The function of an energy management strategy (EMS) in electric vehicles (EVs) is to ensure optimal performance conditions for the vehicles in terms of fuel economy and reduced toxic gas emissions. In the Volkswagen (VW) of the Department of Vehicles Engineering hybrid powertrain, a 2.0 turbocharged direct injection common rail (TDI CR) diesel engine is integrated with a permanent magnet synchronous electrical machine (PMSM) to minimize the vehicle consumption and gas emissions. This dissertation presents the development of a hybrid VW Crafter, implemented with a novel methodology based on an online data acquisition (DAQ) approach for analyzing the vehicle controller area network (CAN) bus in electrical drives. To facilitate the feasibility of transforming an internal combustion engine (ICE) powered vehicle into a hybrid, the vehicle CAN bus data is collected using LabVIEW software, which is based on the hardware-in-the-loop (HIL) method, decoded with the help of a database (DBC) file and analyzed by redesigning the Crafter based on the data measurements conducted and complemented by the model-in-the-loop (MIL) method on the basis of the physical background plant descriptions of the vehicle components with a computer-aided simulation (CAS) in MATLAB/Simulink/Simcape environment. This work analyses the vehicle’s traction using mathematical descriptions of the vehicle to validate its exact power source, considering trade-offs between vehicle size, battery size, engine type, vehicle mass, driving range, and fuel consumption, as well as battery capacity fade over time and its life cycles. Moreover, EMS using a proportional integral derivative-based genetic algorithm (GA-PID), proposing an integral time absolute error (ITAE) as a fitness function, is developed to allocate load demand to the power source, reducing fuel consumption and carbon dioxide (CO_2) emissions. Therefore, the vehicle's pure, conventional, and hybrid versions are developed and compared. The effectiveness of the proposed EMS is verified by the proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) strategies for the hybrid powertrain. This research reduces fuel consumption, CO_2 emissions, and energy consumption by 68.620%, 70.840%, and 25.080%, respectively.