Deep-learning-guided heterogeneous cathode structure engineering for enhanced kinetics in lithium-ion batteries

Abstract

Due to their high energy density and long cycle life, lithium-ion batteries have become a preferred energy-storage solution for electronic devices, electric vehicles and renewable energy storage. In this study, a pseudo-two-dimensional (P2D) model of a half-cell with NCM622 as the cathode material was developed to investigate the effects of electrode thicknesses and heterogeneous porosity distribution on the kinetic performance of lithium-ion batteries. Discharge voltage–capacity curves were calculated under varying electrode thicknesses, discharge rates, and porosity distributions to elucidate their effects on discharge capacity. Based on the established database incorporating electrode thickness, discharge rate, heterogeneous porosity distribution, and discharge voltage–capacity curves, a conditional generative adversarial network (cGAN) was developed to predict discharge voltage–capacity curves. The optimal combinations of electrode thickness and porosity distribution that maximize the discharge capacity of NCM622 half-cells at different discharge rates were then determined using particle swarm optimization (PSO). Compared with the baseline porosity distribution at the same electrode thickness, the optimized heterogeneous porosity distribution increased the discharge capacity by 1.74%, 1.39%, 3.39%, and 1.66% at 0.5, 1, 2, and 4 C, respectively, with the largest improvement observed at 2 C.

Graphical abstract

Keywords

lithium-ion battery / P2D model / deep learning / electrode porosity distribution / electrode thickness

References

[1] Zhao Z , Das S , Xing G . et al. A 3D organically synthesized porous carbon material for lithium-ion batteries. Angewandte Chemie International Edition, 2018, 57(37): 11952–11956

[2] Arshad F , Lin J , Manurkar N . et al. Life cycle assessment of lithium-ion batteries: A critical review. Resources, Conservation and Recycling, 2022, 180: 106164

[3] Xi Y K , Wang M J , Xu L . et al. A new Co-free Ni-rich LiNi_0.8Fe_0.1Mn_0.1O₂ cathode for low-cost Li-ion batteries. ACS Applied Materials & Interfaces, 2021, 13(48): 57341–57349

[4] Chen X H , Zhang C X , Zhang W S . et al. Regulating HOMO energy levels of thiophene-based conjugated polymers to facilitate anion storage for high performance dual-ion batteries. Energy Storage Materials, 2025, 79: 104323

[5] Xu S F , Dai H C , Zhu S L . et al. A branched dihydrophenazine-based polymer as a cathode material to achieve dual-ion batteries with high energy and power density. eScience, 2021, 1(1): 60–68

[6] Zhang H , Wang Y L , Zhang B . et al. Interwoven N-doped carbon nanotubes with capped Ni-doped FeP as double-functional electrocatalysts for overall seawater electrolysis. Science China Materials, 2023, 66(12): 4630–4638

[7] Sun K , Peng Z Q . Intermetallic interphases in lithium metal and lithium ion batteries. InfoMat, 2021, 3(10): 1083–1109

[8] Kuang Y D , Chen C J , Kirsch D . et al. Thick electrode batteries: Principles, opportunities, and challenges. Advanced Energy Materials, 2019, 9(33): 1901457

[9] Guo Y T , Li X H , Guo H J . et al. Visualization of concentration polarization in thick electrodes. Energy Storage Materials, 2022, 51: 476–485

[10] Liu X X , Zeng Y B , Yuan W . et al. Advances in multi-scale design and fabrication processes for thick electrodes in lithium-ion batteries. Energy Reviews, 2024, 3(2): 100066

[11] Wang F , Tang M . Thermodynamic origin of reaction non-uniformity in battery porous electrodes and its mitigation. Journal of the Electrochemical Society, 2020, 167(12): 120543

[12] Fu K , Sun K , Li X Y . et al. Design of thick electrodes for high-performance lithium-ion batteries: A comprehensive perspective under coupled kinetics and thermodynamics. Sustainable Energy & Fuels, 2025, 9(7): 1656–1671

[13] Arnot D J , Mayilvahanan K S , Hui Z Y . et al. Thick electrode design for facile electron and ion transport: Architectures, advanced characterization, and modeling. Accounts of Materials Research, 2022, 3(4): 472–483

[14] Jiang F M , Peng P . Elucidating the performance limitations of lithium-ion batteries due to species and charge transport through five characteristic parameters. Scientific Reports, 2016, 6(1): 32639

[15] Zheng H H , Li J , Song X Y . et al. A comprehensive understanding of electrode thickness effects on the electrochemical performances of Li-ion battery cathodes. Electrochimica Acta, 2012, 71: 258–265

[16] Ebner M , Chung D W , García R E . et al. Tortuosity anisotropy in lithium-ion battery electrodes. Advanced Energy Materials, 2014, 4(5): 1301278

[17] Lu L L , Lu Y Y , Zhu Z X . et al. Extremely fast-charging lithium ion battery enabled by dual-gradient structure design. Science Advances, 2022, 8(17): eabm6624

[18] Chen K H , Namkoong M J , Goel V . et al. Efficient fast-charging of lithium-ion batteries enabled by laser-patterned three-dimensional graphite anode architectures. Journal of Power Sources, 2020, 471: 228475

[19] Billaud J , Bouville F , Magrini T . et al. Magnetically aligned graphite electrodes for high-rate performance Li-ion batteries. Nature Energy, 2016, 1(8): 16097

[20] He R J , Tian G L , Li S P . et al. Enhancing the reversibility of lithium cobalt oxide phase transition in thick electrode via low tortuosity design. Nano Letters, 2022, 22(6): 2429–2436

[21] Fu K , Li X Y , Sun K . et al. Rational design of thick electrodes in lithium-ion batteries by re-understanding the relationship between thermodynamics and kinetics. Advanced Functional Materials, 2024, 34(51): 2409623

[22] Fu K , Li X Y , Sun K . et al. Tuning the electrochemical performance of graphite electrodes in lithium-ion batteries: Thermodynamics versus kinetics. Journal of Power Sources, 2024, 606: 234568

[23] Fu K , Li X Y , Sun K . et al. Clarifying the limiting factor of material utilization in thick electrodes of lithium-ion batteries. Journal of Power Sources, 2024, 591: 233880

[24] Chen Z Q , Danilov D L , Eichel R A . et al. Li⁺ concentration waves in a liquid electrolyte of Li-ion batteries with porous graphite-based electrodes. Energy Storage Materials, 2022, 48: 475–486

[25] Aravindan V , Gnanaraj J , Lee Y S . et al. LiMnPO₄—A next generation cathode material for lithium-ion batteries. Journal of Materials Chemistry A, 2013, 1(11): 3518–3539

[26] Zhang H L , Gong Y , Li J . et al. Selecting substituent elements for LiMnPO₄ cathode materials combined with density functional theory (DFT) calculations and experiments. Journal of Alloys and Compounds, 2019, 793: 360–368

[27] Oukahou S , Maymoun M , Elomrani A . et al. Enhancing the electrochemical performance of olivine LiMnPO₄ as cathode materials for Li-ion batteries by Ni–Fe codoping. ACS Applied Energy Materials, 2022, 5(9): 10591–10603

[28] Huang C , Dontigny M , Zaghib K . et al. Low-tortuosity and graded lithium ion battery cathodes by ice templating. Journal of Materials Chemistry A, 2019, 7(37): 21421–21431

[29] Etiemble A , Besnard N , Bonnin A . et al. Multiscale morphological characterization of process induced heterogeneities in blended positive electrodes for lithium–ion batteries. Journal of Materials Science, 2017, 52(7): 3576–3596

[30] Du Z J , Wood D L , Daniel C . et al. Understanding limiting factors in thick electrode performance as applied to high energy density Li-ion batteries. Journal of Applied Electrochemistry, 2017, 47(3): 405–415

[31] Golmon S , Maute K , Dunn M L . Multiscale design optimization of lithium ion batteries using adjoint sensitivity analysis. International Journal for Numerical Methods in Engineering, 2012, 92(5): 475–494

[32] Yang N X , Yang Y X , Wang H Y . et al. Mechanistic insights into the impact of electrode areal density on electrochemical and thermal behavior of high-rate lithium-ion batteries. Energy, 2025, 339: 139041

[33] Yang N X , Yang Y X , Tan X L . et al. Effects of cathode porosity on electrochemical and thermal behavior of high-rate lithium-ion batteries: Experimental and modeling study. Electrochimica Acta, 2025, 537: 146814

[34] Liow C H , Kang H , Kim S . et al. Machine learning assisted synthesis of lithium-ion batteries cathode materials. Nano Energy, 2022, 98: 107214

[35] Wang Y Z , Wu C Q , Ji W J . et al. Machine learning-assisted precision inverse design research of ternary cathode materials: A new paradigm for material design. Journal of Colloid and Interface Science, 2025, 680: 505–517

[36] Yan P F , Zheng J M , Liu J . et al. Tailoring grain boundary structures and chemistry of Ni-rich layered cathodes for enhanced cycle stability of lithium-ion batteries. Nature Energy, 2018, 3(7): 600–605

[37] Yu H F , Cao Y Q , Chen L . et al. Surface enrichment and diffusion enabling gradient-doping and coating of Ni-rich cathode toward Li-ion batteries. Nature Communications, 2021, 12(1): 4564

[38] Li Z Y , Wang Y , Wang J . et al. Gradient-porous-structured Ni-rich layered oxide cathodes with high specific energy and cycle stability for lithium-ion batteries. Nature Communications, 2024, 15(1): 10216

[39] Guan Z R , Zhang Y D , Sun H . et al. The role of lithiation temperature in tuning structural and electrochemical properties of Ni-rich cathodes with different nickel concentrations. Ionics, 2026, 32(3): 2749–2761

[40] Zhang Y D , Cao H M , Lin H Q . et al. Revealing the impact of dual site modification on the phase transformation and ion transport mechanism of Ni-rich cathode materials. ACS Applied Materials & Interfaces, 2024, 16(32): 42283–42292

[41] Wang B C , Ji Z D , Wang Y . et al. A physics-informed composite network for modeling of electrochemical process of large-scale lithium-ion batteries. IEEE Transactions on Industrial Informatics, 2025, 21(1): 287–296

[42] El Ouazzani H , El Hassani I , Masrour T . Deep learning-based surrogate model for lithium-ion battery behavior prediction with extreme fast charging case study. Energy Technology, 2025, 13(11): 2500651

[43] Wang F Y, Feng H H, Ren M F, et al. An Llm-enhanced physics-informed spatiotemporal neural network for lithium-ion battery modeling. In: Proceedings of the 2025 3rd International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE), Shenyang, China, 2025, 1–6

[44] Goodfellow I J , Pouget-Abadie J , Mirza M . et al. Generative adversarial nets. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal Canada, 2014, 2672–2680

[45] Mirza M , Osindero S . Conditional generative adversarial nets. arXiv preprint: arXiv: 1411.1784, 2014,

[46] Li X Y , Zhang Z Y , Gong L L . et al. Modelling and analysis of the volume change behaviors of Li-ion batteries with silicon-graphene composite electrodes. Chemical Engineering Journal, 2023, 470: 144188

[47] Bruggeman D A G . Berechnung verschiedener physikalischer Konstanten von heterogenen Substanzen. I. Dielektrizitätskonstanten und Leitfähigkeiten der Mischkörper aus isotropen Substanzen. Annalen der Physik, 1935, 416(8): 665–679

[48] Kennedy J , Eberhart R . Particle swarm optimization. In: Proceedings of ICNN'95-International Conference on Neural Networks. Perth, Australia, 1995, 1942–1948

[49] Shi Y, Eberhart R. A modified particle swarm optimizer. In: Proceedings of 1998 IEEE International Conference on Evolutionary Computation (Cat. No. 98TH8360), Anchorage, USA, 1998, 69–73

[50] Clerc M , Kennedy J . The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58–73

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