Energy management control strategy and optimization for plug-in 4WD hybrid electric vehicle

For a plug-in four-wheel-drive hybrid electric vehicle (4WD PHEV), there are 3 power components which can work independently or cooperatively. Therefore, it has many work modes and the energy management control is relatively complicated. And to overcome the inherent defects of fuzzy logic and fuzzy PID (proportion integration differentiation) that they relied on prior knowledge to set the parameters and it was difficult to realize good control effect, it was put forward in this paper that the torque identification coefficient could be obtained through RBF (radial basis function) neural network, whose inputs were the gas pedal travel and its change rate, and the output was the torque identification coefficient. The parameters were obtained through experiments and the neural network model was trained to achieve a better accuracy. With RBF neural network torque request identification, the velocity error was obviously reduced and the fuel consumption was improved by 4.54%, while with the correctional DP-based str