Acknowledgement
This work was supported by the Key Research and Development Program of Tianjin (no. 20YFYSGX00060).
References
- 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
- 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
- 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
- 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
- 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)
- 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
- 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
- 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
- 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
- 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)
- 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
- 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
- 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)
- 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)
- 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)
- 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)
- 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)
- 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
- 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
- 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
- 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
- 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
- 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)
- 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)
- 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
- Idaho: National Engineering & Environmental Laboratory. Battery test manual for plug-in hybrid electric vehicles. INL/EXT07-12536 (2010)
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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