A design concept of energy management and control strategy for hybrid electric car based on neural network and global optimization is proposed. The control strategy can effectively combine the advantages of global optimization algorithm and neural network algorithm. The minimum fuel consumption of the engine model can be derived. Simulation and analysis of the known road cycle conditions were carried out. The simulation platform ADVISOR2002 is used for the secondary development. The control strategy, the power monitoring control strategy and the thermostat control strategy were simulated and compared. The strategy has a strong adaptive capacity which can further improve the fuel economy of hybrid car.
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Neural Network Energy Management Strategy for Series Hybrid Electric Car
Semantic Scholar · Engineering · 2017
Abstract
A design concept of energy management and control strategy for hybrid electric car based on neural network and global optimization is proposed. The control strategy can effectively combine the advantages of global optimization algorithm and neural network algorithm. The minimum fuel consumption of the engine model can be derived. Simulation and analysis of the known road cycle conditions were carried out. The simulation platform ADVISOR2002 is used for the secondary development. The control strategy, the power monitoring control strategy and the thermostat control strategy were simulated and compared. The strategy has a strong adaptive capacity which can further improve the fuel economy of hybrid car.