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- W2287036852 abstract "Model predictive control is a promising approach to exploit the potentials of modern concepts and to fulfill the automotive requirements. Since, it is able to handle constrained multi-input multi-output optimal control problems. However, when it comes to implementation, the MPC computational effort may cause a concern for real-time applications. To maintain the advantage of a predictive control approach and improve its implementation speed, we can solve the problem parametrically. In this paper, we design a power management strategy for a Toyota Prius plug-in hybrid powertrain (PHEV) using explicit model predictive control (eMPC) based on a new control-oriented model to improve the real-time implementation performance. By implementing the controller to a PHEV model through model and hardware-in-the-loop simulation, we get promising fuel economy as well as real-time simulation speed." @default.
- W2287036852 created "2016-06-24" @default.
- W2287036852 creator A5023619833 @default.
- W2287036852 creator A5033006881 @default.
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- W2287036852 date "2015-08-01" @default.
- W2287036852 modified "2023-10-01" @default.
- W2287036852 title "Real-time predictive control strategy for a plug-in hybrid electric powertrain" @default.
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- W2287036852 doi "https://doi.org/10.1016/j.mechatronics.2015.04.020" @default.
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