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State-of-charge estimation hybrid method for lithium-ion batteries using BiGRU and AM co-modified Seq2Seq network and H-infinity filter

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  • Kuang, Pan
  • Zhou, Fei
  • Xu, Shuai
  • Li, Kangqun
  • Xu, Xiaobin

Abstract

Accurate battery state of charge (SOC) estimation can provide guarantee for safety and guide the use and maintenance of power battery. A novel SOC estimation hybrid method which combines deep learning network with filter optimization is proposed. Firstly, a sequence-to-sequence (Seq2Seq) neural network is used to fit the nonlinear relationship between SOC and measured signals. Then, the H-infinity (H∞) filter is used to reduce the noise of neural network. This method avoids the complex mechanism analysis process by establishing a high accurate battery data driven model and greatly improve the estimation accuracy by combining with H∞ filter. The advantage of proposed Seq2Seq network lies in that the model has a front and rear direction information of entire input sequence and larger receptive field by applying the bidirectional gated recurrent units (BiGRU) and the attention mechanism (AM). The proposed Seq2Seq network reduces the MAXE of estimation results by 4.55 % and 2.59 %. The estimation accuracy of the proposed hybrid algorithm is verified with the BJDST test set at 5°C–25 °C. The results show that the MAE, RMSE and MAXE of SOC estimation errors for the hybrid algorithm are less than 0.35 %, 0.4 % and about 0.75 %, respectively.

Suggested Citation

  • Kuang, Pan & Zhou, Fei & Xu, Shuai & Li, Kangqun & Xu, Xiaobin, 2024. "State-of-charge estimation hybrid method for lithium-ion batteries using BiGRU and AM co-modified Seq2Seq network and H-infinity filter," Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224013756
    DOI: 10.1016/j.energy.2024.131602
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    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. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
    3. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Zizhou Lao, 2017. "A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(4), pages 1-15, April.
    4. Wu, Lifeng & Zhang, Yu, 2023. "Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery," Energy, Elsevier, vol. 268(C).
    5. 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).
    6. 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).
    7. Xinghao Zhang & Yan Huang & Zhaowei Zhang & Huipin Lin & Yu Zeng & Mingyu Gao, 2022. "A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter," Energies, MDPI, vol. 15(18), pages 1-26, September.
    8. 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.
    9. 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).
    10. 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.
    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. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    Full references (including those not matched with items on IDEAS)

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