Lihe Xi's research while affiliated with Beijing Jiaotong University and other places
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Publications (4)
This paper presents a linearization method for the vehicle and tire models under the model predictive control (MPC) scheme, and proposes a linear model-based MPC path-tracking steering controller for autonomous vehicles. The steering controller is designed to minimize lateral path-tracking deviation at high speeds. The vehicle model is linearized b...
The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based o...
Driving cycles have been developed for various types of vehicle by different nations and in different areas, as they have a substantial effect on analysis of the fuel economy and the emissions. As the concern about the fuel consumption and the emissions of engineering machinery increases continuously, it has become necessary to develop correspondin...
Range-extender and battery are the main energy sources for Extended-Range Electric Vehicle (E-REV). In order to improve fuel economy, the power demand distribution between range-extender and batteryare the main problem for energy management strategy. This paper considers an energy management strategy for E-REV using dynamic programming, which optim...
Citations
... In the literature further vehicle stability limitations are imposed in terms of yaw rate and/or sideslip angle as hard (J. Zou et al., 2019) or soft constraints Sun et al., 2018;. The typical yaw rate constraint is a direct consequence of (63), under the assumption of steady-state cornering conditions: ...
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... They reduce the real-time computational cost and achieve energy-saving performance close to global optimization approaches but are highly dependent on environmental information. AI-based approaches are promising energy management solutions for MPS-EVs, which include machine learning and deep learning-based knowledge migration methods [13,14,15] and RL-based active control methods [16,17]. The RL demonstrates impressive capabilities for robust regression analysis and strategy development, and the RL-based EMS has shown substantial potential for real-time optimal control in the energy management problem of MPS-EV [18]. ...
... The fuel consumption and emission of vehicles are affected by the real-world driving cycle under different driving conditions. The driving cycle is a critical component in the energy efficiency and emission reduction of vehicles [4,5,6]. The duty cycle appears in construction vehicles, such as WL, excavator, and bulldozer. ...
