RESEARCH PAPER
Early prediction of remaining discharge time for lithium-ion batteries considering parameter correlation between discharge stages
1
School of Automation Science and Electrical Engineering Beihang University No.37 Xueyuan Road, Haidian District, Beijing 100191, China Collaborative Innovation Center of Advanced Aero-Engine, Beijing 100191, China
2
School of Automation Science and Electrical Engineering Beihang University No.37 Xueyuan Road, Haidian District, Beijing 100191, China
3
China Academy of Launch Vehicle Technology R&D center NO.1 Nan Da Hong Men Road, Feng Tai District, Beijing 100191, China
Publication date: 2019-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(1):81-89
KEYWORDS
Lithium-ion batteryparameter correlationparticle swarm optimizationparticle filterremaining discharge time prognostics
ABSTRACT
In this paper, we propose a method for making early predictions of remaining discharge time (RDT) that considers information
about future battery discharge process. Instead of analyzing the entire degradation process of a battery, as in the existing literature, we obtain the information about future battery condition by decomposing the discharge model into three stages, according
to level of voltage loss. Correlation between model parameters at the first and last stages of discharge process allows the values
of model parameters in the future to be used to predict the value of parameters at early stages of discharge. The particle swarm
optimization (PSO) and particle filter (PF) algorithms are employed to update parameters when new voltage data is available.
A case study demonstrates that the proposed approach predicts RDT more accurately than the benchmark PF-based prediction
method, regardless of the degradation period of the battery.
REFERENCES (34)
1.
Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 2002; 50(2):174-188, https://doi.org/10.1109/78.978....
2.
Bole B, Kulkarni C S, Daigle M. Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use, Annual Conference of the Prognostics and Health Management Society, Fort Worth 2014.
3.
Daigle M, Kulkarni C S. Electrochemistry-based battery modeling for prognostics, Annual Conference of the Prognostics and Health Management Society, New Orleans 2013.
4.
Dalal M, Ma J, He D. Lithium-ion battery life prognostic health management system using particle filtering framework. Journal of Risk & Reliability 2015; 225 (1): 81-90.
5.
Dong G, Wei J, Chen Z, Sun H, Yu X. Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter. Journal of Power Sources 2017; 364: 316-327, https://doi.org/10.1016/j.jpow....
6.
He Y, Liu X T, Zhang C B, Chen Z H. A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries. Applied Energy 2013; 101(1): 808-814, https://doi.org/10.1016/j.apen....
7.
Hu C, Jain G, Tamirisa P, Gorka T. Method for estimating capacity and predicting remaining useful life of lithium-ion battery. Applied Energy 2014; 126:182-189, https://doi.org/10.1016/j.apen....
8.
Hu C, Jain G, Zhang P, Schmidt C, Gomadam P, Gorka T. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery. Applied Energy 2014; 129:49-55, https://doi.org/10.1016/j.apen....
9.
Kasprzyk L. Modelling and analysis of dynamic states of the lead-acid batteries in electric vehicles. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2017; 19(2): 229-236, https://doi.org/10.17531/ein.2....
10.
Kennedy J, Eberhart R. Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, Perth 1995; 4: 1942-1948, https://doi.org/10.1109/ICNN.1....
11.
Li K, Wei F, Tseng K J, Soong B H. A practical lithium-ion battery model for state of energy and voltage responses prediction incorporating temperature and ageing effects. IEEE Transactions on Industrial Electronics 2017;99:1-1.
12.
Li Z, Huang J, Liaw B Y, Zhang J. On state-of-charge determination for lithium-ion batteries. Journal of Power Sources 2017; 348: 281-301, https://doi.org/10.1016/j.jpow....
13.
Liu J, Wang W, Ma F, Yang Y B, Yang C S. A data-model-fusion prognostic framework for dynamic system state forecasting. Engineering Applications of Artificial Intelligence 2012; 25(4): 814-823, https://doi.org/10.1016/j.enga....
14.
Lu L, Han X, Li J, Hua J, Ouyang M. A review on the key issues for lithium-ion battery management in electric vehicles. Journal of Power Sources 2013; 226 (3): 272-288, https://doi.org/10.1016/j.jpow....
15.
Miao Q, Xie L, Cui H, Liang W, Pecht M. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectronics Reliability 2013; 53 (6): 805-810, https://doi.org/10.1016/j.micr....
16.
Orchard M E, Cerda M, Olivares B, Silva J F. Sequential Monte Carlo methods for discharge time prognosis in lithium-ion batteries. International Journal of Prognostics & Health Management 2012; 3(2):1-12.
17.
Pattipati B, Sankavaram C, Pattipati K. System identification and estimation framework for pivotal automotive battery management system characteristics. IEEE Transactions on Systems Man & Cybernetics Part C 2011; 41(6): 869-884, https://doi.org/10.1109/ TSMCC.2010.2089979.
18.
Pola D A, Navarrete H F, Orchard M E, Rabié R S, Cerda M A, Olivares B E, Silva J F, Espinoza P A, Pérez A. Particle-filtering-based discharge time prognosis for lithium-ion batteries with a statistical characterization of use profiles. IEEE Transactions on Reliability 2015; 64(2): 710-720, https://doi.org/10.1109/TR.201....
19.
Rahman M A, Anwar S, Izadian A. Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. Journal of Power Sources 2016; 307: 86-97, https://doi.org/10.1016/j.jpow....
20.
Saha B, Goebel K. Modeling Li-ion battery capacity depletion in a particle filtering framework, Annual Conference of the Prognostics and Health Management Society, San Diego 2009.
21.
Saha B, Koshimoto E, Hogge E F, Strom T H, Hill B L, Goebel K. Predicting Battery Life for Electric UAVs, 2011 Infotech@Aerospace Conference, St. Louis 2011.
22.
Saha B, Koshimoto E, Quach C C, Hogge E F, Strom T H, Hill B L, Vazquez S L, Goebel K. Battery health management system for electric UAVs, 2011 IEEE Aerospace Conference, Big Sky 2011; 1-9, https://doi.org/10.1109/AERO.2....
23.
Sbarufatti C, Corbetta M, Giglio M, Cadini F. Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks. Journal of Power Sources 2017; 344: 128-140, https://doi.org/10.1016/j.jpow....
24.
Schwaab M, Jr E C B, Monteiro J L, Pinto J C. Nonlinear parameter estimation through particle swarm optimization. Chemical Engineering Science 2008; 63(6): 1542-1552, https://doi.org/10.1016/j.ces.....
25.
Virkar A V. A model for degradation of electrochemical devices based on linear non-equilibrium thermodynamics and its application to lithium ion batteries. Journal of Power Sources 2011; 196(14): 5970-5984, https://doi.org/10.1016/j.jpow....
26.
Walker E, Rayman S, White R E. Comparison of a particle filter and other state estimation methods for prognostics of lithium-ion batteries. Journal of Power Sources 2015; 287(4): 1-12,https://doi.org/10.1016/j.jpow....
27.
Wang D, Miao Q, Pecht M, Pecht Michael. Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model. Journal of Power Sources 2013; 239(10): 253-264, https://doi.org/10.1016/j.jpow....
28.
Wang Y, Chen Z, Zhang C. On-line remaining energy prediction: A case study in embedded battery management system. Applied Energy 2016; 194: 688-695, https://doi.org/10.1016/j.apen....
29.
Xu X, Chen N. A state-space-based prognostics model for lithium-ion battery degradation. Reliability Engineering & System Safety 2017; 159: 47-57, https://doi.org/10.1016/j.ress....
30.
Xu X, Li Z, Chen N. A hierarchical model for lithium-ion battery degradation prediction. IEEE Transactions on Reliability 2016; 65(1): 310- 325, https://doi.org/10.1109/TR.201....
31.
Yu J, Liang S, Tang D, Liu H. Remaining discharge time prognostics of lithium-ion batteries using Dirichlet process mixture model and particle filtering method. IEEE Transactions on Instrumentation & Measurement 2017; PP(99):1-12.
32.
Zhang X, Wang Y, Liu C, Chen Z H. A novel approach of remaining discharge energy prediction for large format lithium-ion battery pack. Journal of Power Sources 2017; 343: 216-225, https://doi.org/10.1016/j.jpow....
33.
Zheng X, Fang H. An integrated unscented Kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction. Reliability Engineering & System Safety 2015; 144: 74-82, https://doi.org/10.1016/j.ress....
34.
Zio E, Peloni G. Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering & System Safety 2011; 96(3): 403-409, https://doi.org/10.1016/j.ress....
CITATIONS (3):
1.
Remaining useful life prediction model of the space station
Xiaopeng Li, Hong-Zhong Huang, Fuqiu Li, Liming Ren
Ekspolatacja i Niezawodnosc - Maintenance and Reliability
Xiaopeng Li, Hong-Zhong Huang, Fuqiu Li, Liming Ren
Ekspolatacja i Niezawodnosc - Maintenance and Reliability
2.
A closed-loop voltage prognosis for lithium-ion batteries under dynamic loads using an improved equivalent circuit model
Jie Yang, Jinsong Yu, Diyin Tang, Jing Dai
Microelectronics Reliability
Jie Yang, Jinsong Yu, Diyin Tang, Jing Dai
Microelectronics Reliability
3.
Prediction of remaining useful life for lithium-ion battery with multiple health indicators
Chun Su, Hongjing Chen, Zejun Wen
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Chun Su, Hongjing Chen, Zejun Wen
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Share
RELATED ARTICLE
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
