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SummaryAiming at the deficiency of optimal control energy management strategy, a model of energy management controller for hybrid electric vehicle (HEV) is constructed based on Kernel Fuzzy C‐means Clustering (KFCM) and multi‐neural network. Using energy management control strategy based on PMP, the operational parameters of the four driving modes for HEV is extracted; the data cluster corresponding to the driving mode is generated by clustering through the KFCM method and is used as the training samples for the feedforward neural network. Taking the battery SOC, needed power and speed as the inputs of neural network, and taking engine power as the output of neural network, four sub‐neural network models are established. Taking the vehicle driving needed power at the current moment and the engine output power at the previous moment as characteristic parameters, the corresponding sub‐neural network model is selected for output prediction according to the proportional relationship between the driving demand torque and the engine output power. The simulation results show that, compared with the energy management strategy based on PMP, the calculation time is greatly shortened using the proposed control strategy, and the real‐time performance is better. The fuel economy is a little decreased under the condition of meeting the requirements, but better dynamic performance can be obtained.

The design of an adaptive total sliding mode control (ATSMC) system for the current control of power factor correction (PFC) is addressed in this paper. The designed ATSMC can achieve elegant performance under ideal condition as well as the existence of parameters variations, moreover, the designed controller parameter is adjusted adaptively to overcome the potential chattering. The merits of the designed ATSMC is verified by a boost type PFC prototype, the simulation and experiment results show that the PFC circuit can achieve unity power factor and sinusoidal grid current, simultaneously, the output voltage has well dynamic and steady state performance.