A review on the key issues of the lithium ion battery degradation among the whole life cycle

Li-ion battery materials: present and future

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Lithium Batteries: Status, Prospects and Future

Scrosati, B., & Garche, J. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2009.11.048 · Cited by 5,153

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

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Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell

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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 review of the features and analyses of the solid electrolyte interphase in Li-ion batteries

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Comparison of the structural and electrochemical properties of layered Li[NixCoyMnz]O2 (x = 1/3, 0.5, 0.6, 0.7, 0.8 and 0.85) cathode material for lithium-ion batteries

Noh, H.-J., Youn, S., Yoon, C. S., & Sun, Y. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.01.063 · Cited by 2,305

Battery Separators

Arora, P., & Zhang, Z. (2004) · Chemical Reviews · doi:10.1021/cr020738u · Cited by 2,183

The state of understanding of the lithium-ion-battery graphite solid electrolyte interphase (SEI) and its relationship to formation cycling

An, S. J., Li, J., Daniel, C., Mohanty, D., Nagpure, S., & Wood, D. L. (2016) · Carbon · doi:10.1016/j.carbon.2016.04.008 · Cited by 2,121

Degradation diagnostics for lithium ion cells

Birkl, C. R., Roberts, M. R., McTurk, E., Bruce, P. G., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.12.011 · Cited by 1,945

Prediction of Health Level of Multiform Lithium Sulfur Batteries Based on Incremental Capacity Analysis and an Improved LSTM

Zhang, H., Sun, H., Kang, L., Zhang, Y., Wang, L., & Wang, K. (2024) · Protection and Control of Modern Power Systems · doi:10.23919/pcmp.2023.000280 · Cited by 61

Insights Into Thermal Runaway of Li–Ion Cells by Accelerating Rate Calorimetry Coupled with External Sensors and Online Gas Analysis

Abdellatif, A., Sichler, P., Kasper, M., Waldmann, T., & Wohlfahrt‐Mehrens, M. (2021) · Batteries & Supercaps · doi:10.1002/batt.202100023 · Cited by 60

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

XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries

Jafari, S., & Byun, Y.-C. (2022) · Sensors · doi:10.3390/s22239522 · Cited by 63

Microgrid Optimal Energy Scheduling Considering Neural Network Based Battery Degradation

Zhao, C., & Li, X. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3239113 · Cited by 64

Hybrid PEM Fuel Cell Power Plants Fuelled by Hydrogen for Improving Sustainability in Shipping: State of the Art and Review on Active Projects

Dall’Armi, C., Pivetta, D., & Taccani, R. (2023) · Energies · doi:10.3390/en16042022 · Cited by 64

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

Opportunities and challenges in transformer neural networks for battery state estimation: Charge, health, lifetime, and safety

Zhao, J., Han, X., Wu, Y., Wang, Z., & Burke, A. F. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.11.011 · Cited by 66

Issues and challenges of layered lithium nickel cobalt manganese oxides for lithium-ion batteries

Chen, S., Zhang, X., Xia, M., Wei, K., Zhang, L., Zhang, X., Cui, Y., & Shu, J. (2021) · Journal of Electroanalytical Chemistry · doi:10.1016/j.jelechem.2021.115412 · Cited by 59

Emerging Processes for Sustainable Li‐Ion Battery Cathode Recycling

Bhattacharyya, S., Roy, S., & Vajtai, R. (2024) · Small · doi:10.1002/smll.202400557 · Cited by 63

Lithium-ion battery remaining useful life prediction based on interpretable deep learning and network parameter optimization

Zhao, B., Zhang, W., Zhang, Y., Zhang, C., Zhang, C., & Zhang, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124713 · Cited by 62

State of health estimation of lithium-ion battery based on full life cycle acoustic emission signals

He, Y., Zeng, Q., Tang, L., Liu, F., Li, Q., Yin, Y., Xu, S., & Deng, B. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118725 · Cited by 59

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Capacity Estimation and Box-Cox Transformation

Xue, Q., Shen, S., Li, G., Zhang, Y., Chen, Z., & Liu, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3039553 · Cited by 57

A decomposed electrode model for real-time anode potential observation of lithium-ion batteries

Lu, Y., Han, X., Chu, Z., Feng, X., Qin, Y., Ouyang, M., & Lu, L. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230529 · Cited by 58

A joint state-of-health and state-of-energy estimation method for lithium-ion batteries through combining the forgetting factor recursive least squares and unscented Kalman filter

Lai, X., Weng, J., Huang, Y., Yuan, M. H., Yao, Y., Han, X., & Zheng, Y. (2022) · Measurement · doi:10.1016/j.measurement.2022.112187 · Cited by 61

Probabilistic lithium-ion battery state-of-health prediction using convolutional neural networks and Gaussian process regression

Buchanan, S. P., & Crawford, C. A. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.109799 · Cited by 58

A review of the combined effects of environmental and operational factors on lithium-ion battery performance: temperature, vibration, and charging/discharging cycles

Shi, D., Cui, Y., Shen, X., Gao, Z., Ma, X., Xu, L. H., Fang, Y., Wang, S., & Fang, S. E. (2025) · RSC Advances · doi:10.1039/d5ra00934k · Cited by 62

Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework

Yang, K., Zhang, L., Zhang, Z., Yu, H., Wang, W., Ouyang, M., Zhang, C., Sun, Q., Yan, X., Yang, S., et al. (2023) · Batteries · doi:10.3390/batteries9070351 · Cited by 58

The Recent Progress on Halide Perovskite-Based Self-Powered Sensors Enabled by Piezoelectric and Triboelectric Effects

Ippili, S., Jella, V., Thomas, A. M., & Yoon, S. (2021) · Nanoenergy Advances · doi:10.3390/nanoenergyadv1010002 · Cited by 57

Electrochemical models: methods and applications for safer lithium-ion battery operation

Sarkar, S., Halim, S. Z., El‐Halwagi, M. M., & Khan, F. I. (2022) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ac8ee2 · Cited by 57

Study on thermo-electric-hydrogen conversion mechanisms and synergistic operation on hydrogen fuel cell and electrochemical battery in energy flexible buildings

Zhou, L., & Zhou, Y. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.116610 · Cited by 57

Battery health prognostic with sensor-free differential temperature voltammetry reconstruction and capacity estimation based on multi-domain adaptation

Che, Y., Vilsen, S. B., Meng, J., Sui, X., & Teodorescu, R. (2023) · eTransportation · doi:10.1016/j.etran.2023.100245 · Cited by 59

Recycling metal resources from various spent batteries to prepare electrode materials for energy storage: A critical review

Gao, P., Peng, Y., Yue, T., Zhao, X., & Shen, B. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107652 · Cited by 57

Big field data-driven battery pack health estimation for electric vehicles: A deep-fusion transfer learning approach

Liu, H., Deng, Z., Che, Y., Xu, L., Wang, B., Wang, Z., Xie, Y., & Hu, X. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111585 · Cited by 58

Multi-level intelligence empowering lithium-ion batteries

Zhang, G.-X., Zhu, J.-G., Dai, H.-F., & Wei, X.-Z. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.06.020 · Cited by 54

Vehicle-to-Grid technology: Opportunities, challenges, and future prospects for sustainable transportation

Rao, S. P., Tomomewo, O. S., Ranganathan, P., Köse, U., & Goveas, N. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2024.114927 · Cited by 56

A study of external surface pressure effects on the properties for lithium‐ion pouch cells

Zhou, L., Xing, L., Zheng, Y., Lai, X., Su, J., Deng, C., & Sun, T. (2020) · International Journal of Energy Research · doi:10.1002/er.5415 · Cited by 56

Power Quality Phenomena, Standards, and Proposed Metrics for DC Grids

Mariscotti, A. (2021) · Energies · doi:10.3390/en14206453 · Cited by 56

A mechanistic calendar aging model of lithium‐ion battery considering solid electrolyte interface growth

Zhu, W., Zhou, P., Ren, D., Yang, M., Rui, X., Jin, C., Shen, T., Han, X., Zheng, Y., Lu, L., et al. (2022) · International Journal of Energy Research · doi:10.1002/er.8249 · Cited by 55

Deep Learning Powered Online Battery Health Estimation Considering Multitimescale Aging Dynamics and Partial Charging Information

Fei, Z., Zhang, Z., & Tsui, K. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3264438 · Cited by 55

Toward MBenes Battery Electrode Materials: Layered Molybdenum Borides for Li‐Ion Batteries

Majed, A., Torkamanzadeh, M., Nwaokorie, C. F., Eisawi, K., Dun, C., Buck, A., Urban, J. J., Montemore, M. M., Presser, V., & Naguib, M. (2023) · Small Methods · doi:10.1002/smtd.202300193 · Cited by 55

Mechanistic calendar aging model for lithium-ion batteries

Karger, A., Schmitt, J., Kirst, C., Singer, J., Wildfeuer, L., & Jossen, A. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.233208 · Cited by 57

Prediction of nonlinear degradation knee-point and remaining useful life for lithium-ion batteries using relaxation voltage

Fan, W., Zhu, J., Qiao, D., Jiang, B., Wang, X., Wei, X., & Dai, H. (2024) · Energy · doi:10.1016/j.energy.2024.130900 · Cited by 56

A SOH estimation method of lithium-ion batteries based on partial charging data

Gao, R., Zhang, Y., & Lyu, Z. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114309 · Cited by 56

Thermal management of fuel cell-battery electric vehicles: Challenges and solutions

Najmi, A.-U.-H., Wahab, A., Prakash, R., Schopen, O., Esch, T., & Shabani, B. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125635 · Cited by 55

Repurposing Second-Life EV Batteries to Advance Sustainable Development: A Comprehensive Review

Akram, M. N., & Abdul-Kader, W. (2024) · Batteries · doi:10.3390/batteries10120452 · Cited by 50

Cost optimization of a microgrid considering vehicle-to-grid technology and demand response

Beyazıt, M. A., Taşçıkaraoğlu, A., & Catalào, J. P. S. (2022) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2022.100924 · Cited by 54

Machine learning-inspired battery material innovation

Ng, M., Sun, Y., & Seh, Z. W. (2023) · Energy Advances · doi:10.1039/d3ya00040k · Cited by 59

Molecular regulated polymer electrolytes for solid-state lithium metal batteries: Mechanisms and future prospects

Shen, M., Wang, Z., Cheng, D., Hang, C. W., Xu, H., & Huang, Y. (2023) · eTransportation · doi:10.1016/j.etran.2023.100264 · Cited by 54

Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review

Liu, C., Li, H., Li, K., Wu, Y., & Lv, B. (2025) · Energies · doi:10.3390/en18061463 · Cited by 67

An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation

Cheng, Y., Song, D., Wang, Z., Lü, C., & Noureddine Zerhouni, A. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114817 · Cited by 54

A sequential capacity estimation for the lithium-ion batteries combining incremental capacity curve and discrete Arrhenius fading model

Sun, T., Xu, B., Cui, Y., Feng, X., Han, X., & Zheng, Y. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.229248 · Cited by 54

Scalable Synthesis of Few-Layered 2D Tungsten Diselenide (2H-WSe2) Nanosheets Directly Grown on Tungsten (W) Foil Using Ambient-Pressure Chemical Vapor Deposition for Reversible Li-Ion Storage

Konar, R., Rosy, R., Perelshtein, I., Teblum, E., Telkhozhayeva, M., Tkachev, M., Richter, J. J., Cattaruzza, E., Charmet, A. P., Stoppa, P., et al. (2020) · ACS Omega · doi:10.1021/acsomega.0c01155 · Cited by 53

Internal field study of 21700 battery based on long-life embedded wireless temperature sensor

Yang, L., Li, N., Hu, L., Wang, S., Wang, L., Zhou, J., Song, W., Сун, Л., Pan, T., Chen, H., et al. (2021) · Acta Mechanica Sinica · doi:10.1007/s10409-021-01103-0 · Cited by 53

Weight optimized unscented Kalman filter for degradation trend prediction of lithium-ion battery with error compensation strategy

Zhang, Y., Tu, L., Xue, Z., Li, S., Tian, L., & Zheng, X. (2022) · Energy · doi:10.1016/j.energy.2022.123890 · Cited by 53

A copper nanoparticle enhanced phase change material with high thermal conductivity and latent heat for battery thermal management

Ma, C., Zhang, Y., Hu, S., Liu, X., & He, S. (2022) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2022.104814 · Cited by 53

Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network

Xie, J., Xu, J., Wei, Z., & Li, X. (2022) · Energy · doi:10.1016/j.energy.2022.125867 · Cited by 53

Potential and Most Promising Second-Life Applications for Automotive Lithium-Ion Batteries Considering Technical, Economic and Legal Aspects

Michelini, E., Höschele, P., Ratz, F., Stadlbauer, M., Rom, W., Ellersdorfer, C., & Moser, J. (2023) · Energies · doi:10.3390/en16062830 · Cited by 57

Thermal runaway fault prediction in air-cooled lithium-ion battery modules using machine learning through temperature sensors placement optimization

Daniels, R. K., Kumar, V. S., Chouhan, S. S., & Prabhakar, A. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122352 · Cited by 53

Capacity degradation analysis and knee point prediction for lithium-ion batteries

Wang, T., Zhu, Y., Zhao, W., Gong, Y., Zhang, Z., Gao, W., & Shang, Y. (2024) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2024.100171 · Cited by 55

Health Diagnosis for Lithium-Ion Battery by Combining Partial Incremental Capacity and Deep Belief Network During Insufficient Discharge Profile

Wang, Y., Li, K., Peng, P., & Chen, Z. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3224201 · Cited by 52

Electrification and hydrogenation on a PV-battery-hydrogen energy flexible community for carbon–neutral transformation with transient aging and collaboration operation

Zhou, L., Song, A., & Zhou, Y. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117984 · Cited by 57

Optimal planning of distributed photovoltaic generation for the traction power supply system of high-speed railway

Zhong, Z., Zhang, Y., Shen, H., & Li, X. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.121394 · Cited by 52

Empirical Electrical and Degradation Model for Electric Vehicle Batteries

Saldaña, G., Martín, J. I. S., Zamora, I., Asensio, F. J., Onederra, O., & González-Pérez, M. (2020) · IEEE Access · doi:10.1109/access.2020.3019477 · Cited by 58

Use of battery swapping for improving environmental balance and price-performance ratio of electric vehicles

Schmidt, S. (2021) · eTransportation · doi:10.1016/j.etran.2021.100128 · Cited by 52

Battery health evaluation using a short random segment of constant current charging

Deng, Z., Hu, X.-S., Xie, Y., Xu, L., Li, P.-H., Lin, X.-K., & Bian, X. (2022) · iScience · doi:10.1016/j.isci.2022.104260 · Cited by 49

A novel strategy of thermal management system for battery energy storage system based on supercritical CO 2

Khan, S. A., Ahmad, S., Lau, K. T., Dong, K., He, S., Liu, H., & Zhao, J. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.116676 · Cited by 51

A bibliometric analysis of lithium-ion batteries in electric vehicles

Chen, S., Xiong, J., Qiu, Y., Zhao, Y., & Chen, S. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107109 · Cited by 56

A hybrid framework for predicting the remaining useful life of battery using Gaussian process regression

Li, J., Ye, M., Wang, Y., Wang, Q., & Wei, M. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107513 · Cited by 50

Research on green recycling of lithium-ion batteries cathode waste powder

Ding, G., Liu, F., Fan, X., Gao, X., Cao, G., Ban, J., Li, Z., & Hu, J. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.152837 · Cited by 50

Charging strategies and battery ageing for electric vehicles: A review

Leijon, J. (2025) · Energy Strategy Reviews · doi:10.1016/j.esr.2025.101641 · Cited by 51

A Comparative Review of Metal Oxide Surface Coatings on Three Families of Cathode Materials for Lithium Ion Batteries

Somo, T. R., Mabokela, T. E., Teffu, D. M., Sekgobela, T. K., Ramogayana, B., Hato, M. J., & Modibane, K. D. (2021) · Coatings · doi:10.3390/coatings11070744 · Cited by 52

A comprehensively optimized lithium-ion battery state-of-health estimator based on Local Coulomb Counting Curve

Huang, H., Meng, J., Wang, Y., Feng, F., Cai, L., Peng, J., & Liu, T. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119469 · Cited by 50

Energy assessment of the ageing phenomenon in Li-Ion batteries and its impact on the vehicle range efficiency

García, A., Serrano, J. M., Martínez-Boggio, S., & Golke, D. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.116530 · Cited by 50

Stabilization strategies for high-capacity NCM materials targeting for safety and durability improvements

Meng, J., Qu, G., & Huang, Y. (2023) · eTransportation · doi:10.1016/j.etran.2023.100233 · Cited by 51

Detection of jelly roll pressure evolution in large-format Li-ion batteries via in situ thin film flexible pressure sensors

Chen, Z., Lin, J., Zhu, C., Zhuang, Q., Chen, Q., Wei, Y., Wang, S., & Wu, D. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.232960 · Cited by 50

Effect of lanthanum and yttrium doped LiFePO4 cathodes on electrochemical performance of lithium-ion battery

Zhang, Q., Zhou, J. G., Zeng, G., & Ren, S. (2023) · Journal of Materials Science · doi:10.1007/s10853-023-08542-z · Cited by 50

Multiscale Modelling Methodologies of Lithium-Ion Battery Aging: A Review of Most Recent Developments

Ali, M. A., Silva, C. M. D., & Amon, C. H. (2023) · Batteries · doi:10.3390/batteries9090434 · Cited by 57

Remaining useful life prediction using graph convolutional attention networks with temporal convolution-aware nested residual connections

Wei, Y., Wu, D., & Terpenny, J. P. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109776 · Cited by 50

Lithium-ion battery thermal safety evolution during high-temperature nonlinear aging

Zhang, G., Shen, W., & Wei, X. (2024) · Fuel · doi:10.1016/j.fuel.2023.130845 · Cited by 50

A Perspective on the Molecular Modeling of Electrolyte Decomposition Reactions for Solid Electrolyte Interphase Growth in Lithium‐Ion Batteries

Jassar, M. B., Michel, C., Abada, S., de Bruin, T. J. M., Tant, S., Nieto‐Draghi, C., & Steinmann, S. N. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202313188 · Cited by 50

Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

Ansari, S., Ayob, A., Lipu, M. S. H., Hussain, A., & Saad, M. H. M. (2021) · Sustainability · doi:10.3390/su132313333 · Cited by 50

Improved State-of-health prediction based on auto-regressive integrated moving average with exogenous variables model in overcoming battery degradation-dependent internal parameter variation

Kim, S., Lee, P., Lee, M.-Y., Kim, J., & Na, W. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103888 · Cited by 49

Quantitative Analysis of Aging and Rollover Failure Mechanisms of Lithium‐Ion Batteries at Accelerated Aging Conditions

Zhang, H., Peng, Y., Hu, Y., Pan, S., Tang, S., Luo, Y. H., Liang, Y., Liao, Y., Lin, Y., Zhang, K., et al. (2025) · Advanced Energy Materials · doi:10.1002/aenm.202404997 · Cited by 49

A method for estimating lithium-ion battery state of health based on physics-informed hybrid neural network

Luo, Y., Ju, S., Li, P., & Zhang, H. (2025) · Electrochimica Acta · doi:10.1016/j.electacta.2025.146110 · Cited by 49

Robust Operation of Shipboard Microgrids With Multiple-Battery Energy Storage System Under Navigation Uncertainties

Fang, S., Xu, Y. H., Wang, H., Shang, C., & Feng, X. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3011117 · Cited by 51

A Dry Room-Free High-Energy Density Lithium-ion Batteries Enabled by Impurity Scavenging Separator Membrane

Son, H. B., Shin, M., Song, W., Han, D., Choi, S., Cha, H., Nam, S., Cho, J., Choi, S., Yoo, S., et al. (2021) · Energy storage materials · doi:10.1016/j.ensm.2021.01.018 · Cited by 49

Joint state estimation of lithium-ion batteries combining improved equivalent circuit model with electrochemical mechanism and diffusion process

Xu, X., Tang, S., Ren, H., Han, X., Wu, Y., Lu, L., Feng, X., Yu, C., Xie, J., Ouyang, M., et al. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106135 · Cited by 48

Advanced cycling ageing-driven circular economy with E-mobility-based energy sharing and lithium battery cascade utilisation in a district community

Song, A., & Zhou, Y. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.137797 · Cited by 49

Review of Cell Level Battery (Calendar and Cycling) Aging Models: Electric Vehicles

Yarimca, G., & Çetkin, E. (2024) · Batteries · doi:10.3390/batteries10110374 · Cited by 61

Phase change materials in thermal management of Li-ion batteries: A state-of-the-art review

Talebizadehsardari, P., Hosseinzadeh, K., Mohammed, H. I., Maarof, H. A., Rashid, F. L., Togun, H., Biswas, N., Miaari, A. A., Ali, H. M., Rocca, A. L., et al. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119782 · Cited by 48

Optimization of the lumped parameter thermal model for hard-cased li-ion batteries

Xifeng, C., Zeng, J. C., Zhang, H., Yang, J., Qiao, J., Li, J., & Li, W. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101758 · Cited by 48

Differential Capacity as a Tool for SOC and SOH Estimation of Lithium Ion Batteries Using Charge/Discharge Curves, Cyclic Voltammetry, Impedance Spectroscopy, and Heat Events: A Tutorial

Kurzweil, P. R., Scheuerpflug, W., Frenzel, B., Schell, C., & Schottenbauer, J. (2022) · Energies · doi:10.3390/en15134520 · Cited by 47

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

Unveiling Capacity Degradation Mechanism of Li‐ion Battery in Fast‐charging Process

Zhang, S. S. (2020) · ChemElectroChem · doi:10.1002/celc.201902050 · Cited by 47

State of health estimation of lithium-ion batteries based on the regional frequency

Huang, S., Liu, C., Sun, H., & Liao, Q. (2021) · Journal of Power Sources · doi:10.1016/j.jpowsour.2021.230773 · Cited by 48

Robust structural stability of flower-like δ-MnO2 as cathode for aqueous zinc ion battery

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