Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Prognostic predictions need to cope with uncertainties coming from measurement, operation environment, and the model itself, arising from model structure and uncertain parameters.
“Prognostic predictions need to cope with uncertainties coming from the measurement, the operation environment and the model itself -these arise from the structure of the model and uncertain parameters [110].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [110]
Cites
A Mathematical Theory of Evidence
Shafer, G. R. (2020) · Princeton University Press eBooks · doi:10.2307/j.ctv10vm1qb · Cited by 11,786 · see citing works
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531 · see citing works
On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,466 · see citing works
Ageing mechanisms in lithium-ion batteries
Vetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K.-C., Besenhard, J. O., Winter, M., Wohlfahrt-Mehrens, M., Vogler, C., & Hammouche, A. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.01.006 · Cited by 3,987 · see citing works
A mathematical theory of evidence
Rota, G. (1977) · Advances in Mathematics · doi:10.1016/0001-8708(77)90069-x · Cited by 1,785 · see citing works
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768 · 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
Self-citationPrognostics 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
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
Prognostics in Battery Health Management
Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2008.4579269 · Cited by 594 · see citing works
Basir, O., & Yuan, X. (2005) · Information Fusion · doi:10.1016/j.inffus.2005.07.003 · Cited by 462 · see citing works
The Dempster–Shafer theory of evidence: an alternative approach to multicriteria decision modelling
Beynon, M. J., Curry, B., & Morgan, P. (2000) · Omega · doi:10.1016/s0305-0483(99)00033-x · Cited by 446 · see citing works
Kim, I.-S. (2009) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2009.2034966 · Cited by 438 · see citing works
Uncertainty Modeling and Analysis in Engineering and the Sciences
Ayyub, B. M., & Klir, G. J. (2006) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420011456 · Cited by 373 · see citing works
Capacity fade analysis of a lithium ion cell
Zhang, Q., & White, R. E. (2008) · Journal of Power Sources · doi:10.1016/j.jpowsour.2008.01.028 · Cited by 289 · see citing works
Dempster-Shafer theory for sensor fusion in autonomous mobile robots
Murphy, R. R. (1998) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.681240 · Cited by 238 · see citing works
An expert system for multi-criteria decision making using Dempster Shafer theory
Beynon, M. J., Cosker, D. P., & Marshall, D. (2001) · Expert Systems with Applications · doi:10.1016/s0957-4174(01)00020-3 · Cited by 215 · see citing works
Recursive approximate weighted total least squares estimation of battery cell total capacity
Plett, G. L. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2010.09.048 · Cited by 202 · see citing works
Wright, R. B., Motloch, C. G., Belt, J. R., Christophersen, J. P., Ho, C. D., Richardson, R. A., Bloom, I., Jones, S. A., Battaglia, V., Henriksen, G. L., et al. (2002) · Journal of Power Sources · doi:10.1016/s0378-7753(02)00210-0 · Cited by 197 · see citing works
Sensor Systems for Prognostics and Health Management
Cheng, S., Azarian, M. H., & Pecht, M. G. (2010) · Sensors · doi:10.3390/s100605774 · Cited by 188 · see citing works
Self-citationInterdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory
Inagaki, T. (1991) · IEEE Transactions on Reliability · doi:10.1109/24.87125 · Cited by 185 · see citing works
Bauer, M. (1997) · International Journal of Approximate Reasoning · doi:10.1016/s0888-613x(97)00013-3 · Cited by 178 · see citing works
2004 IEEE Aerospace Conference Proceedings
(2004) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2004.1367910 · Cited by 157 · see citing works
Interdependence between safety-control policy and multiplesensor schemes via Dempster-Shafer theory
(1992) · Microelectronics Reliability · doi:10.1016/0026-2714(92)90516-n · Cited by 130 · see citing works
Monte Carlo-based filtering for fatigue crack growth estimation
Cadini, F., Zio, E., & Avram, D. (2008) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2008.10.002 · Cited by 128 · 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
Self-citationA 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
Self-citationValve Regulated Lead Acid battery float service life estimation using a Kalman filter
Burgess, W. L. (2009) · Journal of Power Sources · doi:10.1016/j.jpowsour.2008.12.123 · Cited by 64 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634 · see citing works
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Wang, B., Lei, Y., Li, N., & Li, N. (2018) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2882682 · Cited by 1,939 · see citing works
Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326 · see citing works
Self-citationHu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320 · see citing works
Self-citationData-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
Waag, W., Fleischer, C., & Sauer, D. U. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.02.064 · Cited by 1,059 · see citing works
Xing, Y., He, W., Pecht, M., & Tsui, K. L. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.07.008 · Cited by 977 · see citing works
Self-citationPhysics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
Wang, F., Zhai, Z., Zhao, Z., Di, Y., & Chen, X. (2024) · Nature Communications · doi:10.1038/s41467-024-48779-z · Cited by 897 · see citing works
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
Gaussian process regression for forecasting battery state of health
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.004 · Cited by 732 · see citing works
A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life
Ren, L., Dong, J., Wang, X., Meng, Z., Zhao, L., & Deen, M. J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3008223 · Cited by 690 · see citing works
Rezvanizaniani, S. M., Liu, Z., Chen, Y., & Lee, J. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.01.085 · Cited by 688 · see citing works
Stochastic modelling and analysis of degradation for highly reliable products
Ye, Z., & Xie, M. (2014) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.2063 · Cited by 687 · see citing works
An ensemble model for predicting the remaining useful performance of lithium-ion batteries
Xing, Y., Eden, W. M., Tsui, K., & PECHT, M. G. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.003 · Cited by 675 · see citing works
Self-citationA brief review on key technologies in the battery management system of electric vehicles
Liu, K., Li, K., Peng, Q., & Zhang, C. (2019) · Frontiers of Mechanical Engineering · doi:10.1007/s11465-018-0516-8 · Cited by 635 · see citing works
Effect of Temperature on the Aging rate of Li Ion Battery Operating above Room Temperature
Leng, F., Tan, C. M., & PECHT, M. G. (2015) · Scientific Reports · doi:10.1038/srep12967 · Cited by 620 · see citing works
Self-citationBattery management systems in electric and hybrid vehicles
Xing, Y., Ma, E. W. M., Tsui, K. L., & Pecht, M. (2011) · Energies · doi:10.3390/en4111840 · Cited by 540 · see citing works
“He [37] developed a degradation model for lithium-ion batteries according to a new exponential model.”
BackgroundSelf-citationWu, J., Zhang, C., & Chen, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.057 · Cited by 531 · see citing works
Hu, X., Jiang, J., Cao, D., & Egardt, B. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2461523 · Cited by 504 · see citing works
Remaining useful life prediction of lithium-ion battery with unscented particle filter technique
Miao, Q., Xie, L., Cui, H., Liang, W. Z., & PECHT, M. G. (2012) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.12.004 · Cited by 479 · see citing works
Self-citationA comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C. K., & Hu, Z. Y. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03425-4 · Cited by 471 · see citing works
Lipu, M. S. H., Hannan, M. A., Karim, T. F., Hussain, A., Saad, M. H. M., Ayob, A., Miah, M. S., & Mahlia, T. M. I. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126044 · Cited by 461 · see citing works
Xiong, R., Zhang, Y., Wang, J., He, H., Peng, S., & PECHT, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2864688 · Cited by 450 · see citing works
Self-citationRemaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach
Ren, L., Li, Z., Hong, S., Zhao, S., Wang, H. H., & Zhang, L. (2018) · IEEE Access · doi:10.1109/access.2018.2858856 · Cited by 434 · see citing works
Ge, M., Liu, Y., Jiang, X., & Liu, J. (2021) · Measurement · doi:10.1016/j.measurement.2021.109057 · Cited by 425 · see citing works
Lu, J., Xiong, R., Tian, J., Wang, C., & Sun, F. (2023) · Nature Communications · doi:10.1038/s41467-023-38458-w · Cited by 424 · see citing works
Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
Che, Y., Hu, X., Lin, X., Guo, J., & Teodorescu, R. (2023) · Energy & Environmental Science · doi:10.1039/d2ee03019e · Cited by 421 · see citing works
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
Chen, D., Hong, W., & Zhou, X. (2022) · IEEE Access · doi:10.1109/access.2022.3151975 · Cited by 420 · see citing works
Dai, H., Jiang, B., Hu, X., Lin, X., Wei, X., & PECHT, M. G. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110480 · Cited by 408 · see citing works
Self-citationA failure modes, mechanisms, and effects analysis (FMMEA) of lithium-ion batteries
Hendricks, C., Williard, N., Mathew, S., & Pecht, M. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.07.100 · Cited by 381 · see citing works
Self-citationLithium-ion battery data and where to find it
dos Reis, G., Strange, C., Yadav, M., & Li, S. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100081 · Cited by 377 · see citing works
Tan, Y., & Zhao, G. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2946551 · Cited by 376 · see citing works
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning
Hu, X., Che, Y., Lin, X., & Onori, S. (2020) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2020.3017090 · Cited by 376 · see citing works
Particle filter-based prognostics: Review, discussion and perspectives
Jouin, M., Gouriveau, R., Hissel, D., Péra, M., & Noureddine Zerhouni, A. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.11.008 · Cited by 355 · see citing works
Wang, D., Miao, Q., & Pecht, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.03.129 · Cited by 351 · see citing works
Self-citationYang, F., Wang, D., Zhao, Y., Tsui, K.-L., & Bae, S. J. (2018) · Energy · doi:10.1016/j.energy.2017.12.144 · Cited by 349 · see citing works
State of charge estimation for electric vehicle batteries using unscented kalman filtering
He, W., Williard, N., Chen, C., & PECHT, M. G. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2012.11.010 · Cited by 349 · see citing works
Self-citationA Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem
Wang, Y., Peng, Y., Zi, Y., Jin, X., & Tsui, K. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2535368 · Cited by 344 · see citing works
Ma, G., Zhang, Y. A., Cheng, C., Zhou, B., Hu, P., & Yuan, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113626 · Cited by 343 · see citing works
Deep neural network battery charging curve prediction using 30 points collected in 10 min
Tian, J., Xiong, R., Shen, W., Lu, J., & Yang, X. (2021) · Joule · doi:10.1016/j.joule.2021.05.012 · Cited by 343 · see citing works
A naive Bayes model for robust remaining useful life prediction of lithium-ion battery
Ng, S. S. Y., Xing, Y., & Tsui, K. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.12.020 · Cited by 340 · see citing works
Rauf, H., Khalid, M., & Arshad, N. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111903 · Cited by 331 · see citing works
Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability
Williard, N., He, W., Hendricks, C., & PECHT, M. G. (2013) · Energies · doi:10.3390/en6094682 · Cited by 322 · see citing works
Self-citationGuha, A., & Patra, A. (2018) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2017.2776558 · Cited by 318 · see citing works
Datong, L., Zhou, J., Liao, H., Yu, P., & Peng, X. (2015) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2015.2389757 · Cited by 311 · see citing works
Dong, H., Jin, X., Lou, Y., & Wang, C. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.176 · Cited by 310 · see citing works
Hu, C. K., Jain, G., Zhang, P., Schmidt, C., Gomadam, P. M., & Gorka, T. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.04.077 · Cited by 304 · see citing works
Early prediction of battery lifetime via a machine learning based framework
Fei, Z., Yang, F., Tsui, K., Li, L., & Zhang, Z. (2021) · Energy · doi:10.1016/j.energy.2021.120205 · Cited by 302 · see citing works
Luo, K., Chen, X., Zheng, H., & Shi, Z. (2022) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2022.06.049 · Cited by 292 · see citing works
Prognostics of PEM fuel cell in a particle filtering framework
Jouin, M., Gouriveau, R., Hissel, D., Péra, M., & Noureddine Zerhouni, A. (2013) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2013.10.054 · Cited by 292 · see citing works
Guha, A., & Patra, A. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2809138 · Cited by 292 · see citing works
A novel health indicator for on-line lithium-ion batteries remaining useful life prediction
Zhou, Y., Huang, M., Chen, Y., & Tao, Y. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.04.119 · Cited by 289 · see citing works
A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries
Xiong, R., Zhang, Y., He, H., Zhou, X., & Pecht, M. G. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2733475 · Cited by 285 · see citing works
Self-citationWang, D., Yang, F., Tsui, K., Zhou, Q., & Bae, S. J. (2016) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2016.2534258 · Cited by 285 · see citing works
Hasib, S. A., Islam, S., Chakrabortty, R. K., Ryan, M. J., Saha, D. K., Ahamed, M. H., Moyeen, S. I., Das, S. K., Ali, M. F., Islam, M. R., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3089032 · Cited by 281 · see citing works
Tang, S., Yu, C., Wang, X., Guo, X., & Si, X. (2014) · Energies · doi:10.3390/en7020520 · Cited by 279 · see citing works
A review on prognostics and health monitoring of proton exchange membrane fuel cell
Sutharssan, T., Montalvão, D., Chen, Y., Wang, W.-C., Pisac, C., & Elemara, H. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.11.009 · Cited by 272 · see citing works
Yue, M., Jemeï, S., Gouriveau, R., & Noureddine Zerhouni, A. (2019) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2019.01.190 · Cited by 269 · see citing works
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
Zhang, S., Guo, X., Dou, X., & Zhang, X. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228740 · Cited by 263 · see citing works
Battery state of health estimation: a structured review of models, methods and commercial devices
Ungurean, L., Carstoiu, G. N., Micea, M. V., & Groza, V. F. (2016) · International Journal of Energy Research · doi:10.1002/er.3598 · Cited by 260 · see citing works
Elmahallawy, M., Elfouly, T., Alouani, A. T., & Massoud, A. M. (2022) · IEEE Access · doi:10.1109/access.2022.3221137 · Cited by 260 · see citing works
Zheng, X., & Fang, H. (2015) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2015.07.013 · Cited by 259 · see citing works
Analysis of ageing inhomogeneities in lithium-ion battery systems
Paúl, S., Diegelmann, C., Kabza, H., & Tillmetz, W. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.01.068 · Cited by 251 · see citing works
Yang, Y. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116897 · Cited by 250 · see citing works
Lu, J., Xiong, R., Tian, J., Wang, C., Hsu, C., Tsou, N., Sun, F., & Li, J. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.05.007 · Cited by 246 · see citing works
Long, B., Xian, W., Jiang, L., & Liu, Z. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.01.006 · Cited by 244 · see citing works
Battery Health Prognosis Using Brownian Motion Modeling and Particle Filtering
Dong, G., Chen, Z., Wei, J., & Ling, Q. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2813964 · Cited by 243 · see citing works
Zhang, Y., Xiong, R., He, H., & PECHT, M. G. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2808918 · Cited by 243 · see citing works
Self-citationA new hybrid method for the prediction of the remaining useful life of a lithium-ion battery
Chang, Y. L., Fang, H., & Zhang, Y. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.09.106 · Cited by 231 · see citing works
Wen, Y., Rahman, M. F., Xu, H., & Tseng, T.-L. (2021) · Measurement · doi:10.1016/j.measurement.2021.110276 · Cited by 231 · see citing works
Shu, X., Shen, J., Li, G., Zhang, Y., Chen, Z., & Liu, Y. (2021) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2021.3074638 · Cited by 230 · see citing works
Anomaly Detection and Fault Prognosis for Bearings
Jin, X., Sun, Y., Que, Z., Wang, Y., & Chow, T. W. S. (2016) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2016.2570398 · Cited by 227 · see citing works
Liu, D., Luo, Y., Liu, J., Peng, Y., Guo, L., & PECHT, M. G. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1520-x · Cited by 226 · see citing works
Self-citationAtamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Noureddine Zerhouni, A. (2020) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2017.v8i3.2667 · Cited by 225 · see citing works
Sbarufatti, C., Corbetta, M., Giglio, M., & Cadini, F. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.01.105 · Cited by 222 · see citing works
Zhang, H., Miao, Q., Zhang, X., & Liu, Z. (2018) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.12.036 · Cited by 217 · see citing works
Yang, F., Wang, D., Xu, F., Huang, Z., & Tsui, K. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228654 · Cited by 216 · see citing works
Battery health prediction under generalized conditions using a Gaussian process transition model
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.03.022 · Cited by 212 · see citing works
Evaluating the impact of V2G services on the degradation of batteries in PHEV and EV
Bishop, J. D. K., Axon, C. J., Bonilla, D., Tran, M., Banister, D., & McCulloch, M. D. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.04.094 · Cited by 212 · see citing works
Estimation of remaining useful life of ball bearings using data driven methodologies
Sutrisno, E., Oh, H., Vasan, A. S. S., & PECHT, M. G. (2012) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm.2012.6299548 · Cited by 204 · see citing works
Self-citationWu, L., Fu, X., & Guan, Y. (2016) · Applied Sciences · doi:10.3390/app6060166 · Cited by 203 · see citing works
A Battery Management System With a Lebesgue-Sampling-Based Extended Kalman Filter
Yan, W., Zhang, B., Zhao, G., Tang, S., Niu, G., & Wang, X. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2842782 · Cited by 203 · see citing works
Tagade, P., Hariharan, K. S., Ramachandran, S., Khandelwal, A., Naha, A., Kolake, S. M., & Han, S. H. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.227281 · Cited by 201 · see citing works
Battery Degradation in Electric and Hybrid Electric Vehicles: A Survey Study
Timilsina, L., Badr, P. R., Hoang, P. H., Özkan, G., Papari, B., & Edrington, C. S. (2023) · IEEE Access · doi:10.1109/access.2023.3271287 · Cited by 201 · see citing works
Zhao, Q., Qin, X., Zhao, H., & Feng, W. (2018) · Microelectronics Reliability · doi:10.1016/j.microrel.2018.04.007 · Cited by 199 · see citing works
Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery
Song, Y., Liu, D., Chen, Y., & Peng, Y. (2017) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.06.045 · Cited by 195 · see citing works
Model Migration Neural Network for Predicting Battery Aging Trajectories
Tang, X., Liu, K., Wang, X., Gao, F., Macro, J., & Widanage, W. D. (2020) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2020.2979547 · Cited by 195 · see citing works
Qian, C., Xu, B., Chang, L., Sun, B., Feng, Q., Yang, D., Ren, Y., & Wang, Z. (2021) · Energy · doi:10.1016/j.energy.2021.120333 · Cited by 193 · see citing works
Zheng, Y., Han, X., Lu, L., Li, J., & Ouyang, M. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.09.015 · Cited by 192 · see citing works
Failure statistics for commercial lithium ion batteries: A study of 24 pouch cells
Harris, S. J., Harris, D. J., & Li, C. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.12.083 · Cited by 192 · see citing works
Li, S., Fang, H., & Shi, B. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107542 · Cited by 190 · see citing works
Tang, X., Wang, Y., Zou, C., Yao, K., Xia, Y., & Gao, F. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2018.10.082 · Cited by 190 · see citing works
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C. K., & Hu, Z. Y. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03410-x · Cited by 191 · see citing works
Liu, C., Wang, Y., & Chen, Z. (2018) · Energy · doi:10.1016/j.energy.2018.10.131 · Cited by 189 · see citing works
A Review on Prognostics Methods for Engineering Systems
Guo, J. H., Li, Z., & Li, M. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2019.2957965 · Cited by 189 · see citing works
Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells
Diao, W., Saxena, S., & PECHT, M. G. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.226830 · Cited by 188 · see citing works
Self-citationState of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges
S, V., Che, H. S., Selvaraj, J., Tey, K. S., Lee, J. W., Shareef, H., & Errouissi, R. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123542 · Cited by 190 · see citing works
Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles
Hu, X., Li, S. E., Jia, Z., & Egardt, B. (2014) · Energy · doi:10.1016/j.energy.2013.11.061 · Cited by 187 · see citing works
Liu, D., Wang, H., Peng, Y., Xie, W. H., & Liao, H. (2013) · Energies · doi:10.3390/en6083654 · Cited by 186 · see citing works
- Authors
- He, W., Williard, N., Osterman, M., & Pecht, M.
- Affiliations
- University of Maryland, College Park (US)
- Corresponding author
- Pecht, Michael
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2011-12
- Volume
- 196
- Issue
- 23
- Pages
- 10314-10321
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2078279667
- Cited by
- 1,105
- References
- 30
- Citations by year
- 2026: 138 · 2025: 140 · 2024: 126 · 2023: 132 · 2022: 90 · 2021: 76 · 2020: 73 · 2019: 61 · 2018: 61 · 2017: 54 · 2016: 42 · 2015: 37 · 2014: 29 · 2013: 29 · 2012: 16
- FWCI
- 49.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- lithium-ion batteries, battery prognostics, remaining useful life prediction, state of health estimation, Dempster-Shafer theory, Bayesian Monte Carlo, battery degradation modeling, model parameter updating
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
