ENERGY MANAGEMENT STRATEGY OF A-ECMS BASED ON GRU-RBFNN SPEED PREDICTION

In order to improve the fuel economy of hybrid electric vehicles, an adaptive equivalent consumption minimization strategy (A-ECMS) based on vehicle speed prediction is proposed. The VISSIM software was used to establish a microscopic traffic simulation model in the field and obtain traffic information. Based on the PyTorch framework, a gated recurrent unit-radial basis function neural network prediction model considering spatio-temporal characteristics was constructed. The dynamic model of parallel hybrid electric vehicle was built in MATLAB/Simulink/Stateflow, and a comparative study was carried out between the A-ECMS based on vehicle speed prediction and the fixed equivalent consumption minimization strategy (F-ECMS). The results show that compared with the F-ECMS, the SOC fluctuation of A-ECMS is smaller, and the automobile fuel economy is improved by 8.97%.