4.4 Article

AER adaptive control strategy via energy prediction for PHEV

Journal

IET INTELLIGENT TRANSPORT SYSTEMS
Volume 13, Issue 12, Pages 1822-1831

Publisher

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-its.2018.5582

Keywords

energy management systems; hybrid electric vehicles; energy consumption; adaptive control; internal combustion engines; fuel economy; neural nets; particle swarm optimisation; control strategy; energy prediction; PHEV; electrical energy; plug-in hybrid electric vehicle; internal combustion engine; grid power; fuel consumption; all-electric range; expected route characteristic; forthcoming charge opportunity; AER adaptive energy management strategy; equivalent consumption minimisation strategy; forthcoming energy consumption prediction; equivalent factor; EF; REPD; corresponding correction factor; particle swarm optimisation

Funding

  1. National Natural Science Foundation of China [51505086]

Ask authors/readers for more resources

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 equivalent 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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available