Cited by 116Battery Modeling and ManagementAdvancements in Battery MaterialsExtraction and Separation Processes

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

A comprehensive single-particle-degradation model for battery state-of-health prediction

Open access

Li, J., Landers, R.G., Park, J.·2020·Journal of Power Sources

DOI 10.1016/j.jpowsour.2020.227950

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.jpowsour.2020.227950

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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 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
Self-citation
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
A comparative study of equivalent circuit models for Li-ion batteries
Hu, X., Li, S., & Peng, H. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.10.013 · Cited by 2,179
Capacity Fade Mechanisms and Side Reactions in Lithium‐Ion Batteries
Arora, P., White, R. E., & Doyle, M. (1998) · Journal of The Electrochemical Society · doi:10.1149/1.1838857 · Cited by 1,408
Phase diagram of Li x C 6
Dahn, J. R. (1991) · Physical review. B, Condensed matter · doi:10.1103/physrevb.44.9170 · Cited by 995
Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles
Rahimi-Eichi, H., Ojha, U., Baronti, F., & Chow, M. (2013) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2013.2250351 · Cited by 965
Theory of SEI formation in rechargeable batteries: capacity fade, accelerated aging and lifetime prediction
Pinson, M. B., & Bazant, M. Z. (2013) · Journal of The Electrochemical Society · doi:10.1149/2.044302jes · Cited by 946
Numerical Simulation of Intercalation-Induced Stress in Li-Ion Battery Electrode Particles
Zhang, X., Shyy, W., & Sastry, A. M. (2007) · Journal of The Electrochemical Society · doi:10.1149/1.2759840 · Cited by 894
Dissolution of Spinel Oxides and Capacity Losses in 4 V Li / Li x Mn2 O 4 Cells
Jang, D. H., Shin, Y. J., & Oh, S. M. (1996) · Journal of The Electrochemical Society · doi:10.1149/1.1836981 · Cited by 854
A Single Particle Model with Chemical/Mechanical Degradation Physics for Lithium Ion Battery State of Health (SOH) Estimation
Li, J., Adewuyi, K., Lotfi, N., Landers, R. G., & Park, J. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.01.011 · Cited by 623
Self-citation

Show all 33 references

Research works printed but not linked: 1

Lin, 2013, A comprehensive capacity fade model and analysis for Li-ion batteries, J. Electrochem. Soc., 160, A1701, 10.1149/2.040310jes · DOI

Sorted by citations

Lithium ion battery degradation: what you need to know
Edge, J. S., O’Kane, S. E. J., Prosser, R., Kirkaldy, N., Patel, A. N., Hales, A., Ghosh, A., Ai, W., Chen, J., Jiang, Y., et al. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp00359c · Cited by 1,304
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 426
Lithium-ion battery degradation: how to model it
O'Kane, S. E. J., Ai, W., Madabattula, G., Alonso-Alvarez, D., Timms, R., Sulzer, V., Edge, J. S., Wu, B., Offer, G. J., & Marinescu, M. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp00417h · Cited by 400
A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction
Elmahallawy, M., Elfouly, T., Alouani, A. T., & Massoud, A. M. (2022) · IEEE Access · doi:10.1109/access.2022.3221137 · Cited by 260
Machine learning for predicting battery capacity for electric vehicles
Zhao, J., Ling, H., Liu, J., Wang, J., Burke, A. F., & Lian, Y. (2022) · eTransportation · doi:10.1016/j.etran.2022.100214 · Cited by 206
Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries
Liu, K., Gao, Y., Zhu, C., Li, K., Fei, M., Chen, P., Zhang, X., & Han, Q. (2022) · Control Engineering Practice · doi:10.1016/j.conengprac.2022.105176 · Cited by 179
A survey on lithium-ion battery internal and external degradation modeling and state of health estimation
Vennam, G., Sahoo, A., & Ahmed, S. M. F. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104720 · Cited by 146
Aging mechanisms, prognostics and management for lithium-ion batteries: Recent advances
Wang, Y., Xiang, H., Soo, Y.-Y., & Fan, X. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.114915 · Cited by 142
A state-of-health estimation method of lithium-ion batteries based on multi-feature extracted from constant current charging curve
Guo, Y., Huang, K., & Hu, X. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102372 · Cited by 116
Implementation and evaluation of a practical electrochemical- thermal model of lithium-ion batteries for EV battery management system
Gao, Y., Zhu, C., Zhang, X., & Guo, B. (2020) · Energy · doi:10.1016/j.energy.2020.119688 · Cited by 111
A comparative study of battery state-of-health estimation based on empirical mode decomposition and neural network
Li, K., Wang, Y., & Chen, Z. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105333 · Cited by 94
Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?
Diddens, D., Appiah, W. A., Mabrouk, Y., Heuer, A., Vegge, T., & Bhowmik, A. (2022) · Advanced Materials Interfaces · doi:10.1002/admi.202101734 · Cited by 90
An electrochemical-thermal-aging effects coupled model for lithium-ion batteries performance simulation and state of health estimation
Chen, S., Zhang, Q., Wang, F., Wang, D., & He, Z. (2023) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2023.122128 · Cited by 91
A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle
Zou, W., Li, J., Yang, Q., Wan, X., He, Y., & Lan, H. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120667 · Cited by 79
Self-citation
Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge
Jo, S., Jung, S., & Roh, T. (2021) · Energies · doi:10.3390/en14217206 · Cited by 76
A systematic review of electrochemical model-based lithium-ion battery state estimation in battery management systems
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
A voltage reconstruction model based on partial charging curve for state-of-health estimation of lithium-ion batteries
Yang, S., Zhang, C., Jiang, J., Zhang, W., Gao, Y., & Zhang, L. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102271 · Cited by 67
State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining
Huang, K., Yao, K., Guo, Y., & Lv, Z. (2023) · Energy · doi:10.1016/j.energy.2023.128739 · Cited by 65
Mathematical modelling of electrochemical, thermal and degradation processes in lithium-ion cells—A comprehensive review
Mehta, R., & Gupta, A. K. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114264 · Cited by 66
Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review
Ding, S., Li, Y., Dai, H., Wang, L., & He, X. (2023) · Advanced Energy Materials · doi:10.1002/aenm.202301452 · Cited by 67
Optimal configuration of battery energy storage system with multiple types of batteries based on supply-demand characteristics
Jiang, Y., Kang, L., & Liu, Y. (2020) · Energy · doi:10.1016/j.energy.2020.118093 · Cited by 63
Multi-objective optimization of an air cooling battery thermal management system considering battery degradation and parasitic power loss
Li, W., Wang, N., Garg, A., & Gao, L. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106382 · Cited by 62
SOC and SOH Joint Estimation of Lithium-Ion Battery Based on Improved Particle Filter Algorithm
Wu, T., Liu, S., Wang, Z., & Huang, Y. (2022) · Journal of Electrical Engineering & Technology · doi:10.1007/s42835-021-00861-y · Cited by 61
Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation
Shamarova, N., Suslov, K., ILYUSHIN, P. V., & Shushpanov, I. (2022) · Energies · doi:10.3390/en15196967 · Cited by 73
Toward more realistic microgrid optimization: Experiment and high-efficient model of Li-ion battery degradation under dynamic conditions
Wei, Y., Wang, S., Han, X., Lu, L., Li, W., Zhang, F., & Ouyang, M. (2022) · eTransportation · doi:10.1016/j.etran.2022.100200 · Cited by 60
State of health estimation of large-cycle lithium-ion batteries based on error compensation of autoregressive model
Feng, H., & Yan, H. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104869 · Cited by 59
An improved single-particle model with electrolyte dynamics for high current applications of lithium-ion cells
Mehta, R., & Gupta, A. K. (2021) · Electrochimica Acta · doi:10.1016/j.electacta.2021.138623 · Cited by 57
Battery state of health estimation under fast charging via deep transfer learning
Zhao, J., Li, D., Li, Y., Shi, D., Nan, J., & Burke, A. F. (2025) · iScience · doi:10.1016/j.isci.2025.112235 · Cited by 53
“24–27 SOH estimation methods based on physical models typically require precise battery parameters and complex experimental measurement equipment, making them difﬁcult to apply broadly in large-scale and diverse application scenarios.”
Research Progress of Battery Life Prediction Methods Based on Physical Model
Wang, X., Ye, P., Liu, S., Zhu, Y., Deng, Y., Yuan, Y., & Ni, H. (2023) · Energies · doi:10.3390/en16093858 · Cited by 50
Analysis of the lithium-ion battery capacity degradation behavior with a comprehensive mathematical model
Zhou, F., & Bao, C. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230630 · Cited by 43
A novel data-driven method for online parameter identification of an electrochemical model based on cuckoo search and particle swarm optimization algorithm
Huang, S., Lin, N., Wang, Z., Zhang, Z., Wen, S., Zhao, Y., & Li, Q. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234261 · Cited by 42
Physics-based, reduced order degradation model of lithium-ion batteries
Jana, A., Mitra, A. S., Das, S., Chueh, W. C., Bazant, M. Z., & Garcı́a, R. E. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.231900 · Cited by 40
State-of-Health Prediction for Li-ion Batteries for Efficient Battery Management System Using Hybrid Machine Learning Model
Myilsamy, V., Sengan, S., Alroobaea, R., & Alsafyani, M. (2023) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-023-01564-2 · Cited by 39
Resource-efficient artificial intelligence for battery capacity estimation using convolutional FlashAttention fusion networks
Lv, Z., & Zhao, J. (2024) · eTransportation · doi:10.1016/j.etran.2024.100383 · Cited by 37
A novel characteristic-based degradation model of Li-ion batteries for maximum financial benefits of energy storage system during peak demand reductions
Lam, D. H. C., Lim, Y. S., Wong, J., Allahham, A., & Patsios, C. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121206 · Cited by 35
Parameter sensitivity analysis of a multi-physics coupling aging model of lithium-ion batteries
Wang, Y., Li, J., Guo, S., Sun, M., Zhao, H., Wu, Y., Zhao, L., & Wang, Z. (2024) · Electrochimica Acta · doi:10.1016/j.electacta.2024.143811 · Cited by 33
Self-citation
An efficient optimal sizing strategy for a hybrid electric air-ground vehicle using adaptive spiral optimization algorithm
Wang, W., Chen, Y., Yang, C., Li, Y., Xu, B., Huang, K., & Xiang, C. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230704 · Cited by 33
A Review on the Degradation Implementation for the Operation of Battery Energy Storage Systems
García-Miguel, P. L. C., Alonso-Martínez, J., Arnaltes, S., Plaza, M. G., & Asensio, A. P. (2022) · Batteries · doi:10.3390/batteries8090110 · Cited by 33
Increasing generalization capability of battery health estimation using continual learning
Che, Y., Zheng, Y., Onori, S., Hu, X., & Teodorescu, R. (2023) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2023.101743 · Cited by 34
Rapid estimation of residual capacity for retired LiFePO4 batteries using voltage interval at low state of charge
Ni, Y., Xu, J., Zhu, C., Zhang, H., Yu, Y., Song, K., & Wu, C. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.12.005 · Cited by 33
A digital twin to quantitatively understand aging mechanisms coupled effects of NMC battery using dynamic aging profiles
Guo, W., Li, Y., Sun, Z., Vilsen, S. B., & Stroe, D. (2023) · Energy storage materials · doi:10.1016/j.ensm.2023.102965 · Cited by 29
Unveiling the secrets behind physics-based modeling of lithium-ion battery degradation and its key applications
Fan, G., Zhou, B., Ye, S., Shen, H., Huo, D., & Zhang, X. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114086 · Cited by 29
Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning
Wang, Y., Li, K., & Chen, Z. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105779 · Cited by 27
Internal fault diagnosis method for lithium batteries based on a failure physical model
Li, J., Li, T., Fang, D., Wang, Y., Guo, S., Wang, Z., & Yu, Q. (2023) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2023.107714 · Cited by 26
Self-citation
Review on State of charge prediction of battery management system in electric vehicles
Karthick, A. (2025) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070251380931 · Cited by 25
Novel strategy based on improved Kalman filter algorithm for state of health evaluation of hybrid electric vehicles Li-ion batteries during short- and longer term operating conditions
Ren, P., Wang, S., He, M., & Cao, W. (2021) · Journal of Power Electronics · doi:10.1007/s43236-021-00253-5 · Cited by 25
Estimation of maximum available capacity of lithium-ion battery based on multi-view features extracted from reconstructed charging curve
Huang, K., Guo, Y., & Li, S. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.04.087 · Cited by 26
Battery health prediction under data scarcity: A cross-domain physics-informed 5-shot framework with GRU-Transformer
Nie, X., Pan, Y., Zhang, Y., Luo, Z., & Wang, S. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.127012 · Cited by 25
Physics-based fractional-order model and parameters identification of liquid metal battery
Shi, Q., Guo, Z., Wang, S., Yan, S., Zhou, X., Li, H., Wang, K., & Jiang, K. (2022) · Electrochimica Acta · doi:10.1016/j.electacta.2022.140916 · Cited by 23
Simulation Study on Heat Generation Characteristics of Lithium-Ion Battery Aging Process
Huang, R. N., Xu, Y., Wu, Q., Chen, J., Chen, F., & Yu, X. (2023) · Electronics · doi:10.3390/electronics12061444 · Cited by 23
A CNN-LSTM hybrid network with transfer learning for accurate lithium-ion battery state of health estimation
Zhang, X., Wang, L., Gong, Q., & Wang, Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119450 · Cited by 17
Control-Oriented Physics-Based Modeling and Observer Design for State-of-Charge Estimation of Lithium-Ion Cells for High Current Applications
Nath, A., Mehta, R., Gupta, R., Bahga, S. S., Gupta, A. K., & Bhasin, S. (2022) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2022.3152446 · Cited by 17
State of health indicators for second life battery through non-destructive test approaches from repurposer perspective
Vignesh, S., Seng, H., Selvaraj, J. A.-L., & Tey, K. S. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111656 · Cited by 19
Rapid Simulation of Electro-Chemo-Mechanical Deformation of Li-ion Batteries Based On Porous Electrode Theory
Ipers, G., Jiao, J., Pathak, S., Fang, R., Berliner, M. D., Li, W., Li, W., Braatz, R. D., Bazant, M. Z., & Zhu, J. (2024) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ad4f1e · Cited by 19
Effect of porous structure and morphology of cathode on the degradation of lithium-ion batteries
Lee, Y. K. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104788 · Cited by 16
Battery health prediction using two-dimensional multi-channel ensemble models
Yao, L. Y., Zhao, C., Xiao, Y., Dai, H., Fei, Z., & Zhang, L. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111304 · Cited by 16
Lithium-ion battery health prognosis via electrochemical impedance spectroscopy using CNN-BiLSTM model
Liu, Z., Sun, Y., Yu-tian, L., Liu, Y., Chen, Y., & Zhang, Y. (2024) · Journal of Materials Informatics · doi:10.20517/jmi.2024.09 · Cited by 18
Data-driven modeling of lithium-ion battery degradation using XGBoost with extended Kalman filter-based internal resistance correction
Apribowo, C. H. B., Ashidqi, M. D., Nizam, M., & Purwanto, A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.108100 · Cited by 14
State‐of‐Health Estimation of Lithium‐Ion Batteries Based on Thermal Characteristics Mining and Multi‐Gaussian Process Regression Strategy
Guo, Y., Yu, X., Wang, Y., Zhang, R., & Huang, K. (2022) · Energy Technology · doi:10.1002/ente.202200151 · Cited by 14
State-of-health estimation of lithium-ion batteries based on QPSO-BPNN
Yao, Y., Li, F., Li, H., Liu, J., Wang, X., & Li, T. (2024) · Ionics · doi:10.1007/s11581-024-05968-6 · Cited by 13
Nanofluid thermoelectric cooler based advanced battery thermal management system for battery degradation mitigation in real driving cycles
Khalili, H., Ahmadi, P., Ashjaee, M., & Houshfar, E. (2022) · International Journal of Energy Research · doi:10.1002/er.8065 · Cited by 13
Battery Pack State of Charge Estimation Toward Transportation Electrification: Challenges and Opportunities
Fan, C., Liu, K., Gu, C., Yu, Q., Wang, R., Fang, J., & Peng, Q. (2025) · IEEE Journal of Emerging and Selected Topics in Industrial Electronics · doi:10.1109/jestie.2025.3545746 · Cited by 12
Data-Driven Semi-Empirical Model Approximation Method for Capacity Degradation of Retired Lithium-Ion Battery Considering SOC Range
Xu, W., Cao, H., Lin, X., Shu, F., Du, J., Wang, J., & Tang, J. (2023) · Applied Sciences · doi:10.3390/app132111943 · Cited by 13
Unravelling degradation mechanisms and overpotential sources in aged and non-aged batteries: A non-invasive diagnosis
Appiah, W. A., Rieger, L., Flores, E., Vegge, T., & Bhowmik, A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111000 · Cited by 12
Towards interactional management for power batteries of electric vehicles
He, R., Xie, W., Wu, B., Brandon, N. P., Liu, X., Li, X., & Yang, S. (2023) · RSC Advances · doi:10.1039/d2ra06004c · Cited by 18
Improving the convergence rate of Newman’s battery model using 2nd order finite element method
Ai, W., & Liu, Y. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107512 · Cited by 12
Determination of constant current to constant voltage switch-over point for health-aware fast charging using heuristic algorithm
Bose, B., Garg, A., Panigrahi, B. K., & Kim, J. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107543 · Cited by 12
Accurate and adaptive state of health estimation for lithium-ion battery based on patch learning framework
Li, Y., Zhang, X., Li, Z., Li, X., Liu, G., & Gao, W. J. (2025) · Measurement · doi:10.1016/j.measurement.2025.117083 · Cited by 12
Identification of thermodiffusion coefficients in an aqueous potassium ferri/ferrocyanide thermocell using open-circuit voltage data
Gao, Q., Li, Z., Huang, R. N., Chen, J., & Yu, X. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.07.075 · Cited by 11
Estimation of remaining capacity of lithium-ion batteries based on X-ray computed tomography
Hou, J., Wu, W., Li, L., Tong, X., Hu, R., Wu, W., Cai, W., & Wang, H. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105369 · Cited by 11
Multiscale Mechanical Failures in Lithium-Ion Batteries: Experimental and Theoretical Approaches
Chen, C., Wu, Y., Fan, J., Wang, Z., Sun, L., & Chen, H. (2025) · Acta Mechanica Solida Sinica · doi:10.1007/s10338-025-00584-x · Cited by 10
Surface effects in cylindrical anode particles: Mechanical versus electrochemical performance determined by charging condition in lithium-ion batteries
Sengupta, A., & Chakraborty, J. (2021) · Journal of Applied Physics · doi:10.1063/5.0051370 · Cited by 9
Comprehensive feature extraction for battery health prognostics: Identifying predictive indicators of state of health
Sylvestrin, G. R., Maciel, J. N., & Ando, O. H. (2026) · Journal of Power Sources · doi:10.1016/j.jpowsour.2026.239389 · Cited by 9
Capacity degradation and reliability modeling of lithium-ion batteries based on electrochemical mechanism with epistemic uncertainty
Chen, W.-B., Li, X., Li, F., & Guo, Z.-C. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113080 · Cited by 8
Advancements in battery state prediction methods: A comprehensive review of data-driven approaches and degradation mechanisms
Zheng, W., Wu, D., Yuan, C., Jiang, H., & Wang, S. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.238192 · Cited by 8
The hotspot ignition nature of thermal runaway in Li-ion batteries cycled under low temperatures
Zhang, L., Liu, L., Terekhov, A. Y., Warnberg, D., Zawodzinski, T. A., & Zhao, P. F. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117272 · Cited by 8
State of Health analysis for Lithium-ion Batteries considering temperature effect
Lashgari, F., Petkovski, E., & Cristaldi, L. (2022) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine54828.2022.9967550 · Cited by 7
Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network
Wu, Y. T., & Li, W. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12040256 · Cited by 8
A Deep Learning Method for the Health State Prediction of Lithium-Ion Batteries Based on LUT-Memory and Quantization
Al-Meer, M. H. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15020038 · Cited by 8
sP2D: Simplified pseudo 2D battery model by piecewise sinusoidal/quadratic functions of potential curves
Ai, W., & Liu, Y. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111386 · Cited by 5
Early prediction of lithium-ion battery health in electric vehicles using small-sample learning and hybrid models
Zhao, C., Yao, L. Y., Xiao, Y., Cui, G., Qu, C., Dai, H., Chen, Z. B., Wang, T., & Fei, Z. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111924 · Cited by 4
Damage mechanics of battery electrodes under applied stress and electrochemical cycling: Damage characterization using EIS and DMA
Metlich, C., Boyd, J. G., & Lagoudas, D. C. (2025) · Electrochimica Acta · doi:10.1016/j.electacta.2025.147630 · Cited by 4
Modeling and state estimation of liquid metal batteries
Badr, M. M., Alshammari, L. S., Zeeshan, M., Faiad, A. A., Venkatraman, M. S., Calvet, N., & Ahmed, S. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116495 · Cited by 4
Self-supervised learning for electric vehicle battery remaining useful life prediction using real-world unlabeled data
Lv, Z., Ou, S. S., Jing, H., Wu, G., & Shi, D. (2026) · Energy · doi:10.1016/j.energy.2026.141302 · Cited by 4
Assessment of Core-Shell and Agglomerate Particle Design for All-Solid-State Batteries
Cistjakov, W., Laue, V., Röder, F., & Krewer, U. (2022) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ac67f8 · Cited by 4
A review of modeling the aging mechanism of LIBs: Multi-physics field coupling analysis from LLI to LAM
Long, Z., Chen, K., Tang, M., Zheng, X., Wang, H., Luo, Y., Li, Y., Nie, G., Zhang, X., Gao, G., et al. (2026) · International Journal of Green Energy · doi:10.1080/15435075.2026.2619449 · Cited by 3
Joint non-invasive identification of an electrochemical and thermal model for an ultra high-power Li-ion pouch cell
Trivella, A., Radrizzani, S., Corno, M., & Savaresi, S. M. (2025) · Control Engineering Practice · doi:10.1016/j.conengprac.2024.106228 · Cited by 3
Parameter Identification and Optimization for Lithium-Ion Battery State of Health Detection
Sahay, R., & Raghavan, N. (2024) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm61352.2024.10626309 · Cited by 3
Autoregressive model-based parameter correlation for state of charge and state of health of lithium-ion batteries using built-in piezoelectric transducer induced ultrasonic waves
Ahmed, S., Farhangdoust, S., & Chang, F. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.115829 · Cited by 4
Development of DS-CC-CT-CV Charging Strategy for Health-Aware Battery Fast Charging Using Quad Objective Genetic Algorithm
Bose, B., Vandana, V., & Garg, A. (2023) · IEEE International Conference on Sustainable Energy and Future Electric Transportation (SEFET) · doi:10.1109/sefet57834.2023.10244811 · Cited by 2
Lithium battery capacity prediction method based on sparrow algorithm to optimize convolutional neural network and bidirectional long short-term memory network model
Yang, W., Du, J., & Ye, H. (2024) · Asia Energy and Electrical Engineering Symposium (AEEES) · doi:10.1109/aeees61147.2024.10544821 · Cited by 2
Online SOH Estimation in Lithium-Ion Batteries Using Electrochemical Model and Unscented Particle Filter
Lyu, Z., Wu, L., & Chen, Y. (2024) · CPSS & IEEE International Symposium on Energy Storage and Conversion (ISESC) · doi:10.1109/isesc63657.2024.10785472 · Cited by 2
A Disturbance Rejection Control Strategy of a Single Converter Hybrid Electrical System Integrating Battery Degradation
Zhou, Y., Obeid, H., Laghrouche, S., Hilairet, M., & Djerdir, A. (2020) · Energies · doi:10.3390/en13112781 · Cited by 2
Onlinebcp: Online Update Model for Multi-Parameter Bus Battery Prediction with Selective State Space ModulePreprint
Wang, Z., Zhang, C., Shi, B., Zhu, X., Zhou, J., & Li, B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5178664 · Cited by 2
Dual-channel machine learning proxy for pseudo-two-dimensional model with enhanced extrapolation correction
Cui, Y., Wang, Y., Guo, S., Deng, L., Li, J., Zhao, L., & Wang, Z. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113036 · Cited by 1
Self-citation
End-to-end millisecond-level battery aging diagnostics for electric vehicles
Zhao, J., Lv, Z., Che, Y., Li, Y., & Burke, A. F. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119638 · Cited by 2
Mathematical modeling of electrode-electrolyte interfaces: From Li-ion batteries to water electrolysis
Ren, Z., Li, X., & Zheng, S. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.239130 · Cited by 1
Rethinking battery degradation in presence of surface effects: mechanical versus electrochemical peformance mediated by charging conditionPreprint
Sengupta, A., & Chakraborty, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.11264
Mathematical modeling of lithium–sulfur batteries
Wang, S., Xia, L., Yu, C., Guerrero, J. M., & Xie, Y. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91934-0.00005-3
Electrochemical Modeling of Energy Storage Lithium-Ion Battery
Huang, Q. P., Wang, S., Chen, Z., Xiong, R., Fernández, C., & Stroe, D. (2023) · Springer eBooks · doi:10.1007/978-981-99-5344-8_2

Authors: Li, J., Landers, R. G., & Park, J.
Affiliations: Missouri University of Science and Technology (US)
Corresponding author: Park, J.
ORCID: Li, J.
Landers, R.G.
Venue: Journal of Power Sources
Publisher: Elsevier BV
Published: 2020-04
Volume: 456
Pages: 227950
ISSN: 0378-7753 (ISSN-L)
DOI: 10.1016/j.jpowsour.2020.227950
OpenAlex: W3012214204
Cited by: 116
References: 33
Citations by year: 2026: 8 · 2025: 23 · 2024: 22 · 2023: 21 · 2022: 25 · 2021: 14 · 2020: 3
FWCI: 5.83
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 347 d)
Locations: Journal of Power Sources (acceptedVersion)
Funders: National Science Foundation
Topics: Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
Physical Sciences › Materials Science › Metals and Alloys › Extraction and Separation Processes
Keywords: lithium-ion battery degradation, battery state of health estimation, single particle model, battery management systems, capacity fade, metal dissolution, lithium loss, anode degradation, cathode degradation, effective transport properties

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

A comprehensive single-particle-degradation model for battery state-of-health prediction

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks