Sequential Monte Carlo methods for Discharge Time Prognosis in Lithium-Ion Batteries
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Published
Oct 18, 2020
Marcos E. Orchard
Matías A. Cerda
Benjamín E. Olivares
Jorge F. Silva
Abstract
This paper presents the implementation of a particlefiltering- based prognostic framework that allows estimating the state-of-charge (SOC) and predicting the discharge time of energy storage devices (more specifically lithium-ion batteries). The proposed approach uses an empirical statespace model inspired in the battery phenomenology and particle-filtering to study the evolution of the SOC in time; adapting the value of unknown model parameters during the filtering stage and enabling fast convergence for the state estimates that define the initial condition for the prognosis stage. SOC prognosis is implemented using a particlefiltering- based framework that considers a statistical characterization of uncertainty for future discharge profiles.
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Keywords
particle filtering, state of charge estimation, Energy storage devices, state of charge prognosis
References
Saha, B. and Goebel, K., (2009). “Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework,” Annual Conference of the Prognostics and Health Management Society, San Diego, CA.
Ranjbar, A.H., Banaei, A., Khoobroo, A., Fahimi, B., (2012). “Online Estimation of State of Charge in Li-Ion Batteries Using Impulse Response Concept,” Smart Grid, IEEE Transactions on , Vol. 3, No.1, pp.360-367.
Pattipati, B., Sankavaram, C., and Pattipati, K., (2011). “System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, Vol.41, No.6, pp.869-884.
Orchard, M., and Vachtsevanos, G., (2009). “A Particle Filtering Approach for On-Line Fault Diagnosis and Failure Prognosis,” Transactions of the Institute of Measurement and Control, vol. 31, no. 3-4, pp. 221-246.
Salkind, A.J., Fennie, C., Singh, P., Atwater, T., Reisner, D.E., (1999). “Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology,” Journal of Power Sources, Vol. 80, Issue 1-2, pp. 293-300.
Charkhgard, M., and Farrokhi, M., (2010). “State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF,” Industrial Electronics, IEEE Transactions on, Vol.57, No.12, pp.4178-4187.
Vinh Do, D., Forgez, C., El Kadri Benkara, K., Friedrich, G., (2009). “Impedance Observer for a Li-Ion Battery Using Kalman Filter,” Vehicular Technology, IEEE Transactions on, Vol.58, No.8, pp. 3930-3937.
Ran, L., Junfeng, W., Haiying, W., Gechen, L., (2010). “Prediction of state of charge of Lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory,” Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on, Vol., No., pp.684-688.
Cadar, D.V., Petreus, D.M., Orian, C.A., (2009). “A method of determining a lithium-ion battery’s state of charge,” 15th International Symposium for Design and Technology of Electronics Packages (SIITME) 2009, pp.257-260.
Qingsheng, S., Chenghui, Z., Naxin, C., Xiaoping, Z., (2010). “Battery State-Of-Charge estimation in Electric Vehicle using Elman neural network method,” 29th Chinese Control Conference (CCC) 2010, pp. 5999-6003.
Di Z., Yan, M., Qing-Wen, B., (2011). “Estimation of Lithium-ion battery state of charge,” 30th Chinese Control Conference (CCC) 2011, pp. 6256-6260.
Tang, X., Mao, X., Lin, J., Koch, B., (2011). “Li-ion battery parameter estimation for state of charge,” American Control Conference (ACC) 2011, Vol., No., pp. 941-946.
Saha, B., Goebel, K., Poll, S., Christophersen, J., (2009). “Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework,” IEEE Transactions on Instrumentation and Measurement, Vol.58, No.2, pp.291-296.
Dalal, M., Ma, J., and He, D., (2011). “Lithium-ion battery life prognostic health management system using particle filtering framework,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 225: 81-90.
Lee S., Kim J., Lee J., and Cho B.H., (2011). “Discrimination of Li-ion batteries based on Hamming network using discharging–charging voltage pattern recognition for improved state-of-charge estimation,” Journal of Power Sources, v196, n4, pp. 2227-2240.
Santhanagopalan S., and White R.E., (2010). “State of charge estimation using an unscented filter for high power lithium ion cells,” International Journal of Energy Research, v34, n2, pp. 152-163.
Hu C., Youn B.D., and Chung J., (2012). “A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation,” Applied Energy, v92, pp. 694–704.
He Y., Liu X.T., Zhang C.B., Chen Z.H., (2013). “A new model for State-of-Charge (SOC) estimation for highpower Li-ion batteries”, Applied Energy, v101, pp. 808-814.
Orchard, M., Tang, L., Saha, B., Goebel, K., and Vachtsevanos, G., (2010) “Risk-Sensitive Particle- Filtering-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices,” Studies in Informatics and Control, Vol. 19, Issue 3, pp. 209-218.
Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T., (2002). “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking,”’ IEEE Transactions on Signal Processing, Vol. 50.
Andrieu C., Doucet A., Punskaya E., (2001). “Sequential Monte Carlo Methods for Optimal Filtering,” in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. NY: Springer-Verlag.
Doucet A., de Freitas N., Gordon N., (2001). “An introduction to Sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. NY: Springer-Verlag.
Engel, S.J., Gilmartin, B.J., Bongort, K., Hess, A., (2000). “Prognostics, the real issues involved with predicting life remaining,” Aerospace Conference Proceedings, 2000 IEEE, Vol.6, pp.457-469.
Orchard, M., Tobar, F., Vachtsevanos, G., (2009) “Outer Feedback Correction Loops in Particle Filtering-based Prognostic Algorithms: Statistical Performance Comparison,” Studies in Informatics and Control, vol. 18, Issue 4, pp. 295-304.
Edwards, D., Orchard, M., Tang, L., Goebel, K., Vachtsevanos, G., (2010) “Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems,” Annual Conference of the Prognostics and Health Management Society 2010, Portland, OR, USA.
Chen, C. Vachtsevanos, G., Orchard, M., (2011) “Machine Condition Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering," IEEE Transactions on Industrial Electronics, vol. 58, no. 9, pp. 4353-4364.
Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M., and Vachtsevanos, G., (2011). “A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection,” IEEE Transactions on Industrial Electronics, Vol. 58, No. 5, pp. 2011-2018.
Saxena, A., Celaya, J., Saha, B., Saha, S., Goebel, K., (2010). “Evaluating prognostics performance for algorithms incorporating uncertainty estimates,” Aerospace Conference, 2010 IEEE , pp.1-11.
Tang, L., Orchard, M.E., Goebel, K., Vachtsevanos, G., (2011). “Novel metrics and methodologies for the verification and validation of prognostic algorithms,” Aerospace Conference, 2011 IEEE, pp.1-8.
Gonzalez G.D., Orchard, M., Cerda J.L., Casali A. and Vallebuona, G., (2003). “Local models for soft-sensors in a rougher flotation bank," Minerals Engineering, vol. 16, no.5, pp. 441-453.
Hunter, J. S., (1986). "The exponentially weighted moving average," J. Qual. Technol., Vol. 18, pp. 203-209.
Ranjbar, A.H., Banaei, A., Khoobroo, A., Fahimi, B., (2012). “Online Estimation of State of Charge in Li-Ion Batteries Using Impulse Response Concept,” Smart Grid, IEEE Transactions on , Vol. 3, No.1, pp.360-367.
Pattipati, B., Sankavaram, C., and Pattipati, K., (2011). “System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, Vol.41, No.6, pp.869-884.
Orchard, M., and Vachtsevanos, G., (2009). “A Particle Filtering Approach for On-Line Fault Diagnosis and Failure Prognosis,” Transactions of the Institute of Measurement and Control, vol. 31, no. 3-4, pp. 221-246.
Salkind, A.J., Fennie, C., Singh, P., Atwater, T., Reisner, D.E., (1999). “Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology,” Journal of Power Sources, Vol. 80, Issue 1-2, pp. 293-300.
Charkhgard, M., and Farrokhi, M., (2010). “State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF,” Industrial Electronics, IEEE Transactions on, Vol.57, No.12, pp.4178-4187.
Vinh Do, D., Forgez, C., El Kadri Benkara, K., Friedrich, G., (2009). “Impedance Observer for a Li-Ion Battery Using Kalman Filter,” Vehicular Technology, IEEE Transactions on, Vol.58, No.8, pp. 3930-3937.
Ran, L., Junfeng, W., Haiying, W., Gechen, L., (2010). “Prediction of state of charge of Lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory,” Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on, Vol., No., pp.684-688.
Cadar, D.V., Petreus, D.M., Orian, C.A., (2009). “A method of determining a lithium-ion battery’s state of charge,” 15th International Symposium for Design and Technology of Electronics Packages (SIITME) 2009, pp.257-260.
Qingsheng, S., Chenghui, Z., Naxin, C., Xiaoping, Z., (2010). “Battery State-Of-Charge estimation in Electric Vehicle using Elman neural network method,” 29th Chinese Control Conference (CCC) 2010, pp. 5999-6003.
Di Z., Yan, M., Qing-Wen, B., (2011). “Estimation of Lithium-ion battery state of charge,” 30th Chinese Control Conference (CCC) 2011, pp. 6256-6260.
Tang, X., Mao, X., Lin, J., Koch, B., (2011). “Li-ion battery parameter estimation for state of charge,” American Control Conference (ACC) 2011, Vol., No., pp. 941-946.
Saha, B., Goebel, K., Poll, S., Christophersen, J., (2009). “Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework,” IEEE Transactions on Instrumentation and Measurement, Vol.58, No.2, pp.291-296.
Dalal, M., Ma, J., and He, D., (2011). “Lithium-ion battery life prognostic health management system using particle filtering framework,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 225: 81-90.
Lee S., Kim J., Lee J., and Cho B.H., (2011). “Discrimination of Li-ion batteries based on Hamming network using discharging–charging voltage pattern recognition for improved state-of-charge estimation,” Journal of Power Sources, v196, n4, pp. 2227-2240.
Santhanagopalan S., and White R.E., (2010). “State of charge estimation using an unscented filter for high power lithium ion cells,” International Journal of Energy Research, v34, n2, pp. 152-163.
Hu C., Youn B.D., and Chung J., (2012). “A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation,” Applied Energy, v92, pp. 694–704.
He Y., Liu X.T., Zhang C.B., Chen Z.H., (2013). “A new model for State-of-Charge (SOC) estimation for highpower Li-ion batteries”, Applied Energy, v101, pp. 808-814.
Orchard, M., Tang, L., Saha, B., Goebel, K., and Vachtsevanos, G., (2010) “Risk-Sensitive Particle- Filtering-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices,” Studies in Informatics and Control, Vol. 19, Issue 3, pp. 209-218.
Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T., (2002). “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking,”’ IEEE Transactions on Signal Processing, Vol. 50.
Andrieu C., Doucet A., Punskaya E., (2001). “Sequential Monte Carlo Methods for Optimal Filtering,” in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. NY: Springer-Verlag.
Doucet A., de Freitas N., Gordon N., (2001). “An introduction to Sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. NY: Springer-Verlag.
Engel, S.J., Gilmartin, B.J., Bongort, K., Hess, A., (2000). “Prognostics, the real issues involved with predicting life remaining,” Aerospace Conference Proceedings, 2000 IEEE, Vol.6, pp.457-469.
Orchard, M., Tobar, F., Vachtsevanos, G., (2009) “Outer Feedback Correction Loops in Particle Filtering-based Prognostic Algorithms: Statistical Performance Comparison,” Studies in Informatics and Control, vol. 18, Issue 4, pp. 295-304.
Edwards, D., Orchard, M., Tang, L., Goebel, K., Vachtsevanos, G., (2010) “Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems,” Annual Conference of the Prognostics and Health Management Society 2010, Portland, OR, USA.
Chen, C. Vachtsevanos, G., Orchard, M., (2011) “Machine Condition Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering," IEEE Transactions on Industrial Electronics, vol. 58, no. 9, pp. 4353-4364.
Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M., and Vachtsevanos, G., (2011). “A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection,” IEEE Transactions on Industrial Electronics, Vol. 58, No. 5, pp. 2011-2018.
Saxena, A., Celaya, J., Saha, B., Saha, S., Goebel, K., (2010). “Evaluating prognostics performance for algorithms incorporating uncertainty estimates,” Aerospace Conference, 2010 IEEE , pp.1-11.
Tang, L., Orchard, M.E., Goebel, K., Vachtsevanos, G., (2011). “Novel metrics and methodologies for the verification and validation of prognostic algorithms,” Aerospace Conference, 2011 IEEE, pp.1-8.
Gonzalez G.D., Orchard, M., Cerda J.L., Casali A. and Vallebuona, G., (2003). “Local models for soft-sensors in a rougher flotation bank," Minerals Engineering, vol. 16, no.5, pp. 441-453.
Hunter, J. S., (1986). "The exponentially weighted moving average," J. Qual. Technol., Vol. 18, pp. 203-209.
Section
Technical Papers