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About the Project
This PhD project is sponsored by a global leading OEM in hybrid and electric vehicles, and this is a fantastic opportunity to work with the expert team in the Birmingham CASE Automotive Research and Education Centre (a cross-campus research institute in a global top 100 university) and engineers from the industry partner. The overall goal of this project is to incorporate state-of-the-art artificial intelligence technology into the R&D process of PHEV control software helping the industry partner attain better control quality with less R&D cost by applying AI in both design and production stages. The outcomes of this project include 1) new data processing methods; 2) AI models for emission prediction; and 3) new control software that can be implemented on a real dedicated hybrid engine.
Application requirement:
1. First-class degrees with a strong background in Automotive Engineering, Mechanical Engineering, Control Engineering, or Computer Science.
2. Be familiar with industrial development software i.e., Matlab, and Simulink.
3. Related research or industrial experience is desirable.
4. Related research publications.
5. Experience in industrial projects.
Contact:
Please email Professor Hongming Xu (h.m.xu@bham.ac.uk) or Dr Ji LI (j.li.1@bham.ac.uk) if you are interested in this position. Attach a CV and explain briefly how you fulfil the above requirements. It should be noted that candidates who have related research publications are desirable. Candidates that do not fulfil the mandatory requirements should not expect an answer.
Excellent PhD applicants will have the opportunity to be sponsored by University’s PhD scholarship or industry funding. Besides, we can support applications for external funding (e.g., CSC PhD Scholarship).
References
1] Li, J., Zhou, Q., Williams, H., Lu, G., & Xu, H. (2022). Statistics-Guided Accelerated Swarm Feature Selection in Data-Driven Soft Sensors for Hybrid Engine Performance Prediction. IEEE Transactions on Industrial Informatics.
[2] Li, J., Zhou, Q., Williams, H., Xu, P., Xu, H., & Lu, G. (2022). Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines. Applied Energy, 310, 118534.
[2] Li, J., Zhou, Q., Williams, H., Xu, P., Xu, H., & Lu, G. (2022). Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines. Applied Energy, 310, 118534.
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