Abstract
Coordinatively controlling the engine and several motor/generators (MGs) during a dynamic process is a challenging problem because they are coupled together by the electromechanical transmission (EMT) system and all of them have strong nonlinear characteristics. We develop a novel nonlinear optimal control approach based on the multiobjective dynamic optimization model of the hybrid electric vehicle (HEV), which is equipped with an EMT system. In this approach, the current states of the components are obtained by using the state observation algorithm based on Kalman filtering; the future states of the components and the feasible region of the control variables are estimated by using the dynamic prediction algorithm based on the nonlinear model of the EMT system. Then, the control variables are achieved by using the optimal decision algorithm based on the hierarchical optimization and nonlinear programming, and the influence of the model error and the external disturbance are modified by using the feedback compensation algorithm. The simulation results illustrate the efficiency of the proposed control approach, and the test results verify its real-time performance.
Highlights
Hybrid electric vehicles (HEVs) have received much attention from researchers, governments, and manufacturers because of their high fuel economy and low emissions
We develop a novel nonlinear optimal control approach based on the multiobjective dynamic optimization model of the hybrid electric vehicle (HEV), which is equipped with an electromechanical transmission (EMT) system
The existing control strategies of the HEVs mainly deal with energy management between the engine and the battery to realize the best fuel economy and emission performance
Summary
Hybrid electric vehicles (HEVs) have received much attention from researchers, governments, and manufacturers because of their high fuel economy and low emissions. To overcome the problem of the DP approach being dependent on the driving cycles, some researchers utilized the stochastic process to describe the possible vehicle speeds and proposed the stochastic dynamic programming (SDP) approach [17, 18] This approach needs the Markov model, which is built on the basis of a large number of driving cycles. The MPC approach has become a general model-based control method, which can be divided into different types according to the prediction models and the optimization algorithms. The real-time modifications of the model parameters and the control parameters are done through the feedback compensation algorithm by utilizing the state deviation derived from the feedback As it comprehensively uses the current and future information, the OPDC approach can fully explore the potential of the system.
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