A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries

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

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Ageing mechanisms in lithium-ion batteries

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Cycle-life model for graphite-LiFePO4 cells

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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

Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements

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Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements

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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

Synthesize battery degradation modes via a diagnostic and prognostic model

Dubarry, M., Truchot, C., & Liaw, B. Y. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.07.016 · Cited by 1,065

A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification

Han, X., Ouyang, M., Lu, L., Li, J., Zheng, Y., & Li, Z. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.11.029 · Cited by 822

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

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

Understanding and applying coulombic efficiency in lithium metal batteries

Xiao, J., Li, Q., Bi, Y., Cai, M., Dunn, B., Glossmann, T., Liu, J., Ōsaka, T., Sugiura, R., Wu, B., et al. (2020) · Nature Energy · doi:10.1038/s41560-020-0648-z · Cited by 1,056

State-of-charge estimation of lithium-ion batteries using LSTM and UKF

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Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications

Yang, S., Zhang, C., Jiang, J., Zhang, W., Zhang, L., & Wang, Y. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.128015 · Cited by 435

Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine

Pan, H., Lü, Z., Wang, H., Wei, H., & Chen, L. (2018) · Energy · doi:10.1016/j.energy.2018.06.220 · Cited by 322

Life‐Cycle Assessment Considerations for Batteries and Battery Materials

Porzio, J., & Scown, C. D. (2021) · Advanced Energy Materials · doi:10.1002/aenm.202100771 · Cited by 345

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

Polysulfide-based redox flow batteries with long life and low levelized cost enabled by charge-reinforced ion-selective membranes

Li, Z., & Lu, Y. (2021) · Nature Energy · doi:10.1038/s41560-021-00804-x · Cited by 240

State of health estimation of lithium-ion batteries based on the constant voltage charging curve

Wang, Z., Zeng, S., Guo, J., & Qin, T. (2018) · Energy · doi:10.1016/j.energy.2018.11.008 · Cited by 220

Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model

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

Exploring Lithium-Ion Battery Degradation: A Concise Review of Critical Factors, Impacts, Data-Driven Degradation Estimation Techniques, and Sustainable Directions for Energy Storage Systems

Rahman, T., & Alharbi, T. (2024) · Batteries · doi:10.3390/batteries10070220 · Cited by 235

Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries

Kong, J., Yang, F., Zhang, X., Pan, E., Peng, Z., & Wang, D. (2021) · Energy · doi:10.1016/j.energy.2021.120114 · Cited by 204

Lithium-ion battery cell formation: status and future directions towards a knowledge-based process design

Schomburg, F., Heidrich, B., Wennemar, S., Drees, R., Roth, T., Kurrat, M., Heimes, H. H., Jossen, A., Winter, M., Cheong, J. Y., et al. (2024) · Energy & Environmental Science · doi:10.1039/d3ee03559j · Cited by 199

Intelligent state of health estimation for lithium-ion battery pack based on big data analysis

Song, L., Zhang, K., Liang, T., Han, X., & Zhang, Y. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101836 · Cited by 197

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 215

Convolutional Gated Recurrent Unit–Recurrent Neural Network for State-of-Charge Estimation of Lithium-Ion Batteries

Huang, Z., Yang, F., Xu, F., Song, X., & Tsui, K. (2019) · IEEE Access · doi:10.1109/access.2019.2928037 · Cited by 192

Temperature, Ageing and Thermal Management of Lithium-Ion Batteries

Spitthoff, L., Shearing, P. R., & Burheim, O. S. (2021) · Energies · doi:10.3390/en14051248 · Cited by 186

A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis

Madani, S. S., Shabeer, Y., Allard, F., Fowler, M. W., Ziebert, C., Wang, Z., Panchal, S., Chaoui, H., Mekhilef, S., Dou, S. X., et al. (2025) · Batteries · doi:10.3390/batteries11040127 · Cited by 208

A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries

Yang, F., Song, X., Dong, G., & Tsui, K. (2019) · Energy · doi:10.1016/j.energy.2019.01.083 · Cited by 159

A high-rate and long-life organic–oxygen battery

Cong, G., Wang, W., Lai, N., Liang, Z., & Lu, Y. (2019) · Nature Materials · doi:10.1038/s41563-019-0286-7 · Cited by 151

Environmental feasibility of secondary use of electric vehicle lithium-ion batteries in communication base stations

Yang, J., Гу, Ф., & Guo, J. (2020) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2020.104713 · Cited by 160

Life prediction of lithium-ion batteries based on stacked denoising autoencoders

Xu, F., Yang, F., Fei, Z., Huang, Z., & Tsui, K. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107396 · Cited by 150

Accelerated degradation model for C-rate loading of lithium-ion batteries

Saxena, S., Xing, Y., Kwon, D., & PECHT, M. G. (2018) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2018.12.016 · Cited by 149

Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods

Tian, Y., Lin, C. W., Li, H., Du, J., & Xiong, R. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117386 · Cited by 146

Dynamic Bayesian Network-Based Lithium-Ion Battery Health Prognosis for Electric Vehicles

Dong, G., Han, W., & Wang, Y. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3034855 · Cited by 122

A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression

Pang, X., Liu, X., Jia, J., Wen, J., Shi, Y., Zeng, J., & Zhao, Z. (2021) · Microelectronics Reliability · doi:10.1016/j.microrel.2021.114405 · Cited by 116

Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles

Meng, J., Cai, L., Stroe, D., Luo, G., Sui, X., & Teodorescu, R. (2019) · Energy · doi:10.1016/j.energy.2019.07.127 · Cited by 114

Environmental impact assessment of second life and recycling for LiFePO4 power batteries in China

Wang, Y., Tang, B.-J., Shen, M., Wu, Y., Qu, S., Hu, Y., & Feng, Y. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.115083 · Cited by 119

Effect of low temperature on thermal runaway and fire behaviors of 18650 lithium-ion battery: A comprehensive experimental study

Kong, D.-P., Zhao, H., Ping, P., Zhang, Y., & Wang, G.-Q. (2023) · Chemical engineering research & design · doi:10.1016/j.psep.2023.04.017 · Cited by 104

Data-Driven Battery Health Prognosis Using Adaptive Brownian Motion Model

Dong, G., Yang, F., Wei, Z., Wei, J., & Tsui, K. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2948018 · Cited by 110

Does energy storage provide a profitable second life for electric vehicle batteries?

Wu, W., Lin, B., Xie, C., Elliott, R., & Radcliffe, J. W. (2020) · Energy Economics · doi:10.1016/j.eneco.2020.105010 · Cited by 108

Energy efficiency of lithium-ion batteries: Influential factors and long-term degradation

Lin, Z., Li, D., & Zou, Y. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109386 · Cited by 97

Dehydrogenation-driven Li metal-free prelithiation for high initial efficiency SiO-based lithium storage materials

Chung, D. J., Youn, D., Kim, S., Ma, D., Lee, J., Jeong, W. J., Park, E., Kim, J.-S., Moon, C., Lee, J. Y., et al. (2021) · Nano Energy · doi:10.1016/j.nanoen.2021.106378 · Cited by 92

Battery eruption triggered by plated lithium on an anode during thermal runaway after fast charging

Li, Y., Gao, X., Feng, X., Ren, D., Li, Y., Hou, J., Wu, Y., Du, J., Lu, L., & Ouyang, M. (2021) · Energy · doi:10.1016/j.energy.2021.122097 · Cited by 92

Developing a real-time data-driven battery health diagnosis method, using time and frequency domain condition indicators

Khaleghi, S., Firouz, Y., Mierlo, J. V., & den Bossche, P. V. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113813 · Cited by 87

State-of-Charge Estimation of Lithium-ion Batteries Using LSTM Deep Learning Method

Chung, D., Ko, J.-H., & Yoon, K.-Y. (2022) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-021-00954-8 · Cited by 85

The role of batteries in meeting the PV terawatt challenge

Peters, I. M., Breyer, C., Jaffer, S. A., Kurtz, S., Reindl, T., Sinton, R. A., & Vetter, M. (2021) · Joule · doi:10.1016/j.joule.2021.03.023 · Cited by 84

Are electric vehicle batteries being underused? A review of current practices and sources of circularity

Etxandi-Santolaya, M., Casals, L. C., Montes, T., & Corchero, C. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.117814 · Cited by 94

Li-ion battery degradation modes diagnosis via Convolutional Neural Networks

Costa, N., Sánchez, L., Anseán, D., & Dubarry, M. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105558 · Cited by 81

Degradation identification of LiNi0.8Co0.1Mn0.1O2/graphite lithium-ion batteries under fast charging conditions

Xie, W., He, R., Gao, X., Li, X., Wang, H., Liu, X., Yan, X., & Yang, S. (2021) · Electrochimica Acta · doi:10.1016/j.electacta.2021.138979 · Cited by 80

An Advanced Microstructural and Electrochemical Datasheet on 18650 Li-Ion Batteries with Nickel-Rich NMC811 Cathodes and Graphite-Silicon Anodes

Heenan, T. M. M., Jnawali, A., Kok, M. D. R., Tranter, T. G., Tan, C., Dimitrijevic, A., Jervis, R., Brett, D. J. L., & Shearing, P. R. (2020) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/abc4c1 · Cited by 78

Adaptive sliding window LSTM NN based RUL prediction for lithium-ion batteries integrating LTSA feature reconstruction

Wang, Z., Liu, N., & Guo, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.09.025 · Cited by 79

Constructing a health indicator for roller bearings by using a stacked auto-encoder with an exponential function to eliminate concussion

Xu, F., Huang, Z., Yang, F., Wang, D., & Tsui, K. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106119 · Cited by 79

Prospective strategies for extending long-term cycling performance of anode-free lithium metal batteries

Liu, S., Jiao, K., & Yan, J. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.11.021 · Cited by 77

Practical state estimation using Kalman filter methods for large-scale battery systems

Wang, Z., Gladwin, D. T., Smith, M. J., & Haass, S. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117022 · Cited by 75

Graphene decorated polymeric flexible materials for lightweight high areal energy lithium-ion batteries

Aslam, S., Sagar, R. U. R., Liu, Y., Anwar, T., Zhang, L., Zhang, M., Mahmood, N., & Qiu, Y. (2019) · Applied Materials Today · doi:10.1016/j.apmt.2019.08.003 · Cited by 72

A facile way to regenerate FePO4∙2H2O precursor from spent lithium iron phosphate cathode powder: Spontaneous precipitation and phase transformation in an acidic medium

Lou, W., Zhang, Y., Zhang, Y., Zheng, S., Sun, P., Wang, X., Qiao, S., Li, J., Zhang, Y., Liu, D.-Y., et al. (2020) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2020.158148 · Cited by 69

Implications of the Electric Vehicle Manufacturers’ Decision to Mass Adopt Lithium-Iron Phosphate Batteries

Walvekar, H., Beltrán, H., Sripad, S., & PECHT, M. G. (2022) · IEEE Access · doi:10.1109/access.2022.3182726 · Cited by 65

A feature fusion-based convolutional neural network for battery state-of-health estimation with mining of partial voltage curve

Zhen-feng, L., Fei, Z., Wang, B., & Yang, F. (2023) · Energy · doi:10.1016/j.energy.2023.129690 · Cited by 66

Designing a high-performance nitrogen-doped titanium dioxide anode material for lithium-ion batteries by unravelling the nitrogen doping effect

Choi, W. H., Lee, C. H., Kim, H.-E., Lee, S. U., & Bang, J. H. (2020) · Nano Energy · doi:10.1016/j.nanoen.2020.104829 · Cited by 64

Hybrid energy storage system topology approaches for use in transport vehicles: A review

Lencwe, M. J., Chowdhury, S. D., & Olwal, T. O. (2022) · Energy Science & Engineering · doi:10.1002/ese3.1068 · Cited by 64

A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

Samanta, A., & Williamson, S. S. (2021) · Energies · doi:10.3390/en14185960 · Cited by 62

A hybrid data-driven approach for SOH estimation of lithium-ion batteries based on health index extraction and QHDBO-BiTCN-BiGRU

Quan, R., Zhou, Y., Li, W., & Wan, H. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116788 · Cited by 62

Revealing hidden predicaments to lithium-ion battery dynamics for electric vertical take-off and landing aircraft

Ayyaswamy, A., Vishnugopi, B., & Mukherjee, P. (2023) · Joule · doi:10.1016/j.joule.2023.07.014 · Cited by 54

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

Park, J., Lee, M., Kim, G., Park, S., & Kim, J. (2020) · Energies · doi:10.3390/en13092138 · Cited by 59

In situ transmission electron microscopy observations of rechargeable lithium ion batteries

Woods, J., Bhattarai, N., Chapagain, P., Yang, Y.-H., & Neupane, S. (2019) · Nano Energy · doi:10.1016/j.nanoen.2018.11.087 · Cited by 47

Lifetime Extension of Lithium-Ion Batteries With Low-Frequency Pulsed Current Charging

Huang, X., Liu, W., Meng, J., Li, Y., Jin, S., Teodorescu, R., & Stroe, D. (2021) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2021.3130424 · Cited by 55

State of charge estimation by finite difference extended Kalman filter with HPPC parameters identification

He, L., Hu, M., Wei, Y., Liu, B., & Shi, Q. (2019) · Science China Technological Sciences · doi:10.1007/s11431-019-1467-9 · Cited by 55

Solid Solution Engineering of Co–Ni-Based Ternary Molybdate Nanorods toward Hybrid Supercapacitors and Lithium-Ion Batteries as High-Performance Electrodes

Denis, D. K., Sun, X., Zhang, J., Wang, Y., Hou, L., Li, J., & Yuan, C. (2020) · ACS Applied Energy Materials · doi:10.1021/acsaem.0c00353 · Cited by 56

Insight into pulse-charging for lithium plating-free fast-charging lithium-ion batteries

Jeong, Y. T., Shin, H. R., Lee, J. H., Ryu, M.-H., Choi, S., Kim, H., Jung, K., & Lee, J.-W. (2023) · Electrochimica Acta · doi:10.1016/j.electacta.2023.142761 · Cited by 54

A combination state of charge estimation method for ternary polymer lithium battery considering temperature influence

Li, L., Wang, C.-Y., Yan, S., & Zhao, W.-Z. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.229204 · Cited by 48

SOH Estimation and SOC Recalibration of Lithium-Ion Battery with Incremental Capacity Analysis & Cubic Smoothing Spline

Lin, C. P., Cabrera, J., Yu, D. Y. W., Yang, F., & Tsui, K. (2020) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ab8f56 · Cited by 51

Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system

Kim, J., Sin, S., & Kim, J. (2024) · Energy · doi:10.1016/j.energy.2024.131285 · Cited by 50

Coupling Lithium Plating with SEI Formation in a Pseudo-3D Model: A Comprehensive Approach to Describe Aging in Lithium-Ion Cells

Carelli, S., & Bessler, W. G. (2022) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ac716a · Cited by 49

Lithium-ion battery remaining useful life prediction using a two-phase degradation model with a dynamic change point

Wang, R., Zhu, M., Zhang, X., & Pham, H. N. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106457 · Cited by 50

Development and Validation of Cycle and Calendar Aging Model for 144Ah NMC/Graphite Battery at Multi Temperatures, DODs, and C-Rates

Panchal, S., Pierre, V., Cancian, M., Gross, O., Estefanous, F., & Badawy, T. (2023) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2023-01-0503 · Cited by 48

Optimal energy management for hybrid-electric aircraft

Leite, J. P. S. P., & Voskuijl, M. (2020) · Aircraft Engineering and Aerospace Technology · doi:10.1108/aeat-03-2019-0046 · Cited by 47

State of health estimation of lithium-ion battery by removing model redundancy through aging mechanism

Zhu, X., Wang, W., Zou, G., Chen, Z., & Zou, H. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.105018 · Cited by 48

Physics-guided TL-LSTM network for early-stage degradation trajectory prediction of lithium-ion batteries

Liu, Q., Shang, Z., Lu, S., Liu, Y., Liu, Y., & Sheng, Y. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114736 · Cited by 48

Next-generation battery safety management: Machine learning assisted life-time prediction and performance enhancement

Wang, L., Qiu, Y., Yuan, W., Tian, Y., & Zhou, Z. (2025) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2025.05.065 · Cited by 46

A Copula-based battery pack consistency modeling method and its application on the energy utilization efficiency estimation

Jiang, Y., Jiang, J., Zhang, C., Zhang, W., Gao, Y., & Mi, C. C. (2019) · Energy · doi:10.1016/j.energy.2019.116219 · Cited by 46

Thermal Characteristics and Safety Aspects of Lithium-Ion Batteries: An In-Depth Review

Madani, S., Ziebert, C., & Marzband, M. (2023) · Symmetry · doi:10.3390/sym15101925 · Cited by 50

Recent advancements in battery state of power estimation technology: A comprehensive overview and error source analysis

Guo, R., & Shen, W. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114294 · Cited by 50

State of Health Estimation and Battery Management: A Review of Health Indicators, Models and Machine Learning

Li, M., Xu, W., Zhang, S., Liu, L., Hussain, A., Hu, E., Zhang, J., Mao, Z., & Chen, Z. (2025) · Materials · doi:10.3390/ma18010145 · Cited by 51

Investigation of the temperature dependence of lithium plating onset conditions in commercial Li-ion batteries

Cabañero, M. A., Altmann, J., Gold, L., Boaretto, N., Müller, J., Hein, S., Zausch, J., Kallo, J., & Latz, A. (2019) · Energy · doi:10.1016/j.energy.2019.01.017 · Cited by 45

The role of metal substitutions in the development of Li batteries, part I: cathodes

Hebert, A., & McCalla, E. (2021) · Materials Advances · doi:10.1039/d1ma00081k · Cited by 45

The significance of detecting imperceptible physical/chemical changes/reactions in lithium-ion batteries: a perspective

Zhao, H., Lam, W. A., Wang, L., Xu, H., Daoud, W. A., & He, X. (2022) · Energy & Environmental Science · doi:10.1039/d2ee01020h · Cited by 44

Cycling performance of LiFePO4/graphite batteries and their degradation mechanism analysis via electrochemical and microscopic techniques

Sharifi, H., Mosallanejad, B., Mohammadzad, M., Hosseini‐Hosseinabad, S. M., & Ramakrishna, S. (2021) · Ionics · doi:10.1007/s11581-021-04258-9 · Cited by 43

Online electrochemical mass spectrometry on large-format Li-ion cells

Misiewicz, C., Lundström, R., Ahmed, I., Lacey, M. J., Brant, W. R., & Berg, E. J. (2022) · Journal of Power Sources · doi:10.1016/j.jpowsour.2022.232318 · Cited by 43

State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model

Huang, Z., Xu, F., & Yang, F. (2022) · Energy · doi:10.1016/j.energy.2022.125497 · Cited by 43

Understanding Binder–Silicon Interactions during Slurry Processing

Burdette‐Trofimov, M. K., Armstrong, B. L., Rogers, A. M., Heroux, L., Doucet, M., Yang, G. H., Phillip, N. D., Kidder, M. K., & Veith, G. M. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c03660 · Cited by 42

A lithiated gel polymer electrolyte with superior interfacial performance for safe and long-life lithium metal battery

Yuan, J., Sun, C., Fang, L., Song, Y., Yan, Y., Qiu, Z., Shen, Y., Li, H., & Zhu, B. (2020) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2020.06.052 · Cited by 41

SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters

Wang, Q., Gao, T. Y., & Li, X. (2022) · Energies · doi:10.3390/en15165829 · Cited by 40

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

The influence of the voltage plateau on the coulombic efficiency and capacity degradation in LiNi0.5Mn1.5O4 materials

Cui, X., Geng, T., Zhang, F., Zhang, N., Zhao, D., Li, C., & Li, S. (2019) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2019.153443 · Cited by 40

A chronicle of titanium niobium oxide materials for high‐performance lithium‐ion batteries: From laboratory to industry

Peng, C., Liang, S., Yu, Y., Cao, L., Yang, C., Liu, X., Guo, K., Müller‐Buschbaum, P., Cheng, Y., & Wang, C. (2024) · Carbon Neutralization · doi:10.1002/cnl2.177 · Cited by 40

Prognosticating Nonlinear Degradation in Lithium-Ion Batteries: Operando Pressure as an Early Indicator Preceding Other Signals of Capacity Fade and Safety Risks

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Carbonized waste milk powders as cathodes for stable lithium–sulfur batteries with ultra-large capacity and high initial coulombic efficiency

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