J. Jaguemont, L. Boulon and Y. Dube, “Characterization and Modeling of a Hybrid-Electric-Vehicle Lithium-Ion Battery Pack at Low Temperatures,” IEEE Trans. Veh. Technol., vol. 65, no. 1, pp. 1-14, 2016.
 X. Zeng and J. Wang, “A Parallel Hybrid Electric Vehicle Energy Management Strategy Using Stochastic Model Predictive Control with Road Grade Preview,” IEEE Trans. Control Syst. Technol., vol. 23, no. 6, pp. 2416-2423, 2015.
 J. K. Barillas, J. Li, C. Gunther and M. A. Danzer, “A Comparative Study and Validation of State Estimation Algorithms for Li-ion Batteries in Battery Management Systems,” Appl. Energy, vol. 155, pp. 455-462, 2015.
 F. Feng, R.G .Lu, G. Wei and C.B. Zhu, “Online Estimation of Model Parameters and State of Charge of LiFePO4 Batteries using a Novel Open-Circuit Voltage at Various Ambient Temperatures,” Energies2015, vol.8, pp. 2950–2976,2015.
 J.Y.Cao, “A New Method to Estimate the State of Charge of Lithium-ion Batteries based on the Battery Impedance Model,” J. Power Sources 2013,vol. 233, pp.277–284, 2013.
 H. Rahimi-Eichi, F. Baronti and MY. Chow, “Online Adaptive Parameter Identification and State-of-Charge Coestimation for Lithium-Polymer Battery Cells,” IEEE Trans. Ind. Electron., vol. 61, no. 4, pp. 2053-2061, 2014.
 C .Zhang, K. Li, L. Pei and C.B. Zhu, “ An Integrated Approach for Real-Time Model-based state-of Charge Estimation of Lithium-ion Batteries,” J. Power Sources 2015,vol. 283,pp. 24–36, 2015.
 S.Sepasi, L.R. Roose and M.M. Matsuura, “ Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation,” Energies 2015, vol. 8, pp. 5217–5233, 2015.
 C. Hu, B. D. Youn and J. Chung, “A Multiscale Framework with Extended Kalman Filter for Lithium-ion Battery SOC and Capacity Estimation,” Appl Energy., vol. 92, pp. 694-704, Jan. 2012.
 Y.Tian, B.Z. Xia. , W. Sun. Z.H .Xu and W.W .Zheng, “A Modified Model based State of Charge Estimation of Power Lithium-ion Batteries using Unscented Kalman Filter,” J. Power Sources2014,vol.270, pp. 619–626, 2014.
 Q .Yu, et al., “Lithium-ion Battery Parameters and State-of-Charge Joint Estimation based on H-infinity and Unscented Kalman Filters,”IEEE 2017. vol.66, pp. 8693-8701,2017.
 M. Partovibakhsh and G. Liu, “An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots,” IEEE Trans. Control Syst. Technol., vol. 23, no. 1, pp. 357-363, 2015.
 X.J .Tang, Z.B .Liu and J.S .Zhang, " Square-Root Quaternion Cubature Kalman Filtering for Spacecraft Attitude Estimation". Acta Astronautica2012,vol.76,pp. 84–94, 2012.
 I.Arasaratnam, S. Haykin and T.R. Hurd, “Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations,” IEEE Trans. Signal Process., vol.58,pp. 4977,4993, 2010.
 W. Li and Y .Jia, “Location of Mobile Station with Maneuvers using an IMM-based Cubature Kalman Filter,” IEEE Trans. Ind. Electron. 2012, vol. 59, pp. 4338–4348, 2012.
 M. Dahmahi, A.Meche, M. Keche and A. Oramri,. “Reduced Cubature Kalman Filtering Applied to Target Tracking,” In Proceedings of the 2nd International Conference on Control, Instrumentation and Automation (ICCIA’11), Shiraz, Iran, 27–29 December 2011, pp. 1097–1101, 2011.
 Y. Sun, J. Xie and J. Guo, “A New Maneuvering Target Tracking Method using Adaptive Cubature Kalman Filter,” 2014 IEEE International Conference on Control Science and Systems Engineering, Yantai, 2014, pp. 40-44, 2014.
 B. Xia, et al., “State of Charge Estimation of Lithium-ion Batteries using an Adaptive Cubature Kalman Filter,”Energies 2015, vol. 8, pp. 5916-5936, 2015.
 S. Khashirunnisa, B. K. Chand and B. L. Kumari, “Performance Analysis of Kalman Filter, Fuzzy Kalman Filter and wind Driven Optimized Kalman Filter for Tracking Applications,” 2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS), Mathura, 2016, pp. 170-174, 2016.
 Y.Q. Shen, "Adaptive Online State-of-Charge Determination based on Neuro-Controller and Neural Network" Energy Convers.Manag., vol.51, pp. 1093–1098, 2010.
 H. He, R. Xiong, and J.J.E. Fan, “Evaluation of Lithium-ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach,” Energies2011.vol 4,pp. 582-598, 2011.
 B. Diouf and R. Pode, “Potential of Lithium-ion Batteries in Renewable Energy, Renew,” Energy., vol 76, pp.375-380, 2015.
 D .Li , J.Ouyang
, “State of charge Estimation for LiMn2O4 Power Battery based on Strong Tracking Sigma Point Kalman Filter,” Journal of power sources. 2015.vol. 279, pp. 439-449, 2015.
 I. Arasaratnam and S. Haykin, “Cubature Kalman Smoothers,” Automatica2011,vol.47, pp. 2245–2250, 2011.
 Z. Pan, L. Gao, S. Gao and B. Gao, “Adaptive Cubature Kalman Filter for Ultra-Tightly Coupled BDS/INS Integration,” 2016 6th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, 2016, pp. 269-272, 2016.
 N. Gao, M. Wang and L. Zhao, “An Integrated INS/GNSS Urban Navigation System based on Fuzzy Adaptive Kalman Filter,” 2016 35th Chinese Control Conference (CCC), Chengdu, 2016, pp. 5732-5736, 2016.