IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i21p7997-d955461.html
   My bibliography  Save this article

A Paradox over Electric Vehicles, Mining of Lithium for Car Batteries

Author

Listed:
  • John H. T. Luong

    (School of Chemistry, University College Cork, T12 YN60 Cork, Ireland)

  • Cang Tran

    (Department of Chemical Engineering, Syracuse University, Syracuse, NY 13244, USA)

  • Di Ton-That

    (Broadcom Corp, Irvine, CA 92618, USA)

Abstract

Lithium, a silver-white alkali metal, with significantly high energy density, has been exploited for making rechargeable lithium-ion batteries (LiBs). They have become one of the main energy storage solutions in modern electric cars (EVs). Cobalt, nickel, and manganese are three other key components of LiBs that power electric vehicles (EVs). Neodymium and dysprosium, two rare earth metals, are used in the permanent magnet-based motors of EVs. The operation of EVs also requires a high amount of electricity for recharging their LiBs. Thus, the CO 2 emission is reduced during the operation of an EV if the recharged electricity is generated from non-carbon sources such as hydroelectricity, solar energy, and nuclear energy. LiBs in EVs have been pushed to the limit because of their limited storage capacity and charge/discharge cycles. Batteries account for a substantial portion of the size and weight of an EV and occupy the entire chassis. Thus, future LiBs must be smaller and more powerful with extended driving ranges and short charging times. The extended range and longevity of LiBs are feasible with advances in solid-state electrolytes and robust electrode materials. Attention must also be focused on the high-cost, energy, and time-demand steps of LiB manufacturing to reduce cost and turnover time. Solid strategies are required to promote the deployment of spent LiBs for power storage, solar energy, power grids, and other stationary usages. Recycling spent LiBs will alleviate the demand for virgin lithium and 2.6 × 10 11 tons of lithium in seawater is a definite asset. Nonetheless, it remains unknown whether advances in battery production technology and recycling will substantially reduce the demand for lithium and other metals beyond 2050. Technical challenges in LiB manufacturing and lithium recycling must be overcome to sustain the deployment of EVs for reducing CO 2 emissions. However, potential environmental problems associated with the production and operation of EVs deserve further studies while promoting their global deployment. Moreover, the combined repurposing and remanufacturing of spent LiBs also increases the environmental benefits of EVs. EVs will be equipped with more powerful computers and reliable software to monitor and optimize the operation of LiBs.

Suggested Citation

  • John H. T. Luong & Cang Tran & Di Ton-That, 2022. "A Paradox over Electric Vehicles, Mining of Lithium for Car Batteries," Energies, MDPI, vol. 15(21), pages 1-25, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7997-:d:955461
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/21/7997/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/21/7997/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ouyang, Tiancheng & Xu, Peihang & Chen, Jingxian & Su, Zixiang & Huang, Guicong & Chen, Nan, 2021. "A novel state of charge estimation method for lithium-ion batteries based on bias compensation," Energy, Elsevier, vol. 226(C).
    2. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).
    3. Mohammed Al-Saadi & Josu Olmos & Andoni Saez-de-Ibarra & Joeri Van Mierlo & Maitane Berecibar, 2022. "Fast Charging Impact on the Lithium-Ion Batteries’ Lifetime and Cost-Effective Battery Sizing in Heavy-Duty Electric Vehicles Applications," Energies, MDPI, vol. 15(4), pages 1-23, February.
    4. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
    5. Li, Xiaoyu & Huang, Zhijia & Tian, Jindong & Tian, Yong, 2021. "State-of-charge estimation tolerant of battery aging based on a physics-based model and an adaptive cubature Kalman filter," Energy, Elsevier, vol. 220(C).
    6. Shriram S. Rangarajan & Suvetha Poyyamani Sunddararaj & AVV Sudhakar & Chandan Kumar Shiva & Umashankar Subramaniam & E. Randolph Collins & Tomonobu Senjyu, 2022. "Lithium-Ion Batteries—The Crux of Electric Vehicles with Opportunities and Challenges," Clean Technol., MDPI, vol. 4(4), pages 1-23, September.
    7. Jiang, Cong & Wang, Shunli & Wu, Bin & Fernandez, Carlos & Xiong, Xin & Coffie-Ken, James, 2021. "A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter," Energy, Elsevier, vol. 219(C).
    8. Li, Lantian & Wang, Zhenpo & Gao, Feng & Wang, Shuo & Deng, Junjun, 2020. "A family of compensation topologies for capacitive power transfer converters for wireless electric vehicle charger," Applied Energy, Elsevier, vol. 260(C).
    9. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    10. Zhu, Jiangong & Knapp, Michael & Darma, Mariyam Susana Dewi & Fang, Qiaohua & Wang, Xueyuan & Dai, Haifeng & Wei, Xuezhe & Ehrenberg, Helmut, 2019. "An improved electro-thermal battery model complemented by current dependent parameters for vehicular low temperature application," Applied Energy, Elsevier, vol. 248(C), pages 149-161.
    11. Mahmud, Khizir & Town, Graham E. & Morsalin, Sayidul & Hossain, M.J., 2018. "Integration of electric vehicles and management in the internet of energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4179-4203.
    12. Calise, Francesco & Cappiello, Francesco Liberato & Cartenì, Armando & Dentice d’Accadia, Massimo & Vicidomini, Maria, 2019. "A novel paradigm for a sustainable mobility based on electric vehicles, photovoltaic panels and electric energy storage systems: Case studies for Naples and Salerno (Italy)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 97-114.
    13. José Manuel Andújar & Francisca Segura & Jesús Rey & Francisco José Vivas, 2022. "Batteries and Hydrogen Storage: Technical Analysis and Commercial Revision to Select the Best Option," Energies, MDPI, vol. 15(17), pages 1-32, August.
    14. Arno Kwade & Wolfgang Haselrieder & Ruben Leithoff & Armin Modlinger & Franz Dietrich & Klaus Droeder, 2018. "Current status and challenges for automotive battery production technologies," Nature Energy, Nature, vol. 3(4), pages 290-300, April.
    15. William T. Stringfellow & Patrick F. Dobson, 2021. "Technology for the Recovery of Lithium from Geothermal Brines," Energies, MDPI, vol. 14(20), pages 1-72, October.
    16. Davide Castelvecchi, 2021. "Electric cars and batteries: how will the world produce enough?," Nature, Nature, vol. 596(7872), pages 336-339, August.
    17. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
    18. Bhatti, Abdul Rauf & Salam, Zainal & Aziz, Mohd Junaidi Bin Abdul & Yee, Kong Pui & Ashique, Ratil H., 2016. "Electric vehicles charging using photovoltaic: Status and technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 34-47.
    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. Yu, Donglei & Wenhui, Xiong & Anser, Muhammad Khalid & Nassani, Abdelmohsen A. & Imran, Muhammad & Zaman, Khalid & Haffar, Mohamed, 2023. "Navigating the global mineral market: A study of resource wealth and the energy transition," Resources Policy, Elsevier, vol. 82(C).
    2. Sergey Zhironkin & Ekaterina Taran, 2023. "Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review," Energies, MDPI, vol. 16(9), pages 1-31, April.

    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. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
    2. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).
    3. Shi, Haotian & Wang, Shunli & Huang, Qi & Fernandez, Carlos & Liang, Jianhong & Zhang, Mengyun & Qi, Chuangshi & Wang, Liping, 2024. "Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries," Applied Energy, Elsevier, vol. 353(PB).
    4. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    5. Yong Tian & Qianyuan Dong & Jindong Tian & Xiaoyu Li, 2023. "Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks," Energies, MDPI, vol. 16(2), pages 1-18, January.
    6. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    7. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    8. Yang, Bowen & Wang, Dafang & Sun, Xu & Chen, Shiqin & Wang, Xingcheng, 2023. "Offline order recognition for state estimation of Lithium-ion battery using fractional order model," Applied Energy, Elsevier, vol. 341(C).
    9. Chen, Laien & Zeng, Xiaoyong & Xia, Xiangyang & Sun, Yaoke & Yue, Jiahui, 2024. "A modeling and state of charge estimation approach to lithium-ion batteries based on the state-dependent autoregressive model with exogenous inputs," Energy, Elsevier, vol. 300(C).
    10. Lai, Xin & Huang, Yunfeng & Gu, Huanghui & Han, Xuebing & Feng, Xuning & Dai, Haifeng & Zheng, Yuejiu & Ouyang, Minggao, 2022. "Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects," Energy, Elsevier, vol. 238(PA).
    11. Yan Cheng & Xuesen Zhang & Xiaoqiang Wang & Jianhua Li, 2022. "Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform," Energies, MDPI, vol. 15(6), pages 1-16, March.
    12. Hou, Jie & Liu, Jiawei & Chen, Fengwei & Li, Penghua & Zhang, Tao & Jiang, Jincheng & Chen, Xiaolei, 2023. "Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter," Energy, Elsevier, vol. 271(C).
    13. Wojciech Lewicki & Mariusz Niekurzak & Ewelina Sendek-Matysiak, 2024. "Electromobility Stage in the Energy Transition Policy—Economic Dimension Analysis of Charging Costs of Electric Vehicles," Energies, MDPI, vol. 17(8), pages 1-16, April.
    14. Ana Carolina Kulik & Édwin Augusto Tonolo & Alberto Kisner Scortegagna & Jardel Eugênio da Silva & Jair Urbanetz Junior, 2021. "Analysis of Scenarios for the Insertion of Electric Vehicles in Conjunction with a Solar Carport in the City of Curitiba, Paraná—Brazil," Energies, MDPI, vol. 14(16), pages 1-15, August.
    15. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    16. Seyfettin Vadi & Ramazan Bayindir & Alperen Mustafa Colak & Eklas Hossain, 2019. "A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies," Energies, MDPI, vol. 12(19), pages 1-27, September.
    17. 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).
    18. Surender Reddy Salkuti, 2023. "Advanced Technologies for Energy Storage and Electric Vehicles," Energies, MDPI, vol. 16(5), pages 1-7, February.
    19. Wang, Limei & Jin, Mengjie & Cai, Yingfeng & Lian, Yubo & Zhao, Xiuliang & Wang, Ruochen & Qiao, Sibing & Chen, Long & Yan, Xueqing, 2023. "Construction of electrochemical model for high C-rate conditions in lithium-ion battery based on experimental analogy method," Energy, Elsevier, vol. 279(C).
    20. Peng Song & Zhisheng Zhang, 2023. "Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM," Energies, MDPI, vol. 16(9), pages 1-13, April.

    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:15:y:2022:i:21:p:7997-:d:955461. 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.