关键词: 车辆工程；异质车队；协同式自适应巡航控制；模糊控制；模型预测控制

Abstract: Taking the heterogeneous fleet consisting of electric vehicles with different dynamic parameters as the research object, a cooperative adaptive cruise control (CACC) system utilizing fuzzy control theory to modify the fixed weights of the original model predictive control (MPC) was proposed, which aimed to meet the performance requirements of the mixed heterogeneous traffic flow such as following, safety and comfort. The longitudinal dynamic models between the vehicles in the CACC system were constructed, and a discrete-state space equation was established. Corresponding objective optimization functions were created and solved with constraints. Fuzzy rules were designed to change the fixed weight coefficients of MPC related performance. Finally, a joint simulation model combining Simulink and CarSim was constructed for testing. Experiment results demonstrate that the improved fuzzy MPC control algorithm outperforms the traditional MPC control algorithm in terms of control effect under the following driving conditions, which can better respond to the performance of heterogeneous fleet such as following, safety and comfort, and better meet the actual driving situation of vehicles.

Key words: vehicle engineering; heterogeneous fleet; cooperative adaptive cruise control; fuzzy control; model predictive control