IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v238y2022ipbs0360544221021022.html
   My bibliography  Save this article

Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation

Author

Listed:
  • Zuo, Hongyan
  • Zhang, Bin
  • Huang, Zhonghua
  • Wei, Kexiang
  • Zhu, Hong
  • Tan, Jiqiu

Abstract

In this work, a coupled electrochemical-thermal model of the power lithium manganate battery under discharging process is established and verified, and its maximum surface temperature errors at the test point under 0.5C (C is discharging rate) and 1.0 C are 1.08 K and 0.95 K, respectively. Moreover, the SOC values of the established model under different discharging conditions are estimated by the improved functional link neural network (FLNN) model. The results show that the improved functional link neural network model is of higher SOC estimation accuracy, namely its maximum estimation errors is 1.82 % and the maximum average errors is 0.98 %, respectively. And SOC estimation results indicate that battery should be properly heated and convection heat transfer coefficient should be suitably reduced at lower ambient temperature during the initial working phase of the battery, SOC value deviation from the new battery increase due to battery aging which increases the SOC estimation error and it should be taken as an important factor to improve the SOC estimation accuracy.

Suggested Citation

  • Zuo, Hongyan & Zhang, Bin & Huang, Zhonghua & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2022. "Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation," Energy, Elsevier, vol. 238(PB).
  • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221021022
    DOI: 10.1016/j.energy.2021.121854
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221021022
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.121854?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. E, Jiaqiang & Zhao, Xiaohuan & Liu, Guanlin & Zhang, Bin & Zuo, Qingsong & Wei, Kexiang & Li, Hongmei & Han, Dandan & Gong, Jinke, 2019. "Effects analysis on optimal microwave energy consumption in the heating process of composite regeneration for the diesel particulate filter," Applied Energy, Elsevier, vol. 254(C).
    2. 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.
    3. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    4. 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).
    5. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    6. Bian, Chong & He, Huoliang & Yang, Shunkun, 2020. "Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 191(C).
    7. Xu, Meng & Zhang, Zhuqian & Wang, Xia & Jia, Li & Yang, Lixin, 2015. "A pseudo three-dimensional electrochemical–thermal model of a prismatic LiFePO4 battery during discharge process," Energy, Elsevier, vol. 80(C), pages 303-317.
    8. Dong, Guangzhong & Wei, Jingwen & Zhang, Chenbin & Chen, Zonghai, 2016. "Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method," Applied Energy, Elsevier, vol. 162(C), pages 163-171.
    9. Zhang, Xiliang & Karplus, Valerie J. & Qi, Tianyu & Zhang, Da & He, Jiankun, 2016. "Carbon emissions in China: How far can new efforts bend the curve?," Energy Economics, Elsevier, vol. 54(C), pages 388-395.
    10. 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.
    11. Zuo, Hongyan & Tan, Jiqiu & Wei, Kexiang & Huang, Zhonghua & Zhong, Dingqing & Xie, Fuchun, 2021. "Effects of different poses and wind speeds on wind-induced vibration characteristics of a dish solar concentrator system," Renewable Energy, Elsevier, vol. 168(C), pages 1308-1326.
    12. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    13. Cai, Tao & Zhao, Dan & Sun, Yuze & Ni, Siliang & Li, Weixuan & Guan, Di & Wang, Bing, 2021. "Evaluation of NOx emissions characteristics in a CO2-Free micro-power system by implementing a perforated plate," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    14. Luo, Xiaohu & Caron, Justin & Karplus, Valerie J. & Zhang, Da & Zhang, Xiliang, 2016. "Interprovincial migration and the stringency of energy policy in China," Energy Economics, Elsevier, vol. 58(C), pages 164-173.
    15. Wen, Jianping & Zhao, Dan & Zhang, Chuanwei, 2020. "An overview of electricity powered vehicles: Lithium-ion battery energy storage density and energy conversion efficiency," Renewable Energy, Elsevier, vol. 162(C), pages 1629-1648.
    16. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    17. E, Jiaqiang & Zeng, Yan & Jin, Yu & Zhang, Bin & Huang, Zhonghua & Wei, Kexiang & Chen, Jingwei & Zhu, Hao & Deng, Yuanwang, 2020. "Heat dissipation investigation of the power lithium-ion battery module based on orthogonal experiment design and fuzzy grey relation analysis," Energy, Elsevier, vol. 211(C).
    18. Zhang, Bin & E, Jiaqiang & Gong, Jinke & Yuan, Wenhua & Zuo, Wei & Li, Yu & Fu, Jun, 2016. "Multidisciplinary design optimization of the diesel particulate filter in the composite regeneration process," Applied Energy, Elsevier, vol. 181(C), pages 14-28.
    19. 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).
    20. Michael R. Davidson & Da Zhang & Weiming Xiong & Xiliang Zhang & Valerie J. Karplus, 2016. "Modelling the potential for wind energy integration on China’s coal-heavy electricity grid," Nature Energy, Nature, vol. 1(7), pages 1-7, July.
    21. Deyu Cui & Bizhong Xia & Ruifeng Zhang & Zhen Sun & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network," Energies, MDPI, vol. 11(4), pages 1-18, April.
    22. Qian Zhang & Xunmin Ou & Xiaoyu Yan & Xiliang Zhang, 2017. "Electric Vehicle Market Penetration and Impacts on Energy Consumption and CO 2 Emission in the Future: Beijing Case," Energies, MDPI, vol. 10(2), pages 1-15, February.
    23. 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.
    24. Wang, Hailin & Ou, Xunmin & Zhang, Xiliang, 2017. "Mode, technology, energy consumption, and resulting CO2 emissions in China's transport sector up to 2050," Energy Policy, Elsevier, vol. 109(C), pages 719-733.
    25. Xingtao Liu & Chaoyi Zheng & Ji Wu & Jinhao Meng & Daniel-Ioan Stroe & Jiajia Chen, 2020. "An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries," Energies, MDPI, vol. 13(2), pages 1-16, January.
    26. Vidadili, Nurtaj & Suleymanov, Elchin & Bulut, Cihan & Mahmudlu, Ceyhun, 2017. "Transition to renewable energy and sustainable energy development in Azerbaijan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1153-1161.
    27. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
    Full references (including those not matched with items on IDEAS)

    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. E, Jiaqiang & Zhang, Bin & Zeng, Yan & Wen, Ming & Wei, Kexiang & Huang, Zhonghua & Chen, Jingwei & Zhu, Hao & Deng, Yuanwang, 2022. "Effects analysis on active equalization control of lithium-ion batteries based on intelligent estimation of the state-of-charge," Energy, Elsevier, vol. 238(PB).
    2. E, Jiaqiang & Zeng, Yan & Jin, Yu & Zhang, Bin & Huang, Zhonghua & Wei, Kexiang & Chen, Jingwei & Zhu, Hao & Deng, Yuanwang, 2020. "Heat dissipation investigation of the power lithium-ion battery module based on orthogonal experiment design and fuzzy grey relation analysis," Energy, Elsevier, vol. 211(C).
    3. Hu, Wenyu & E, Jiaqiang & Tan, Yan & Zhang, Feng & Liao, Gaoliang, 2022. "Modified wind energy collection devices for harvesting convective wind energy from cars and trucks moving in the highway," Energy, Elsevier, vol. 247(C).
    4. 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.
    5. E, Jiaqiang & Yi, Feng & Li, Wenjie & Zhang, Bin & Zuo, Hongyan & Wei, Kexiang & Chen, Jingwei & Zhu, Hong & Zhu, Hao & Deng, Yuanwang, 2021. "Effect analysis on heat dissipation performance enhancement of a lithium-ion-battery pack with heat pipe for central and southern regions in China," Energy, Elsevier, vol. 226(C).
    6. 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.
    7. 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).
    8. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    9. Zhang, Zhiqing & Dong, Rui & Tan, Dongli & Duan, Lin & Jiang, Feng & Yao, Xiaoxue & Yang, Dixin & Hu, Jingyi & Zhang, Jian & Zhong, Weihuang & Zhao, Ziheng, 2023. "Effect of structural parameters on diesel particulate filter trapping performance of heavy-duty diesel engines based on grey correlation analysis," Energy, Elsevier, vol. 271(C).
    10. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    11. Ma, Ying & Yang, Heng & Zuo, Hongyan & Zuo, Qingsong & He, Xiaoxiang & Chen, Wei & Wei, Rongrong, 2023. "EG@Bi-MOF derived porous carbon/lauric acid composite phase change materials for thermal management of batteries," Energy, Elsevier, vol. 272(C).
    12. Zhang, Zhiqing & Li, Jiangtao & Tian, Jie & Dong, Rui & Zou, Zhi & Gao, Sheng & Tan, Dongli, 2022. "Performance, combustion and emission characteristics investigations on a diesel engine fueled with diesel/ ethanol /n-butanol blends," Energy, Elsevier, vol. 249(C).
    13. 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).
    14. Zhang, Zhiqing & Lv, Junshuai & Xie, Guanglin & Wang, Su & Ye, Yanshuai & Huang, Gaohua & Tan, Donlgi, 2022. "Effect of assisted hydrogen on combustion and emission characteristics of a diesel engine fueled with biodiesel," Energy, Elsevier, vol. 254(PA).
    15. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
    16. E, Shengxin & Cui, Yaxin & Liu, Yuxian & Yin, Huichun, 2023. "Effects of the different phase change materials on heat dissipation performances of the ternary polymer Li-ion battery pack in hot climate," Energy, Elsevier, vol. 282(C).
    17. 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).
    18. Kuo Yang & Yugui Tang & Zhen Zhang, 2021. "Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter," Energies, MDPI, vol. 14(4), pages 1-15, February.
    19. 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.
    20. Lin, Cheng & Tang, Aihua & Xing, Jilei, 2017. "Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 394-404.

    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:238:y:2022:i:pb:s0360544221021022. 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.