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Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model

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  • Yang, Xiaolong
  • Chen, Yongji
  • Li, Bin
  • Luo, Dong

Abstract

The rational design of the battery management system requires a high-fidelity battery state estimation method. However, the nonlinear varieties of battery parameters, external interference and the dynamic behaviors of battery capacity will cause big problems for battery state estimation. A new state estimation method for lithium-ion batteries is proposed. A hybrid battery model is firstly established to better reflect the dynamic behaviors of battery capacity and voltage. Then the model parameters are identified online using the exponential decay particle swarm optimization (EDPSO). Finally, a proportional-integral observer (PIO) is designed for battery state-of-charge (SOC) estimation. In addition, the battery maximum available capacity (Cmax) is online estimated using the accumulated charge and variation of battery open-circuit voltage (OCV), which helps to update SOC estimation at different aging cycles. A verifying experiment is carried out based on the urban dynamometer driving schedule (UDDS) cycles. The results indicate that the proposed method has good performance and high accuracy. The online estimated parameters consist well with experimental data, the error of the terminal voltage is less than 0.02 V. The error of the estimated SOC can be controlled within 1%. Moreover, the estimated capacity could converge in 12 min with an error less than 2%.

Suggested Citation

  • Yang, Xiaolong & Chen, Yongji & Li, Bin & Luo, Dong, 2020. "Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219322042
    DOI: 10.1016/j.energy.2019.116509
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    2. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    3. Zhu, Rui & Duan, Bin & Zhang, Junming & Zhang, Qi & Zhang, Chenghui, 2020. "Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter," Applied Energy, Elsevier, vol. 277(C).
    4. Xin Lai & Ming Yuan & Xiaopeng Tang & Yi Yao & Jiahui Weng & Furong Gao & Weiguo Ma & Yuejiu Zheng, 2022. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing," Energies, MDPI, vol. 15(19), pages 1-20, October.
    5. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    6. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    7. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).

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