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State-of-charge estimation combination algorithm for lithium-ion batteries with Frobenius-norm-based QR decomposition modified adaptive cubature Kalman filter and H-infinity filter based on electro-thermal model

Author

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  • Li, Kangqun
  • Zhou, Fei
  • Chen, Xing
  • Yang, Wen
  • Shen, Junjie
  • Song, Zebin

Abstract

A novel algorithm containing an adaptive cubature Kalman filter (ACKF) modified by Frobenius-norm-based (fro-norm-based) QR decomposition (QR) and H-infinity(H∞) filter based on electro-thermal model is proposed to estimate the state of charge (SOC) of lithium-ion batteries (LIBS). First, an electro-thermal model with a second-order RC equivalent circuit model (ECM) and a lumped thermal model is employed to identify the internal parameters of LIBS at different temperatures. Then, to solve the non-positive definiteness of the error covariance matrix, an adaptive cubature Kalman filter is modified by fro-norm-based QR decomposition (ACKF-QR). Finally, to cope with uncertain noises especially non-Gaussian noises, the H∞ filter is combined with ACKF-QR to estimate the battery SOC (ACKF-QR-H∞). The ACKF-QR-H∞ algorithm is validated under different working conditions at different temperatures with incorrect initial values. The SOC estimation MAXE (Maximum absolute error) of the ACKF-QR-H∞ algorithm is less than 1% and its SOC estimation MAE (Mean absolute error) and RMSE (Root mean square error) are less than 0.32%. As compared with the same algorithm without considering temperature variations, the SOC estimation error of ACKF-QR-H∞ algorithm can almost reduce by half in most cases. When various noises are added manually, the ACKF-QR-H∞ algorithm can remain robust.

Suggested Citation

  • Li, Kangqun & Zhou, Fei & Chen, Xing & Yang, Wen & Shen, Junjie & Song, Zebin, 2023. "State-of-charge estimation combination algorithm for lithium-ion batteries with Frobenius-norm-based QR decomposition modified adaptive cubature Kalman filter and H-infinity filter based on electro-th," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222026494
    DOI: 10.1016/j.energy.2022.125763
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    1. Shen, Jiangwei & Ma, Wensai & Xiong, Jian & Shu, Xing & Zhang, Yuanjian & Chen, Zheng & Liu, Yonggang, 2022. "Alternative combined co-estimation of state of charge and capacity for lithium-ion batteries in wide temperature scope," Energy, Elsevier, vol. 244(PB).
    2. 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).
    3. Jiandong Duan & Peng Wang & Wentao Ma & Xinyu Qiu & Xuan Tian & Shuai Fang, 2020. "State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter," Energies, MDPI, vol. 13(16), pages 1-18, August.
    4. 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).
    5. Ye, Min & Guo, Hui & Cao, Binggang, 2017. "A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter," Applied Energy, Elsevier, vol. 190(C), pages 740-748.
    6. Li, Junqiu & Sun, Danni & Jin, Xin & Shi, Wentong & Sun, Chao, 2019. "Lithium-ion battery overcharging thermal characteristics analysis and an impedance-based electro-thermal coupled model simulation," Applied Energy, Elsevier, vol. 254(C).
    7. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
    8. 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).
    9. Perez Estevez, Manuel Antonio & Calligaro, Sandro & Bottesi, Omar & Caligiuri, Carlo & Renzi, Massimiliano, 2021. "An electro-thermal model and its electrical parameters estimation procedure in a lithium-ion battery cell," Energy, Elsevier, vol. 234(C).
    10. 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.
    11. 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.
    12. Shuaishuai Zhang & Youhong Wan & Jie Ding & Yangyang Da, 2021. "State of Charge (SOC) Estimation Based on Extended Exponential Weighted Moving Average H ∞ Filtering," Energies, MDPI, vol. 14(6), pages 1-15, March.
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