IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p8072-d693771.html
   My bibliography  Save this article

Degradation of Lithium-Ion Batteries in an Electric Transport Complex

Author

Listed:
  • Nickolay I. Shchurov

    (Faculty of Mechatronics and Automation, Novosibirsk State Technical University, 20 Karla Marksa Ave., 630073 Novosibirsk, Russia)

  • Sergey I. Dedov

    (Faculty of Mechatronics and Automation, Novosibirsk State Technical University, 20 Karla Marksa Ave., 630073 Novosibirsk, Russia)

  • Boris V. Malozyomov

    (Faculty of Mechatronics and Automation, Novosibirsk State Technical University, 20 Karla Marksa Ave., 630073 Novosibirsk, Russia)

  • Alexander A. Shtang

    (Faculty of Mechatronics and Automation, Novosibirsk State Technical University, 20 Karla Marksa Ave., 630073 Novosibirsk, Russia)

  • Nikita V. Martyushev

    (Department of Materials Science, Tomsk Polytechnic University, 30 Lenina Ave., 634050 Tomsk, Russia)

  • Roman V. Klyuev

    (Department of Low Temperature Engineering, Moscow Polytechnic University, 33 B. Semenovskaya Str., 107023 Moscow, Russia)

  • Sergey N. Andriashin

    (Faculty of Mechatronics and Automation, Novosibirsk State Technical University, 20 Karla Marksa Ave., 630073 Novosibirsk, Russia)

Abstract

The article provides an overview and comparative analysis of various types of batteries, including the most modern type—lithium-ion batteries. Currently, lithium-ion batteries (LIB) are widely used in electrical complexes and systems, including as a traction battery for electric vehicles. Increasing the service life of the storage devices used today is an important scientific and technical problem due to their rapid wear and tear and high cost. This article discusses the main approaches and methods for researching the LIB resource. First of all, a detailed analysis of the causes of degradation was carried out and the processes occurring in lithium-ion batteries during charging, discharging, resting and difficult operating conditions were established. Then, the main factors influencing the service life are determined: charging and discharging currents, self-discharge current, temperature, number of cycles, discharge depth, operating range of charge level, etc. when simulating a real motion process. The work considers the battery management systems (BMS) that take into account and compensate for the influence of the factors considered. In the conclusion, the positive and negative characteristics of the presented methods of scientific research of the residual life of LIB are given and recommendations are given for the choice of practical solutions to engineers and designers of batteries. The work also analyzed various operating cycles of electric transport, including heavy forced modes, extreme operating modes (when the amount of discharge and discharge of batteries is greater than the nominal value) and their effect on the degradation of lithium-ion batteries.

Suggested Citation

  • Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8072-:d:693771
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/8072/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/23/8072/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Weiping Diao & Chetan Kulkarni & Michael Pecht, 2021. "Development of an Informative Lithium-Ion Battery Datasheet," Energies, MDPI, vol. 14(17), pages 1-19, September.
    2. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Yewen Wei & Shuailong Dai & Jiayu Wang & Zhifei Shan & Jie Min, 2019. "Switch Matrix Algorithm for Series Lithium Battery Pack Equilibrium Based on Derived Acceleration Information Gauss-Seidel," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-9, February.
    4. Jian Chen & Jiaqi Li & Yicheng Zhang & Guannan Bao & Xiaohui Ge & Peng Li, 2018. "A Hierarchical Optimal Operation Strategy of Hybrid Energy Storage System in Distribution Networks with High Photovoltaic Penetration," Energies, MDPI, vol. 11(2), pages 1-20, February.
    5. Lysander De Sutter & Gert Berckmans & Mario Marinaro & Jelle Smekens & Yousef Firouz & Margret Wohlfahrt-Mehrens & Joeri Van Mierlo & Noshin Omar, 2018. "Comprehensive Aging Analysis of Volumetric Constrained Lithium-Ion Pouch Cells with High Concentration Silicon-Alloy Anodes," Energies, MDPI, vol. 11(11), pages 1-21, October.
    6. Jorge Nájera & Pablo Moreno-Torres & Marcos Lafoz & Rosa M. De Castro & Jaime R. Arribas, 2017. "Approach to Hybrid Energy Storage Systems Dimensioning for Urban Electric Buses Regarding Efficiency and Battery Aging," Energies, MDPI, vol. 10(11), pages 1-16, October.
    7. Tae-Won Noh & Jung-Hoon Ahn & Byoung Kuk Lee, 2019. "Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries," Energies, MDPI, vol. 12(17), pages 1-14, September.
    8. John Bryant, 2015. "Entropy Man," Books, Economic Consultancy, Vocat International, edition 0, number em2015, July.
    9. Narayan, Nishant & Papakosta, Thekla & Vega-Garita, Victor & Qin, Zian & Popovic-Gerber, Jelena & Bauer, Pavol & Zeman, Miroslav, 2018. "Estimating battery lifetimes in Solar Home System design using a practical modelling methodology," Applied Energy, Elsevier, vol. 228(C), pages 1629-1639.
    10. Zhiwei He & Mingyu Gao & Caisheng Wang & Leyi Wang & Yuanyuan Liu, 2013. "Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model," Energies, MDPI, vol. 6(8), pages 1-18, August.
    11. Sophia Gantenbein & Michael Schönleber & Michael Weiss & Ellen Ivers-Tiffée, 2019. "Capacity Fade in Lithium-Ion Batteries and Cyclic Aging over Various State-of-Charge Ranges," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    12. Uddin, Kotub & Gough, Rebecca & Radcliffe, Jonathan & Marco, James & Jennings, Paul, 2017. "Techno-economic analysis of the viability of residential photovoltaic systems using lithium-ion batteries for energy storage in the United Kingdom," Applied Energy, Elsevier, vol. 206(C), pages 12-21.
    13. Saeed Sepasi & Leon R. Roose & Marc M. Matsuura, 2015. "Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    14. Gavin Harper & Roberto Sommerville & Emma Kendrick & Laura Driscoll & Peter Slater & Rustam Stolkin & Allan Walton & Paul Christensen & Oliver Heidrich & Simon Lambert & Andrew Abbott & Karl Ryder & L, 2019. "Recycling lithium-ion batteries from electric vehicles," Nature, Nature, vol. 575(7781), pages 75-86, November.
    15. Kuo-Hsin Tseng & Jin-Wei Liang & Wunching Chang & Shyh-Chin Huang, 2015. "Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 8(4), pages 1-19, April.
    16. Qiaohua Fang & Xuezhe Wei & Tianyi Lu & Haifeng Dai & Jiangong Zhu, 2019. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model," Energies, MDPI, vol. 12(7), pages 1-18, April.
    17. Nickolay I. Shchurov & Sergey V. Myatezh & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergei I. Dedov, 2021. "Determination of Inactive Powers in a Single-Phase AC Network," Energies, MDPI, vol. 14(16), pages 1-13, August.
    18. Allafi, Walid & Uddin, Kotub & Zhang, Cheng & Mazuir Raja Ahsan Sha, Raja & Marco, James, 2017. "On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model," Applied Energy, Elsevier, vol. 204(C), pages 497-508.
    19. Damian Burzyński & Robert Pietracho & Leszek Kasprzyk & Andrzej Tomczewski, 2019. "Analysis and Modeling of the Wear-Out Process of a Lithium-Nickel-Manganese-Cobalt Cell during Cycling Operation under Constant Load Conditions," Energies, MDPI, vol. 12(20), pages 1-12, October.
    20. Simone Barcellona & Luigi Piegari, 2017. "Lithium Ion Battery Models and Parameter Identification Techniques," Energies, MDPI, vol. 10(12), pages 1-24, December.
    21. Natthawuth Somakettarin & Achara Pichetjamroen, 2019. "Characterization of a Practical-Based Ohmic Series Resistance Model under Life-Cycle Changes for a Lithium-Ion Battery," Energies, MDPI, vol. 12(20), pages 1-11, October.
    22. Caiping Zhang & Jiuchun Jiang & Linjing Zhang & Sijia Liu & Leyi Wang & Poh Chiang Loh, 2016. "A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery," Energies, MDPI, vol. 9(11), pages 1-16, November.
    23. Xue Li & Jiuchun Jiang & Caiping Zhang & Le Yi Wang & Linfeng Zheng, 2015. "Robustness of SOC Estimation Algorithms for EV Lithium-Ion Batteries against Modeling Errors and Measurement Noise," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-14, October.
    24. Prakash Venugopal & Vigneswaran T., 2019. "State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition," Energies, MDPI, vol. 12(22), pages 1-29, November.
    25. Bizhong Xia & Guanghao Chen & Jie Zhou & Yadi Yang & Rui Huang & Wei Wang & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2019. "Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization," Energies, MDPI, vol. 12(15), pages 1-20, July.
    26. Cheng Siong Chin & Zuchang Gao & Joel Hay King Chiew & Caizhi Zhang, 2018. "Nonlinear Temperature-Dependent State Model of Cylindrical LiFePO 4 Battery for Open-Circuit Voltage, Terminal Voltage and State-of-Charge Estimation with Extended Kalman Filter," Energies, MDPI, vol. 11(9), pages 1-28, September.
    27. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    28. Uddin, Kotub & Jackson, Tim & Widanage, Widanalage D. & Chouchelamane, Gael & Jennings, Paul A. & Marco, James, 2017. "On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system," Energy, Elsevier, vol. 133(C), pages 710-722.
    29. Zhu, Qiao & Xu, Mengen & Liu, Weiqun & Zheng, Mengqian, 2019. "A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter," Energy, Elsevier, vol. 187(C).
    30. Tiansi Wang & Lei Pei & Tingting Wang & Rengui Lu & Chunbo Zhu, 2015. "On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries," Energies, MDPI, vol. 8(8), pages 1-15, August.
    31. Joris De Hoog & Joris Jaguemont & Mohamed Abdel-Monem & Peter Van Den Bossche & Joeri Van Mierlo & Noshin Omar, 2018. "Combining an Electrothermal and Impedance Aging Model to Investigate Thermal Degradation Caused by Fast Charging," Energies, MDPI, vol. 11(4), pages 1-15, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    2. Boris V. Malozyomov & Nikita V. Martyushev & Elena V. Voitovich & Roman V. Kononenko & Vladimir Yu. Konyukhov & Vadim Tynchenko & Viktor Alekseevich Kukartsev & Yadviga Aleksandrovna Tynchenko, 2023. "Designing the Optimal Configuration of a Small Power System for Autonomous Power Supply of Weather Station Equipment," Energies, MDPI, vol. 16(13), pages 1-30, June.
    3. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    4. Xiuli Wang & Junkai Wei & Fushuan Wen & Kai Wang, 2023. "A Trading Mode Based on the Management of Residual Electric Energy in Electric Vehicles," Energies, MDPI, vol. 16(17), pages 1-23, August.
    5. Boris V. Malozyomov & Nikita V. Martyushev & Vladislav V. Kukartsev & Vadim S. Tynchenko & Vladimir V. Bukhtoyarov & Xiaogang Wu & Yadviga A. Tyncheko & Viktor A. Kukartsev, 2023. "Overview of Methods for Enhanced Oil Recovery from Conventional and Unconventional Reservoirs," Energies, MDPI, vol. 16(13), pages 1-48, June.
    6. Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of Mechanical Forces and Power Balance in Electromechanical Energy Converter," Mathematics, MDPI, vol. 11(10), pages 1-11, May.
    7. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport," Mathematics, MDPI, vol. 11(15), pages 1-31, July.
    8. Boris V. Malozyomov & Nikita V. Martyushev & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Nikolay A. Zagorodnii & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    9. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    10. Sebastian Pohlmann & Ali Mashayekh & Manuel Kuder & Antje Neve & Thomas Weyh, 2023. "Data Augmentation and Feature Selection for the Prediction of the State of Charge of Lithium-Ion Batteries Using Artificial Neural Networks," Energies, MDPI, vol. 16(18), pages 1-14, September.
    11. Petr Bača & Petr Vanýsek, 2023. "Issues Concerning Manufacture and Recycling of Lead," Energies, MDPI, vol. 16(11), pages 1-20, June.
    12. Zifa Liu & Chengchen Li, 2023. "Low-Carbon Economic Optimization of Integrated Energy System Considering Refined Utilization of Hydrogen Energy and Generalized Energy Storage," Energies, MDPI, vol. 16(15), pages 1-23, July.
    13. Genwei Wang & Xuanfu Guo & Jingyi Chen & Pengfei Han & Qiliang Su & Meiqing Guo & Bin Wang & Hui Song, 2023. "Safety Performance and Failure Criteria of Lithium-Ion Batteries under Mechanical Abuse," Energies, MDPI, vol. 16(17), pages 1-25, September.
    14. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.
    15. Pranjal Barman & Lachit Dutta & Brian Azzopardi, 2023. "Electric Vehicle Battery Supply Chain and Critical Materials: A Brief Survey of State of the Art," Energies, MDPI, vol. 16(8), pages 1-23, April.
    16. Takele Ferede Agajie & Armand Fopah-Lele & Ahmed Ali & Isaac Amoussou & Baseem Khan & Mahmoud Elsisi & Wirnkar Basil Nsanyuy & Om Prakash Mahela & Roberto Marcelo Álvarez & Emmanuel Tanyi, 2023. "Integration of Superconducting Magnetic Energy Storage for Fast-Response Storage in a Hybrid Solar PV-Biogas with Pumped-Hydro Energy Storage Power Plant," Sustainability, MDPI, vol. 15(13), pages 1-30, July.
    17. Sergi Obrador Rey & Juan Alberto Romero & Lluis Trilla Romero & Àlber Filbà Martínez & Xavier Sanchez Roger & Muhammad Attique Qamar & José Luis Domínguez-García & Levon Gevorkov, 2023. "Powering the Future: A Comprehensive Review of Battery Energy Storage Systems," Energies, MDPI, vol. 16(17), pages 1-21, September.
    18. Boris V. Malozyomov & Nikita V. Martyushev & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Sergei Vasilievich Tynchenko & Roman V. Klyuev & Nikolay A. Zagorodnii & Yadviga Aleksandr, 2023. "Study of Supercapacitors Built in the Start-Up System of the Main Diesel Locomotive," Energies, MDPI, vol. 16(9), pages 1-24, May.
    19. Denis Pelin & Andrej Brandis & Mario Kovačević & Filip Halak, 2022. "Design and Testing of a Multimode Capable Passive Battery Management System," Energies, MDPI, vol. 15(12), pages 1-21, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    2. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    3. Bizhong Xia & Haiqing Wang & Yong Tian & Mingwang Wang & Wei Sun & Zhihui Xu, 2015. "State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 8(6), pages 1-21, June.
    4. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    5. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    6. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    7. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Zizhou Lao, 2017. "A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(4), pages 1-15, April.
    8. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).
    9. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    10. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    11. Yin Hua & Min Xu & Mian Li & Chengbin Ma & Chen Zhao, 2015. "Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles," Energies, MDPI, vol. 8(5), pages 1-22, April.
    12. Jiangong Zhu & Yixiu Wang & Yuan Huang & R. Bhushan Gopaluni & Yankai Cao & Michael Heere & Martin J. Mühlbauer & Liuda Mereacre & Haifeng Dai & Xinhua Liu & Anatoliy Senyshyn & Xuezhe Wei & Michael K, 2022. "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    13. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2016. "A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty," Energy, Elsevier, vol. 109(C), pages 933-946.
    14. Kobashi, Takuro & Yoshida, Takahiro & Yamagata, Yoshiki & Naito, Katsuhiko & Pfenninger, Stefan & Say, Kelvin & Takeda, Yasuhiro & Ahl, Amanda & Yarime, Masaru & Hara, Keishiro, 2020. "On the potential of “Photovoltaics + Electric vehicles” for deep decarbonization of Kyoto’s power systems: Techno-economic-social considerations," Applied Energy, Elsevier, vol. 275(C).
    15. Shipman, Rob & Roberts, Rebecca & Waldron, Julie & Naylor, Sophie & Pinchin, James & Rodrigues, Lucelia & Gillott, Mark, 2021. "We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network," Energy, Elsevier, vol. 221(C).
    16. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    17. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    18. Ethelbert Ezemobi & Mario Silvagni & Ahmad Mozaffari & Andrea Tonoli & Amir Khajepour, 2022. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions," Energies, MDPI, vol. 15(3), pages 1-20, February.
    19. Xingxing Wang & Yujie Zhang & Hongjun Ni & Shuaishuai Lv & Fubao Zhang & Yu Zhu & Yinnan Yuan & Yelin Deng, 2022. "Influence of Different Ambient Temperatures on the Discharge Performance of Square Ternary Lithium-Ion Batteries," Energies, MDPI, vol. 15(15), pages 1-22, July.
    20. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8072-:d:693771. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.