Abstract: In response to the technical bottleneck that lightweight and crashworthiness are difficult to be improved collaboratively for conventional automotive B-pillars, a super-hybrid automotive B-pillar assembly structure composed of an ultra-high-strength steel outer panel with variable cross-sectional thickness and a carbon fiber reinforced polymer(CFRP) inner reinforcing panel was proposed. The outer panel was treated with sandblasting and interface intercalation treatment, and the B-pillar assembly structure was integrally formed by combining it with the CFRP inner panel via the prepreg compression molding process. We carried out a collaborative optimization on the cross-sectional thickness of the outer panel and the ply parameters of the CFRP inner panel via OptiStruct software, and formulated a multi-angle crash test scheme for the B-pillar subsystem to replace the full-vehicle side impact test. Finite element simulations and physical experimental validations were conducted for both static three-point bending and multi-angle crash test conditions. The results show that the new solution achieves a 17.4% weight reduction for the B-pillar assembly structure, with the CFRP inner reinforcing panel contributing a 63.2% weight reduction compared to the original metal inner panel. Meanwhile, the intrusion displacement of the B-pillar subsystem under crashworthiness conditions is reduced by 13.8% to 27.8%, and the occupant survival space in the full-vehicle side impact test is increased 11.1%, representing a significant improvement in crash safety performance. The deviation between finite element simulation results and experimental data is less than 9.9%, which verifies the accuracy and reliability of the proposed method. Thus, the super-hybrid structure provides a feasible technical path for the collaborative optimization of lightweight and crashworthiness for key load-bearing components of automobiles.

Key words: carbon fiber reinforced polymer(CFRP), tailor rolled blank(TRB), B-pillar, lightweight, crashworthiness, collaborative optimization