IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v221y2021ics036054422032795x.html
   My bibliography  Save this article

Implementation and evaluation of a practical electrochemical- thermal model of lithium-ion batteries for EV battery management system

Author

Listed:
  • Gao, Yizhao
  • Zhu, Chong
  • Zhang, Xi
  • Guo, Bangjun

Abstract

To realize the electrochemical-thermal model in the battery management system (BMS), a control-oriented electrochemical model of lithium-ion batteries and its real-time implementation in the BMS of an electric vehicle (EV) are presented in this paper. Firstly, the improved pseudo-two-dimensional electrochemical model is derived to obtain the transfer function between the cell voltage and current by simplifying the partial differential equations with the Laplace transform, Pade approximation, etc. The temperature-sensitive parameters are identified at different temperatures to obtain the model temperature dependency. Then these temperature-dependent parameters are extracted for model decoupling. The presented model is validated to achieve high accuracy over a wide range of state-of-charges and temperatures with offline data. The root-mean-square (RMS) error in the prediction of terminal voltage could be limited within 28 mV when temperature decreases to −10 °C. Finally, the model is discretized and embedded into the targeted algorithm framework in the BMS of an EV to verify its effectiveness in real-time. The RMS error of the online estimated voltage is less than 15.8 mV under realistic city driving conditions at −13 °C, indicating the feasibility of the developed electrochemical model for broader EV applications.

Suggested Citation

  • Gao, Yizhao & Zhu, Chong & Zhang, Xi & Guo, Bangjun, 2021. "Implementation and evaluation of a practical electrochemical- thermal model of lithium-ion batteries for EV battery management system," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s036054422032795x
    DOI: 10.1016/j.energy.2020.119688
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422032795X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119688?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. Tanim, Tanvir R. & Rahn, Christopher D. & Wang, Chao-Yang, 2015. "State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model," Energy, Elsevier, vol. 80(C), pages 731-739.
    2. Chang, Chia-Chin & Huang, Sin-Yi & Chen, Wei-Hsin, 2019. "Thermal and solid electrolyte interphase characterization of lithium-ion battery," Energy, Elsevier, vol. 174(C), pages 999-1011.
    3. Lin, Qian & Wang, Jun & Xiong, Rui & Shen, Weixiang & He, Hongwen, 2019. "Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries," Energy, Elsevier, vol. 183(C), pages 220-234.
    4. Šeruga, Domen & Gosar, Aleš & Sweeney, Caoimhe A. & Jaguemont, Joris & Van Mierlo, Joeri & Nagode, Marko, 2021. "Continuous modelling of cyclic ageing for lithium-ion batteries," Energy, Elsevier, vol. 215(PB).
    5. Chen, Jingwei & E, Jiaqiang & Kang, Siyi & Zhao, Xiaohuan & Zhu, Hao & Deng, Yuanwang & Peng, Qingguo & Zhang, Zhiqing, 2019. "Modeling and characterization of the mass transfer and thermal mechanics of the power lithium manganate battery under charging process," Energy, Elsevier, vol. 187(C).
    6. Li, Xiaoyu & Fan, Guodong & Rizzoni, Giorgio & Canova, Marcello & Zhu, Chunbo & Wei, Guo, 2016. "A simplified multi-particle model for lithium ion batteries via a predictor-corrector strategy and quasi-linearization," Energy, Elsevier, vol. 116(P1), pages 154-169.
    7. Deng, Zhongwei & Yang, Lin & Deng, Hao & Cai, Yishan & Li, Dongdong, 2018. "Polynomial approximation pseudo-two-dimensional battery model for online application in embedded battery management system," Energy, Elsevier, vol. 142(C), pages 838-850.
    8. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2016. "A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty," Energy, Elsevier, vol. 109(C), pages 933-946.
    9. Xu, Meng & Reichman, Benjamin & Wang, Xia, 2019. "Modeling the effect of electrode thickness on the performance of lithium-ion batteries with experimental validation," Energy, Elsevier, vol. 186(C).
    10. Liang, Jialin & Gan, Yunhua & Li, Yong & Tan, Meixian & Wang, Jianqin, 2019. "Thermal and electrochemical performance of a serially connected battery module using a heat pipe-based thermal management system under different coolant temperatures," Energy, Elsevier, vol. 189(C).
    11. Deng, Zhongwei & Deng, Hao & Yang, Lin & Cai, Yishan & Zhao, Xiaowei, 2017. "Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery," Energy, Elsevier, vol. 138(C), pages 509-519.
    12. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    13. Miranda, D. & Almeida, A.M. & Lanceros-Méndez, S. & Costa, C.M., 2019. "Effect of the active material type and battery geometry on the thermal behavior of lithium-ion batteries," Energy, Elsevier, vol. 185(C), pages 1250-1262.
    14. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation," Energy, Elsevier, vol. 207(C).
    15. Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
    16. 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.
    17. Miranda, D. & Gören, A. & Costa, C.M. & Silva, M.M. & Almeida, A.M. & Lanceros-Méndez, S., 2019. "Theoretical simulation of the optimal relation between active material, binder and conductive additive for lithium-ion battery cathodes," Energy, Elsevier, vol. 172(C), pages 68-78.
    18. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    19. Liang, Jialin & Gan, Yunhua & Tan, Meixian & Li, Yong, 2020. "Multilayer electrochemical-thermal coupled modeling of unbalanced discharging in a serially connected lithium-ion battery module," Energy, Elsevier, vol. 209(C).
    20. Li, Dongdong & Yang, Lin & Li, Chun, 2021. "Control-oriented thermal-electrochemical modeling and validation of large size prismatic lithium battery for commercial applications," Energy, Elsevier, vol. 214(C).
    21. 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).
    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. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    2. Ren, Ruyang & Zhao, Yaohua & Diao, Yanhua & Liang, Lin, 2022. "Experimental study on preheating thermal management system for lithium-ion battery based on U-shaped micro heat pipe array," Energy, Elsevier, vol. 253(C).
    3. Huang, Zhiliang & Wang, Huaixing & Zou, Wei & Zhang, Rongchuan & Wang, Yuhan & Chen, Jie & Wu, Shengben, 2024. "An online evaluation model for mechanical/thermal states in prismatic lithium-ion batteries under fast charging/discharging," Energy, Elsevier, vol. 302(C).
    4. Gu, Yuxuan & Wang, Jianxiao & Chen, Yuanbo & Xiao, Wei & Deng, Zhongwei & Chen, Qixin, 2023. "A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control," Energy, Elsevier, vol. 264(C).
    5. Lu, Xin & Chen, Ning & Li, Hui & Guo, Shiyu & Chen, Zengtao, 2023. "Simulation of the temperature distribution of lithium-ion battery module considering the time-delay effect of the porous electrodes," Energy, Elsevier, vol. 284(C).
    6. Torregrosa, Antonio José & Broatch, Alberto & Olmeda, Pablo & Agizza, Luca, 2023. "A generalized equivalent circuit model for lithium-iron phosphate batteries," Energy, Elsevier, vol. 284(C).
    7. Yan, Lisen & Peng, Jun & Gao, Dianzhu & Wu, Yue & Liu, Yongjie & Li, Heng & Liu, Weirong & Huang, Zhiwu, 2022. "A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery," Energy, Elsevier, vol. 243(C).
    8. Lei, Deyong & Wang, Yun & Fu, Jingfei & Zhu, Xiaobao & Shi, Jing & Wang, Yachao, 2024. "Electrochemical-thermal analysis of large-sized lithium-ion batteries: Influence of cell thickness and cooling strategy in charging," Energy, Elsevier, vol. 307(C).
    9. Rodríguez-Iturriaga, Pablo & García, Víctor Manuel & Rodríguez-Bolívar, Salvador & Valdés, Enrique Ernesto & Anseán, David & López-Villanueva, Juan Antonio, 2024. "A coupled electrothermal lithium-ion battery reduced-order model including heat generation due to solid diffusion," Applied Energy, Elsevier, vol. 367(C).
    10. Astaneh, Majid & Andric, Jelena & Löfdahl, Lennart & Stopp, Peter, 2022. "Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications," Energy, Elsevier, vol. 239(PB).
    11. Stefano Leonori & Luca Baldini & Antonello Rizzi & Fabio Massimo Frattale Mascioli, 2021. "A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells," Energies, MDPI, vol. 14(21), pages 1-29, November.
    12. Shan, Shuai & Li, Li & Xu, Qiang & Ling, Lei & Xie, Yajun & Wang, Hongkang & Zheng, Keqing & Zhang, Lanchun & Bei, Shaoyi, 2023. "Numerical investigation of a compact and lightweight thermal management system with axially mounted cooling tubes for cylindrical lithium-ion battery module," Energy, Elsevier, vol. 274(C).
    13. Miao, Mengqi & Yang, Pu & Yue, Shang & Zhou, Ruixu & Yu, Jianbo, 2024. "Multi-source self-supervised domain adaptation network for VRLA battery anomaly detection of data center under non-ideal conditions," Energy, Elsevier, vol. 299(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. Li, Changlong & Cui, Naxin & Wang, Chunyu & Zhang, Chenghui, 2021. "Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods," Energy, Elsevier, vol. 221(C).
    2. Xu, Meng & Wang, Xia & Zhang, Liwen & Zhao, Peng, 2021. "Comparison of the effect of linear and two-step fast charging protocols on degradation of lithium ion batteries," Energy, Elsevier, vol. 227(C).
    3. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    4. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2017. "A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique," Energy, Elsevier, vol. 141(C), pages 1402-1415.
    5. Li, Dongdong & Yang, Lin & Li, Chun, 2021. "Control-oriented thermal-electrochemical modeling and validation of large size prismatic lithium battery for commercial applications," Energy, Elsevier, vol. 214(C).
    6. Gu, Yuxuan & Wang, Jianxiao & Chen, Yuanbo & Xiao, Wei & Deng, Zhongwei & Chen, Qixin, 2023. "A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control," Energy, Elsevier, vol. 264(C).
    7. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    8. Astaneh, Majid & Andric, Jelena & Löfdahl, Lennart & Stopp, Peter, 2022. "Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications," Energy, Elsevier, vol. 239(PB).
    9. Gao, Yizhao & Liu, Chenghao & Chen, Shun & Zhang, Xi & Fan, Guodong & Zhu, Chong, 2022. "Development and parameterization of a control-oriented electrochemical model of lithium-ion batteries for battery-management-systems applications," Applied Energy, Elsevier, vol. 309(C).
    10. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
    11. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    12. Deng, Zhongwei & Deng, Hao & Yang, Lin & Cai, Yishan & Zhao, Xiaowei, 2017. "Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery," Energy, Elsevier, vol. 138(C), pages 509-519.
    13. Zhang, Shuzhi & Zhang, Chen & Jiang, Shiyong & Zhang, Xiongwen, 2022. "A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation," Energy, Elsevier, vol. 246(C).
    14. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
    15. 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.
    16. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    17. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    18. 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).
    19. Simone Barcellona & Luigi Piegari, 2017. "Lithium Ion Battery Models and Parameter Identification Techniques," Energies, MDPI, vol. 10(12), pages 1-24, December.
    20. Lai, Xin & Huang, Yunfeng & Gu, Huanghui & Han, Xuebing & Feng, Xuning & Dai, Haifeng & Zheng, Yuejiu & Ouyang, Minggao, 2022. "Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects," Energy, Elsevier, vol. 238(PA).

    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:221:y:2021:i:c:s036054422032795x. 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.