AI-Driven Computational Insights into Electrochemical Energy Storage: A Review of Emerging Trends in Battery Chemistry

Authors

  • Ansar B. Adam Department of Chemistry, Federal University Wukari, Wukari, Nigeria Author
  • Raymond B. Donatus Department of Chemistry, Federal University Wukari, Wukari, Nigeria Author
  • Emmanuel B. Attahdaniel Department of Chemistry, Federal University Wukari, Wukari, Nigeria Author
  • Hyelalibiya Ataitiya Department of Chemistry, Federal University Wukari, Wukari, Nigeria Author
  • Oluwadolapo J. Ewenifa Department of Inustrial Chemistry, Federal University Wukari, Wukari, Nigeria Author
  • Musa Y. Abubakar Department of Inustrial Chemistry, Federal University Wukari, Wukari, Nigeria Author
  • Abubakar M. Shittu Department of Chemistry, University of Jos, Jos, Nigeria Author

DOI:

https://doi.org/10.63623/3vw8qf18

Keywords:

Artificial intelligence, Battery chemistry, Computational modeling, Electrochemical energy storage, Digital twins

Abstract

Modern battery technologies depend on electrochemical storage systems. However, the performance of such systems is limited by complicated multi-scale processes. These processes include transport of ions, interfacial reactions, and degradation processes. These coupled physicochemical dynamics across scales cannot be captured accurately by traditional computational methods. This review analyzes more than 150 recently published studies (2018-2025) on the seamless integration of artificial intelligence like machine learning and deep learning and physics-informed hybrid models with standard computational chemistry frameworks. As materials discovery and performance optimization are increasingly accelerated by AI-powered methods, this review provides a systematic overview of their applications, successes, and challenges in electrochemical energy storage. AI-powered techniques are speeding the discovery of high-performing electrode materials (Ni-rich cathodes, solid-state electrolytes). AI can increase battery lifetime prediction accuracy by 30-50%. It can also lead to the development of digital twins for real-time monitoring and optimization of systems. Also, the review notes developing trends towards autonomous laboratories and self-optimizing battery systems, where AI connects data-driven insights to fundamental chemical understanding. Our study reveal the immense potential of AI in developing next-generation, sustainable, and circular electrochemical energy storage technologies. Further, they also highlight the challenges and research directions for effective deployment.

References

[1]Vidas L, Castro R. Recent developments on hydrogen production technologies: state-of-the-art review with a focus on green-electrolysis. Applied Sciences, 2021, 11(23), 11363. DOI: 10.3390/APP112311363

[2]Qin Z, Ma J, Zhu M, Khan TA. Advancements in energy storage technologies: Implications for sustainable energy strategy and electricity supply towards sustainable development goals. Energy Strategy Reviews, 2025, 59, 101710. DOI: 10.1016/j.esr.2025.101710

[3]Razmjoo A, Ghazanfari A, Østergaard PA, Jahangiri M, Sumper A, Ahmadzadeh S, et al. Moving toward the expansion of energy storage systems in renewable energy systems A techno-institutional investigation with artificial intelligence consideration. Sustainability, 2024, 16(22), 9926. DOI: 10.3390/su16229926

[4]Xin H, Mou T, Pillai HS, Wang S, Huang Y. Interpretable machine learning for catalytic materials design toward sustainability. Accounts of Materials Research, 2023, 5(1), 22-34. DOI: 10.1021/accountsmr.3c00131

[5]Lombardo T, Duquesnoy M, El-Bouysidy H, Årén F, Gallo-Bueno A, Jørgensen PB, et al. Artificial intelligence applied to battery research: hype or reality? Chemical reviews, 2021, 122(12), 10899. DOI: 10.1021/acs.chemrev.1c00108

[6]Yao Z, Lum Y, Johnston A, Mejía-Mendoza LM, Zhou X, Wen Y, et al. Machine learning for a sustainable energy future. Nature Reviews Materials, 2022, 8(3), 202. DOI: 10.1038/s41578-022-00490-5

[7]Brindha R, Rao RP, Chellappan V, Ramakrishna S. Towards sustainable fuel cells and batteries with an AI perspective. Sustainability, 2022, 14(23), 16001. DOI: 10.3390/su142316001

[8]Barrett DH, Haruna AB. Artificial intelligence and machine learning for targeted energy storage solutions. Current Opinion in Electrochemistry, 2020, 21, 160. DOI: 10.1016/j.coelec.2020.02.002

[9]Sanghvi AH, Manjoo A, Rajput P, Mahajan N, Rajamohan N, Abrar I. Advancements in biohydrogen production__a comprehensive review of technologies, lifecycle analysis, and future scope. RSC Advances, 2024, 14(49), 36868-36885. DOI: 10.1039/D4RA06214K

[10]Martins VL, Neves HR, Monje IE, Leite M, Oliveira PFM de, Antoniassi RM, et al. An overview on the development of electrochemical capacitors and batteries__part II. Anais da Academia Brasileira de Ciências, 2020, 92(2), e20200800. DOI: 10.1590/0001-3765202020200800

[11]Liao C. Electrolytes and additives for batteries Part I: fundamentals and insights on cathode degradation mechanisms. eTransportation, 2020, 5, 100068. DOI: 10.1016/j.etran.2020.100068

[12]Richardson G, Korotkin I, Ranom R, Castle M, Foster JM. Generalised single particle models for high-rate operation of graded lithium-ion electrodes: Systematic derivation and validation. Electrochim Acta, 2020, 339, 135862. DOI: 10.1016/j.electacta.2020.135862

[13]Adebanjo IA, Eko J, Agbeyegbe AG, Yuk SF, Cowart SV, Nagelli EA, et al. A comprehensive review of lithium-ion battery components degradation and operational considerations: A safety perspective. Energy Advances, 2025, 4(7), 820-877. DOI: 10.1039/d5ya00065c

[14]Guo Y, Zhang Q, Chen J, Wan L. Preface: Special topic on rechargeable battery technology. Science China Chemistry, 2023, 67(1), 1. DOI: 10.1007/s11426-023-1907-y

[15]Saldarini A, Longo M, Brenna M, Zaninelli D. Battery electric storage systems: advances, challenges, and market trends. Energies, 2023, 16(22), 7566. DOI: 10.3390/en16227566

[16]Pokhriyal A, Rueda-García D, Gómez-Romero P. To flow or not to flow. A perspective on large-scale stationary electrochemical energy storage. Sustainable Energy Fuels, 2023, 7(23), 5473-5482. DOI: 10.1039/d3se00955f

[17]Grey CP. Prospects for lithium-ion batteries and beyond__A 2030 vision. Nature Communications, 2020, 11(1), 6279. DOI: 10.1038/s41467-020-19991-4

[18]Kaliaperumal M, Dharanendrakumar MS, Prasanna S, Abhishek KV, Chidambaram RK, Adams S, et al. Cause and mitigation of lithium-ion battery failure__A review. Materials, 2021, 14(19), 5676. DOI: 10.3390/ma14195676

[19]Rahman MA, Chowdhury MA, Hasan MM. Exploring lithium-ion battery degradation: A concise review. Batteries, 2024 10(7), 220. DOI: 10.3390/batteries10070220

[20]Xie XH, Guan SJ, Murayama M, Zhao X. A review on research progress in electrolytes for sodium-ion batteries. Scientia Sinica Technologica, 2019, 50, 247-260. DOI: 10.1360/sst-2019-0218

[21]Karahan Toprakci H A, Toprakci O. Recent advances in new-generation electrolytes for sodium-ion batteries. Energies, 2023, 16(7), 3169. DOI: 10.3390/en16073169

[22]Radjendirane AC, Maurya DK, Ren J, Hou H, Algadi H, Xu BB, et al. Overview of inorganic electrolytes for all-solid-state sodium batteries. Langmuir, 2024, 40(32), 16690-16712. DOI: 10.1021/acs.langmuir.4c01845

[23]Massaro A, Squillantini L, De Giorgio F, Scaramuzzo FA, Pasquali M, Brutti S. Advancements in solid-state sodium-based batteries: A comprehensive review. arXiv preprint arXiv, 2025, 04391. DOI: 10.48550/arXiv.2505.04391

[24]Yu J, Duquesnoy M, Liu C, Franco AA. Optimization of the microstructure of carbon felt electrodes by applying the lattice Boltzmann method and Bayesian optimizer. Journal of Power Sources, 2023, 575, 233182. DOI: 10.1016/j.jpowsour.2023.233182

[25]Owen RE, Du W, Millichamp J, Shearing PR, Brett DJL, Robinson JB. Visualising the effect of areal current density on the performance and degradation of lithium sulfur batteries using operando optical microscopy. Journal of The Electrochemical Society, 2024, 171(12), 120523. DOI: 10.1149/1945-7111/ad9cc6

[26]Parvizi P, Jalilian M, Amidi AM, Zangeneh MR, Riba J. From present innovations to future potential: The promising journey of Lithium-Ion batteries. Micromachines, 2025 ,16(2), 194. DOI: 10.3390/mi16020194

[27]Vasta E, Scimone T, Nobile G, Eberhardt O, Dugo D, Benedetti MMD, et al. Models for battery health assessment: A comparative evaluation. Energies, 2023, 16(2), 632. DOI: 10.3390/en16020632

[28]Miao Y, Liu L, Xu K, Li J. High Concentration Heightens Risk for Power Lithium-ion Battery Supply Chains Globally. DOI: 10.21203/rs.3.rs-2083016/v1

[29]Bhowmik A, Berecibar M, Casas-Cabanas M, Csányi G, Dominko R, Hermansson K, et al. Implications of the BATTERY 2030+ AI‐assisted toolkit on future low‐TRL battery discoveries and chemistries. Advanced Energy Materials, 2021, 12(17), 2102698. DOI: 10.1002/aenm.202102698

[30]Stenina I, Yaroslavtsev A. Modern technologies of hydrogen production. Processes, 2022, 11(1), 56. DOI: 10.3390/PR11010056

[31]van Hees A, Zhang ZY, Sudhama A, Zhang C. Molecular modelling of aqueous batteries. arXiv preprint arXiv, 2024, 2406.00468. DOI: 10.48550/arxiv.2406.00468

[32]Atkins D, Ayerbe E, Benayad A, Capone FG, Capria E, Castelli IE, et al. Understanding battery interfaces by combined characterization and simulation approaches: challenges and perspectives. Advanced Energy Materials, 2021, 12(17), 2102687. DOI: 10.1002/aenm.202102687

[33]Zhang S, Steubing B, Potter HK, Hansson PA, Nordberg Å. Future climate impacts of sodium-ion batteries. Resources, Conservation and Recycling, 2023, 202, 107362. DOI: 10.1016/j.resconrec.2023.107362

[34]Li G, Monroe CW. Multiscale lithium-battery modeling from materials to cells. Annual Review of Chemical and Biomolecular Engineering, 2020, 11(1), 277. DOI: 10.1146/annurev-chembioeng-012120-083016

[35]Morgan LM, Islam MM, Yang H, O’Regan K, Patel AN, Ghosh A, et al. From atoms to cells: Multiscale modeling of LiNixMnyCozO2 cathodes for Li-Ion batteries. ACS Energy Letters, 2021, 7(1), 108-122. DOI: 10.1021/acsenergylett.1c02028

[36]Leung K. DFT modelling of explicit solid–solid interfaces in batteries: methods and challenges. Physical Chemistry Chemical Physics, 2020, 22(19), 10412-10425. DOI: 10.1039/c9cp06485k

[37]Dai FZ, Sun Y, Wen B, Xiang H, Zhou Y. Temperature dependent thermal and elastic properties of high entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2) B2: molecular dynamics simulation by deep learning potential. Journal of Materials Science & Technology, 2021, 72, 8-15. DOI: 10.1016/j.jmst.2020.07.014

[38]Nekahi A, Dorri M, Rezaei M, Bouguern MD, Madikere Raghunatha Reddy AK, Li X, et al. Comparative issues of metal-ion batteries toward sustainable energy storage: lithium vs. sodium. Batteries. 2024, 10(8), 279. DOI: 10.3390/batteries10080279

[39]Liu S, Xu H, Ai Y, Li H, Bengio Y, Guo H. Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization. arXiv preprint arXiv:2507.16110. 2025 Jul 21. DOI: 10.48550/arxiv.2507.16110

[40]Jha S, Yen M, Salinas YS, Palmer EM, Villafuerte JA, Liang H. Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges. Journal of Materials Chemistry A, 2023, 11(8), 3904. DOI:10.1039/d2ta07148g

[41]Valizadeh A, Amirhosseini MH. Machine learning in lithium-ion battery: applications, challenges, and future trends. SN Computer Science, 2024, 5(6), 717. DOI:10.1007/s42979-024-03046-2

[42]Wang Y. Application-oriented design of machine learning paradigms for battery science. NPJ Computational Materials, 2025, 11(1), 89 DOI:10.1038/s41524-025-01575-9

[43]Xue P, Qiu R, Peng C, Peng Z, Ding K, Long R, et al. Solutions for lithium battery materials data issues in machine learning: overview and future outlook. Advanced Science, 2024, 11(48), 2410065. DOI: 10.1002/advs.202410065

[44]Brewer W, Gainaru A, Suter F, Wang F, Emani M, Jha S. AI-coupled HPC workflow applications, middleware and performance. arXiv preprint arXiv:2406.14315, 2024. DOI: 10.48550/arxiv.2406.14315

[45]Zhu S, Yu T, Xu T, Chen H, Dustdar S, Gigan S, et al. Intelligent computing: The latest advances, challenges and future. Intelligent Computing, 2023, 2, 0006. DOI: 10.34133/icomputing.0006

[46]Navaux POA, Lorenzon AF, Serpa MS. Challenges in high-performance computing. Journal of the Brazilian Computer Society, 2023, 29(1), 51-62. DOI:10.5753/jbcs.2023.2219

[47]Gill SS, Golec M, Hu J, Xu M, Du J, Wu H, et al. Edge AI: A taxonomy, systematic review and future directions. Cluster Comput, 2025, 28(1), 18. DOI: 10.1007/s10586-024-04686-y

[48]Ficili I, Giacobbe M, Tricomi G, Puliafito A. From sensors to data intelligence: Leveraging IoT, cloud, and edge computing with AI. Sensors, 2025, 25(6), 1763. DOI: 10.3390/s25061763

[49]Chappidi SR. Agricultural Intelligence: AI-driven performance frameworks for modern farming. International Journal of Science and Research Archive, 2025, 14(1), 1078-1084. DOI: 10.30574/ijsra.2025.14.1.0160

[50]Weber P, Carl KV, Hinz O. Applications of explainable artificial intelligence in Finance a systematic review of finance, information systems, and computer science literature.Management Review Quarterly, 2024, 74(2), 867-907. DOI: 10.1007/s11301-023-00320-0

[51]Phogat P, Dey S, Wan M. Powering the sustainable future: A review of emerging battery technologies and their environmental impact. RSC Sustainability, 2025, 3, 3266-3306. DOI: 10.1039/d5su00127g

[52]Peters JF, Baumann M, Binder JR, Weil M. On the environmental competitiveness of sodium-ion batteries under a full life cycle perspective__a cell-chemistry specific modelling approach. Sustainable Energy Fuels, 2021, 5(24), 6414. DOI: 10.1039/d1se01292d

[53]Molaiyan P, Bhattacharyya S, dos Reis GS, Sliz R, Paolella A, Lassi U. Towards greener batteries: Sustainable components and materials for next-generation batteries. Green Chemistry, 2024, 26(13), 7508-7531. DOI: 10.1039/d3gc05027k

[54]Wickerts S, Arvidsson R, Nordelöf A, Svanström M, Johansson P. Prospective life cycle assessment of sodium‐ion batteries made from abundant elements. Journal of Industrial Ecology, 2023, 28(1), 116-129. DOI:10.1111/jiec.13452

[55]Putsche VL, Pattany J, Ghosh T, Atnoorkar S, Zuboy J, Carpenter A, et al. A framework for integrating supply chain, environmental, and social justice factors during early stationary battery research. Frontiers in Sustainability, 2023, 4, 1287423. DOI: 10.3389/frsus.2023.1287423

[56]Martin N, Madrid-López C, Villalba G, Talens-Peiró L. New techniques for assessing critical raw material aspects in energy and other technologies. Environmental science & technology, 2022, 56(23), 17236-17245. DOI: 10.1021/acs.est.2c05308

[57]Tao H, Zhuang S, Xue R, Cao W, Tian J, Shan Y. Environmental Finance: An Interdisciplinary Review. Technological Forecasting and Social Change, 2022, 179, 121639. DOI:10.1016/j.techfore.2022.121639

[58]Mahnoor M, Chandio R, Inam A, Ahad IU. Critical and strategic raw materials for energy storage devices. Batteries, 2025, 11(4), 163. DOI:10.3390/batteries11040163

[59]Navidi S, Thelen A, Li T, Hu C. Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods. Energy Storage Materials, 2024, 68, 103343.

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Published

2025-12-05

Issue

Vol. 2 No. 4 (2025)

Section

Articles

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Copyright (c) 2025 Ansar B. Adam, Raymond B. Donatus, Emmanuel B. Attahdaniel, Hyelalibiya Ataitiya, Oluwadolapo J. Ewenifa, Musa Y. Abubakar, Abubakar M. Shittu (Author)

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