsevier, vol. 228(C).
    10. Zhou, Zhizuan & Wang, Dong & Peng, Yang & Li, Maoyu & Wang, Boxuan & Cao, Bei & Yang, Lizhong, 2022. "Experimental study on the thermal management performance of phase change material module for the large format prismatic lithium-ion battery," Energy, Elsevier, vol. 238(PC).
    11. Zhang, Yue & Song, Laifeng & Tian, Jiamin & Mei, Wenxin & Jiang, Lihua & Sun, Jinhua & Wang, Qingsong, 2024. "Modeling the propagation of internal thermal runaway in lithium-ion battery," Applied Energy, Elsevier, vol. 362(C).
    12. Sheng Yang & Wenwei Wang & Cheng Lin & Weixiang Shen & Yiding Li, 2019. "Investigation of Internal Short Circuits of Lithium-Ion Batteries under Mechanical Abusive Conditions," Energies, MDPI, vol. 12(10), pages 1-16, May.
    13. Zhang, Zhenying & Wang, Jiayu & Feng, Xu & Chang, Li & Chen, Yanhua & Wang, Xingguo, 2018. "The solutions to electric vehicle air conditioning systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 443-463.
    14. Jin, Changyong & Sun, Yuedong & Wang, Huaibin & Zheng, Yuejiu & Wang, Shuyu & Rui, Xinyu & Xu, Chengshan & Feng, Xuning & Wang, Hewu & Ouyang, Minggao, 2022. "Heating power and heating energy effect on the thermal runaway propagation characteristics of lithium-ion battery module: Experiments and modeling," Applied Energy, Elsevier, vol. 312(C).
    15. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    16. Mesbahi, Tedjani & Ouari, Ahmed & Ghennam, Tarak & Berkouk, El Madjid & Rizoug, Nassim & Mesbahi, Nadhir & Meradji, Moudrik, 2014. "A stand-alone wind power supply with a Li-ion battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 204-213.
    17. Chuanwei Zhang & Zhan Xia & Huaibin Gao & Jianping Wen & Shangrui Chen & Meng Dang & Sujing Gu & Jianing Zhang, 2020. "A Coolant Circulation Cooling System Combining Aluminum Plates and Copper Rods for Li-Ion Battery Pack," Energies, MDPI, vol. 13(17), pages 1-14, August.
    18. Huang, Zonghou & Yu, Yin & Duan, Qiangling & Qin, Peng & Sun, Jinhua & Wang, Qingsong, 2022. "Heating position effect on internal thermal runaway propagation in large-format lithium iron phosphate battery," Applied Energy, Elsevier, vol. 325(C).
    19. Nomura, Takahiro & Zhu, Chunyu & Nan, Sheng & Tabuchi, Kazuki & Wang, Shuangfeng & Akiyama, Tomohiro, 2016. "High thermal conductivity phase change composite with a metal-stabilized carbon-fiber network," Applied Energy, Elsevier, vol. 179(C), pages 1-6.
    20. Jin, Xianrong & Duan, Xiting & Jiang, Wenjuan & Wang, Yan & Zou, Youlan & Lei, Weixin & Sun, Lizhong & Ma, Zengsheng, 2021. "Structural design of a composite board/heat pipe based on the coupled electro-chemical-thermal model in battery thermal management system," Energy, Elsevier, vol. 216(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:eee:energy:v:138:y:2017:i:c:p:118-132. 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.