Estimation and Control of Hybrid Electric Vehicle using Artificial Neural Networks

This paper proposes a hybrid adaptive control strategy to control a hybrid electric vehicle (HEV), and two neural-network-based adaptive estimators of torque and speed, which are of both induction motor (IM) and engine, are proposed too. In order to control HEV effectively, the configuration of the hybrid control system combines a fuzzy neural network (FNN) controller and an adaptive compensated controller. The FNN controller is the main controller to track the expected value of the system; and the compensated controller to compensate the uncertainties of the system; the compensated control law is derived using Lyapunov stability theory. The proposed estimator of IM includes two recurrent neural networks (RNN), one is used to estimate rotor flux and speed, the other is used to estimate stator current. The effectiveness of the proposed control strategy is verified by the simulation results.