IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v194y2017icp560-568.html
   My bibliography  Save this article

Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy

Author

Listed:
  • Lin, Cheng
  • Mu, Hao
  • Xiong, Rui
  • Cao, Jiayi

Abstract

State-of-energy (SoE) is an important index for batteries in electric vehicles and it provides the essential basis of energy application, load equilibrium and security of electricity. To improve the estimation accuracy and reliability of SoE, a novel multi-model fusion estimation approach is proposed against uncertain dynamic load and different temperatures. The main contributions of this work can be summarized as follows: (1) Through analyzing the impact on the estimation accuracy of SoE due to the complexity of models, the necessity of redundant modeling is elaborated. (2) Three equivalent circuit models are selected and their parameters are identified by genetic algorithm offline. Linear matrix inequality (LMI) based H-infinity state observer technique is applied to estimate SoEs on aforementioned models. (3) The concept of fusion estimation is introduced. The estimation results derived by different models are merged under certain weights which are determined by Bayes theorem. (4) Batteries are tested with dynamic load cycles under different temperatures to validate the effectiveness of this method. The results indicate the estimation accuracy and reliability on SoE are elevated after fusion.

Suggested Citation

  • Lin, Cheng & Mu, Hao & Xiong, Rui & Cao, Jiayi, 2017. "Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy," Applied Energy, Elsevier, vol. 194(C), pages 560-568.
    • Handle: RePEc:eee:appene:v:194:y:2017:i:c:p:560-568
    DOI: 10.1016/j.apenergy.2016.05.065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916306626
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.05.065?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. 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.
    2. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2014. "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, Elsevier, vol. 135(C), pages 81-87.
    3. 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.
    4. He, HongWen & Zhang, YongZhi & Xiong, Rui & Wang, Chun, 2015. "A novel Gaussian model based battery state estimation approach: State-of-Energy," Applied Energy, Elsevier, vol. 151(C), pages 41-48.
    5. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
    6. 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.
    7. Subburaj, Anitha S. & Pushpakaran, Bejoy N. & Bayne, Stephen B., 2015. "Overview of grid connected renewable energy based battery projects in USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 219-234.
    8. Lee, Seongjun & Kim, Jonghoon, 2015. "Discrete wavelet transform-based denoising technique for advanced state-of-charge estimator of a lithium-ion battery in electric vehicles," Energy, Elsevier, vol. 83(C), pages 462-473.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).
    3. Zhang, Xu & Wang, Yujie & Wu, Ji & Chen, Zonghai, 2018. "A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter," Applied Energy, Elsevier, vol. 216(C), pages 442-451.
    4. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    5. Qian, Cheng & Guan, Hongsheng & Xu, Binghui & Xia, Quan & Sun, Bo & Ren, Yi & Wang, Zili, 2024. "A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions," Energy, Elsevier, vol. 294(C).
    6. Li, Xiaoyu & Zhang, Zuguang & Wang, Wenhui & Tian, Yong & Li, Dong & Tian, Jindong, 2020. "Multiphysical field measurement and fusion for battery electric-thermal-contour performance analysis," Applied Energy, Elsevier, vol. 262(C).
    7. Changping Li & Xiaohui Wang & Longchen Duan & Bo Lei, 2022. "Study on a Discharge Circuit Prediction Model of High-Voltage Electro-Pulse Boring Based on Bayesian Fusion," Energies, MDPI, vol. 15(10), pages 1-12, May.
    8. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
    9. 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).
    10. Park, Jinhyeong & Kim, Kunwoo & Park, Seongyun & Baek, Jongbok & Kim, Jonghoon, 2021. "Complementary cooperative SOC/capacity estimator based on the discrete variational derivative combined with the DEKF for electric power applications," Energy, Elsevier, vol. 232(C).
    11. 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.

    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. 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).
    2. 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.
    3. 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.
    4. 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.
    5. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2017. "On-line battery state-of-charge estimation based on an integrated estimator," Applied Energy, Elsevier, vol. 185(P2), pages 2026-2032.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Wei, Zhongbao & Lim, Tuti Mariana & Skyllas-Kazacos, Maria & Wai, Nyunt & Tseng, King Jet, 2016. "Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery," Applied Energy, Elsevier, vol. 172(C), pages 169-179.
    11. 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.
    12. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    13. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy," Applied Energy, Elsevier, vol. 137(C), pages 427-434.
    14. Bizhong Xia & Zheng Zhang & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 11(6), pages 1-20, June.
    15. Shifei Yuan & Hongjie Wu & Xuerui Ma & Chengliang Yin, 2015. "Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration," Energies, MDPI, vol. 8(8), pages 1-23, July.
    16. Avvari, G.V. & Pattipati, B. & Balasingam, B. & Pattipati, K.R. & Bar-Shalom, Y., 2015. "Experimental set-up and procedures to test and validate battery fuel gauge algorithms," Applied Energy, Elsevier, vol. 160(C), pages 404-418.
    17. 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.
    18. 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.
    19. 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).
    20. Shichun Yang & Cheng Deng & Yulong Zhang & Yongling He, 2017. "State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model," Energies, MDPI, vol. 10(10), pages 1-14, 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:eee:appene:v:194:y:2017:i:c:p:560-568. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.