Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries
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?
A common way to estimate SOH is through models simulating cell behaviours, followed by optimization algorithms and observers such as Kalman filter family and particle filter to identify parameters and SOH states.
“One common way is through the use of models simulating the behaviours of cells [8,9], followed by various optimization algorithms and observers, such as the Kalman filter family [10] and particle filter [11] to identify the parameters and SOH states [4].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [11]
Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751 · see citing works
Pattern Recognition and Machine Learning
(2007) · Kybernetes · doi:10.1108/03684920710743466 · Cited by 8,540 · see citing works
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886 · 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.2004.02.032 · Cited by 1,927 · 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
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.2004.02.031 · Cited by 1,385 · see citing works
Methods for state-of-charge determination and their applications
Piller, S., Perrin, M., & Jossen, A. (2001) · Journal of Power Sources · doi:10.1016/s0378-7753(01)00560-2 · Cited by 1,169 · see citing works
Improved particle filter for nonlinearproblems
Carpenter, J. R., Clifford, P., & Fearnhead, P. (1999) · IEE Proceedings - Radar Sonar and Navigation · doi:10.1049/ip-rsn:19990255 · Cited by 1,006 · see citing works
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
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering
Lee, J.-M., Nam, O., & Cho, B. H. (2007) · Journal of Power Sources · doi:10.1016/j.jpowsour.2007.03.072 · Cited by 332 · see citing works
Cathode materials: A personal perspective
Goodenough, J. B. (2007) · Journal of Power Sources · doi:10.1016/j.jpowsour.2007.06.217 · Cited by 229 · see citing works
Theoretical evaluation of high-energy lithium metal phosphate cathode materials in Li-ion batteries
HOWARD, W. F. J., & Spotnitz, R. M. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.12.046 · Cited by 185 · see citing works
Development of a voltage-behavior model for NiMH batteries using an impedance-based modeling concept
Thele, M., Bohlen, O. S., Sauer, D. U., & Karden, E. (2007) · Journal of Power Sources · doi:10.1016/j.jpowsour.2007.08.039 · Cited by 124 · see citing works
State Estimation of a Lithium-Ion Battery Through Kalman Filter
Urbain, M., Raël, S., Davat, B., & Desprez, P. (2007) · IEEE Power Electronics Specialists Conference (PESC) · doi:10.1109/pesc.2007.4342463 · Cited by 54 · see citing works
Flow in a Pipe Following an Abrupt Increase in Surface Roughness
Logan, E., Jr., & Jones, J. B. (1963) · Journal of Basic Engineering · doi:10.1115/1.3656525 · Cited by 14 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.001 · Cited by 2,005 · see citing works
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110015 · Cited by 1,394 · 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
“One common way is through the use of models simulating the behaviours of cells [8,9], followed by various optimization algorithms and observers, such as the Kalman filter family [10] and particle filter [11] to identify the parameters and SOH states [4].”
MethodWaag, 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
Xiong, R., Li, L., & Tian, J. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.10.019 · Cited by 1,017 · 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
How, D. N. T., Hannan, M. A., Lipu, M. S. H., & Ker, P. J. (2019) · IEEE Access · doi:10.1109/access.2019.2942213 · Cited by 823 · 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
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
Wei, J., Dong, G., & Chen, Z. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2782224 · Cited by 643 · see citing works
Zheng, F., Xing, Y., Jiang, J., Sun, B., Kim, J., & Pecht, M. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.09.010 · Cited by 574 · see citing works
Nejad, S., Gladwin, D. T., & Stone, D. A. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.03.042 · Cited by 565 · see citing works
Shen, P., Ouyang, M., Lu, L., Li, J., & Feng, X. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2751613 · Cited by 537 · see citing works
Nyamathulla, S., & Dhanamjayulu, C. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111179 · Cited by 518 · see citing works
Zheng, Y., Ouyang, M., Han, X., Lu, L., & Li, J. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.11.094 · Cited by 493 · see citing works
Farmann, A., Waag, W., Marongiu, A., & Sauer, D. U. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.01.129 · Cited by 472 · see citing works
Ali, M. U., Zafar, A., Nengroo, S. H., Hussain, S., Alvi, M. J., & Kim, H. (2019) · Energies · doi:10.3390/en12030446 · Cited by 455 · 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
Gao, Y., Liu, K., Zhu, C., Zhang, X., & Zhang, D. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3066946 · Cited by 422 · see citing works
Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data
Deng, Z., Hu, X., Li, P., Lin, X., & Bian, X. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2021.3134701 · Cited by 364 · see citing works
A comprehensive review on estimation strategies used in hybrid and battery electric vehicles
Cuma, M. U., & Köroğlu, T. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.10.047 · Cited by 344 · see citing works
Xiong, R., Li, L., Li, Z., Yu, Q., & Mu, H. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.03.053 · Cited by 326 · see citing works
Dai, H., Zhao, G., Lin, M., Wu, J., & Zheng, G. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2880703 · Cited by 325 · see citing works
Key challenges for a large-scale development of battery electric vehicles: A comprehensive review
Lebrouhi, B. E., Khattari, Y., Lamrani, B., Maaroufi, M., Zeraouli, Y., & Kousksou, T. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103273 · Cited by 276 · see citing works
Chen, L., Lü, Z., Lin, W., Li, J., & Pan, H. (2017) · Measurement · doi:10.1016/j.measurement.2017.11.016 · Cited by 276 · 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
Zhong, L., Zhang, C., He, Y., & Chen, Z. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.08.008 · Cited by 247 · see citing works
Wassiliadis, N., Adermann, J., Frericks, A., Pak, M., Reiter, C., Lohmann, B., & Lienkamp, M. (2018) · Journal of Energy Storage · doi:10.1016/j.est.2018.07.006 · Cited by 227 · 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
Deng, Y., Ying, H., Jiaqiang, E., Zhu, H., Wei, K., Chen, J., Zhang, F., & Liao, G. (2019) · Energy · doi:10.1016/j.energy.2019.03.177 · Cited by 222 · see citing works
Selvaraj, V., & Vairavasundaram, I. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108777 · Cited by 220 · see citing works
Yang, F., Xing, Y., Wang, D., & Tsui, K. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.11.072 · Cited by 208 · see citing works
Liu, B., Tang, X., & Gao, F. (2020) · Electrochimica Acta · doi:10.1016/j.electacta.2020.136098 · Cited by 199 · see citing works
Battery management strategies: An essential review for battery state of health monitoring techniques
Pradhan, S. K., & Chakraborty, B. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104427 · Cited by 198 · see citing works
Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles
Espedal, I. B., Jinasena, A., Burheim, O. S., & Lamb, J. J. (2021) · Energies · doi:10.3390/en14113284 · Cited by 177 · see citing works
Xiao, B., Liu, Y., & Xiao, B. (2019) · IEEE Access · doi:10.1109/access.2019.2913078 · Cited by 170 · see citing works
Zhang, S., Guo, X., Dou, X., & Zhang, X. (2020) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2020.100752 · Cited by 163 · see citing works
Xia, B., Cui, D., Sun, Z. G., Lao, Z., Zhang, R., Wang, W., Sun, W., Lai, Y., & Wang, M. (2018) · Energy · doi:10.1016/j.energy.2018.04.085 · Cited by 157 · see citing works
Lyu, Z., Gao, R., & Chen, L. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3033297 · Cited by 156 · see citing works
Ye, M., Guo, H., Xiong, R., & Yu, Q. (2017) · Energy · doi:10.1016/j.energy.2017.12.061 · Cited by 152 · see citing works
State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter
Xu, Y., Hu, M., Zhou, A., Li, Y., Li, S., Fu, C., & Gong, C. (2019) · Applied Mathematical Modelling · doi:10.1016/j.apm.2019.09.011 · Cited by 152 · see citing works
A review on state of health estimation for lithium ion batteries in photovoltaic systems
Tian, J., Xiong, R., & Shen, W. (2019) · eTransportation · doi:10.1016/j.etran.2019.100028 · Cited by 150 · see citing works
Sarmah, S. B., Kalita, P., Garg, A., Niu, X., Zhang, X., Peng, X., & Bhattacharjee, D. (2019) · Journal of Electrochemical Energy Conversion and Storage · doi:10.1115/1.4042987 · Cited by 140 · see citing works
Advances in battery state estimation of battery management system in electric vehicles
Jiang, M., Li, D., Li, Z., Chen, Z., Yan, Q., Fu, L., Yu, C., Jiang, B., Wei, X., Yan, W., et al. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234781 · Cited by 137 · see citing works
Recent advances in model-based fault diagnosis for lithium-ion batteries: A comprehensive review
Xu, Y., Ge, X., Guo, R., & Shen, W. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114922 · Cited by 136 · see citing works
A comparative study and review of different Kalman filters by applying an enhanced validation method
Campestrini, C., Heil, T., Kosch, S., & Jossen, A. (2016) · Journal of Energy Storage · doi:10.1016/j.est.2016.10.004 · Cited by 128 · see citing works
Basia, A., Simeu-Abazi, Z., Gascard, E., & Zwolinski, P. (2021) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2021.02.004 · Cited by 125 · see citing works
Bi, Y., Yin, Y., & Choe, S.-Y. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228655 · Cited by 124 · see citing works
Venugopal, P., & Vigneswaran, T. (2019) · Energies · doi:10.3390/en12224338 · Cited by 119 · see citing works
A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer
Xia, B., Chen, C., Tian, Y., Sun, W., Xu, Z., & Zheng, W. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.103 · Cited by 118 · see citing works
Ghaeminezhad, N., Ouyang, Q., Wei, J., Xue, Y., & Wang, Z. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108707 · Cited by 116 · see citing works
Chen, L., Wang, H., Liu, B., Wang, Y., Ding, Y., & Pan, H. (2020) · Energy · doi:10.1016/j.energy.2020.119078 · Cited by 113 · see citing works
An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles
Wang, Y., Zhang, C., & Chen, Z. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.11.087 · Cited by 112 · see citing works
A model for state-of-health estimation of lithium ion batteries based on charging profiles
Bian, X., Liu, L., & Yan, J. (2019) · Energy · doi:10.1016/j.energy.2019.04.070 · Cited by 112 · see citing works
Zheng, C., Chen, Z., & Huang, D. (2019) · Energy · doi:10.1016/j.energy.2019.116504 · Cited by 107 · see citing works
Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries
Zhao, J., Zhu, Y., Zhang, B., Liu, M., Wang, J., Liu, C., & Hao, X. (2023) · Sustainability · doi:10.3390/su15065014 · Cited by 106 · see citing works
A review of state-of-health estimation for lithium-ion battery packs
Li, Q., Song, R., & Wei, Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116078 · Cited by 106 · see citing works
Li, J., Wang, L., Lyu, C., & PECHT, M. G. (2017) · Energy · doi:10.1016/j.energy.2017.05.158 · Cited by 101 · see citing works
Modeling and Estimation for Advanced Battery Management
Lin, X., Kim, Y., Mohan, S. N., Siegel, J. B., & Stefanopoulou, A. G. (2019) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-053018-023643 · Cited by 99 · see citing works
D.V.S.R., S., Badachi, C., & Green II, R. C. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108420 · Cited by 98 · see citing works
Wang, J., Zhang, S., Li, C., Wu, L., & Wang, Y. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3183886 · Cited by 97 · see citing works
Gallardo-Lozano, J., Romero‐Cadaval, E., Milanés‐Montero, M. I., & Guerrero-Martinez, M. A. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.09.005 · Cited by 97 · see citing works
A Novel Hybrid Neural Network-Based SOH and RUL Estimation Method for Lithium-Ion Batteries
Chen, B., Liu, Y., & Xiao, B. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113074 · Cited by 95 · see citing works
Lipu, M. S. H., Hannan, M. A., Hussain, A., & Saad, M. H. M. (2017) · Journal of Renewable and Sustainable Energy · doi:10.1063/1.5008491 · Cited by 93 · see citing works
On-line battery state-of-charge estimation based on an integrated estimator
Wang, Y., Zhang, C., & Chen, Z. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.09.015 · Cited by 93 · see citing works
Lithium–Sulfur Battery State-of-Charge Observability Analysis and Estimation
Fotouhi, A., Auger, D. J., Propp, K., & Longo, S. (2017) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2017.2740223 · Cited by 89 · see citing works
Zhu, F., & Fu, J. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3102990 · Cited by 88 · see citing works
Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering
Zhang, C., Zhu, Y., Dong, G., & Wei, J. (2019) · International Journal of Energy Research · doi:10.1002/er.4820 · Cited by 87 · see citing works
Pang, Z., Yang, K., Song, Z., Niu, P., Chen, G., & Meng, J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108693 · Cited by 87 · see citing works
Tian, Y., Dong, L., Tian, J., & Xia, B. (2016) · Electrochimica Acta · doi:10.1016/j.electacta.2016.12.119 · Cited by 85 · see citing works
Bidirectional Charging in V2G Systems: An In-Cell Variation Analysis of Vehicle Batteries
Khalid, H. M., & Peng, J. C. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2958967 · Cited by 84 · see citing works
Ren, Z. G., Du, C., Wu, Z., Shao, J., & Deng, W. (2021) · International Journal of Energy Research · doi:10.1002/er.6700 · Cited by 79 · see citing works
Li, S., Pischinger, S., He, C., Liang, L., & Stapelbroek, M. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.01.008 · Cited by 78 · see citing works
A partial charging curve-based data-fusion-model method for capacity estimation of Li-Ion battery
Lyu, Z., Gao, R., & Li, X. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.229131 · Cited by 77 · see citing works
SOH and RUL prediction of Li-ion batteries based on improved Gaussian process regression
Feng, H., & Shi, G. (2021) · Journal of Power Electronics · doi:10.1007/s43236-021-00318-5 · Cited by 76 · see citing works
Guo, F., Couto, L. D., Mulder, G., Trad, K., Hu, G., Capron, O., & Haghverdi, K. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113850 · Cited by 75 · see citing works
An intelligent battery management system (BMS) with end-edge-cloud connectivity – a perspective
Mulpuri, S. K., Sah, B., & Kumar, P. (2025) · Sustainable Energy & Fuels · doi:10.1039/d4se01238k · Cited by 74 · see citing works
Feng, F., Lu, R., Wei, G., & Zhu, C. (2015) · Energies · doi:10.3390/en8042950 · Cited by 74 · see citing works
Cui, X., Zhu, J., Luo, M., Guo, Y., & Qiao, H. (2018) · Energies · doi:10.3390/en11010209 · Cited by 74 · see citing works
Sun, B., Jiang, J., Fangdan, Z., Zhao, W., Liaw, B. Y., Ruan, H., Han, Z., & Zhang, W. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.01.106 · Cited by 73 · see citing works
Cui, D., Xia, B., Zhang, R., Sun, Z., Lao, Z., Wang, W., Sun, W., Lai, Y., & Wang, M. (2018) · Energies · doi:10.3390/en11040995 · Cited by 72 · see citing works
Xu, Y., Hu, M., Fu, C., Cao, K., Su, Z., & Yang, Z. Q. (2019) · Electronics · doi:10.3390/electronics8091012 · Cited by 66 · see citing works
Fotouhi, A., Auger, D. J., Propp, K., & Longo, S. (2017) · IET Power Electronics · doi:10.1049/iet-pel.2016.0777 · Cited by 64 · see citing works
Campestrini, C., Horsche, M. F., Zilberman, I., Heil, T., Zimmermann, T., & Jossen, A. (2016) · Journal of Energy Storage · doi:10.1016/j.est.2016.05.007 · Cited by 63 · see citing works
Zhang, M., Wang, K., & Zhou, Y.-T. (2020) · Complexity · doi:10.1155/2020/8231243 · Cited by 62 · see citing works
State estimation of lithium-ion battery for shipboard applications: Key challenges and future trends
Kong, L., Luo, Y., Fang, S., Niu, T., Chen, G., Yang, L., & Liao, R. (2024) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2024.100192 · Cited by 62 · see citing works
Kalman-variant estimators for state of charge in lithium-sulfur batteries
Propp, K., Auger, D. J., Fotouhi, A., Longo, S., & Knap, V. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.12.087 · Cited by 61 · see citing works
Zhao, G., Zhang, C., Duan, B., Shang, Y., Kang, Y., & Zhu, R. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3170630 · Cited by 60 · see citing works
Li, J., Ye, M., Gao, K., Xu, X., Wei, M., & Jiao, S. (2021) · International Journal of Energy Research · doi:10.1002/er.6658 · Cited by 60 · see citing works
An adaptive state of charge estimation approach for lithium-ion series-connected battery system
Peng, S., Zhu, X., Xing, Y., Shi, H., Cai, X., & PECHT, M. G. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.04.101 · Cited by 59 · see citing works
Xia, B., Chen, C., Tian, Y., Wang, M., Sun, W., & Xu, Z. (2015) · Energy · doi:10.1016/j.energy.2015.06.095 · Cited by 59 · see citing works
Waseem, M., Lakshmi, G. S., Amir, M., Ahmad, M., & Suhaib, M. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.236414 · Cited by 58 · see citing works
Shi, E.-W., Xia, F., Peng, D., Li, L., Wang, X., & Yu, B. (2019) · Journal of Renewable and Sustainable Energy · doi:10.1063/1.5065477 · Cited by 55 · see citing works
Li, R., Li, W., & Zhang, H. (2022) · International Journal of Electrochemical Science · doi:10.20964/2022.02.03 · Cited by 54 · see citing works
Cui, X., & Hu, T. (2020) · IEEE Access · doi:10.1109/access.2020.3038182 · Cited by 54 · see citing works
Online parameter and state estimation of lithium-ion batteries under temperature effects
Chaoui, H., & Gualous, H. (2017) · Electric Power Systems Research · doi:10.1016/j.epsr.2016.12.029 · Cited by 50 · see citing works
Qiu, X., Wang, S., & Chen, K. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110281 · Cited by 50 · see citing works
Analysis of Optimal Machine Learning Approach for Battery Life Estimation of Li-Ion Cell
Venugopal, P., Shankar, S. S., Jebakumar, C. P., Agarwal, R., Alhelou, H. H., Reka, S. S., & Golshan, M. E. H. (2021) · IEEE Access · doi:10.1109/access.2021.3130994 · Cited by 50 · see citing works
Girijaprasanna, T., & Dhanamjayulu, C. (2022) · Electronics · doi:10.3390/electronics11111795 · Cited by 48 · see citing works
Wang, C., Cui, N., Cui, Z., Yuan, H., & Zhang, C. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105075 · Cited by 46 · see citing works
- Authors
- Schwunk, S., Armbruster, N., Straub, S., Kehl, J., & Vetter, M.
- Affiliations
- Fraunhofer Institute for Solar Energy Systems (DE)
- Corresponding author
- Schwunk, Simon
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 239
- Pages
- 705-710
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2000885517
- Cited by
- 250
- References
- 16
- Citations by year
- 2026: 6 · 2025: 18 · 2024: 27 · 2023: 28 · 2022: 20 · 2021: 21 · 2020: 23 · 2019: 29 · 2018: 20 · 2017: 19 · 2016: 12 · 2015: 15 · 2014: 8 · 2013: 4
- FWCI
- 15.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- particle filter, state of charge estimation, state of health estimation, lithium iron phosphate batteries, hysteresis, battery degradation, Monte Carlo sampling, electric vehicles, off-grid power supply
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
