The acquisition of multi-dimensional traffic information and constantly increasing computational power enable sophisticated control techniques to be applied in cruise control system. This study proposes a predictive cruise control (PCC) scheme based on model predictive control, which is formulated as a multi-objective nonlinear optimization problem. In order to facilitate the proposed PCC to deal with different driving conditions, a clustering method is used to identify the driving state of the preceding vehicle. Then, Bayesian optimization method is adopted to learn the optimal weighting parameters in the multi-objective optimization function, which can improve the control performance. Simulation results show that 2.83% fuel-saving rate can be obtained by applying Bayesian optimization method compared to fixed weighting parameters while maintaining good tracking ability and driving comfort.
Predictive Cruise Control of Connected Vehicle With Online Parameters Learning
2021-10-29
998559 byte
Conference paper
Electronic Resource
English
Data-driven Predictive Connected Cruise Control
IEEE | 2023
|European Patent Office | 2023
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