Physics-Informed Neural State-Space Modeling of Battery-Electric Vehicle Dynamics for Closed-Loop Automated Parking Simulation

Sirong Pan, Guannan Tian, Pan Song

This paper contributes to vehicle dynamics modeling by introducing a physics-informed neural state-space model tailored for the parking regime of a production battery-electric sedan, identified entirely from field-test maneuvers. At parking speeds the model captures what the kinematic idealization omits, including actuator lag, drivetrain creep, brake-hold transitions through standstill, and frequent reversals of the motion direction. A gear-conditioned velocity constraint is imposed during training, and the yaw rate is read out as a learned residual on a kinematic-bicycle prior, so that the network devotes its capacity to the deviation from physics rather than to its reproduction. These training-time physics make the customary inference-time state limiter redundant. The commanded-to-actual behavior of the drive, brake, and steering actuators is reproduced by dedicated submodels, for which signal fidelity proves an unreliable proxy for closed-loop value; tuning the brake on its velocity consequence rather than on its own signal reverses the verdict reached at the signal level. The model generalizes to held-out maneuvers in fully open-loop simulation, and, despite being identified from only 16 field tests, the assembled command-to-vehicle chain earns Good ratings on the vehicle states under the ISO/TS 18571 objective rating metric. Embedded as the real-time plant of an interactive simulator, it enables a production-representative planning stack to park the vehicle through the learned dynamics. This makes the model suitable for pre-calibrating an automated-parking planning and control stack in the virtual development phase without the manufacturer's proprietary chassis and actuator parameters.