Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control
Abstract In this paper, a real time energy management strategy (EMS) is proposed for a dual-mode power-split hybrid electric vehicle in order to improve the fuel economy and maintain proper battery’s state of charge (SOC) while satisfying all the constraints and the driving demands. The EMS employs a cascaded control concept which includes a velocity predictor, a master controller and a slave controller. The short term vehicle velocity predictor is developed to improve the controller performance based on radial basis function neural network. The master controller based on nonlinear model predictive control is developed with slow sampling time to sustain SOC and to reduce fuel consumption. Forward dynamic programming is employed here to solve the optimal problem. And the PID-based slave controller is developed with fast sampling time to coordinate the engine and the two motors. Simulation and testbed experiments are performed to verify it and the results demonstrate the effectiveness of the proposed approach compared with other methods. Highlights A new cascaded real time energy management strategy is proposed. A velocity predictor is proposed based on radial basis function neural network. Forward dynamic programming is employed in nonlinear model predictive control to improve efficiency. Engine and motors are coordinated with fast sampling time. Results of simulation and experiments showed that fuel economy was improved compared with other methods.
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