<p>Hybrid electric vehicles (HEVs) are powered by an internal combustion engine and one or more electric motors, which use energy stored in batteries. Previous studies on torque and speed control of HEVs encountered chattering phenomena due to angular tracking errors in motor speed, switching period inaccuracies and challenges in managing parameter variations. Hence a novel technique, namely the Neuro Adaptive Super Twisting (NAST) intelligent controller for series-parallel hybrid electric vehicle (SPHEV) with Levenberg–Marquardt Non-linear Vector Autoregressive Exogenous Neural Network (LM-NVARXNN)-assisted supercapacitor/lithium-ion (SC/Li-ion) battery management system (BMS) has been proposed in which accurate rotor position and motor angular velocity measurement enables effective switching control for SPHEV’s SC/Li-ion BMS. Moreover, in the previous techniques for enhancing HEV range, challenges arise from uncontrolled battery cycles, leading to errors in parameter estimation, slow computation and optimization parameters. Hence, a novel SC/Li-ion BMS with an LM-NVARXNN has been introduced which optimizes the parameters, minimizes estimation errors and boosts battery safety where Li-ion batteries are used for energy storage during regenerative braking, enhancing start/stop operation. Overall, the proposed method provides better dynamic stability with controlled speed and torque and increases the range of SPHEV by the improved regenerative capacity with faster computation and control with reduced error.</p>