IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v162y2018icp871-882.html
   My bibliography  Save this article

State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles

Author

Listed:
  • Zahid, Taimoor
  • Xu, Kun
  • Li, Weimin
  • Li, Chenming
  • Li, Hongzhe

Abstract

State of charge estimation is one of the most critical factors to solve the key issues of monitoring and safety concerns of an electric vehicle power battery. In this paper, a state of charge estimation approach using subtractive clustering based neuro-fuzzy system is presented and evaluated by the simulation experiments using advanced vehicle simulator in comparison with back propagation neural network and Elman neural networks. Input parameters to model the state of charge estimation approach using subtractive clustering based neuro-fuzzy system are current, temperature, actual power loss, available and requested power, cooling air temperature and battery thermal factor. Data collected from 10 different drive cycles are utilized for the training and testing stages of the state of charge estimation model. Experimental results illustrated that the proposed model exhibits sufficient accuracy and outperforms both neural network and Elman neural network based models. Thus, the proposed model under different drive cycles show remarkable advancement in state of charge estimation with high potential to overcome the drawbacks in traditional methods and therefore provides an alternative approach in state of charge estimation. In addition, a sensitivity analysis is also performed to determine the importance of each input parameter on output i.e. state of charge.

Suggested Citation

  • Zahid, Taimoor & Xu, Kun & Li, Weimin & Li, Chenming & Li, Hongzhe, 2018. "State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles," Energy, Elsevier, vol. 162(C), pages 871-882.
  • Handle: RePEc:eee:energy:v:162:y:2018:i:c:p:871-882
    DOI: 10.1016/j.energy.2018.08.071
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218316037
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2018.08.071?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. 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.
    2. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Moradi-Dalvand, Mohammad & Zare, Kazem, 2017. "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, Elsevier, vol. 118(C), pages 1168-1179.
    3. 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.
    4. Taimoor Zahid & Weimin Li, 2016. "A Comparative Study Based on the Least Square Parameter Identification Method for State of Charge Estimation of a LiFePO 4 Battery Pack Using Three Model-Based Algorithms for Electric Vehicles," Energies, MDPI, vol. 9(9), pages 1-16, September.
    5. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
    6. Xiaosong Hu & Fengchun Sun & Yuan Zou, 2010. "Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer," Energies, MDPI, vol. 3(9), pages 1-18, September.
    7. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    8. Dai, Haifeng & Guo, Pingjing & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan, 2015. "ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries," Energy, Elsevier, vol. 80(C), pages 350-360.
    9. 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.
    10. Li, Junfu & Lai, Qingzhi & Wang, Lixin & Lyu, Chao & Wang, Han, 2016. "A method for SOC estimation based on simplified mechanistic model for LiFePO4 battery," Energy, Elsevier, vol. 114(C), pages 1266-1276.
    11. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    12. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
    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. 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.
    2. Zhao, Xiaowei & Cai, Yishan & Yang, Lin & Deng, Zhongwei & Qiang, Jiaxi, 2017. "State of charge estimation based on a new dual-polarization-resistance model for electric vehicles," Energy, Elsevier, vol. 135(C), pages 40-52.
    3. 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.
    4. Deng, Zhongwei & Yang, Lin & Cai, Yishan & Deng, Hao & Sun, Liu, 2016. "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, Elsevier, vol. 112(C), pages 469-480.
    5. Li, Xiaoyu & Wang, Zhenpo & Zhang, Lei, 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 174(C), pages 33-44.
    6. Shuqing Li & Chuankun Ju & Jianliang Li & Ri Fang & Zhifei Tao & Bo Li & Tingting Zhang, 2021. "State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network," Energies, MDPI, vol. 14(2), pages 1-21, January.
    7. Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    8. 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).
    9. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    10. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    11. Bizhong Xia & Zizhou Lao & Ruifeng Zhang & Yong Tian & Guanghao Chen & Zhen Sun & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2017. "Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-23, December.
    12. 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.
    13. 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.
    14. 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.
    15. Hu, Xiaosong & Li, Shengbo Eben & Jia, Zhenzhong & Egardt, Bo, 2014. "Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles," Energy, Elsevier, vol. 64(C), pages 953-960.
    16. 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.
    17. 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).
    18. 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.
    19. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
    20. Turksoy, Arzu & Teke, Ahmet & Alkaya, Alkan, 2020. "A comprehensive overview of the dc-dc converter-based battery charge balancing methods in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(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:162:y:2018:i:c:p:871-882. 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.