To optimize the control of automobile air conditioning system and realize the regional thermal management of vehicle cabin,the prediction of local temperature near the driver and passenger is an effective measure.Aiming at the problem of dynamic prediction of cabin local temperature,the spatial and temporal characteristics of cabin temperature field and the air speed characteristics of air conditioning system were comprehensively considered to establish an attribute-augmented spatio-temporal graph convolution network(AST-GCN).The model training and validation tests on multiple sets of data that collected on real vehicle show that the prediction accuracy of AST-GCN is higher than that of the tempo-ral graph convolutional network(T-GCN)and gated recurrent unit network(GRU)when predicting temperature change over a long time range.In addition,the perturbation analysis shows that the AST-GCN has good robustness.
automobile air conditioning systemregional thermal managementtemperature predictionspatio-temporal graph convolution network
NETL
NSTL
万方数据
