A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer

A novel method for Li-ion battery state of charge (SOC) estimation based on a super-twisting sliding mode observer (STSMO) is proposed in this paper. To design the STSMO, the state equation of a second-order RC equivalent circuit model (SRCECM) is derived to represent the dynamic behaviors of the Li-ion battery, and the model parameters are determined by the pulse current discharge approach. The convergence of the STSMO is proven by Lyapunov stability theory. The experiments under three different discharge profiles are conducted on the Li-ion battery. Through comparisons with a conventional sliding mode observer (CSMO) and adaptive extended Kalman filter (AEKF), the superiority of the proposed observer for SOC estimation is validated.

[1]  Azah Mohamed,et al.  A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations , 2017 .

[2]  Maria Skyllas-Kazacos,et al.  Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery , 2016 .

[3]  Zhenwei Cao,et al.  A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles , 2014 .

[4]  Jun Xu,et al.  Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications , 2013 .

[5]  Van-Huan Duong,et al.  Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares , 2015 .

[6]  Chen Long,et al.  State of charge estimation of power Li-ion batteries using a hybrid estimation algorithm based on UKF , 2016 .

[7]  Hongjie Wu,et al.  State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model , 2013 .

[8]  Huiqi Li,et al.  State of charge estimation for LiMn2O4 power battery based on strong tracking sigma point Kalman filter , 2015 .

[9]  Avrie Levent,et al.  Robust exact differentiation via sliding mode technique , 1998, Autom..

[10]  IL-Song Kim,et al.  A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer , 2010, IEEE Transactions on Power Electronics.

[11]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[12]  Wei He,et al.  State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures , 2014 .

[13]  Rui Xiong,et al.  Lithium-Ion Battery Parameters and State-of-Charge Joint Estimation Based on H-Infinity and Unscented Kalman Filters , 2017, IEEE Transactions on Vehicular Technology.

[14]  Hongye Su,et al.  Neural Network-Based State of Charge Observer Design for Lithium-Ion Batteries , 2018, IEEE Transactions on Control Systems Technology.

[15]  Binggang Cao,et al.  The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer , 2014, IEEE Transactions on Vehicular Technology.

[16]  Salah Laghrouche,et al.  Adaptive-Gain Second Order Sliding Mode Observer Design for Switching Power Converters , 2013, ArXiv.

[17]  Salah Laghrouche,et al.  Observer-based higher order sliding mode control of power factor in three-phase AC/DC converter for hybrid electric vehicle applications , 2013, Int. J. Control.

[18]  Wei Sun,et al.  A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer , 2014 .

[19]  Leonid M. Fridman,et al.  Second-order sliding-mode observer for mechanical systems , 2005, IEEE Transactions on Automatic Control.

[20]  Il-Song Kim,et al.  Nonlinear State of Charge Estimator for Hybrid Electric Vehicle Battery , 2008, IEEE Transactions on Power Electronics.

[21]  Zonghai Chen,et al.  Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator , 2017 .

[22]  Jaime A. Moreno,et al.  A Lyapunov approach to second-order sliding mode controllers and observers , 2008, 2008 47th IEEE Conference on Decision and Control.

[23]  Chenbin Zhang,et al.  A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries , 2014 .

[24]  Xiaosong Hu,et al.  Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer , 2010 .

[25]  C. Mi,et al.  A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter , 2013 .

[26]  P. J. García Nieto,et al.  Support Vector Machines Used to Estimate the Battery State of Charge , 2013, IEEE Transactions on Power Electronics.

[27]  Kai Zhao,et al.  Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach , 2013, IEEE Transactions on Vehicular Technology.

[28]  Youyi Wang,et al.  An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles , 2016 .

[29]  Leonid M. Fridman,et al.  Optimal Lyapunov function selection for reaching time estimation of Super Twisting algorithm , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[30]  Weilin Li,et al.  State of Charge Estimation of Lithium-Ion Batteries Using a Discrete-Time Nonlinear Observer , 2017, IEEE Transactions on Industrial Electronics.

[31]  Akshay Kumar Rathore,et al.  Super-Twisting Differentiator-Based High Order Sliding Mode Voltage Control Design for DC-DC Buck Converters , 2016 .

[32]  Cheng Chen,et al.  A Lithium-Ion Battery-in-the-Loop Approach to Test and Validate Multiscale Dual H Infinity Filters for State-of-Charge and Capacity Estimation , 2018, IEEE Transactions on Power Electronics.

[33]  Yves Dube,et al.  A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures , 2016 .