Driving behavior oriented torque demand regulation for electric vehicles with single pedal driving

Driving behaviors, induced by psychological activities and environment stimulation, impose the dominant impact on vehicle driving performance. To exhaustively improve performance of electric vehicles (EVs), information unscrambled from various behaviors is recommended to be incorporated into controlling process. In this context, a novel method presented regulate torque demand EVs with single pedal (SPD) efficiently interpreting intention different for eco driving. Specifically, brand-new behavior identifier (DBI) constructed integrally employing binary dragonfly algorithm (BDA) adaptive neuro-fuzzy inference system particle swarm optimization (ANFIS-PSO). Simultaneously, whale (WOA) generates look-up tables (TDLTs) offline under SPD referring constraints drivability energy efficiency. instant implementation, are identified instantaneously DBI, homologous TDLTs assigned controller, thereby attaining efficient control powertrain. A case study about traction performed validate prospective optimal proposed further evaluate behaviors.