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Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform

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  • He, Hongwen
  • Xiong, Rui
  • Peng, Jiankun

Abstract

To develop an advanced battery estimation unit for electric vehicles application, the state-of-charge (SoC) estimation is proposed with an unscented Kalman filter (UKF) and realized with the RTOS μCOS-II platform. Kalman filters are broadly used to deploy various battery SoC estimators recently. Herein, an UKF algorithm has been employed to develop a systematic adaptive SoC estimation framework. Compared with traditional used extended Kalman filter, it uses an unscented transform to deal with the state estimation problem, thus it has the potential to achieve third order accuracy of the Taylor expansion for tracking posterior estimate of the inner inhabited state. Beneficial from it, the SoC estimation accuracy has been improved with higher tracking accuracy and faster convergence ability. To further evaluate and verify the performance of the proposed online SoC estimation approach, a battery-in-loop platform is built and the SoC estimation is calculated with a RTOS μCOS-II platform. The analog acquisition, communication system and SoC estimation algorithms were programmed, the performance of the proposed SoC estimation with UKF algorithm was finally investigated. The battery management system with UKF algorithm and RTOS μCOS-II platform has good performance and it can apply for electric vehicles.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:162:y:2016:i:c:p:1410-1418
    DOI: 10.1016/j.apenergy.2015.01.120
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    8. Zheng, Fangdan & Xing, Yinjiao & Jiang, Jiuchun & Sun, Bingxiang & Kim, Jonghoon & Pecht, Michael, 2016. "Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 183(C), pages 513-525.
    9. 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.
    10. Li, Shuangqi & He, Hongwen & Li, Jianwei, 2019. "Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology," Applied Energy, Elsevier, vol. 242(C), pages 1259-1273.
    11. Lim, KaiChin & Bastawrous, Hany Ayad & Duong, Van-Huan & See, Khay Wai & Zhang, Peng & Dou, Shi Xue, 2016. "Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles," Applied Energy, Elsevier, vol. 169(C), pages 40-48.
    12. 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.
    13. Wang, Shun-Li & Fernandez, Carlos & Zou, Chuan-Yun & Yu, Chun-Mei & Chen, Lei & Zhang, Li, 2019. "A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction," Energy, Elsevier, vol. 171(C), pages 444-455.
    14. Shulin Liu & Naxin Cui & Chenghui Zhang, 2017. "An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 10(9), pages 1-14, September.
    15. Haitao Zhang & Ming Zhou & Xudong Lan, 2019. "State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter," Energies, MDPI, vol. 12(20), pages 1-15, October.
    16. Diao, Weiping & Xue, Nan & Bhattacharjee, Vikram & Jiang, Jiuchun & Karabasoglu, Orkun & Pecht, Michael, 2018. "Active battery cell equalization based on residual available energy maximization," Applied Energy, Elsevier, vol. 210(C), pages 690-698.
    17. Wei, Jingwen & Dong, Guangzhong & Chen, Zonghai & Kang, Yu, 2017. "System state estimation and optimal energy control framework for multicell lithium-ion battery system," Applied Energy, Elsevier, vol. 187(C), pages 37-49.
    18. Zheng, Linfeng & Zhang, Lei & Zhu, Jianguo & Wang, Guoxiu & Jiang, Jiuchun, 2016. "Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model," Applied Energy, Elsevier, vol. 180(C), pages 424-434.
    19. Pan, Haihong & Lü, Zhiqiang & Lin, Weilong & Li, Junzi & Chen, Lin, 2017. "State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model," Energy, Elsevier, vol. 138(C), pages 764-775.
    20. Aron Kondoro & Imed Ben Dhaou & Hannu Tenhunen & Nerey Mvungi, 2021. "A Low Latency Secure Communication Architecture for Microgrid Control," Energies, MDPI, vol. 14(19), pages 1-26, October.
    21. Zhibing Zeng & Jindong Tian & Dong Li & Yong Tian, 2018. "An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-16, January.

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