MPC Based Car-Following Control for Electric Vehicles Considering Comfort 2023-01-0683

This paper proposed a model predictive control(MPC) based car-following control strategy for electric vehicles considering comfort, in order to improve the comfort of the car-following control system of electric vehicles. The MPC algorithm is improved in the following three aspects to improve the comfort: Firstly, a five-state longitudinal car-following model is adopted, so that the MPC algorithm can optimize the acceleration and acceleration change rate of the ego vehicle. Secondly, for the weight coefficients of the output vector and the input vector of the objective function, the fixed weight coefficients are changed into variable weight coefficients by the way of Nash equilibrium game, so that the control system can improve the weight of the parameters used to control the comfort under suitable driving conditions. Finally, The NGSIM natural driving data set is used to change the fixed constraint on acceleration into the variable constraint that follows the velocity change, so as to avoid the impact of emergency acceleration and deceleration on comfort. The improved MPC algorithm continuously calculates the desired acceleration of the ego vehicle at the current moment through online rolling optimization, than transmits it to the lower-level controller. Due to the type of controlled vehicle is changed from fuel vehicle to electric vehicle, the motor model is used in the lower-level controller to replace the engine model. The control signal of the upper-level controller is converted into the desired motor torque or the desired braking pressure in the lower-level controller. The simulation results show that, on the basis of ensuring the safety and tracking capability, the fluctuation range of acceleration is reduced by 24.4% and the standard deviation of acceleration is reduced by 12.5% compared with the basic MPC algorithm. At the same time, the sudden change of acceleration is eliminated. Thus the comfort of the car-following process is significantly improved.