An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries

A new look at the statistical model identification

Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · see citing works

Particle swarm optimization: An overview

Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101 · see citing works

Testing for unit roots in heterogeneous panels

Im, K. S., Pesaran, M. H., & Shin, Y. (2003) · Journal of Econometrics · doi:10.1016/s0304-4076(03)00092-7 · Cited by 15,435 · see citing works

A modified particle swarm optimizer

Shi, Y., & Eberhart, R. (1998) · 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) · doi:10.1109/icec.1998.699146 · Cited by 11,497 · see citing works

Particle Swarm Optimization

(2019) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789811205675_0004 · Cited by 6,316 · see citing works

Time Series Analysis: Forecasting and Control.

Anderson, O. D., Box, G. E. P., & Jenkins, G. M. (1978) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2988198 · Cited by 5,368 · see citing works

Time Series Analysis, Forecasting and Control.

Kendall, M. G., Box, G. E. P., & Jenkins, G. M. (1971) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344246 · Cited by 2,784 · see citing works

The particle swarm optimization algorithm: convergence analysis and parameter selection

Trelea, I.-C. (2003) · Information Processing Letters · doi:10.1016/s0020-0190(02)00447-7 · Cited by 2,656 · see citing works

Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method

He, W., Williard, N., Osterman, M., & Pecht, M. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.040 · Cited by 1,105 · see citing works

Prognostics and Health Management of Electronics

PECHT, M. G. (2008) · Wiley eBooks · doi:10.1002/9780470385845 · Cited by 848 · see citing works

Prognostics methods for battery health monitoring using a bayesian framework

Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831 · see citing works

A review on prognostics and health monitoring of Li-ion battery

Zhang, J., & Lee, J. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.03.101 · Cited by 792 · see citing works

Time Series and System Analysis with Applications.

Parzen, E., Pandit, S. M., & Wu, S.-M. (1985) · Journal of the American Statistical Association · doi:10.2307/2288103 · Cited by 682 · see citing works

Identify capacity fading mechanism in a commercial LiFePO4 cell

Dubarry, M., & Liaw, B. Y. (2009) · Journal of Power Sources · doi:10.1016/j.jpowsour.2009.05.036 · Cited by 675 · see citing works

Issue and challenges facing rechargeable thin film lithium batteries

Patil, A., Patil, V., Shin, D. W., Choi, J., Paik, D.-S., & Yoon, S.-J. (2007) · Materials Research Bulletin · doi:10.1016/j.materresbull.2007.08.031 · Cited by 601 · see citing works

Locating and tracking multiple dynamic optima by a particle swarm model using speciation

Parrott, D., & Li, X. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.859468 · Cited by 547 · see citing works

The Estimation of the Order of an ARMA Process

Hannan, E. J. (1980) · The Annals of Statistics · doi:10.1214/aos/1176345144 · Cited by 501 · see citing works

Intelligent prognostics for battery health monitoring based on sample entropy

Widodo, A., Shim, M.-C., Caesarendra, W., & Yang, B.-S. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.03.063 · Cited by 392 · see citing works

Particle Swarm Optimization

Yang, X. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821986-7.00015-9 · Cited by 367 · see citing works

Factors that affect cycle-life and possible degradation mechanisms of a Li-ion cell based on LiCoO2

Choi, S. S., & Lim, H. S. (2002) · Journal of Power Sources · doi:10.1016/s0378-7753(02)00305-1 · Cited by 333 · see citing works

Prognostics and Health Management of Electronics

PECHT, M. G. (2008) · Encyclopedia of Structural Health Monitoring · doi:10.1002/9780470061626.shm118 · Cited by 288 · see citing works

Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering

Chen, C., Zhang, B., Vachtsevanos, G., & Orchard, M. E. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2098369 · Cited by 250 · see citing works

ARMA model order estimation based on the eigenvalues of the covariance matrix

Liang, G., Wilkes, D. M., & CADZOW, J. A. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.277805 · Cited by 140 · see citing works

Prediction of Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms

Chen, C., Zhang, B., & Vachtsevanos, G. (2011) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2011.2169182 · Cited by 136 · see citing works

Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition

Wang, D., Miao, Q., & Kang, R. (2009) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2009.02.013 · Cited by 136 · see citing works

Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models

Miao, Q., & Makiš, V. (2006) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.01.009 · Cited by 130 · see citing works

Prognostics implementation of electronics under vibration loading

Gu, J., Barker, D., & PECHT, M. G. (2007) · Microelectronics Reliability · doi:10.1016/j.microrel.2007.02.015 · Cited by 115 · see citing works

Life prediction and reliability assessment of lithium secondary batteries

Eom, S.-W., Kim, M., Kim, I.-J., Moon, S., Sun, Y., & Kim, H. (2007) · Journal of Power Sources · doi:10.1016/j.jpowsour.2007.06.208 · Cited by 84 · see citing works

A Wireless Sensor System for Prognostics and Health Management

Cheng, S., Tom, K. F., Thomas, L., & PECHT, M. G. (2010) · IEEE Sensors Journal · doi:10.1109/jsen.2009.2035817 · Cited by 83 · see citing works

Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression

Liu, D., Pang, J., Zhou, J., & Peng, Y. (2012) · Prognostics and System Health Management Conference · doi:10.1109/phm.2012.6228848 · Cited by 71 · see citing works

Prognostics-based risk mitigation for telecom equipment under free air cooling conditions

Dai, J., Das, D., & PECHT, M. G. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.05.055 · Cited by 62 · see citing works

Diagnostics of Filtered Analog Circuits with Tolerance Based on LS-SVM Using Frequency Features

Long, B., Tian, S., & Wang, H. (2012) · Journal of Electronic Testing · doi:10.1007/s10836-011-5275-y · Cited by 60 · see citing works

Research on failure rate forecasting method based on ARMA model

Kang, R. (2008) · Systems engineering and electronics · Cited by 28 · see citing works

Remaining useful performance analysis of batteries

He, W., Williard, N., Osterman, M. D., & PECHT, M. G. (2011) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2011.6024341 · Cited by 27 · see citing works

Research on features for diagnostics of filtered analog circuits based on LS-SVM

Long, B., Tian, S., Miao, Q., et al. (2011) · Conference record (1995) - Autotestcon/Conference record - Autotestcon · doi:10.1109/autest.2011.6058746 · Cited by 27 · see citing works

Prognostics-based health management for free air cooling of data centers

Dai, J., Das, D., & PECHT, M. G. (2010) · Prognostics and System Health Management Conference · doi:10.1109/phm.2010.5413498 · Cited by 16 · see citing works

NEW TECHNIQUE FOR DETERMINING THE ORDER OF ARMA MODEL BASED ON GENETIC ALGORITHM

Guo, C. G. (2005) · Journal of Mechanical Engineering · doi:10.3901/jme.2005.01.041 · Cited by 4 · see citing works

Untitled work

Papke (1996) · doi:10.1002/(sici)1099-1255(199611)11:6

Battery Lifetime Prognostics

Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320 · see citing works

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193 · see citing works

A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: challenges and recommendations

Lipu, M. S. H., Hannan, M. A., Hussain, A., Hoque, M. M., Ker, P. J., Saad, M. H. M., & Ayob, A. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.09.065 · Cited by 755 · see citing works

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

Liu, D., Zhou, J., Pan, D., Peng, Y., & Peng, X. (2015) · Measurement · doi:10.1016/j.measurement.2014.11.031 · Cited by 266 · see citing works

SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators

Jia, J., Liang, J., Shi, Y., Wen, J., Pang, X., & Zeng, J. (2020) · Energies · doi:10.3390/en13020375 · Cited by 223 · see citing works

State-of-Health Estimation for Lithium-Ion Batteries Based on the Multi-Island Genetic Algorithm and the Gaussian Process Regression

Wang, Z., Ma, J., & Zhang, L. (2017) · IEEE Access · doi:10.1109/access.2017.2759094 · Cited by 205 · see citing works

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Wu, L., Fu, X., & Guan, Y. (2016) · Applied Sciences · doi:10.3390/app6060166 · Cited by 203 · see citing works

Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO–SVR model

Qin, T., Zeng, S., & Guo, J. (2015) · Microelectronics Reliability · doi:10.1016/j.microrel.2015.06.133 · Cited by 176 · see citing works

Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering

Bai, G., Su, Y., Rahman, M. M., & Wang, Z. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108944 · Cited by 76 · see citing works

A review on prognostics approaches for remaining useful life of lithium-ion battery

Su, C., & Chen, H. J. (2017) · IOP Conference Series: Earth and Environmental Science · doi:10.1088/1755-1315/93/1/012040 · Cited by 64 · see citing works

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

Qin, T., Zeng, S., Guo, J., & Skaf, Z. (2016) · Energies · doi:10.3390/en9110896 · Cited by 52 · see citing works

Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system

Kim, J., Sin, S., & Kim, J. (2024) · Energy · doi:10.1016/j.energy.2024.131285 · Cited by 50 · see citing works

A novel concept and assessment method for trustworthiness of prognostics

Sun, B., Jiang, X., Ye, T., et al. (2016) · Advances in Mechanical Engineering · doi:10.1177/1687814016638807 · Cited by 1 · see citing works

Challenges and Opportunities of System-Level Prognostics

Kim, S., Choi, J.-H., & Kim, N. H. (2021) · Sensors · doi:10.3390/s21227655 · Cited by 37 · see citing works

A review of data-driven prognostics in power electronics

Kabir, A., Bailey, C., Lu, H., & Stoyanov, S. (2012) · International Spring Seminar on Electronics Technology · doi:10.1109/isse.2012.6273136 · Cited by 39 · see citing works

Health Assessment, Prognostics and Remaining Useful Life – Part A

Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016) · doi:10.1002/9781119371052.ch4 · Cited by 2 · see citing works

How to tell the good from the bad in failure prognostics methods

Leão, B. P., Gomes, J. P. P., Galvão, R. K. H., et al. (2010) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2010.5446829 · Cited by 9 · see citing works