State of health estimation for lithium ion batteries based on an equivalenthydraulic model: An iron phosphate application

Couto, Luis; Schorsch, Julien; Job, Nathalie; Léonard, Alexandre; Kinnaert, Michel

doi:10.1016/j.est.2018.11.001

Article (Scientific journals)

State of health estimation for lithium ion batteries based on an equivalenthydraulic model: An iron phosphate application

Couto, Luis; Schorsch, Julien; Job, Nathalie et al.

2019 • In Journal of Energy Storage, 21, p. 259-271

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/232502

DOI
10.1016/j.est.2018.11.001

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Keywords :

Li-ion battery; State estimation; State-of-charge; State-of-health; System identification

Abstract :

[en] A two-step approach for state-of-health (SOH) estimation of a lithium-ion (Li-ion) battery is developed. In the first step, state-of-charge (SOC) estimation is performed by a constrained extended Kalman filter (EKF) based on the so-called equivalent-hydraulic model. The latter model allows to characterize the internal battery state and main physical parameters while being suitable for on-line computation. The internal battery states are further exploited in the second step of the approach to obtain parameter-based SOH indicators that characterize the long term evolution of the diffusion and charge transfer processes associated to aging. Capacity and power fade indicators are determined by using notably an instrumental variable method in order to obtain unbiased parameter estimates in the presence of heteroscedastic colored noise. The methodology is validated on both simulation and experimental data for a lithium iron phosphate (LFP) half battery cell. This also provides insight on the properties of the LFP electrodes

Disciplines :

Materials science & engineering

Author, co-author :

Couto, Luis; Université Libre de Bruxelles - ULB > Department of Control Engineering and System Analysis

Schorsch, Julien; Université Libre de Bruxelles - ULB > Department of Control Engineering and System Analysis

Job, Nathalie ; Université de Liège - ULiège > Department of Chemical Engineering > Ingéniérie électrochimique

Léonard, Alexandre ; Université de Liège - ULiège > Department of Chemical Engineering > Ingéniérie électrochimique

Kinnaert, Michel; Université Libre de Bruxelles - ULB > Department of Control Engineering and System Analysis

Language :

English

Title :

State of health estimation for lithium ion batteries based on an equivalenthydraulic model: An iron phosphate application

Publication date :

2019

Journal title :

Journal of Energy Storage

ISSN :

2352-1538

eISSN :

2352-152X

Publisher :

Elsevier, Oxford, United Kingdom

Volume :

Pages :

259-271

Peer reviewed :

Peer Reviewed verified by ORBi

Name of the research project :

BATWAL

Available on ORBi :

since 04 February 2019

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Lu, L., Han, X., Li, J., Hua, J., Ouyang, M., A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 226 (2013), 272–288, 10.1016/j.jpowsour.2012.10.060.
Rezvanizaniani, S., Liu, Z., Chen, Y., Lee, J., Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. J. Power Sources 256 (2014), 110–124, 10.1016/j.jpowsour.2014.01.085.
Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., Van den Bossche, P., Critical review of state of health estimation methods of Li-ion batteries for real applications. Renew. Sustain. Energy Rev. 56 (2016), 572–587, 10.1016/j.rser.2015.11.042.
Arora, P., White, R., Doyle, M., Capacity fade mechanisms and side reactions in lithium-ion batteries. J. Electrochem. Soc. 145:10 (1998), 3647–3667, 10.1149/1.1838857.
Vetter, J., Novák, P., Wagner, M., Veit, C., Möller, K., Besenhard, J., Winter, M., Wohlfahrt-Mehrens, M., Vogler, C., Hammouche, A., Ageing mechanisms in lithium-ion batteries. J. Power Sources 147:1-2 (2005), 269–281, 10.1016/j.jpowsour.2005.01.006.
De Hoog, J., Fleurbaey, K., Nikolian, A., Omar, N., Timmermans, J.-M., Van Den Bossche, P., Van Mierlo, J., Ageing phenomena of lithium ion batteries. European Electric Vehicle Congress, Brussels, Belgium, 2014.
Safari, M., Morcrette, M., Teyssot, A., Delacourt, C., Multimodal physics-based aging model for life prediction of Li-ion batteries. J. Electrochem. Soc. 156:3 (2009), A145–A153, 10.1149/1.3043429.
Christensen, J., Modeling diffusion-induced stress in Li-ion cells with porous electrodes. J. Electrochem. Soc. 157:3 (2010), A366–A380, 10.1149/1.3269995.
Barai, P., Mukherjee, P.P., Mechano-electrochemical model for acoustic emission characterization in intercalation electrodes. J. Electrochem. Soc. 161:11 (2014), F3123–F3136, 10.1149/2.0201411jes.
Dai, Y., Cai, L., White, R.E., Capacity fade model for spinel LiMn2O4 electrode. J. Electrochem. Soc. 160:1 (2013), A182–A190, 10.1149/2.026302jes.
Christensen, J., Newman, J., Effect of anode film resistance on the charge/discharge capacity of a lithium-ion battery. J. Electrochem. Soc. 150:11 (2003), A1416–A1420, 10.1149/1.1612501.
Ramadass, P., Haran, B., White, R., Popov, B., Mathematical modeling of the capacity fade of Li-ion cells. J. Power Sources 123:2 (2003), 230–240, 10.1016/S0378-7753(03)00531-7.
Ramadesigan, V., Chen, K., Burns, N., Boovaragavan, V., Braatz, R., Subramanian, V., Parameter estimation and capacity fade analysis of lithium-ion batteries using reformulated models. J. Electrochem. Soc. 158:9 (2011), A1048–A1054, 10.1149/1.3609926.
Uddin, K., Perera, S., Widanage, W., Somerville, L., Marco, J., Characterising lithium-ion battery degradation through the identification and tracking of electrochemical battery model parameters. Batteries 2:13 (2016), 1–17, 10.3390/batteries2020013.
Zhang, L., Wang, L., Lyu, C., Li, J., Zheng, J., Non-destructive analysis of degradation mechanisms in cycle-aged graphite/LiCoO2 batteries. Energies 7 (2014), 6282–6305, 10.3390/en7106282.
He, Y.-J., Shen, J.-N., Shen, J.-F., Ma, Z.-F., State of health estimation of lithium-ion batteries: a multiscale Gaussian process regression modeling approach. AIChE J. 61:5 (2015), 1589–1600, 10.1002/aic.14760.
Hu, X., Li, S., Peng, H., A comparative study of equivalent circuit models for Li-ion batteries. J. Power Sources 198 (2012), 359–367, 10.1016/j.jpowsour.2011.10.013.
Doyle, M., Fuller, T., Newman, J., Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J. Electrochem. Soc. 140:6 (1993), 1526–1533, 10.1149/1.2221597.
Fuller, T., Doyle, M., Newman, J., Simulation and optimization of the dual lithium ion insertion cell. J. Electrochem. Soc. 141:1 (1994), 1–10, 10.1149/1.2054684.
Newman, J., Thomas-Alyea, K., Electrochemical Systems. 3rd edition, 2004, John Wiley & Sons, Inc., Hoboken, New Jersey.
Farkhondeh, M., Delacourt, C., Mathematical modeling of commercial LiFePO4 electrodes based on variable solid-state diffusivity. J. Electrochem. Soc. 159:2 (2012), A177–A192, 10.1149/2.073202jes.
Farkhondeh, M., Safari, M., Pritzker, M., Fowler, M., Han, T., Wang, J., Delacourt, C., Full-range simulation of a commercial LiFePO4 electrode accounting for bulk and surface effects: a comparative analysis. J. Electrochem. Soc. 161:3 (2014), A201–A212, 10.1149/2.094401jes.
Padhi, A., Nanjundaswamy, K., Goodenough, J., Phospho-olivines as positive-electrode materials for rechargeable lithium batteries. J. Electrochem. Soc. 144:4 (1997), 1188–1194, 10.1149/1.1837571.
Srinivasan, V., Newman, J., Discharge model for the lithium iron-phosphate electrode. J. Electrochem. Soc. 151:10 (2004), A1517–A1529, 10.1149/1.1785012.
Srinivasan, V., Newman, J., Existence of path-dependence in the LiFePO4 electrode. Electrochem. Solid-State Lett. 9:3 (2006), A110–A114, 10.1149/1.2159299.
Thomas-Alyea, K., Modeling resistive-reactant and phase-change materials in battery electrodes. ECS Trans. 16:13 (2008), 155–165, 10.1149/1.2987767.
Wang, C., Kasavajjula, U., Arce, P., A discharge model for phase transformation electrodes: formulation, experimental validation, and analysis. J. Phys. Chem. C 111:44 (2007), 16656–16663, 10.1021/jp074490u.
Kasavajjula, U., Wang, C., Arce, P., Discharge model for LiFePO4 accounting for the solid solution range. J. Electrochem. Soc. 155:11 (2008), A866–A874, 10.1149/1.2980420.
Dargaville, S., Farrell, T., Predicting active material utilization in LiFePO4 electrodes using a multiscale mathematical model. J. Electrochem. Soc. 157:7 (2010), A830–A840, 10.1149/1.3425620.
Dreyer, W., Jamnik, J., Guhlke, C., Huth, R., Moskon, J., Gaberscek, M., The thermodynamic origin of hysteresis in insertion batteries. Nat. Mater. 9:5 (2010), 448–453, 10.1038/nmat2730.
Farkhondeh, M., Pritzker, M., Fowler, M., Safari, M., Delacourt, C., Mesoscopic modeling of Li insertion in phase-separating electrode materials: application to lithium iron phosphate. Phys. Chem. Chem. Phys. 16:41 (2014), 22555–22565, 10.1039/C4CP03530E.
Bai, P., Cogswell, D., Bazant, M., Suppression of phase separation in LiFePO4 nanoparticles during battery discharge. Nano Lett. 11:11 (2011), 4890–4896, 10.1021/nl202764f.
Cogswell, D., Bazant, M., Coherency strain and the kinetics of phase separation in LiFePO4 nanoparticles. ACS Nano 6:3 (2012), 2215–2225, 10.1021/nn204177u.
Safari, M., Delacourt, C., Mathematical modeling of lithium iron phosphate electrode: galvanostatic charge/discharge and path dependence. J. Electrochem. Soc. 158:2 (2011), A63–A73, 10.1149/1.3515902.
Mastali Majdabadi, M., Farhad, S., Farkhondeh, M., Fraser, R., Fowler, M., Simplified electrochemical multi-particle model for LiFePO4 cathodes in lithium-ion batteries. J. Power Sources 275 (2015), 633–643, 10.1016/j.jpowsour.2014.11.066.
Santhanagopalan, S., Guo, Q., Ramadass, P., White, R., Review of models for predicting the cycling performance of lithium ion batteries. J. Power Sources 156:2 (2006), 620–628, 10.1016/j.jpowsour.2005.05.070.
Di Domenico, D., Stefanopoulou, A., Fiengo, G., Lithium-ion battery state of charge and critical surface charge estimation using an electrochemical model-based extended Kalman filter. J. Dyn. Syst. Meas. Control, 132(6), 2010, 10.1115/1.4002475, 061302.1-061302.11.
Smith, K., Rahn, C., Wang, C., Control oriented 1D electrochemical model of lithium ion battery. Energy Convers. Manag. 48:9 (2007), 2565–2578, 10.1016/j.enconman.2007.03.015.
Klein, R., Chaturvedi, N., Christensen, J., Ahmed, J., Findeisen, R., Kojic, A., Electrochemical model based observer design for a lithium-ion battery. IEEE Trans. Control Syst. Technol. 21:2 (2013), 289–301, 10.1109/tcst.2011.2178604.
Subramanian, V., Boovaragavan, V., Ramadesigan, V., Arabandi, M., Mathematical model reformulation for lithium-ion battery simulations: galvanostatic boundary conditions. J. Electrochem. Soc. 156:4 (2009), A260–A271, 10.1149/1.3065083.
Fang, H., Wang, Y., Sahinoglu, Z., Wada, T., Hara, S., State of charge estimation for lithium-ion batteries: an adaptive approach. Control Eng. Pract. 25 (2014), 45–54, 10.1016/j.conengprac.2013.12.006.
Manwell, J., McGowan, J., Lead acid battery storage model for hybrid energy systems. Sol. Energy 50:5 (1993), 399–405, 10.1016/0038-092X(93)90060-2.
Milocco, R., Thomas, J., Castro, B., Generic dynamic model of rechargeable batteries. J. Power Sources 246 (2014), 609–620, 10.1016/j.jpowsour.2013.08.006.
Delacourt, C., Safari, M., Analysis of lithium deinsertion/insertion in LiyFePO4 with a simple mathematical model. Electrochim. Acta 56:14 (2011), 5222–5229, 10.1016/j.electacta.2011.03.030.
Barré A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., Riu, D., A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. J. Power Sources 241 (2013), 680–689, 10.1016/j.jpowsour.2013.05.040.
Waag, W., Fleischer, C., Sauer, D.U., Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J. Power Sources 258 (2014), 321–339, 10.1016/j.jpowsour.2014.02.064.
Santhanagopalan, S., Zhang, Q., Kumaresan, K., White, R., Parameter estimation and life modeling of lithium-ion cells. J. Electrochem. Soc. 155:4 (2008), A345–A353, 10.1149/1.2839630.
Boovaragavan, V., Harinipriya, S., Subramanian, V., Towards real-time (milliseconds) parameter estimation of lithium-ion batteries using reformulated physics-based models. J. Power Sources 183:1 (2008), 361–365, 10.1016/j.jpowsour.2008.04.077.
Prasad, G., Rahn, C., Model based identification of aging parameters in lithium ion batteries. J. Power Sources 232 (2013), 79–85, 10.1016/j.jpowsour.2013.01.041.
Schmidt, A., Bitzer, M., Imre, A., Guzzella, L., Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries. J. Power Sources 195:22 (2010), 7634–7638, 10.1016/j.jpowsour.2010.06.011.
Moura, S., Chaturvedi, N., Krstić M., Adaptive partial differential equation observer for battery state-of-charge/state-of-health estimation via an electrochemical model. J. Dyn. Syst. Meas. Control, 136(1), 2014, 10.1115/1.4024801, 011015-1-011015-11.
Zhang, D., Dey, S., Perez, H., Moura, S., Remaining useful life estimation of lithium-ion batteries based on thermal dynamics. American Control Conference (ACC), Seattle, WA, USA, 2017, 4042–4047, 10.23919/ACC.2017.7963575.
Dey, S., Ayalew, B., Pisu, P., Combined estimation of state-of-charge and state-of-health of Li-ion battery cells using SMO on electrochemical model. 2014 13th International Workshop on Variable Structure Systems (VSS), 2014, 1–6, 10.1109/VSS.2014.6881140.
Dey, S., Ayalew, B., A diagnostic scheme for detection, isolation and estimation of electrochemical faults in lithium-ion cells. ASME 2015 Dynamic Systems and Control Conference, Columbus, OH, USA, 2015, 10.1115/DSCC2015-9699 pp. V001T13A001–10.
Dey, S., Ayalew, B., Pisu, P., Nonlinear adaptive observer for a lithium-ion battery cell based on coupled electrochemical-thermal model. J. Dyn. Syst. Meas. Control, 137(11), 2015, 10.1115/1.4030972 110005–111005-12.
Zheng, L., Zhang, L., Zhu, J., Wang, G., Jiang, J., Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model. Appl. Energy 180 (2016), 424–434, 10.1016/j.apenergy.2016.08.016.
Rahimian, S., Rayman, R., White, State of charge and loss of active material estimation of a lithium ion cell under low earth orbit condition using Kalman filtering approaches. J. Electrochem. Soc. 159:6 (2012), A860–A872, 10.1149/2.098206jes.
Fang, H., Zhao, X., Wang, Y., Sahinoglu, Z., Wada, T., Hara, S., de Callafon, R., Improved adaptive state-of-charge estimation for batteries using a multi-model approach. J. Power Sources 254 (2014), 258–267, 10.1016/j.jpowsour.2013.12.005.
Zou, C., Kallapur, A.G., Manzie, C., Nešić D., PDE battery model simplification for SOC and SOH estimator design. 2015 54th IEEE Conference on Decision and Control (CDC), 2015, 1328–1333, 10.1109/CDC.2015.7402395.
Bartlett, A., Marcicki, J., Onori, S., Rizzoni, G., Yang, X.G., Miller, T., Electrochemical model-based state of charge and capacity estimation for a composite electrode lithium-ion battery. IEEE Trans. Control Syst. Technol. 24:2 (2016), 384–399, 10.1109/TCST.2015.2446947.
Samadi, M.F., Alavi, S.M.M., Saif, M., Online state and parameter estimation of the Li-ion battery in a bayesian framework. 2013 American Control Conference (ACC), 2013, 4693–4698, 10.1109/ACC.2013.6580563.
Wang, Y., Fang, H., Sahinoglu, Z., Wada, T., Hara, S., Adaptive estimation of the state of charge for lithium-ion batteries: nonlinear geometric observer approach. IEEE Trans. Control Syst. Technol. 23:3 (2015), 948–962, 10.1109/TCST.2014.2356503.
Young, P., Jakeman, A., Refined instrumental variable methods of recursive time-series analysis. Part III. Extensions. Int. J. Control 31:4 (1980), 741–764, 10.1080/00207178008961080.
Young, P., Recursive Estimation and Time-Series Analysis: An Introduction for the Student and Practitioner. 2nd edition, 2011, Springer-Verlag, Berlin.
Schorsch, J., Garnier, H., Gilson, M., Young, P., Instrumental variable methods for identifying partial differential equation models. Int. J. Control 86:12 (2013), 2325–2335, 10.1080/00207179.2013.813690.
Marcicki, J., Canova, M., Conlisk, A., Rizzoni, G., Design and parametrization analysis of a reduced-order electrochemical model of graphite/LiFePO4 cells for SOC/SOH estimation. J. Power Sources 237 (2013), 310–324, 10.1016/j.jpowsour.2012.12.120.
Thomas-Alyea, K., Newman, J., Darling, R., Mathematical Modeling of Lithium Batteries, Book Section 12. 1st edition, 2002, Springer US, New York, 345–392.
Safari, M., Delacourt, C., Modeling of a commercial graphite/LiFePO4 cell. J. Electrochem. Soc. 158:5 (2011), A562–A571, 10.1149/1.3567007.
Jacobsen, T., West, K., Diffusion impedance in planar, cylindrical and spherical symmetry. Electrochim. Acta 40:2 (1995), 255–262, 10.1016/0013-4686(94)E0192-3.
Forman, J., Bashash, S., Stein, J., Fathy, H., Reduction of an electrochemistry-based Li-ion battery model via quasi-linearization and Padé approximation. J. Electrochem. Soc. 158:2 (2011), A93–A101, 10.1149/1.3519059.
Couto, L., Schorsch, J., Nicotra, M., Kinnaert, M., SOC and SOH estimation for Li-ion batteries based on an equivalent hydraulic model. Part I: SOC and surface concentration estimation. American Control Conference (ACC), Boston, MA, USA, 2016, 4022–4028, 10.1109/ACC.2016.7525553.
Schorsch, J., Couto, L., Kinnaert, M., SOC and SOH estimation for Li-ion battery based on an equivalent hydraulic model. Part II: SOH power fade. American Control Conference (ACC), Boston, MA, USA, 2016, 4029–4034, 10.1109/ACC.2016.7525554.
Simon, D., Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theory Appl. 4:8 (2010), 1303–1318, 10.1049/iet-cta.2009.0032.
Meng, J., Luo, G., Ricco, M., Swierczynski, M., Stroe, D.-I., Teodorescu, R., Overview of lithium-ion battery modeling methods for state-of-charge estimation in electrical vehicles. Appl. Sci. 8:5 (2018), 659–676, 10.3390/app8050659.
Garnier, H., Direct continuous-time approaches to system identification. overview and benefits for practical applications. Eur. J. Control 24 (2015), 50–62, 10.1016/j.ejcon.2015.04.003.
Chaturvedi, N., Klein, R., Christensen, J., Ahmed, J., Kojic, A., Algorithms for advanced battery-management systems. IEEE Control Syst. Mag. 30:3 (2010), 49–68, 10.1109/mcs.2010.936293.
Plett, G.L., Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation. J. Power Sources 134:2 (2004), 277–292, 10.1016/j.jpowsour.2004.02.033.
Pavković D., Krznar, M., Komljenović A., Hrgetić M., Zorc, D., Dual EKF-based state and parameter estimator for a LiFePO4 battery cell. J. Power Electron. 17:2 (2017), 398–410, 10.6113/JPE.2017.17.2.398.
Lin, X., Perez, H.E., Mohan, S., Siegel, J.B., Stefanopoulou, A.G., Ding, Y., Castanier, M.P., A lumped-parameter electro-thermal model for cylindrical batteries. J. Power Sources 257 (2014), 1–11, 10.1016/j.jpowsour.2014.01.097.
Ecker, M., Nieto, N., Kbitz, S., Schmalstieg, J., Blanke, H., Warnecke, A., Sauer, D.U., Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries. J. Power Sources 248 (2014), 839–851, 10.1016/j.jpowsour.2013.09.143.