Speed Optimization And Energy Saving Control Of Intelligent Networked Plug-in Hybrid Vehicle Under Following Conditions

Currently,the automotive industry’s primary focus is on energy efficiency,safety,comfort,and environmental protection,with the most prominent research areas being intelligent vehicles,networked cars,and electrification.This paper centers on energy saving optimization control of intelligent snatched plug-in hybrids,also known as plug-in hybrid electric vehicles(PHEVs),aiming to enhance vehicle fuel economy,traffic flow,comfort,and safety by utilizing the model predictive control(MPC)method.The main focus of this paper is outlined below:1)This study examines the power drive system architecture of intelligent networked PHEVs and develops a mathematical model for vehicle energy management.The energy flow of intelligent networked PHEVs is examined in detail across five different energy supply modes,alongside the working modes of the primary components of the power transmission system.These findings serve as the basis for simulation verification of the optimization strategy presented in this paper.2)This paper focuses on studying the optimization of speed for intelligent networked vehicles under urban road conditions.Firstly,based on the speed limit and vehicle power system information in urban road conditions,a speed optimization algorithm framework was developed using a rolling optimization method to enable energy-saving and safe following of target vehicles.Then,by utilizing real-time communication among vehicles in a networking environment and road traffic environment information,the optimal target speed is determined through preliminary optimization using sequential quadratic programming(SQP)algorithm.Finally,the proposed optimization algorithm for determining the optimal target speed is evaluated through real-time simulation,demonstrating its ability to quickly optimize the economical following speed track while ensuring vehicle driving safety and good following performance.Furthermore,the simulation results reveal that the algorithm effectively improves both fuel economy and driving comfort of the vehicle.3)A study was conducted on the energy management and control strategy for intelligent networked vehicles on urban roads.The study utilized the stochastic dynamic programming algorithm(SDP)to develop a real-time energy management control for hybrids called equivalent strategy(ECMS),which minimizes fuel consumption.The study first optimized the energy management control of PHEVs by introducing the energy cost minimization problem of PHEVs under different energy management strategies.The study then designed an objective function expression and applied relevant constraints to construct an energy management control architecture based on ECMS and SDP strategies.Simulation results of different standard working conditions proved that the proposed energy management strategy(EMS)effectively minimizes fuel consumption for intelligent networked PHEVs.4)A layered optimization control strategy was developed to reduce the energy consumption of plug-in hybrid electric vehicles while ensuring vehicle safety in an intelligent network environment.The strategy included layer speed optimization control,based on traffic lights signal phase and timing(SPa T)information and MPC algorithm,and energy management control,based on ECMS.By optimizing the power distribution of the vehicle during acceleration and deceleration,the strategy achieved efficient collaborative optimization in speed planning and energy management.Simulation results demonstrated that the speed optimization layer control method effectively avoided red traffic and collision accidents,reducing red traffic stops,and that energy management based on ECMS enabled good vehicle speed following and maintained the balance of power battery SOC,significantly improving vehicle fuel economy and driving safety.