Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model

This paper considers the estimation of the state of charge and state of health for lithium-ion batteries, while an inclusive model is taken into account. The model includes two RC subnetworks, which represent the fast and slow transient responses of the terminal voltage. Nevertheless, the linear part of the model is unobservable. On the other hand, the nonlinear behavior of the open-circuit voltage versus state of charge is also included in the model. The proposed observer tackles the aforementioned problems to attain a reliable estimation of the state of charge. Moreover, as opposed to the methods in which the nonlinearities or uncertainties in the model are disregarded or those terms are discarded using a conventional sliding-mode observer, an analytical method is considered to estimate the additive nonlinear or uncertainty term in the model. This approach leads to a very accurate model of the battery to be used in a battery management system. Moreover, an online parameter estimation method is proposed to estimate the battery's state of health. The proposed scheme benefits from an adaptive rule for the online estimation of the series resistance in the lithium-ion battery based on the accurately identified model. Experimental tests certify the performance and feasibility of the proposed schemes.

[1]  A. Miraoui,et al.  Review of adaptive systems for lithium batteries State-of-Charge and State-of-Health estimation , 2012, 2012 IEEE Transportation Electrification Conference and Expo (ITEC).

[2]  Christopher Edwards,et al.  Adaptive Sliding-Mode-Observer-Based Fault Reconstruction for Nonlinear Systems With Parametric Uncertainties , 2008, IEEE Transactions on Industrial Electronics.

[3]  Zechang Sun,et al.  Online SOCEstimation ofHigh-power Lithium-ion Batteries UsedonHEVs , 2006 .

[4]  Yiran Hu,et al.  Battery state of charge estimation in automotive applications using LPV techniques , 2010, Proceedings of the 2010 American Control Conference.

[5]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

[6]  F.R. Salmasi,et al.  State of charge estimation for batteries in HEV using locally linear model tree (LOLIMOT) , 2007, 2007 International Conference on Electrical Machines and Systems (ICEMS).

[7]  Chenghui Cai,et al.  Artificial neural network in estimation of battery state of-charge (SOC) with nonconventional input variables selected by correlation analysis , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[8]  Mohammad Farrokhi,et al.  Online State-of-Health Estimation of VRLA Batteries Using State of Charge , 2013, IEEE Transactions on Industrial Electronics.

[9]  Sun Zechang,et al.  A new SOH prediction concept for the power lithium-ion battery used on HEVs , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[10]  Jonghoon Kim,et al.  State-of-Charge Estimation and State-of-Health Prediction of a Li-Ion Degraded Battery Based on an EKF Combined With a Per-Unit System , 2011, IEEE Transactions on Vehicular Technology.

[11]  Alain Oustaloup,et al.  On Lead-Acid-Battery Resistance and Cranking-Capability Estimation , 2010, IEEE Transactions on Industrial Electronics.

[12]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation , 2006 .

[13]  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.

[14]  Seongjun Lee,et al.  State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .

[15]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[16]  Guangjun Liu,et al.  Estimation of Battery State of Charge With $H_{\infty}$ Observer: Applied to a Robot for Inspecting Power Transmission Lines , 2012, IEEE Transactions on Industrial Electronics.

[17]  Babak Fahimi,et al.  Online Estimation of State of Charge in Li-Ion Batteries Using Impulse Response Concept , 2012, IEEE Transactions on Smart Grid.

[18]  Giovanni Fiengo,et al.  Lithium-ion battery state of charge estimation with a Kalman Filter based on a electrochemical model , 2008, 2008 IEEE International Conference on Control Applications.

[19]  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 .

[20]  David A. Stone,et al.  Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles , 2005, IEEE Transactions on Vehicular Technology.

[21]  Chunbo Zhu,et al.  State-of-Charge Determination From EMF Voltage Estimation: Using Impedance, Terminal Voltage, and Current for Lead-Acid and Lithium-Ion Batteries , 2007, IEEE Transactions on Industrial Electronics.

[22]  Guangjun Liu,et al.  A battery state of charge estimation method using sliding mode observer , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[23]  Tsorng-Juu Liang,et al.  Estimation of Battery State of Health Using Probabilistic Neural Network , 2013, IEEE Transactions on Industrial Informatics.

[24]  C. Fennie,et al.  Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries , 2004 .

[25]  Xu Wang,et al.  A nonlinear adaptive observer approach for state of charge estimation of lithium-ion batteries , 2011, Proceedings of the 2011 American Control Conference.

[26]  Sergio M. Savaresi,et al.  Kalman Filter SoC estimation for Li-Ion batteries , 2011, 2011 IEEE International Conference on Control Applications (CCA).

[27]  Mohammad Farrokhi,et al.  State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF , 2010, IEEE Transactions on Industrial Electronics.

[28]  Yuang-Shung Lee,et al.  Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems , 2008, IEEE Transactions on Industrial Electronics.

[29]  C. Kral,et al.  A method for online capacity estimation of lithium ion battery cells using the state of charge and the transferred charge , 2010, 2010 IEEE International Conference on Sustainable Energy Technologies (ICSET).

[30]  Seongjun Lee,et al.  Complementary Cooperation Algorithm Based on DEKF Combined With Pattern Recognition for SOC/Capacity Estimation and SOH Prediction , 2012, IEEE Transactions on Power Electronics.

[31]  Il-Song Kim,et al.  The novel state of charge estimation method for lithium battery using sliding mode observer , 2006 .

[32]  Christopher Edwards,et al.  Sliding-mode observers , 2007 .

[33]  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.