Real‑Time Predictive Control of Path Following to Stabilize Autonomous Electric Vehicles Under Extreme Drive Conditions

A novel real-time predictive control strategy is proposed for path following(PF)and vehicle stability of autonomous electric vehicles under extreme drive conditions.The investigated vehicle configuration is a distributed drive electric vehicle,which allows to independently control the torques of each in-wheel motor(IWM)for superior stability,but bringing control com-plexities.The control-oriented model is established by the Magic Formula tire function and the single-track vehicle model.For PF and direct yaw moment control,the nonlinear model predictive control(NMPC)strategy is developed to minimize PF tracking error and stabilize vehicle,outputting front tires’lateral force and external yaw moment.To mitigate the calcu-lation burdens,the continuation/general minimal residual algorithm is proposed for real-time optimization in NMPC.The relaxation function method is adopted to handle the inequality constraints.To prevent vehicle instability and improve steering capacity,the lateral velocity differential of the vehicle is considered in phase plane analysis,and the novel stable bounds of lateral forces are developed and online applied in the proposed NMPC controller.Additionally,the Lyapunov-based constraint is proposed to guarantee the closed-loop stability for the PF issue,and sufficient conditions regarding recursive feasibility and closed-loop stability are provided analytically.The target lateral force is transformed as front steering angle command by the inversive tire model,and the external yaw moment and total traction torque are distributed as the torque commands of IWMs by optimization.The validations prove the effectiveness of the proposed strategy in improved steering capacity,desirable PF effects,vehicle stabilization,and real-time applicability.

Forecasting the spatial and temporal charging demand of fully electrified urban private car transportation based on large-scale traffic simulation

To support power grid operators to detect and evaluate potential power grid congestions due to the electrification of urban private cars,accurate models are needed to determine the charging energy and power demand of battery electric vehicles(BEVs)with high spatial and temporal resolution.Typically,e-mobility traffic simulations are used for this purpose.In particular,activity-based mobility models are used because they individually model the activity and travel patterns of each person in the considered geographical area.In addition to inaccuracies in determining the spatial distribution of BEV charging demand,one main limitation of the activity-based models proposed in the literature is that they rely on data describing traffic flow in the considered area.However,these data are not available for most places in the world.Therefore,this paper proposes a novel approach to develop an activity-based model that overcomes the spatial limitations and does not require traffic flow data as an input parameter.Instead,a route assignment procedure assigns a destination to each BEV trip based on the evaluation of all possible destinations.The basis of this evaluation is the travel distance and speed between the origin of the trip and the destination,as well as the car-access attractiveness and the availability of parking spots at the destinations.The applicability of this model is demonstrated for the urban area of Berlin,Germany,and its 448 sub-districts.For each district in Berlin,both the required daily BEV charging energy demand and the power demand are determined.In addition,the load shifting potential is investigated for an exemplary district.The results show that peak power demand can be reduced by up to 31.7%in comparison to uncontrolled charging.