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Recent advances in the modeling of fundamental processes in liquid metal batteries

Author

Listed:
  • Agarwal, Daksh
  • Potnuru, Rakesh
  • Kaushik, Chiranjeev
  • Darla, Vinay Rajesh
  • Kulkarni, Kaustubh
  • Garg, Ashish
  • Gupta, Raju Kumar
  • Tiwari, Naveen
  • Nalwa, Kanwar Singh

Abstract

Liquid Metal Batteries (LMBs) have a potential to emerge as a cost-effective solution for grid-scale energy storage to overcome the intermittency of renewable energy generation and to facilitate the management of peak loading requirements. They have significant advantages over other battery types such as high-power density and cyclability, use of earth-abundant materials, self-healing capability, high coulombic efficiency, and ease of scalability. The successful adoption of LMBs for grid storage requires a thorough understanding of the underlying processes that govern the performance of LMBs to prevent any detrimental effects at higher storage capacities. However, most of the research work in this relatively new field has focused on developing new electrode materials to achieve higher performance and lower operating temperature. In this review, we focus on other critical aspects such as heat and mass transfer, electric potential, instabilities, high temperature sealing and state of charge, which are vital to the functioning of LMBs. The models that have been developed to study these processes and attributes of LMBs, and their learning advancements have been summarized. Moreover, the challenges and outlook of research on modeling of LMBs are presented which are expected to significantly contribute to the development of a comprehensive model combining these effects that will offer insights into optimization of design and operating conditions of LMBs resulting in accelerated scaling and commercialization.

Suggested Citation

  • Agarwal, Daksh & Potnuru, Rakesh & Kaushik, Chiranjeev & Darla, Vinay Rajesh & Kulkarni, Kaustubh & Garg, Ashish & Gupta, Raju Kumar & Tiwari, Naveen & Nalwa, Kanwar Singh, 2022. "Recent advances in the modeling of fundamental processes in liquid metal batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
  • Handle: RePEc:eee:rensus:v:158:y:2022:i:c:s1364032122000946
    DOI: 10.1016/j.rser.2022.112167
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    as
    1. Huayi Yin & Brice Chung & Fei Chen & Takanari Ouchi & Ji Zhao & Nobuyuki Tanaka & Donald R. Sadoway, 2018. "Faradaically selective membrane for liquid metal displacement batteries," Nature Energy, Nature, vol. 3(2), pages 127-131, February.
    2. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    3. Yang Jin & Kai Liu & Jialiang Lang & Denys Zhuo & Zeya Huang & Chang-an Wang & Hui Wu & Yi Cui, 2018. "An intermediate temperature garnet-type solid electrolyte-based molten lithium battery for grid energy storage," Nature Energy, Nature, vol. 3(9), pages 732-738, September.
    4. Ismail, M.S. & Ingham, D.B. & Hughes, K.J. & Ma, L. & Pourkashanian, M., 2014. "An efficient mathematical model for air-breathing PEM fuel cells," Applied Energy, Elsevier, vol. 135(C), pages 490-503.
    5. Xiaosong Hu & Fengchun Sun & Yuan Zou, 2010. "Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer," Energies, MDPI, vol. 3(9), pages 1-18, September.
    6. Basu, Suman & Hariharan, Krishnan S. & Kolake, Subramanya Mayya & Song, Taewon & Sohn, Dong Kee & Yeo, Taejung, 2016. "Coupled electrochemical thermal modelling of a novel Li-ion battery pack thermal management system," Applied Energy, Elsevier, vol. 181(C), pages 1-13.
    7. Hussain, Akhtar & Arif, Syed Muhammad & Aslam, Muhammad, 2017. "Emerging renewable and sustainable energy technologies: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 12-28.
    8. Yue, Meiling & Lambert, Hugo & Pahon, Elodie & Roche, Robin & Jemei, Samir & Hissel, Daniel, 2021. "Hydrogen energy systems: A critical review of technologies, applications, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    9. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
    10. Daehyun Kim & Keunhwi Koo & Jae Jin Jeong & Taedong Goh & Sang Woo Kim, 2013. "Second-Order Discrete-Time Sliding Mode Observer for State of Charge Determination Based on a Dynamic Resistance Li-Ion Battery Model," Energies, MDPI, vol. 6(10), pages 1-14, October.
    11. Pan, Haihong & Lü, Zhiqiang & Lin, Weilong & Li, Junzi & Chen, Lin, 2017. "State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model," Energy, Elsevier, vol. 138(C), pages 764-775.
    12. Liu, Guoan & Xu, Cheng & Li, Haomiao & Jiang, Kai & Wang, Kangli, 2019. "State of charge and online model parameters co-estimation for liquid metal batteries," Applied Energy, Elsevier, vol. 250(C), pages 677-684.
    13. Richard Van Noorden, 2014. "The rechargeable revolution: A better battery," Nature, Nature, vol. 507(7490), pages 26-28, March.
    14. Lim, KaiChin & Bastawrous, Hany Ayad & Duong, Van-Huan & See, Khay Wai & Zhang, Peng & Dou, Shi Xue, 2016. "Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles," Applied Energy, Elsevier, vol. 169(C), pages 40-48.
    15. Kangli Wang & Kai Jiang & Brice Chung & Takanari Ouchi & Paul J. Burke & Dane A. Boysen & David J. Bradwell & Hojong Kim & Ulrich Muecke & Donald R. Sadoway, 2014. "Lithium–antimony–lead liquid metal battery for grid-level energy storage," Nature, Nature, vol. 514(7522), pages 348-350, October.
    16. Xiaopeng Tang & Boyang Liu & Furong Gao & Zhou Lv, 2016. "State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer," Energies, MDPI, vol. 9(9), pages 1-12, August.
    17. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Deyu Cui & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2017. "A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries," Energies, MDPI, vol. 10(8), pages 1-14, August.
    18. Xian Wang & Zhengxiang Song & Kun Yang & Xuyang Yin & Yingsan Geng & Jianhua Wang, 2019. "State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries," Energies, MDPI, vol. 12(1), pages 1-22, January.
    19. Zhao, Rui & Liu, Jie & Gu, Junjie, 2015. "The effects of electrode thickness on the electrochemical and thermal characteristics of lithium ion battery," Applied Energy, Elsevier, vol. 139(C), pages 220-229.
    20. Ye, Min & Guo, Hui & Xiong, Rui & Yu, Quanqing, 2018. "A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries," Energy, Elsevier, vol. 144(C), pages 789-799.
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