Abstract:

In order to improve the lightweight level of electric commercial vehicles， in this paper a multi-level and multi-condition topology optimization design method is proposed for the aluminum alloy frame driven by Internet of Vehicles （IoV） big data. Firstly， the big data of the IoV is analyzed to obtain the proportion of working conditions to determine the objective weight， and the subjective weight is obtained through the Analytic Hierarchy Process （AHP）. Subsequently， a game theory approach integrates these weights through compromise programming to normalize conditions， establishing a multi-condition topology optimization objective function. Then， taking an aluminum alloy frame of a tractor as the research object， the Solid Isotropic Material with Penalization （SIMP） method is used to carry out the overall multi-condition topology optimization design of the first level of the frame， and the number and relative position information of the beams are obtained. Finally， the multi-body dynamic model is used to extract the multi-condition loads between the longitudinal and cross beams， and the second level cross beam section topology optimization is carried out on the frame， leading to the extraction of the cross-section shape of the cross beam. The results show that after topology optimization， the weight of the frame is reduced by 31.4% on the premise of performance improvement.

Key words: aluminum alloy frame, topology optimization, lightweight, weight coefficient, internet of vehicles big data