Parametric Modeling for Personalized Braking of Electric Vehicles in Full-Stop Scenarios

This study proposes a parametric model for per-sonalized braking of electric vehicles (EVs) in a full-stop scenario, with the goal of achieving more comfortable One-Pedal Driving. The proposed model is designed to capture two operations: coasting and active braking. Firstly, coasting behavior is analyzed using real-world driving data from three vehicles to determine a threshold distance where active braking initiates. Secondly, a modified skewed sine (MSS) function-based model is introduced to capture human behavior during active braking periods. With the proposed MSS and a second-order polynomial approximation of the coasting period thresh-old, braking during full-stop scenarios can be reasonably well captured. On average, the medians of the root mean squared error in vehicle speed, the median of minimum acceleration error, and the minimum/maximum jerk errors between the model and the data are 0.76 m/s, 0.18 m/s², and 0.02/-0.54 m/s³, respectively. Furthermore, a parametric study was conducted to investigate the influence of the model parameter and the duration of coasting periods on energy recuperation for a specific EV. The results reveal that when compared to a scenario where the model parameter is tuned with human driving data, energy recuperation can be increased by up to 11.75% through reductions in the model parameter value and coasting distance.