Research on eco-velocity planning and air-path control of connected vehicles

ZHAO Jing-hua; WANG Hao-nan; WANG Jie-yu; GONG Xun; XIE Fang-xi; GAO Bing-zhao

doi:10.7641/CTA.2024.40005

Abstract：The mixing process of fresh air and exhaust gas has strong nonlinearity and multi-scale time delay,which poses high challenges for fuel economy improvement and emissions reduction of the air-path control of gasoline engines with VGT and EGR systems.With increasingly stringent regulations,future vehicle fuel consumption and emission testing are required to be conducted in real-world driving conditions.Traffic lights,slopes,and other road conditions can alter the operating conditions of vehicles,causing significant interference with fuel and emission controls.The development and popularization of vehicle connected technology have made it possible to obtain road condition information in advance,and has also promoted the development of eco-velocity planning for fuel economy improvement and emissions reduction.Targeted at the competition requirements for engine air-path and eco-velocity planning of"internal combustion power intel-ligent algorithm challenge",a dual closed-loop real-time optimization control strategy based on neural network modeling and model predictive control technology is proposed in this paper.Based on the high-precision vehicle model provided by the competition,the performance of real-time neural network models as feedforward controllers for air-path control is tested.Under the conditions of obtaining networked information such as traffic lights and speed limits,the effectiveness of the eco-velocity planning within the model predictive control framework is verified,and the sensitivity of sampling period,prediction time domain,optimization objective weight,and vehicle mass changes to control effectiveness is analyzed.