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MODEL PREDICTIVE COORDINATED CONTROL FOR DUAL-MODE POWER-SPLIT HYBRID ELECTRIC VEHICLE
Yunlong Qi,Changle Xiang,Weida Wang,Boxuan Wen,Feng Ding 한국자동차공학회 2018 International journal of automotive technology Vol.19 No.2
Power-split hybrid electric vehicles (HEVs) have great potential fuel efficiency and have attracted extensive research attention with regard to their control system. The coordinated controller in HEV plays an important role in tracking the optimal state reference generated by the energy management strategy (EMS), so as to reach the desired fuel efficiency. Meanwhile, the coordinated controller also has a significant impact on driving performance. To improve its performance, the design of a model predictive control (MPC) based coordinated controller in power-split HEV is presented. First, a non-linear, time-varying constrained control oriented transmission model of a dual-mode power-split HEV is formulated to describe this control problem. Then, to solve this problem, the non-linear part in the transmission model is linearised, and a linear MPC is used to obtain the control signals for the motors and engine at each time step. To meet the requirements of real-time computation, a fast MPC method is also applied to reduce the online computation effort. Simulations and experiments demonstrate the effectiveness of the proposed MPC-based coordinated controller.
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Cong Liu,Hui Liu,Lijin Han,Changle Xiang 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.1
This paper proposes a novel integrated multi-algorithm fusion localization and trajectory tracking framework for autonomous vehicles under extreme conditions with non-Gaussian noises. Firstly, in order to solve the problem that GPS signals are interfered with non-Gaussian noises or lost, a localization method based on Particle Filter (PF) is designed, which takes full advantage of the reference objects position information and vehicle driving state information, thus realizing the self-localization for high-speed autonomous vehicles. Besides, considering the accumulated errors of the model-driven Inertial Measurement Unit (IMU) in the long-horizon positioning prediction, an online future driving state prediction algorithm based on multi-order variable-step Markov model (MM) is proposed to calculate the future vehicle position in scenarios without reference. The fusion of these two methods can give full play to their respective advantages, thus improving the accuracy and robustness of the whole localization algorithm in scenes with non-Gaussian noises. Then, the location information and the future driving state are applied to the trajectory tracking controller based on adaptive model predictive control (AMPC). Finally, the CarSim-Matlab/Simulink cGAOo-simulations results show the effectiveness of the proposed framework when GPS signal is interfered with non-Gaussian noises, which further improve the positioning accuracy and autonomous tracking stability.
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