Stable trajectory planning and energyefficience control allocation of lane change maneuver for autonomous electric vehicle

Purpose–The purpose of this paper is to investigate problems in performing stable lane changes and tofind a solution to reduce energy consumption of autonomous electric vehicles.Design/methodology/approach–An optimization algorithm,model predictive control(MPC)and Karush–Kuhn–Tucker(KKT)conditions are adopted to resolve the problems of obtaining optimal lane time,tracking dynamic reference and energy-efficient allocation.In this paper,the dynamic constraints of vehicles during lane change arefirst established based on the longitudinal and lateral force coupling characteristics and the nominal reference trajectory.Then,by optimizing the lane change time,the yaw rate and lateral acceleration that connect with the lane change time are limed.Furthermore,to assure the dynamic properties of autonomous vehicles,the real system inputs under the restraints are obtained by using the MPC method.Based on the gained inputs and the efficient map of brushless direct-current in-wheel motors(BLDC IWMs),the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is adopted.Findings–The effectiveness of the proposed control system is verified by numerical simulations.Consequently,the proposed control system can successfully achieve stable trajectory planning,which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries,which accomplishes accurate tracking control and decreases obvious energy consumption.Originality/value–This paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric vehicles.Different from previous path planning researches in which only the geometric constraints are involved,this paper considers vehicle dynamics,and stability boundaries are established in path planning to ensure the feasibility of the generated reference path.