Nonlinear Adaptive Observer for a Lithium-Ion Battery Cell Based on Coupled Electrochemical–Thermal Model
Tóm tắt
Real-time estimation of battery internal states and physical parameters is of the utmost importance for intelligent battery management systems (BMS). Electrochemical models, derived from the principles of electrochemistry, are arguably more accurate in capturing the physical mechanism of the battery cells than their counterpart data-driven or equivalent circuit models (ECM). Moreover, the electrochemical phenomena inside the battery cells are coupled with the thermal dynamics of the cells. Therefore, consideration of the coupling between electrochemical and thermal dynamics inside the battery cell can be potentially advantageous for improving the accuracy of the estimation. In this paper, a nonlinear adaptive observer scheme is developed based on a coupled electrochemical–thermal model of a Li-ion battery cell. The proposed adaptive observer scheme estimates the distributed Li-ion concentration and temperature states inside the electrode, and some of the electrochemical model parameters, simultaneously. These states and parameters determine the state of charge (SOC) and state of health (SOH) of the battery cell. The adaptive scheme is split into two separate but coupled observers, which simplifies the design and gain tuning procedures. The design relies on a Lyapunov's stability analysis of the observers, which guarantees the convergence of the combined state-parameter estimates. To validate the effectiveness of the scheme, both simulation and experimental studies are performed. The results show that the adaptive scheme is able to estimate the desired variables with reasonable accuracy. Finally, some scenarios are described where the performance of the scheme degrades.
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Tài liệu tham khảo
Hatzell, K. B., Sharma, A., and Fathy, H. K., 2012, “A Survey of Long-Term Health Modeling, Estimation, and Control of Lithium-Ion Batteries: Challenges and Opportunities,” American Control Conference (ACC), Montreal, Canada, June 27–29, pp. 584–591.10.1109/ACC.2012.6315578
Saha, B., Goebel, K., Poll, S., and Christophersen, J., 2007, “An Integrated Approach to Battery Health Monitoring Using Bayesian Regression and State Estimation,” IEEEAutotestcon Conference, Baltimore, MD, Sept. 17–20, pp. 646–653.10.1109/AUTEST.2007.4374280
2009, Enhanced Coulomb Counting Method for Estimating State-of-Charge and State-of-Health of Lithium-Ion Batteries, Appl. Energy, 86, 1506, 10.1016/j.apenergy.2008.11.021
2004, Extended Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs: Part 3. State and Parameter Estimation, J. Power Sources, 134, 277, 10.1016/j.jpowsour.2004.02.033
2011, State-of-Health Monitoring of Lithium-Ion Batteries in Electric Vehicles by On-Board Internal Resistance Estimation, J. Power Sources, 196, 5357, 10.1016/j.jpowsour.2010.08.035
2010, A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer, IEEE Trans. Power Electron., 25, 1013, 10.1109/TPEL.2009.2034966
2009, New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observer Techniques, IEEE Trans. Veh. Technol., 58, 3905, 10.1109/TVT.2009.2028348
2010, Algorithms for Advanced Battery-Management Systems, IEEE Control Syst. Mag., 30, 49, 10.1109/MCS.2010.936293
1993, Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell, J. Electrochem. Soc., 140, 1526, 10.1149/1.2221597
Kehs, M. A., Beeney, M. D., and Fathy, H. K., 2014, “Computational Efficiency of Solving the DFN Battery Model Using Descriptor Form With Legendre Polynomials and Galerkin Projections,” American Control Conference (ACC), Portland, OR, June 4–6, pp. 260–267.10.1109/ACC.2014.6858858
2010, Model-Based Electrochemical Estimation and Constraint Management for Pulse Operation of Lithium Ion Batteries, IEEE Trans. Control Syst. Technol., 18, 654, 10.1109/TCST.2009.2027023
2013, Electrochemical Model Based Observer Design for a Lithium-Ion Battery, IEEE Trans. Control Syst. Technol., 21, 289, 10.1109/TCST.2011.2178604
2011, Reduction of an Electrochemistry-Based Li-Ion Battery Model Via Quasi-Linearization and Pade Approximation, J. Electrochem. Soc., 158, A93, 10.1149/1.3519059
2014, Development and Experimental Parameterization of a Physics-Based Second-Order Lithium-Ion Battery Model, ASME
2006, Online Estimation of the State of Charge of a Lithium Ion Cell, J. Power Sources, 161, 1346, 10.1016/j.jpowsour.2006.04.146
2010, Lithium-Ion Battery State of Charge and Critical Surface Charge Estimation Using an Electrochemical Model-Based Extended Kalman Filter, ASME J. Dyn. Syst., Meas., Control, 132, 061302, 10.1115/1.4002475
Moura, S. J., Chaturvedi, N. A., and Krstic, M., 2012, “PDE Estimation Techniques for Advanced Battery Management Systems—Part I: SOC Estimation,” American Control Conference (ACC), pp. 559–565.
Dey, S., and Ayalew, B., 2014, “Nonlinear Observer Designs for State-of-Charge Estimation of Lithium-Ion Batteries,” American Control Conference (ACC), Portland, OR, June 4–6, pp. 248–253.10.1109/ACC.2014.6858766
Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model, IEEE Trans. Control Syst. Technol.
Samadi, M. F., Alavi, S. M., and Saif, M., 2013, “Online State and Parameter Estimation of the Li-Ion Battery in a Bayesian Framework,” American Control Conference (ACC), Washington, DC, June 17–19, pp. 4693–4698.10.1109/ACC.2013.6580563
2010, Model-Based Distinction and Quantification of Capacity Loss and Rate Capability Fade in Li-Ion Batteries, J. Power Sources, 195, 7634, 10.1016/j.jpowsour.2010.06.011
Fang, H., Wang, Y., Sahinoglu, Z., Wada, T., and Hara, S., 2013, “Adaptive Estimation of State of Charge for Lithium-Ion Batteries,” American Control Conference (ACC), pp. 3485–3491.
2014, State of Charge Estimation for Lithium-Ion Batteries: An Adaptive Approach, Control Eng. Pract., 25, 45, 10.1016/j.conengprac.2013.12.006
2013, Adaptive PDE Observer for Battery SOC/SOH Estimation Via an Electrochemical Model, ASME J. Dyn. Syst., Meas., Control, 136
Wang, Y., Fang, H., Sahinoglu, Z., Wada, T., and Hara, S., 2013, “Nonlinear Adaptive Estimation of the State of Charge for Lithium-Ion Batteries,” 52nd Annual Conference on Decision and Control, pp. 4405–4410.
2015, Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach, IEEE Trans. Control Syst. Technol., 23, 948, 10.1109/TCST.2014.2356503
Dey, S., Ayalew, B., and Pisu, P., 2014, “Combined Estimation of State-of-Charge and State-of-Health of Li-Ion Battery Cells Using SMO on Electrochemical Model,” 13th International Workshop on Variable Structure Systems, Nantes, France, June 29–July 2, pp. 1–6.
Zhou, X., Ersal, T., Stein, J. L., and Bernstein, D. S., 2013, “Battery Health Diagnostics Using Retrospective-Cost Subsystem Identification: Sensitivity to Noise and Initialization Errors,” ASME Paper No. DSCC2013-3953.10.1115/DSCC2013-3953
Zhou, X., Ersal, T., Stein, J. L., and Bernstein, D. S., 2014, “Battery State of Health Monitoring by Side Reaction Current Density Estimation Via Retrospective-Cost Subsystem Identification,” ASME Paper No. DSCC2014-6254.10.1115/DSCC2014-6254
2013, An Observer Looks at the Cell Temperature in Automotive Battery Packs, Control Eng. Pract., 21, 1035, 10.1016/j.conengprac.2013.03.001
2014, A Lumped-Parameter Electro-Thermal Model for Cylindrical Batteries, J. Power Sources, 257, 1, 10.1016/j.jpowsour.2014.01.097
2013, Online Parameterization of Lumped Thermal Dynamics in Cylindrical Lithium Ion Batteries for Core Temperature Estimation and Health Monitoring, IEEE Trans. Control Syst. Technol., 21, 1745, 10.1109/TCST.2012.2217143
2014, The Estimation of Temperature Distribution in Cylindrical Battery Cells Under Unknown Cooling Conditions, IEEE Trans. Control Syst. Technol., 22, 2277, 10.1109/TCST.2014.2309492
2013, Parameterization and Observability Analysis of Scalable Battery Clusters for Onboard Thermal Management, Oil Gas Sci. Technol., 68, 165, 10.2516/ogst/2012075
2015, State of Charge Estimation of a Lithium Ion Cell Based on a Temperature Dependent and Electrolyte Enhanced Single Particle Model, Energy, 80, 731
Klein, R., Chaturvedi, N. A., Christensen, J., Ahmed, J., Findeisen, R., and Kojic, A., 2010, “State Estimation of a Reduced Electrochemical Model of a Lithium-Ion Battery,” American Control Conference (ACC), Baltimore, MD, June 30–July 2, pp. 6618–6623.10.1109/ACC.2010.5531378
2011, A Critical Review of Thermal Issues in Lithium-Ion Batteries, J. Electrochem. Soc., 158, R1, 10.1149/1.3515880
Dey, S., Ayalew, B., and Pisu, P., 2014, “Adaptive Observer Design for a Li-Ion Cell Based on Coupled Electrochemical-Thermal Model,” ASME Paper No. DSCC2014-5986.10.1115/DSCC2014-5986
2011, Single-Particle Model for a Lithium-Ion Cell: Thermal Behavior, J. Electrochem. Soc., 158, A122, 10.1149/1.3521314
2003, Mathematical Modeling of the Capacity Fade of Li-Ion Cells, J. Power Sources, 123, 230, 10.1016/S0378-7753(03)00531-7
2006, Power and Thermal Characterization of a Lithium-Ion Battery Pack for Hybrid-Electric Vehicles, J. Power Sources, 160, 662, 10.1016/j.jpowsour.2006.01.038
2013, Extension of Physics-Based Single Particle Model for Higher Charge–Discharge Rates, J. Power Sources, 224, 180, 10.1016/j.jpowsour.2012.09.084
2013, A New Extension of Physics-Based Single Particle Model for Higher Charge–Discharge Rates, J. Power Sources, 241, 295, 10.1016/j.jpowsour.2013.04.129
2014, A Temperature Dependent, Single Particle, Lithium Ion Cell Model Including Electrolyte Diffusion, ASME J. Dyn. Syst., Meas., Control, 137, 011005
2005, Ageing Mechanisms in Lithium-Ion Batteries, J. Power Sources, 147, 269
1997, A Systematic Approach to Adaptive Observer Synthesis for Nonlinear Systems, IEEE Trans. Autom. Control, 42, 534, 10.1109/9.566664
1995, Adaptive Observers for Active Automotive Suspensions: Theory and Experiment, IEEE Trans. Control Syst. Technol., 3, 86, 10.1109/87.370713
1991, Applied Nonlinear Control
1989, Stable Adaptive Systems
2012, Simplified Electrochemical and Thermal Model of LiFePO4–Graphite Li-Ion Batteries for Fast Charge Applications, J. Electrochem. Soc., 159, A1508, 10.1149/2.064209jes
2000, Comparison Between Computer Simulations and Experimental Data for High-Rate Discharges of Plastic Lithium-Ion Batteries, J. Power Sources, 88, 219, 10.1016/S0378-7753(99)00527-3
2011, Analysis of Lithium Deinsertion/Insertion in LiyFePO4 With a Simple Mathematical Model, Electrochim. Acta, 56, 5222, 10.1016/j.electacta.2011.03.030
2006, Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries, J. Power Sources, 156, 620, 10.1016/j.jpowsour.2005.05.070