Author(s)

Dongxu Guo ¹, Kai Shen ¹, Yuejiu Zheng ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China

Corresponding Author

Yuejiu Zheng

ABSTRACT

Engineering education in power battery systems faces persistent bottlenecks, including prohibitively expensive experimental resources, severe safety risks during abuse testing, and a steep cognitive gap between abstract electrochemical mechanisms and dynamic operational phenomena. To overcome the limitations of traditional, static virtual simulation tools, this paper proposes a novel large-model-driven virtual simulation experiment paradigm tailored for power battery courses. First, we systematically review the current constraints of virtual experiment teaching regarding resource supply, boundary-condition exploration, cognitive barriers, and assessment limitations. Second, a comprehensive methodological framework is proposed, integrating a high-fidelity simulation engine, a model integration layer, and a domain-specific large-model interaction layer. By synergizing open-source battery simulators, scientific computing models, and domain large models, this architecture enables natural-language scenario generation, real-time diagnostic feedback, personalized AI tutoring, and process-oriented multidimensional assessment. Finally, we discuss the pedagogical value and implementation challenges of this approach. The proposed framework effectively transforms virtual simulation from a rigid verification tool into an intelligent, closed loop learning companion, offering a scalable and safe pathway to cultivate innovative engineering talents under the Emerging Engineering Education initiative.

KEYWORDS

Virtual Simulation Experiment, Artificial Intelligence, Multi-Modal Large Model, Emerging Engineering Education, Power Battery Education

CITE THIS PAPER

Dongxu Guo, Kai Shen, Yuejiu Zheng. Large-Model-Driven Virtual Simulation Paradigm for Power Battery Engineering Education. Transactions on Comparative Education (2026). Vol. 8, No.1, 25-32. DOI: http://dx.doi.org/10.23977/trance.2026.080104.

REFERENCES

[1] Li J, Liang W. Effectiveness of virtual laboratory in engineering education: A meta-analysis[J]. PloS one, 2024, 19(12): e0316269.
[2] Boumediene H. The AI Evolution in Higher Education: Enhancing Teaching with ChatGPT[J]. Journal of Studies in Language, Culture and Society (JSLCS), 2025, 8(1): 15-29. 
[3] Ariza J Á, Restrepo M B, Hernández C H. Generative AI in engineering and computing education: A scoping review of empirical studies and educational practices[J]. IEEE Access, 2025.
[4] Rahman F, Mim M S, Baishakhi F B, et al. A systematic review on interactive virtual reality laboratory[C]//Proceedings of the 2nd international conference on computing advancements. 2022: 491-500.
[5] Filippi S, Motyl B. Large language models (LLMs) in engineering education: A systematic review and suggestions for practical adoption[J]. Information, 2024, 15(6): 345. 
[6] Yan L, Sha L, Zhao L, et al. Practical and ethical challenges of large language models in education: A systematic scoping review[J]. British Journal of Educational Technology, 2024, 55(1): 90-112.
[7] Knoth N, Tolzin A, Janson A, et al. AI literacy and its implications for prompt engineering strategies[J]. Computers and Education: Artificial Intelligence, 2024, 6: 100225.
[8] Feng X, Ouyang M, Liu X, et al. Thermal runaway mechanism of lithium ion battery for electric vehicles: A review[J]. Energy storage materials, 2018, 10: 246-267. 
[9] Finegan D P, Scheel M, Robinson J B, et al. In-operando high-speed tomography of lithium-ion batteries during thermal runaway[J]. Nature communications, 2015, 6(1): 6924. 
[10] Sulzer V, Marquis S G, Timms R, et al. Python battery mathematical modelling (PyBaMM)[J]. Journal of Open Research Software, 2021, 9(1). 
[11] Amiri M N, Håkansson A, Burheim O S, et al. Lithium-ion battery digitalization: Combining physics-based models and machine learning[J]. Renewable and Sustainable Energy Reviews, 2024, 200: 114577.
[12] Huang S C, Tseng K H, Liang J W, et al. An online SOC and SOH estimation model for lithium-ion batteries[J]. Energies, 2017, 10(4): 512. 
[13] Li S, Jiang Z, Zhu Z, et al. A framework of joint SOC and SOH estimation for lithium-ion batteries: Using BiLSTM as a battery model[J]. Journal of Power Sources, 2025, 635: 236342. 
[14] Long P, Siemens G. Penetrating the fog: Analytics in learning and education[J]. EDUCAUSE Review (Online), 2011. 
[15] Shute V J. Focus on formative feedback[J]. Review of educational research, 2008, 78(1): 153-189.

Sponsors, Associates, and Links

---

• Advances in Educational Technology and Psychology
  
  
• Curriculum and Teaching Methodology
  
  
• Applied & Educational Psychology
  
  
• Advances in Vocational and Technical Education
  
  
• Adult and Higher Education