Remaining useful life assessment of lithium-ion batteries in implantable medical devices

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

A review on the key issues for lithium-ion battery management in electric vehicles

Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837 · 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

Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries

Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009) · Applied Energy · doi:10.1016/j.apenergy.2008.11.021 · Cited by 1,636 · see citing works

A Tutorial on Particle Filtering and Smoothing: Fifteen years later

Doucet, A., & Johansen, A. M. (2008) · Cited by 1,385 · 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

An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo

Cappé, O., Godsill, S., & Moulines, É. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2007.893250 · Cited by 1,009 · 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

Residual-life distributions from component degradation signals: A Bayesian approach

Gebraeel, N., Lawley, M., LI, R., & Ryan, J. K. (2005) · IIE Transactions · doi:10.1080/07408170590929018 · Cited by 790 · see citing works

Particle filters for state estimation of jump Markov linear systems

Arnaud Doucet, B., Gordon, N., & Kroshnamurthy, V. (2001) · IEEE Transactions on Signal Processing · doi:10.1109/78.905890 · Cited by 727 · see citing works

State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge

Lee, S., Kim, J., Lee, J.-M., & Cho, B. H. (2008) · Journal of Power Sources · doi:10.1016/j.jpowsour.2008.08.103 · Cited by 699 · see citing works

Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods

Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.11.146 · Cited by 664 · see citing works

Recurrent neural networks for remaining useful life estimation

Heimes, F. O. (2008) · International Conference on Prognostics and Health Management (PHM) · doi:10.1109/phm.2008.4711422 · Cited by 655 · see citing works

Simulation of capacity fade in lithium-ion batteries

Spotnitz, R. M. (2002) · Journal of Power Sources · doi:10.1016/s0378-7753(02)00490-1 · Cited by 575 · see citing works

A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation

Hu, C. K., Youn, B. D., & Chung, J. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.08.002 · Cited by 561 · see citing works

Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs

Plett, G. L. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.06.004 · Cited by 547 · see citing works

A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles

Xiong, R., Sun, F., Chen, Z., & He, H. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.07.061 · Cited by 529 · 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

Behavior and State-of-Health Monitoring of Li-ion Batteries Using Impedance Spectroscopy and Recurrent Neural Networks

Eddahech, A., Briat, O., Bertrand, N., Delétage, J.-Y., & Vinassa, J.-M. (2012) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2012.04.050 · Cited by 462 · see citing works

A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation

Si, X., Wang, W., Hu, C.-H., Chen, M., & Zhou, D.-H. (2012) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2012.08.016 · Cited by 460 · see citing works

Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles

Chiang, Y., Sean, W., & Ke, J.-C. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.01.005 · Cited by 396 · 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

Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model

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

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

Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery

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

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

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter

Wang, 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

An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries

Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2010.v2i1.1896 · Cited by 274 · see citing works

A Comprehensive Capacity Fade Model and Analysis for Li-Ion Batteries

Lin, X., Park, J., Liu, L., Lee, Y., Sastry, A. M., & Lu, W. D. (2013) · Journal of The Electrochemical Society · doi:10.1149/2.040310jes · Cited by 267 · see citing works

System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics

Pattipati, B., Sankavaram, C., & Pattipati, K. R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2089979 · Cited by 267 · see citing works

Model-Based Prognostic Techniques Applied to a Suspension System

Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2008.2001055 · Cited by 234 · see citing works

Prognostic Degradation Models for Computing and Updating Residual Life Distributions in a Time-Varying Environment

Gebraeel, N., & Pan, J. W. (2008) · IEEE Transactions on Reliability · doi:10.1109/tr.2008.928245 · Cited by 223 · see citing works

Interacting multiple model particle filter

Boers, Y., & Driessen, J. N. (2003) · IEE Proceedings - Radar Sonar and Navigation · doi:10.1049/ip-rsn:20030741 · Cited by 218 · see citing works

Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination

Waag, W., & Sauer, D. U. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.05.001 · Cited by 216 · see citing works

Complementary Cooperation Algorithm Based on DEKF Combined With Pattern Recognition for SOC/Capacity Estimation and SOH Prediction

Kim, J., Lee, S., & Cho, B. H. (2011) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2011.2158554 · Cited by 207 · see citing works

Multiple model bootstrap filter for maneuvering target tracking

McGinnity, S., & Irwin, G. W. (2000) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/7.869522 · Cited by 195 · see citing works

A data-model-fusion prognostic framework for dynamic system state forecasting

Liu, J., Wang, W., Ma, F., Yang, Y., & Yang, C. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.02.015 · Cited by 191 · see citing works

Online estimation of lithium-ion battery capacity using sparse Bayesian learning

Hu, C., Jain, G., Schmidt, C., Strief, C., & Sullivan, M. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.04.166 · Cited by 180 · see citing works

Method for estimating capacity and predicting remaining useful life of lithium-ion battery

Hu, C. K., Jain, G., Tamirisa, P. A., & Gorka, T. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.03.086 · Cited by 162 · see citing works

Capacity-fading prediction of lithium-ion batteries based on discharge curves analysis

Honkura, K., Takahashi, K., & Horiba, T. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.08.020 · Cited by 148 · see citing works

Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries

Schmidt, A., Bitzer, M., Imre, Á. W., & Guzzella, L. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2010.06.011 · Cited by 134 · see citing works

A generic model-free approach for lithium-ion battery health management

Bai, G., Wang, P., Hu, C. K., & PECHT, M. G. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.08.059 · Cited by 134 · see citing works

Adaptive, multi-parameter battery state estimator with optimized time-weighting factors

Verbrugge, M. W. (2007) · Journal of Applied Electrochemistry · doi:10.1007/s10800-007-9291-7 · Cited by 86 · see citing works

Fusing Competing Prediction Algorithms for Prognostics

Goebel, K., Eklund, N. H. W., & Bonanni, P. G. (2006) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2006.1656116 · Cited by 59 · see citing works

Prognostics of Lithium-ion batteries based on state space modeling with heterogeneous noise variances

Wang, D., Yang, F., Zhao, Y., & Tsui, K. (2017) · Microelectronics Reliability · doi:10.1016/j.microrel.2017.06.002 · Cited by 30 · see citing works

A Practical Longevity Model for Lithium-Ion Batteries: De-coupling the Time and Cycle-Dependence of Capacity Fade

Scott, E., Brown, J. R., Schmidt, C., et al. (2006) · ECS Meeting Abstracts · doi:10.1149/ma2005-02/4/239 · Cited by 12 · 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

Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries

Shen, S., Sadoughi, M., Li, M., Wang, Z., & Hu, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114296 · Cited by 537 · see citing works

Lithium-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

Integrating Physics-Based Modeling and Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries

Thelen, A., Lui, Y. H., Shen, S., Laflamme, S., Hu, S., Ye, H., & Hu, C. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.05.047 · Cited by 148 · see citing works

Batteries State of Health Estimation via Efficient Neural Networks with Multiple Channel Charging Profiles

Khan, N., Ullah, F. U. M., Afnan, Ullah, A., Lee, M. Y., & Baik, S. W. (2021) · IEEE Access · doi:10.1109/access.2020.3047732 · Cited by 98 · 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

Alphasat experiment in Madrid: modeling considerations on fade and inter-fade duration results

Pimienta‐del‐Valle, D., Riera, J. M., García-del-Pino, P., et al. (2018) · European Conference on Antennas and Propagation · doi:10.1049/cp.2018.1493 · Cited by 4 · see citing works

Fade Slope Analysis of Alphasat Satellite Measurements at Ka and Q Bands

Kelmendi, A., Hrovat, A., Švigelj, A., et al. (2021) · European Conference on Antennas and Propagation · doi:10.23919/eucap51087.2021.9411105 · Cited by 7 · see citing works

Analysis of 11 GHz slant path fade duration and fade slope

Dintelmann, F. (1981) · Electronics Letters · doi:10.1049/el:19810189 · Cited by 16 · see citing works

Fade Dynamics on Earth-Space Paths at Ku-Band in Fukuoka, Japan Fade-Slope Evaluation, Comparison, and Model

Franklin, F. F., Fujisaki, K., & Tateiba, M. (2006) · IEEE Antennas and Wireless Propagation Letters · doi:10.1109/lawp.2006.872430 · Cited by 10 · see citing works

Dynamic characteristics of fading on a 50 GHz slant path

García-del-Pino, P., Riera, J. M., & Benarroch, A. (2006) · European Conference on Antennas and Propagation · doi:10.1109/eucap.2006.4584795 · Cited by 5 · see citing works