Xinning Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xinning Li has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Hu Wu
School of Mechanical Engineering, Shandong University of Technology, Zibo, China
1 shared publication - Qun He
School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China
1 shared publication - Wenjie Mao
School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China
1 shared publication - Xianhai Yang
School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China
1 shared publication
Latest Publications
- Open Access
- 9 March 2024
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 15 (3) , 107
Research on Vehicle Frame Optimization Methods Based on the Combination of Size Optimization and Topology Optimization
The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck as the research object, a force analysis of its frame was conducted under six typical working conditions. A size optimization method based on a hybrid model of the Kriging model and the analytic hierarchy process (AHP) is proposed. An approximate model of the mass and maximum stress of the frame was established using the Kriging model, and the Kriging model was optimized by using the multi-objective genetic optimization algorithm and the AHP method. Meanwhile, topology optimization was introduced to improve the structural performance of the frame and reduce its weight. The optimization results show that the overall weight of the frame is reduced by 11.96% compared to the pre-optimization period, though it still meets the material performance specifications. By comparing the iterative curves of the single Kriging model with those of the AHP model, it can be seen that the initial optimization efficiency of the hybrid model is about twice as much as that of the AHP model, and the final optimization result is improved by about 3.6% compared with the Kriging model. This validates the hybrid model as an effective tool for the multi-objective optimization of electric vehicle frames, providing more efficient and accurate optimization results for frame design.
- 9 March 2024
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 15 (3) , 107
Open AccessResearch on Vehicle Frame Optimization Methods Based on the Combination of Size Optimization and Topology Optimization
The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck as the research object, a force analysis of its frame was conducted under six typical working conditions. A size optimization method based on a hybrid model of the Kriging model and the analytic hierarchy process (AHP) is proposed. An approximate model of the mass and maximum stress of the frame was established using the Kriging model, and the Kriging model was optimized by using the multi-objective genetic optimization algorithm and the AHP method. Meanwhile, topology optimization was introduced to improve the structural performance of the frame and reduce its weight. The optimization results show that the overall weight of the frame is reduced by 11.96% compared to the pre-optimization period, though it still meets the material performance specifications. By comparing the iterative curves of the single Kriging model with those of the AHP model, it can be seen that the initial optimization efficiency of the hybrid model is about twice as much as that of the AHP model, and the final optimization result is improved by about 3.6% compared with the Kriging model. This validates the hybrid model as an effective tool for the multi-objective optimization of electric vehicle frames, providing more efficient and accurate optimization results for frame design.