In the China VI regulations for light-duty vehicles, the (RDE) test is introduced as a supplementary test procedure. In the actual test process, the RDE results are more significantly influenced by driving behavior and vehicle type. To reduce the test cost, the NOx and PN prediction models are established based on the GA-BP method. The results showed that the coefficients of determination of the GA-BP model for NOx and PN predictions are all greater than 0.9 and are linearly highly correlated at the instantaneous emission level. At the overall emission level, the overall error of the GA-BP model is less than 7% for NOx prediction and less than 6% for PN prediction. The model has high accuracy for both instantaneous and overall emissions of light-duty vehicles. This provides practical engineering value for guiding the RDE test.

Accurate gas mass flow allocation is important for multi-component gas mixing systems and fuel-cell test benches, especially when a wide total-flow range is covered by several parallel mass flow controllers (MFCs). This study evaluates the coordinated allocation of three parallel MFCs using traceable third-party calibration data and a reproducible numerical simulation workflow. The calibration curves define a constrained flow-allocation problem in which several MFC setpoint combinations can satisfy the same total target flow, while producing different full-scale error levels. A modified particle swarm optimization (PSO) strategy is compared with a rule-based allocation strategy and standard PSO. The numerical evaluation was extended with 500-run reliability statistics, ablation analysis, parameter sensitivity analysis, expanded switching-region evaluation, numerical perturbation checks, and benchmark optimizer comparisons. Across the main 12 operating points, the probability of obtaining a solution within 0.10 %FS (percentage of full scale (%FS)) increased from 0.207 with standard PSO to 0.310 with the improved PSO, and the probability within 0.05 %FS increased from 0.135 to 0.193. The positive-improvement rate relative to the rule baseline increased from 0.384 to 0.547. The results support the use of calibration-curve-based optimization to reduce switching-region error peaks and to improve repeated-run low-error solution probability in parallel-MFC allocation.

Electric dynamometer test benches generate heterogeneous multi-channel logs whose structural completeness, physical consistency, and operating-condition support vary across files. We propose Coupling-Gated Data Quality Index (CG-DQI) sample weighting, a fold-local preparation protocol for 60 s next-window temperature-change prediction. Deployable weights use training-side structural and physical quality signals, while a target-magnitude coupling gate screens candidate association with the absolute target. The dataset contains 30,093 valid windows from 134 files and 30 date-defined campaigns. File-level leave-one-file-out validation reduced Ridge MAE from 0.7035 to 0.6932 °C and the mean MAE across three fixed Random Forest initializations from 0.4556 to 0.4489 °C per window. Strict nested leave-one-campaign-out analysis retained sparse CG-DQI in all 30 folds and yielded a campaign-level Ridge MAE difference of 0.0152 °C with a 95% bootstrap confidence interval (CI) of 0.0054–0.0309, while the 99th percentile absolute error (P99) increased by 0.0710 °C. Fixed-parameter XGBoost preserved a positive mean MAE direction, although its campaign interval crossed zero. CG-DQI, therefore, provides a traceable MAE-oriented preparation option within the present bench and campaign range; tail-sensitive and cross-device use requires an explicit constraint or recalibration.