Research On Adaptive Cruise Control Strategy Of Pure Electric Vehicle

As an important part of advanced driver assistance system,adaptive cruise control system plays an important role in improving driving safety,economy and comfort.With the development of Internet technology,information transfer rate is significantly improved,and adaptive cruise control is more and more recognized by people.At the same time,in order to deal with the increasingly prominent environmental problems,electric vehicles will become a new trend of the development of the automotive industry in the future.Therefore,the research on the adaptive cruise control system of pure electric vehicles fits the two themes of safety and environmental protection,and has important practical significance to reduce traffic accidents and environmental protection.In this paper,in order to further improve the driving safety,following and comfort,pure electric vehicle adaptive cruise control system as the research object,according to the theory of vehicle longitudinal dynamics,electric vehicles and control theory as the foundation,the system of the space control strategy,the upper control algorithm and lower control strategy were studied,so as to make pure electric vehicles in complex traffic environment realize the function of adaptive cruise.In this paper,the pure electric vehicle was taken as the control object.Firstly,a vehicle inverse longitudinal dynamics model was established,and the expected motor output torque and expected brake pressure were calculated according to the expected acceleration from the upper control.Permanent magnet synchronous motor(PMSM)was used as the power source of electric vehicle.The PMSM model was established,and the output torque was controlled by vector control with more stable output torque.Finally,the vehicle dynamics model was established in Carsim according to vehicle parameters.In adaptive cruise control system,spacing strategy is the first step of system design.In order to make the safe distance as accurate and reasonable as possible in response to the dynamic changes of driving conditions,the variable distance strategy based on variable headway was adopted.Based on the precise division of the dynamic changes of driving conditions,an adaptive cruise distance strategy based on 3d fuzzy control was proposed.Finally,the Matlab/Simulink and Carsim co-simulation platform was built for comparative test and detailed analysis.The simulation results show that the designed spacing strategy can quickly and accurately adopt a reasonable safe spacing according to the dynamic changes of the vehicle in front,and has good adaptability to different driving conditions.Based on model predictive control theory,an adaptive cruise control algorithm with multi-objective optimization was designed.The longitudinal vehicle spacing model and state space equation were established and discretized according to the following process.Objective function and constraints of vehicle following,driving safety and ride comfort were selected to achieve multi-objective optimization of the system.In order to avoid no solution to the optimization problem,relaxation factor was introduced to soften and expand the constraints.Finally,the optimization problem of the system was transformed into a quadratic programming problem with constraints,and the desired acceleration was obtained by solving the problem to complete the upper control of the system.In the lower level control of adaptive cruise control system,the driving and braking mode switching strategy based on threshold was adopted.Considering the influence of external environment and vehicle parameters on the reference acceleration,it is necessary to identify the parameters that affect the vehicle running state,and a vehicle mass and road slope estimation method based on BP neural network and extended Kalman filter was proposed.Firstly,the BP neural network model was established,and the driving pedal opening and speed characteristic parameters of each sampling period were taken as the input of the BP neural network model.The estimated vehicle mass and road slope were output through the model,and then the estimated results were further optimized by the extended Kalman filter algorithm.The final estimation results were applied to the driving braking mode switching strategy of the lower control algorithm.The final estimation results were applied to the driving braking mode switching strategy of the lower control algorithm.Finally,the accuracy of the estimation results was verified by simulation.Finally,the adaptive cruise control system was co-simulated by Matlab/Simulink and Carsim,and the simulation test of uniform speed,insertion,smooth following and cycle conditions of the front vehicle was carried out respectively.The simulation results show that the adaptive cruise control system designed in this paper can better complete the following of the self-vehicle to the front vehicle under a certain road slope,make the self-vehicle track the front vehicle more smoothly,and control the distance between the two vehicles within a reasonable range,which not only ensures the driving safety in the following process,but also improves the road utilization to a certain extent.