Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms

The battery state of charge (SoC), whose estimation is one of the basic functions of battery management system (BMS), is a vital input parameter in the energy management and power distribution control of electric vehicles (EVs). In this paper, two methods based on an extended Kalman filter (EKF) and unscented Kalman filter (UKF), respectively, are proposed to estimate the SoC of a lithium-ion battery used in EVs. The lithium-ion battery is modeled with the Thevenin model and the model parameters are identified based on experimental data and validated with the Beijing Driving Cycle. Then space equations used for SoC estimation are established. The SoC estimation results with EKF and UKF are compared in aspects of accuracy and convergence. It is concluded that the two algorithms both perform well, while the UKF algorithm is much better with a faster convergence ability and a higher accuracy.

[1]  Song Yan,et al.  Integrated control method for a fuel cell hybrid system , 2009 .

[2]  Amir Vasebi,et al.  A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended Kalman filter for hybrid electric vehicle applications , 2007 .

[3]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[4]  Max Åhman,et al.  Primary energy efficiency of alternative powertrains in vehicles , 2001 .

[5]  Hongwen He,et al.  Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach , 2011 .

[6]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[7]  Hongwen He,et al.  Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach , 2012 .

[8]  Hongwen He,et al.  Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles , 2012 .

[9]  Hongwen He,et al.  Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles , 2012 .

[10]  D. Sauer,et al.  Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries , 2011 .

[11]  Karim Salahshoor,et al.  Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller , 2012 .

[12]  Barry W. Johnson,et al.  A battery state-of-charge indicator for electric wheelchairs , 1992, IEEE Trans. Ind. Electron..

[13]  Der-fa Chen,et al.  Design and implementation of a battery charger with a state-of-charge estimator , 2000 .

[14]  Myoungho Sunwoo,et al.  State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter , 2009 .

[15]  Dean Patterson,et al.  Use of lithium-ion batteries in electric vehicles , 2000 .

[16]  Hongwen He,et al.  State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model , 2011, IEEE Transactions on Vehicular Technology.

[17]  Andreas Jossen,et al.  Methods for state-of-charge determination and their applications , 2001 .

[18]  Jasim Ahmed,et al.  Algorithms for Advanced Battery-Management Systems , 2010, IEEE Control Systems.

[19]  C.W. Chan,et al.  Detection of satellite attitude sensor faults using the UKF , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[20]  Yiwen Zhao,et al.  Application of Unscented Kalman Filter in the SOC Estimation of Li-ion Battery for Autonomous Mobile Robot , 2006, 2006 IEEE International Conference on Information Acquisition.

[21]  Lajos Hanzo,et al.  Preamble Design Using Embedded Signaling for OFDM Broadcast Systems Based on Reduced-Complexity Distance Detection , 2011, IEEE Transactions on Vehicular Technology.