Journal of Power Electronics

  • 제22권3호
  • /
  • Pages.490-502
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  • 2022
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  • 1598-2092(pISSN)
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  • 2093-4718(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
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DOI QR코드

DOI QR Code

Joint SOC-SOP estimation method for lithium-ion batteries based on electro-thermal model and multi-parameter constraints

  • Qin, Peijun (Key Laboratory of Smart Grid of Education Ministry, Tianjin University) ;
  • Che, Yanbo (Key Laboratory of Smart Grid of Education Ministry, Tianjin University) ;
  • Li, Hongfeng (Key Laboratory of Smart Grid of Education Ministry, Tianjin University) ;
  • Cai, Yibin (Key Laboratory of Smart Grid of Education Ministry, Tianjin University) ;
  • Jiang, Mingda (Key Laboratory of Smart Grid of Education Ministry, Tianjin University)
  • 투고 : 2021.06.15
  • 심사 : 2021.12.22
  • 발행 : 2022.03.20
⟨ 이전 논문 다음 논문 ⟩

초록

Accurate estimations of the state of charge (SOC) and the state of power (SOP) are required to ensure efficient and reliable utilization of Li-ion batteries. A new joint estimation method of SOC-SOP based on the electro-thermal model and multi-parameter constraints is proposed in this paper. The proposed method introduces temperature as one of the important constraints for SOP and considers the intrinsic relationship between SOC and SOP as well as the influence of voltage, temperature, and SOC on SOP estimation. First, an electro-thermal model is developed to describe the electric and thermal dynamic characteristics of a battery. Second, the battery SOC is accurately estimated by the unscented Kalman filter method. Then the state of power of the battery is predicted under the condition of multi-parameter constraints. Finally, experiments are conducted to verify the effectiveness of the proposed method. Simulation and experimental results show that this method has a high degree of estimation accuracy and is very simple to calculate. Under the DST condition, the maximum relative voltage error within the electro-thermal model is about 5%. The maximum estimation error of the peak discharge power does not exceed 5 W, and the overall average estimation error is about 1.2 W.

키워드

과제정보

This work was supported by the Key Research and Development Program of Tianjin (no. 20YFYSGX00060).

참고문헌

  1. Hu, X.S., Tang, X.L.: Review of modeling techniques for lithiumion traction batteries in electric vehicles. J. Mech. Eng. 53(16), 20-31 (2017) https://doi.org/10.3901/jme.2017.16.020
  2. Lu, L.G., Han, X.B., Li, J.Q., et al.: A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sourc. 226(3), 272-288 (2013) https://doi.org/10.1016/j.jpowsour.2012.10.060
  3. Plett, G.L.: High-performance battery-pack power estimation using a dynamic cell model. IEEE Trans. Veh. Technol. 53(5), 1586-1593 (2004) https://doi.org/10.1109/TVT.2004.832408
  4. Zhang, W.G., Shi, W., Ma, Z.Y.: Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery. J. Power Sourc. 289, 50-62 (2015) https://doi.org/10.1016/j.jpowsour.2015.04.148
  5. Wang, C.Y., Cui, N.X., Li, C.L., Zhang, C.H.: Peak power prediction of power battery based on electro-thermal coupling model and multi-parameter constraint. J. Mech. Eng. 55(20), 28-35 (2019)
  6. Yang, B., Wang, J.T., Cao, P.L., et al.: Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: a critical comprehensive survey. J Energy Storage. 39, 102572 (2021) https://doi.org/10.1016/j.est.2021.102572
  7. Zhang, Y.H., Song, W.J., Lin, S.L., et al.: A novel model of the initial state of charge estimation for LiFePO4 batteries. J. Power Sources 248, 1028-1033 (2014) https://doi.org/10.1016/j.jpowsour.2013.09.135
  8. Yang, N., Zhang, X., Li, G., et al.: State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting. Electrochim. Acta 151, 63-71 (2015) https://doi.org/10.1016/j.electacta.2014.11.011
  9. Xing, Y.J., He, W., Pecht, M., et al.: State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Appl. Energy 113(1), 106-115 (2014) https://doi.org/10.1016/j.apenergy.2013.07.008
  10. Chen, C., Xiong, R., Shen, W.: A lithium-ion battery-in-the-loop approach to test and validate multiscale dual H infinity filters for state-of-charge and capacity estimation. IEEE Trans. Power Electron. 1(33), 332-342 (2017)
  11. Yang, F., Li, W., Li, C., Miao, Q.: State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy 175, 66-75 (2019) https://doi.org/10.1016/j.energy.2019.03.059
  12. Yao, L., Xiao, Y.Q., Gong, X.Y., et al.: A novel intelligent method for fault diagnosis of electric vehicle battery system based on wavelet neural network. J. Power Sources. 453, 227870 (2020) https://doi.org/10.1016/j.jpowsour.2020.227870
  13. Zhao, Y.Q., Zhou, X.F., Liu, Y.J.: SOC estimation for Li-ion battery based on extended Kalman particle. China Mech. Eng. 26(3), 394-397 (2015)
  14. Ramadan, H.S., Becherif, M., Claude, F.: Extended kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis. Int. J. Hydrogen Energy 290, 33-46 (2017)
  15. Wang, D.S., Wang, X.X.: SOC estimation of lithium-ion battery based on extended Kalman filter. Chin. J. Power Sources. 43(09), 1458-1460 (2019)
  16. Zhang, Y., Wang, L.J., Wu, T.Z., et al.: SOC estimation method of UAV based on improved EKF. Chin. J. Power Sources. 43(02), 320-323 (2019)
  17. Chen, Y., He, Y.G., Li, Z.: Battery variable temperature model parameter identification by likelihood estimation and SOC estimation. J. Electron. Meas. Instrum. 33(12), 1-9 (2019)
  18. He, W., Williard, N., Chen, C.C., et al.: State of charge estimation for electric vehicle batteries using unscented Kalman filtering. Microelectron. Reliab. 53(6), 840-847 (2013) https://doi.org/10.1016/j.microrel.2012.11.010
  19. Chen, Z.W., Yang, L.W., Zhao, X.B., et al.: Online state of charge estimation of Li-ion battery based on an improved unscented kalman filter approach. Appl. Math. Model. 70, 532-544 (2019) https://doi.org/10.1016/j.apm.2019.01.031
  20. Dong, G.Z., Wei, J.W., Chen, Z.H., et al.: Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter. J. Power Sources 364, 316-327 (2017) https://doi.org/10.1016/j.jpowsour.2017.08.040
  21. Xiong, R., He, H., Sun, F., et al.: Model-based state of charge and peak power capability joint estimation of lithium-ion battery in plug-in hybrid electric vehicles. J. Power Sources 229, 159-169 (2013) https://doi.org/10.1016/j.jpowsour.2012.12.003
  22. Sun, F., Xiong, R., He, H.: Estimation of state-of-charge and state of-power capability of lithium-ion battery considering varying health conditions. J. Power Sources 259(7), 166-176 (2014) https://doi.org/10.1016/j.jpowsour.2014.02.095
  23. Jin, X.N., Gu, Q.M., Pan, Y.W., et al.: Online state of power estimation methods for lithium-ion batteries in EV. Chin. J. Power Sources. 43(09), 1448-1452 (2019)
  24. Zhu, H., Zhang, W.B., Deng, Y.W., et al.: Peak power estimation of power battery discharge based on SA + BP hybrid algorithm. J. Jiangsu Univ. (Nat. Sci. Edn.). 041(002), 192-198 (2020)
  25. Wang, Y.J., Tian, J.Q., Sun, Z.D., et al.: A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renew. Sustain. Energy Rev. 131, 110015 (2020) https://doi.org/10.1016/j.rser.2020.110015
  26. Idaho: National Engineering & Environmental Laboratory. Battery test manual for plug-in hybrid electric vehicles. INL/EXT07-12536 (2010)
  27. Chen, Z., Xiong, R., Wang, K., et al.: Optimal energy management strategy of a plug-in hybrid electric vehicle based on a particle swarm optimization algorithm. Energies 8(5), 3661-3678 (2015) https://doi.org/10.3390/en8053661
  28. Dong, G., Wei, J., Chen, Z.: Kalman filter for onboard state of charge estimation and peak power capability analysis of lithiumion batteries. J. Power Sources 328, 615-626 (2016) https://doi.org/10.1016/j.jpowsour.2016.08.065
  29. Xiong, R., Sun, F.C., He, H.W., et al.: A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles. Energy 63, 295-308 (2013) https://doi.org/10.1016/j.energy.2013.10.027
  30. Wang, Y.J., Pan, R., Liu, C., et al.: Power capability evaluation for lithium iron phosphate batteries based on multi-parameter constraints estimation. J. Power Sources 374, 12-23 (2018) https://doi.org/10.1016/j.jpowsour.2017.11.019
  31. Wang, Y.J., Zhang, X., Liu, C., et al.: Multi-timescale power and energy assessment of lithium-ion battery and supercapacitor hybrid system using extended Kalman filter. J. Power Sources 389, 93-105 (2018) https://doi.org/10.1016/j.jpowsour.2018.04.012
  32. Feng, T.H., Yang, L., Zhao, X.W., et al.: Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction. J. Power Sources 281, 192-203 (2015) https://doi.org/10.1016/j.jpowsour.2015.01.154
  33. Jiang, J.C., Liu, S.J., Ma, Z.Y., et al.: Butler-volmer equationbased model and its implementation on state of power prediction of high-power lithium titanate batteries considering temperature effects. Energy 117, 58-72 (2016) https://doi.org/10.1016/j.energy.2016.10.087
  34. Zheng, L.F., Zhu, J.H., Wang, G.X., et al.: Lithium-ion battery instantaneous available power prediction using surface lithium concentration of solid particles in a simplified electrochemical model. IEEE Trans. Power Electron. 11(33), 9551-9560 (2018) https://doi.org/10.1109/TPEL.2018.2791965
  35. Tang, X., Wang, Y., Yao, K., et al.: Model migration based battery power capability evaluation considering uncertainties of temperature and aging. J. Power Sources. 440, 227141 (2019) https://doi.org/10.1016/j.jpowsour.2019.227141
  36. Xie, J., Yao, T.: Quantified assessment of internal short-circuit state for 18650 batteries using an extreme learning machine based pseudo-distributed model. IEEE Trans. Transport. Electrif. 7(3), 1303-1313 (2021) https://doi.org/10.1109/TTE.2021.3052579
  37. Xie, J., Li, Z., Jiao, J., Li, X.: Lumped-parameter temperature evolution model for cylindrical Li-ion batteries considering reversible heat and propagation delay. Measurement 173(3), 108567 (2021) https://doi.org/10.1016/j.measurement.2020.108567