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

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

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

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

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

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

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

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

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

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

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

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

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

State estimation for advanced battery management: Key challenges and future trends

Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857 · see citing works

Predicting the state of charge and health of batteries using data-driven machine learning

Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J., & Seh, Z. W. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0156-7 · Cited by 793 · see citing works

Random forest regression for online capacity estimation of lithium-ion batteries

Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J. C.-W., van den Bossche, P., Van Mierlo, J., & Omar, N. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.182 · Cited by 727 · see citing works

A 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

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

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

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

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

Hu, C., Ye, H., Jain, G., & Schmidt, C. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.11.056 · Cited by 110 · see citing works

Online State of Health Estimation for Series-Connected LiFePO4 Battery Pack Based on Differential Voltage and Inconsistency Analysis

Zhou, Z., Duan, B., Kang, Y., Zhang, Q., Shang, Y., & Zhang, C. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3274819 · Cited by 52 · see citing works

Towards an intelligent battery management system for electric vehicle applications: Dataset considerations, algorithmic approaches, and future trends

Lyu, Z., Wu, L., Lyu, M., Yang, J., & Li, X. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113827 · Cited by 28 · see citing works

Fast grading method based on data driven capacity prediction for high-efficient lithium-ion battery manufacturing

Yuan, Y., Kong, X., Hua, J., Pan, Y., Sun, Y., Han, X., Yang, H., Li, Y., Liu, X., Zhou, X., et al. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109143 · Cited by 25 · see citing works

Prediction of Data Classification Based on Support Vector Machine

Wu, X., & Zhou, Y. (2016) · doi:10.2991/iceeecs-16.2016.138 · Cited by 1 · see citing works

Support vector machines, import vector machines and relevance vector machines for hyperspectral classification — A comparison

Braun, A., Weidner, U., & Hinz, S. (2011) · Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing · doi:10.1109/whispers.2011.6080861 · Cited by 29 · see citing works

An improved model of support vector machine for high noise data regression problem

Fang, J., Chen, S., Liang, X., et al. (2007) · China-Ireland International Conference on Information and Communications Technologies (CIICT) · doi:10.1049/cp:20070717

The relation of solution between support vector machine regression and v-support vector machine classification

Li, L. (2004) · Journal of Hubei University · Cited by 1 · see citing works

The Study of Nonlinear System Identification Based on V-Support Vector Machine and E-Support Vector Machine

Yong-jie, Y. (2006) · Journal of Projectiles.Rockets.Missiles and Guidance · Cited by 1 · see citing works