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All issues Volume 4 / No 6 (2025) Natl Sci Open, 4 6 (2025) 20250054 References
Issue
Natl Sci Open
Volume 4, Number 6, 2025
Special Topic: Artificial Intelligence and Energy Revolution
Article Number 20250054
Number of page(s) 32
Section Engineering
DOI https://doi.org/10.1360/nso/20250054
Published online 05 November 2025
  • Goodenough J, Whittingham MS, Yoshino A. Battery revolution to evolution. Nat Energy 2019; 4: 893 [Google Scholar]
  • Padhi AK, Nanjundaswamy KS, Goodenough JB. Phospho-olivines as positive-electrode materials for rechargeable lithium batteries. J Electrochem Soc 1997; 144: 1188-1194. [Article] [Google Scholar]
  • Whittingham MS. Electrical energy storage and intercalation chemistry. Science 1976; 192: 1126-1127. [Article] [Google Scholar]
  • Yoshino A. The birth of the lithium-ion battery. Angew Chem Int Ed 2012; 51: 5798-5800. [Article] [Google Scholar]
  • Liu J, Bao Z, Cui Y, et al. Pathways for practical high-energy long-cycling lithium metal batteries. Nat Energy 2019; 4: 180-186. [Article] [Google Scholar]
  • Armand M, Tarascon JM. Building better batteries. Nature 2008; 451: 652-657. [Article] [Google Scholar]
  • Tarascon JM, Armand M. Issues and challenges facing rechargeable lithium batteries. Nature 2001; 414: 359-367. [Article] [Google Scholar]
  • Kang B, Ceder G. Battery materials for ultrafast charging and discharging. Nature 2009; 458: 190-193. [Article] [Google Scholar]
  • Niu C, Lee H, Chen S, et al. High-energy lithium metal pouch cells with limited anode swelling and long stable cycles. Nat Energy 2019; 4: 551-559. [Article] [Google Scholar]
  • Whittingham MS. Lithium batteries and cathode materials. Chem Rev 2004; 104: 4271-4302. [Article] [Google Scholar]
  • Mizushima K, Jones PC, Wiseman PJ, et al. LixCoO2 (0 < x < −1): A new cathode material for batteries of high energy density. Mater Res Bull 1980; 15: 783-789. [Article] [Google Scholar]
  • Whittingham MS. Electrointercalation in transition-metal disulphides. J Chem Soc Chem Commun 1974; : 328. [Article] [Google Scholar]
  • Lu YC, Gallant BM, Kwabi DG, et al. Lithium-oxygen batteries: Bridging mechanistic understanding and battery performance. Energy Environ Sci 2013; 6: 750. [Article] [Google Scholar]
  • Thackeray MM, Wolverton C, Isaacs ED. Electrical energy storage for transportation—Approaching the limits of, and going beyond, lithium-ion batteries. Energy Environ Sci 2012; 5: 7854. [Article] [Google Scholar]
  • Zhang M, Kitchaev DA, Lebens-Higgins Z, et al. Pushing the limit of 3d transition metal-based layered oxides that use both cation and anion redox for energy storage. Nat Rev Mater 2022; 7: 522-540. [Article] [Google Scholar]
  • Xiao J, Li Q, Bi Y, et al. Understanding and applying coulombic efficiency in lithium metal batteries. Nat Energy 2020; 5: 561-568. [Article] [Google Scholar]
  • Li M, Zhang Y, Zhou H, et al. Lithium inventory tracking as a non-destructive battery evaluation and monitoring method. Nat Energy 2024; 9: 612-621. [Article] [Google Scholar]
  • Yang Y, Okonkwo EG, Huang G, et al. On the sustainability of lithium ion battery industry—A review and perspective. Energy Storage Mater 2021; 36: 186-212. [Article] [Google Scholar]
  • Larcher D, Tarascon JM. Towards greener and more sustainable batteries for electrical energy storage. Nat Chem 2015; 7: 19-29. [Article] [Google Scholar]
  • Díaz-González F, Sumper A, Gomis-Bellmunt O, et al. A review of energy storage technologies for wind power applications. Renew Sustain Energy Rev 2012; 16: 2154-2171. [Article] [Google Scholar]
  • Diouf B, Pode R. Potential of lithium-ion batteries in renewable energy. Renew Energy 2015; 76: 375-380. [Article] [Google Scholar]
  • Fredericks WL, Sripad S, Bower GC, et al. Performance metrics required of next-generation batteries to electrify vertical takeoff and landing (VTOL) aircraft. ACS Energy Lett 2018; 3: 2989-2994. [Article] [Google Scholar]
  • Yang XG, Liu T, Ge S, et al. Challenges and key requirements of batteries for electric vertical takeoff and landing aircraft. Joule 2021; 5: 1644-1659. [Article] [Google Scholar]
  • Whittingham MS. History, evolution, and future status of energy storage. Proc IEEE 2012; 100: 1518-1534. [Article] [Google Scholar]
  • Goodenough JB, Park KS. The Li-ion rechargeable battery: A perspective. J Am Chem Soc 2013; 135: 1167-1176. [Article] [Google Scholar]
  • Whittingham MS. Ultimate limits to intercalation reactions for lithium batteries. Chem Rev 2014; 114: 11414-11443. [Article] [Google Scholar]
  • Whittingham MS. Materials challenges facing electrical energy storage. MRS Bull 2008; 33: 411-419. [Article] [Google Scholar]
  • Goodenough JB, Kim Y. Challenges for rechargeable Li batteries. Chem Mater 2010; 22: 587-603. [Article] [Google Scholar]
  • Etacheri V, Marom R, Elazari R, et al. Challenges in the development of advanced Li-ion batteries: A review. Energy Environ Sci 2011; 4: 3243. [Article] [Google Scholar]
  • Marom R, Amalraj SF, Leifer N, et al. A review of advanced and practical lithium battery materials. J Mater Chem 2011; 21: 9938. [Article] [Google Scholar]
  • Nitta N, Wu F, Lee JT, et al. Li-ion battery materials: Present and future. Mater Today 2015; 18: 252-264. [Article] [Google Scholar]
  • Scrosati B, Hassoun J, Sun YK. Lithium-ion batteries. A look into the future. Energy Environ Sci 2011; 4: 3287. [Article] [Google Scholar]
  • Hassabis D, Kumaran D, Summerfield C, et al. Neuroscience-inspired artificial intelligence. Neuron 2017; 95: 245-258. [Article] [Google Scholar]
  • Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput 2006; 18: 1527-1554. [Article] [Google Scholar]
  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436-444. [Article] [Google Scholar]
  • Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2012; 60: 84-90. [Article] [Google Scholar]
  • Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 1982; 79: 2554-2558. [Article] [Google Scholar]
  • Hopfield JJ, Tank DW. Computing with neural circuits: A model. Science 1986; 233: 625-633. [Article] [Google Scholar]
  • Lombardo T, Duquesnoy M, El-Bouysidy H, et al. Artificial intelligence applied to battery research: Hype or reality?. Chem Rev 2021; 122: 10899-10969. [Article] [Google Scholar]
  • Dave A, Mitchell J, Burke S, et al. Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling. Nat Commun 2022; 13: 5454. [Article] [Google Scholar]
  • Zhu S, Ramsundar B, Annevelink E, et al. Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning. Nat Commun 2024; 15: 8649. [Article] [Google Scholar]
  • Han Z, Chen A, Li Z, et al. Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development. Nat Commun 2024; 15: 8433. [Article] [Google Scholar]
  • Niu Z, Pinfield VJ, Wu B, et al. Towards the digitalisation of porous energy materials: Evolution of digital approaches for microstructural design. Energy Environ Sci 2021; 14: 2549-2576. [Article] [Google Scholar]
  • Sun D, Guo D, Lu Y, et al. AI enabled fast charging of lithium-ion batteries of electric vehicles during their life cycle: Review, challenges and perspectives. Energy Environ Sci 2024; 17: 7512-7542. [Article] [Google Scholar]
  • Ng MF, Zhao J, Yan Q, et al. Predicting the state of charge and health of batteries using data-driven machine learning. Nat Mach Intell 2020; 2: 161-170. [Article] [Google Scholar]
  • Lv C, Zhou X, Zhong L, et al. Machine learning: An advanced platform for materials development and state prediction in lithium-ion batteries. Adv Mater 2022; 34: 2101474. [Article] [Google Scholar]
  • Tao S, Ma R, Zhao Z, et al. Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions. Nat Commun 2024; 15: 10154. [Article] [Google Scholar]
  • Iftikhar M, Shoaib M, Altaf A, et al. A deep learning approach to optimize remaining useful life prediction for Li-ion batteries. Sci Rep 2024; 14: 25838. [Article] [Google Scholar]
  • Bin Kaleem M, Zhou Y, Jiang F, et al. Fault detection for Li-ion batteries of electric vehicles with segmented regression method. Sci Rep 2024; 14: 31922. [Article] [Google Scholar]
  • Tao S, Guo R, Lee J, et al. Immediate remaining capacity estimation of heterogeneous second-life lithium-ion batteries via deep generative transfer learning. Energy Environ Sci 2025; 18: 7413-7426. [Article] [Google Scholar]
  • Tao S, Liu H, Sun C, et al. Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning. Nat Commun 2023; 14: 8032. [Article] [Google Scholar]
  • Mailoa JP, Li X, Zhang S. 3T-VASP: Fast ab-initio electrochemical reactor via multi-scale gradient energy minimization. Nat Commun 2024; 15: 10140. [Article] [Google Scholar]
  • Shrestha S, Barvenik KJ, Chen T, et al. Machine intelligence accelerated design of conductive MXene aerogels with programmable properties. Nat Commun 2024; 15: 4685. [Article] [Google Scholar]
  • Meng YS, Arroyo-de Dompablo ME. First principles computational materials design for energy storage materials in lithium ion batteries. Energy Environ Sci 2009; 2: 589. [Article] [Google Scholar]
  • Ceder G. Opportunities and challenges for first-principles materials design and applications to Li battery materials. MRS Bull 2010; 35: 693-701. [Article] [Google Scholar]
  • Jain A, Shin Y, Persson KA. Computational predictions of energy materials using density functional theory. Nat Rev Mater 2016; 1: 15004. [Article] [Google Scholar]
  • Islam MS, Fisher CAJ. Lithium and sodium battery cathode materials: Computational insights into voltage, diffusion and nanostructural properties. Chem Soc Rev 2014; 43: 185-204. [Article] [Google Scholar]
  • Wang A, Kadam S, Li H, et al. Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries. npj Comput Mater 2018; 4: 15. [Article] [Google Scholar]
  • Bedrov D, Smith GD, van Duin ACT. Reactions of singly-reduced ethylene carbonate in lithium battery electrolytes: A molecular dynamics simulation study using the ReaxFF. J Phys Chem A 2012; 116: 2978-2985. [Article] [Google Scholar]
  • Bhatt MD, O’Dwyer C. Recent progress in theoretical and computational investigations of Li-ion battery materials and electrolytes. Phys Chem Chem Phys 2015; 17: 4799-4844. [Article] [Google Scholar]
  • Wei Y, Zheng J, Cui S, et al. Kinetics tuning of Li-ion diffusion in layered Li(NixMnyCoz)O2. J Am Chem Soc 2015; 137: 8364-8367. [Article] [Google Scholar]
  • Cui S, Wei Y, Liu T, et al. Optimized temperature effect of Li-ion diffusion with layer distance in Li(NixMnyCoz)O2 cathode materials for high performance Li-ion battery. Adv Energy Mater 2016; 6: 1501309. [Article] [Google Scholar]
  • Zhang C, Park SH, Seral-Ascaso A, et al. High capacity silicon anodes enabled by MXene viscous aqueous ink. Nat Commun 2019; 10: 849. [Article] [Google Scholar]
  • Sivonxay E, Aykol M, Persson KA. The lithiation process and Li diffusion in amorphous SiO2 and Si from first-principles. Electrochim Acta 2020; 331: 135344. [Article] [Google Scholar]
  • Chen R, Li Q, Yu X, et al. Approaching practically accessible solid-state batteries: Stability issues related to solid electrolytes and interfaces. Chem Rev 2019; 120: 6820-6877. [Article] [Google Scholar]
  • Zhu Y, He X, Mo Y. First principles study on electrochemical and chemical stability of solid electrolyte-electrode interfaces in all-solid-state Li-ion batteries. J Mater Chem A 2016; 4: 3253-3266. [Article] [Google Scholar]
  • Nolan AM, Zhu Y, He X, et al. Computation-accelerated design of materials and interfaces for all-solid-state lithium-ion batteries. Joule 2018; 2: 2016-2046. [Article] [Google Scholar]
  • Rucci A, Ngandjong AC, Primo EN, et al. Tracking variabilities in the simulation of lithium ion battery electrode fabrication and its impact on electrochemical performance. Electrochim Acta 2019; 312: 168-178. [Article] [Google Scholar]
  • Tan X, Chen M, Zhang J, et al. Decoding electrochemical processes of lithium-ion batteries by classical molecular dynamics simulations. Adv Energy Mater 2024; 14: 2400564. [Article] [Google Scholar]
  • Zhao R, Liu J, Gu J. The effects of electrode thickness on the electrochemical and thermal characteristics of lithium ion battery. Appl Energy 2015; 139: 220-229. [Article] [Google Scholar]
  • Borodin O, Ren X, Vatamanu J, et al. Modeling insight into battery electrolyte electrochemical stability and interfacial structure. Acc Chem Res 2017; 50: 2886-2894. [Article] [Google Scholar]
  • Xu M, Reichman B, Wang X. Modeling the effect of electrode thickness on the performance of lithium-ion batteries with experimental validation. Energy 2019; 186: 115864. [Article] [Google Scholar]
  • Gupta P, Gudmundson P. A multi-scale model for simulation of electrochemically induced stresses on scales of active particles, electrode layers, and battery level in lithium-ion batteries. J Power Sources 2021; 511: 230465. [Article] [Google Scholar]
  • Fang W, Kwon OJ, Wang CY. Electrochemical-thermal modeling of automotive Li-ion batteries and experimental validation using a three-electrode cell. Int J Energy Res 2010; 34: 107-115. [Article] [Google Scholar]
  • Chen Z, Danilov DL, Eichel R, et al. Porous electrode modeling and its applications to Li-ion batteries. Adv Energy Mater 2022; 12: 2201506. [Article] [Google Scholar]
  • Schmidt AP, Bitzer M, Imre ÁW, et al. Experiment-driven electrochemical modeling and systematic parameterization for a lithium-ion battery cell. J Power Sources 2010; 195: 5071-5080. [Article] [Google Scholar]
  • Fuller TF, Doyle M, Newman J. Simulation and optimization of the dual lithium ion insertion cell. J Electrochem Soc 1994; 141: 1-10. [Article] [Google Scholar]
  • Zhang X, Lu J, Yuan S, et al. A novel method for identification of lithium-ion battery equivalent circuit model parameters considering electrochemical properties. J Power Sources 2017; 345: 21-29. [Article] [Google Scholar]
  • Astaneh M, Andric J, Löfdahl L, et al. Multiphysics simulation optimization framework for lithium-ion battery pack design for electric vehicle applications. Energy 2022; 239: 122092. [Article] [Google Scholar]
  • Xu C, Wang H, Jiang F, et al. Modelling of thermal runaway propagation in lithium-ion battery pack using reduced-order model. Energy 2023; 268: 126646. [Article] [Google Scholar]
  • Feng X, Ouyang M, Liu X, et al. Thermal runaway mechanism of lithium ion battery for electric vehicles: A review. Energy Storage Mater 2018; 10: 246-267. [Article] [Google Scholar]
  • Pesaran AA. Battery thermal models for hybrid vehicle simulations. J Power Sources 2002; 110: 377-382. [Article] [Google Scholar]
  • Akbarzadeh M, Kalogiannis T, Jaguemont J, et al. Thermal modeling of a high-energy prismatic lithium-ion battery cell and module based on a new thermal characterization methodology. J Energy Storage 2020; 32: 101707. [Article] [Google Scholar]
  • Liu B, Zhang J, Zhang C, et al. Mechanical integrity of 18650 lithium-ion battery module: Packing density and packing mode. Eng Fail Anal 2018; 91: 315-326. [Article] [Google Scholar]
  • Cicconi P, Landi D, Germani M. Thermal analysis and simulation of a Li-ion battery pack for a lightweight commercial EV. Appl Energy 2017; 192: 159-177. [Article] [Google Scholar]
  • Lai WJ, Ali MY, Pan J. Mechanical behavior of representative volume elements of lithium-ion battery modules under various loading conditions. J Power Sources 2014; 248: 789-808. [Article] [Google Scholar]
  • Ali MY, Lai WJ, Pan J. Computational models for simulation of a lithium-ion battery module specimen under punch indentation. J Power Sources 2015; 273: 448-459. [Article] [Google Scholar]
  • Jordan SM, Schreiber CO, Parhizi M, et al. A new multiphysics modeling framework to simulate coupled electrochemical-thermal-electrical phenomena in Li-ion battery packs. Appl Energy 2024; 360: 122746. [Article] [Google Scholar]
  • Tran MK, Panchal S, Khang TD, et al. Concept review of a cloud-based smart battery management system for lithium-ion batteries: Feasibility, logistics, and functionality. Batteries 2022; 8: 19. [Article] [Google Scholar]
  • Hossain Lipu MS, Hannan MA, Karim TF, et al. Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook. J Clean Prod 2021; 292: 126044. [Article] [Google Scholar]
  • Kosuru VSR, Kavasseri Venkitaraman A. A smart battery management system for electric vehicles using deep learning-based sensor fault detection. World Electric Vehic J 2023; 14: 101. [Article] [Google Scholar]
  • Panwar NG, Singh S, Garg A, et al. Recent advancements in battery management system for Li-ion batteries of electric vehicles: Future role of digital twin, cyber-physical systems, battery swapping technology, and nondestructive testing. Energy Tech 2021; 9: 2000984. [Article] [Google Scholar]
  • Wang Y, Xu R, Zhou C, et al. Digital twin and cloud-side-end collaboration for intelligent battery management system. J Manuf Syst 2022; 62: 124-134. [Article] [Google Scholar]
  • Dai H, Jiang B, Hu X, et al. Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends. Renew Sustain Energy Rev 2021; 138: 110480. [Article] [Google Scholar]
  • Singh S, Ebongue YE, Rezaei S, et al. Hybrid modeling of lithium-ion battery: Physics-informed neural network for battery state estimation. Batteries 2023; 9: 301. [Article] [Google Scholar]
  • Wen P, Ye ZS, Li Y, et al. Physics-informed neural networks for prognostics and health management of lithium-ion batteries. IEEE Trans Intell Veh 2023; 9: 2276-2289. [Article] [Google Scholar]
  • Bhatti G, Mohan H, Raja Singh R. Towards the future of smart electric vehicles: Digital twin technology. Renew Sustain Energy Rev 2021; 141: 110801. [Article] [Google Scholar]
  • Gao Y, Zhu C, Zhang X, et al. Implementation and evaluation of a practical electrochemical-thermal model of lithium-ion batteries for EV battery management system. Energy 2021; 221: 119688. [Article] [Google Scholar]
  • Lin J, Liu X, Li S, et al. A review on recent progress, challenges and perspective of battery thermal management system. Int J Heat Mass Transfer 2021; 167: 120834. [Article] [Google Scholar]
  • Kumar RR, Bharatiraja C, Udhayakumar K, et al. Advances in batteries, battery modeling, battery management system, battery thermal management, SOC, SOH, and charge/discharge characteristics in EV applications. IEEE Access 2023; 11: 105761-105809. [Article] [Google Scholar]
  • Basu S, Hariharan KS, Kolake SM, et al. Coupled electrochemical thermal modelling of a novel Li-ion battery pack thermal management system. Appl Energy 2016; 181: 1-13. [Article] [Google Scholar]
  • Liu Z, He H. Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter. Appl Energy 2017; 185: 2033-2044. [Article] [Google Scholar]
  • Hu X, Zhang K, Liu K, et al. Advanced fault diagnosis for lithium-ion battery systems: A review of fault mechanisms, fault features, and diagnosis procedures. EEE Ind Electron Mag 2020; 14: 65-91. [Article] [Google Scholar]
  • Zhang K, Hu X, Liu Y, et al. Multi-fault detection and isolation for lithium-ion battery systems. IEEE Trans Power Electron 2021; 37: 971-989. [Article] [Google Scholar]
  • Sadeeq MM, Abdulkareem NM, Zeebaree SR, et al. IoT and cloud computing issues, challenges and opportunities: A review. Qubahan Acad J 2021; 1: 1 [Google Scholar]
  • Duan S, Wang D, Ren J, et al. Distributed artificial intelligence empowered by end-edge-cloud computing: A survey. IEEE Commun Surv Tutorials 2023; 25: 591-624. [Article] [Google Scholar]
  • Tang H, Wu Y, Cai Y, et al. Design of power lithium battery management system based on digital twin. J Energy Storage 2022; 47: 103679. [Article] [Google Scholar]
  • Binsahaq A, Sheltami TR, Salah K. A survey on autonomic provisioning and management of QoS in SDN networks. IEEE Access 2019; 7: 73384-73435. [Article] [Google Scholar]
  • Yang S, Zhang Z, Cao R, et al. Implementation for a cloud battery management system based on the CHAIN framework. Energy AI 2021; 5: 100088. [Article] [Google Scholar]
  • Aguilar J, Ardila D, Avendaño A, et al. An autonomic cycle of data analysis tasks for the supervision of HVAC systems of smart building. Energies 2020; 13: 3103. [Article] [Google Scholar]
  • Genkin M, McArthur JJ. B-SMART: A reference architecture for artificially intelligent autonomic smart buildings. Eng Appl Artif Intel 2023; 121: 106063. [Article] [Google Scholar]
  • Bai Y, Muralidharan N, Sun YK, et al. Energy and environmental aspects in recycling lithium-ion batteries: Concept of battery identity global passport. Mater Today 2020; 41: 304-315. [Article] [Google Scholar]
  • Weng A, Dufek E, Stefanopoulou A. Battery passports for promoting electric vehicle resale and repurposing. Joule 2023; 7: 837-842. [Article] [Google Scholar]
  • Clark S, Bleken FL, Stier S, et al. Toward a unified description of battery data. Adv Energy Mater 2022; 12: 2102702. [Article] [Google Scholar]
  • Peiseler L, Bauer C, Beuse M, et al. Toward a European carbon footprint rule for batteries. Science 2022; 377: 1386-1388. [Article] [Google Scholar]
  • Melin HE, Rajaeifar MA, Ku AY, et al. Global implications of the EU battery regulation. Science 2021; 373: 384-387. [Article] [Google Scholar]
  • Barkhausen R, Fick K, Durand A, et al. Analysing policy change towards the circular economy at the example of EU battery legislation. Renew Sustain Energy Rev 2023; 186: 113665. [Article] [Google Scholar]
  • Naseri F, Gil S, Barbu C, et al. Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms. Renew Sustain Energy Rev 2023; 179: 113280. [Article] [Google Scholar]
  • Biswal BK, Zhang B, Thi Minh Tran P, et al. Recycling of spent lithium-ion batteries for a sustainable future: Recent advancements. Chem Soc Rev 2024; 53: 5552-5592. [Article] [Google Scholar]
  • Fan E, Li L, Wang Z, et al. Sustainable recycling technology for Li-ion batteries and beyond: Challenges and future prospects. Chem Rev 2020; 120: 7020-7063. [Article] [Google Scholar]
  • Qian J, Henderson WA, Xu W, et al. High rate and stable cycling of lithium metal anode. Nat Commun 2015; 6: 6362. [Article] [Google Scholar]
  • Harper G, Sommerville R, Kendrick E, et al. Recycling lithium-ion batteries from electric vehicles. Nature 2019; 575: 75-86. [Article] [Google Scholar]
  • Lv W, Wang Z, Cao H, et al. A critical review and analysis on the recycling of spent lithium-ion batteries. ACS Sustain Chem Eng 2018; 6: 1504-1521. [Article] [Google Scholar]
  • Chen M, Ma X, Chen B, et al. Recycling end-of-life electric vehicle lithium-ion batteries. Joule 2019; 3: 2622-2646. [Article] [Google Scholar]
  • Georgi-Maschler T, Friedrich B, Weyhe R, et al. Development of a recycling process for Li-ion batteries. J Power Sources 2012; 207: 173-182. [Article] [Google Scholar]
  • Gaines L. The future of automotive lithium-ion battery recycling: Charting a sustainable course. Sustain Mater Techno 2014; 1-2: 2-7. [Article] [Google Scholar]
  • Wegener K, Chen WH, Dietrich F, et al. Robot assisted disassembly for the recycling of electric vehicle batteries. Procedia CIRP 2015; 29: 716-721. [Article] [Google Scholar]
  • Mrozik W, Rajaeifar MA, Heidrich O, et al. Environmental impacts, pollution sources and pathways of spent lithium-ion batteries. Energy Environ Sci 2021; 14: 6099-6121. [Article] [Google Scholar]
  • Madani SS, Shabeer Y, Fowler M, et al. Artificial intelligence and digital twin technologies for intelligent lithium-ion battery management systems: A comprehensive review of state estimation, lifecycle optimization, and cloud-edge integration. Batteries 2025; 11: 298. [Article] [Google Scholar]
  • Wu B, Widanage WD, Yang S, et al. Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems. Energy AI 2020; 1: 100016. [Article] [Google Scholar]
  • Shi D, Cai M, Che Y, et al. Towards artificial intelligence-enabled autonomous battery prognostics and management. J Energy Chem 2026; 113: 905-939. [Article] [Google Scholar]
  • Schmidt J, Marques MRG, Botti S, et al. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater 2019; 5: 1. [Article] [Google Scholar]
  • Liu Y, Zhao T, Ju W, et al. Materials discovery and design using machine learning. J Materiomics 2017; 3: 159-177. [Article] [Google Scholar]
  • Liu Y, Guo B, Zou X, et al. Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Mater 2020; 31: 434-450. [Article] [Google Scholar]
  • Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater 2016; 4: 053208. [Article] [Google Scholar]
  • Berecibar M, Gandiaga I, Villarreal I, et al. Critical review of state of health estimation methods of Li-ion batteries for real applications. Renew Sustain Energy Rev 2016; 56: 572-587. [Article] [Google Scholar]
  • Vidal C, Malysz P, Kollmeyer P, et al. Machine learning applied to electrified vehicle battery state of charge and state of health estimation: State-of-the-art. IEEE Access 2020; 8: 52796-52814. [Article] [Google Scholar]
  • Hannan MA, Lipu MSH, Hussain A, et al. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations. Renew Sustain Energy Rev 2017; 78: 834-854. [Article] [Google Scholar]
  • Severson KA, Attia PM, Jin N, et al. Data-driven prediction of battery cycle life before capacity degradation. Nat Energy 2019; 4: 383-391. [Article] [Google Scholar]
  • Hossain Lipu MS, Hannan MA, Hussain A, et al. Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends. J Clean Prod 2020; 277: 124110. [Article] [Google Scholar]
  • Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 2019; 378: 686-707. [Article] [Google Scholar]
  • Niu YL, Chen X, Zhang TC, et al. A large-scale on-the-fly machine learning molecular dynamics simulation to explore lithium metal battery interfaces. J Energy Chem 2025; 110: 356-362. [Article] [Google Scholar]
  • Wang F, Sun Y, Cheng J. Switching of redox levels leads to high reductive stability in water-in-salt electrolytes. J Am Chem Soc 2023; 145: 4056-4064. [Article] [Google Scholar]
  • Yang Y, Yao N, Gao Y, et al. Data-knowledge-dual-driven electrolyte design for fast-charging lithium ion batteries. Angew Chem Int Ed 2025; 64: e202505212. [Article] [Google Scholar]
  • Qin T, Yang H, Wang L, et al. Molecule design for non-aqueous wide-temperature electrolytes via the intelligentized screening method. Angew Chem Int Ed 2024; 63: e202408902. [Article] [Google Scholar]
  • Han C, Gao Y, Chen X, et al. A self-adaptive, data-driven method to predict the cycling life of lithium-ion batteries. InfoMat 2024; 6: e12521. [Article] [Google Scholar]
  • Liu X, Zhang XQ, Chen X, et al. A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries. J Energy Chem 2022; 68: 548-555. [Article] [Google Scholar]
  • Lin T, Chen S, Harris SJ, et al. Investigating explainable transfer learning for battery lifetime prediction under state transitions. eScience 2024; 4: 100280. [Article] [Google Scholar]
  • Su L, Tao S, Li W, et al. Data sufficiency for transferable lithium-ion battery periodical SOH estimation under resource constraints. Cell Rep Phys Sci 2025; 6: 102901. [Article] [Google Scholar]
  • Wang F, Zhai Z, Zhao Z, et al. Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat Commun 2024; 15: 4332. [Article] [Google Scholar]
  • Tao S, Zhang M, Zhao Z, et al. Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning. Energy Environ Sci 2025; 18: 1544-1559. [Article] [Google Scholar]
  • Tang Q, Peng H, Wang Y, et al. Few-shot state-of-health prediction for lithium-ion batteries with LSTM network. Energy 2025; 335: 138078. [Article] [Google Scholar]
  • Han Z, Tao S, Jia Y, et al. Data-driven insight into the universal structure-property relationship of catalysts in lithium-sulfur batteries. J Am Chem Soc 2025; 147: 22851-22863. [Article] [Google Scholar]
  • Piao Z, Han Z, Tao S, et al. Deciphering failure paths in lithium metal anodes by electrochemical curve fingerprints. Natl Sci Rev 2025; 12: nwaf158. [Article] [Google Scholar]
  • Wang S, Liu J, Song X, et al. Artificial intelligence empowers solid-state batteries for material screening and performance evaluation. Nano-Micro Lett 2025; 17: 287. [Article] [Google Scholar]
  • Schwietert TK, Ombrini P, Ootes LS, et al. Phase-field computational framework for addressing challenges in solid-state batteries. PRX Energy 2023; 2: 033014. [Article] [Google Scholar]
  • Jia W, Yi Z, Yu H, et al. New insights into the effects of atomic-scale disordered structures in hard carbon anodes on Na diffusion behaviors: A DFT study. Surf Interfaces 2025; 62: 106175. [Article] [Google Scholar]
  • Li Z, Chen M, Wu Q, et al. A metal-organic framework neuron. Nat Sci Rev 2025; 12: nwaf213. [Google Scholar]

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