Surrogate-Assisted Multiobjective Optimization of Electric Vehicle Frontal Crashworthiness via Response-Gated Residual Compensation and Uncertainty Calibration

High-dimensional mixed variables, strongly nonlinear responses, and unreliable decisions near constraint boundaries remain major challenges in frontal crash optimization of electric vehicles. This study develops a response-gated error-compensated surrogate model (RGEC-SM) and an uncertainty-constrained surrogate-assisted optimization method (UC-SB-NSGA-II) for the 100% frontal rigid-barrier impact of an electric sport utility vehicle. RGEC-SM uses a shared encoder with response-specific feature gates to represent multi-response mappings and applies out-of-fold residual radial-basis-function compensation to responses with stable local bias. Bootstrap ensemble standard deviations are converted, through an independent calibration set, into one-sided conservative prediction bounds for baseline-referenced safety constraint screening. On an independent test set, RGEC-SM achieved R2 values of 81.74–91.86% for 13 responses, with a mean NRMSE of 13.4% and a Boundary-NRMSE of 19.4% for safety responses. After conservative constraints were constructed to target 90% one-sided pooled marginal coverage, the false-acceptance rate in the candidate FE audit decreased from 27% to 10.3%, while acceptance precision increased from 73.0% to 89.7%. Under a fixed surrogate-evaluation budget, UC-SB-NSGA-II maintained a conservative-feasible hypervolume comparable to that of NSGA-II, but increased the final-generation conservative baseline-referenced feasible ratio from 48.7% to 79.6%. FE re-simulation of the TOPSIS-selected candidate confirmed that all 11 safety responses were no worse than the baseline design. Body mass decreased from 438.6 kg to 420.4 kg, and vehicle acceleration decreased from 39.4 m/s2 to 36.9 m/s2, corresponding to improvements of 4.15% and 6.35%, respectively. The proposed method reduces the risk of falsely classifying practically infeasible designs as feasible during surrogate-based optimization.