All-electric Range Adaptive Control Strategy via Energy Prediction for Plug-in HEV
IET Intelligent Transport Systems
The electrical energy of a plug-in hybrid electric vehicle (PHEV) is provided by the internal combustion engine and grid power. The fuel consumption of a PHEV can be minimised through optimising the operation at all-electric range (AER). The AER may vary with the state of charge (SOC), the expected route characteristic, traffic and the electrical energy available dominated by the forthcoming charge opportunity. This research proposes an AER adaptive energy management strategy based on the
... lent consumption minimisation strategy (ECMS) and the forthcoming energy consumption prediction. The model of the equivalent factor (EF) is developed based on the required energy per unit distance (REPD). The corresponding correction factor of EF is optimised with the particle swarm optimisation and developed as a function of REPD, SOC and the AER. The artificial neural network is used to predict REPD which is applied to update the EF estimated model online. The proposed strategy is validated by the numerical simulation and hardware-in-loop experiment (HIL). The simulation and HIL experiment results demonstrate that the proposed strategy can further improve the fuel economy of PHEVs when compared with the traditional ECMS under different driving cycles. fuel consumption of the proposed Ē − ECMS is less than that obtained with s const − ECMS . It is derived that the longer the trip distance, the more improvement in fuel economy performance. Conclusion The AER adaptive control strategy-based energy prediction and combined with ECMS is proposed to improve the fuel economy performance for a plug-in parallel HEV in this research. In this approach, the EF can be adaptive to the changes with REPD, SOC, DTC and AER under different driving cycles using the intelligent optimisation method with information provided by the V2I communication channels. Both the simulations and HIL experiment were performed on a real-world driving cycle and the traffic information is given as prior data. Compared to traditional ECMS, the application of the proposed Ē − ECMS can improve the fuel consumption of a PHEV more obvious during the trip distance increase beyond the AER.