Future transportation advancements are anticipated to be dependent on electric cars (EVs). The performance of batteries in terms of power density and energy density, however, remains a barrier to the widespread use of electric cars. The energy management system (EMS) of a hybrid electric vehicle (HEV) is necessary for the conversion from a conventional automobile to a pure electric vehicle (PEV). As hybrid electrical sources are often used to power HEVs, choosing the best one is essential for improving HEV performance, cutting fuel use, and lowering nitrogen oxide and hydrocarbon emissions. This research introduces the improved Grey Wolf Optimization (GWO), which replaced weak member strategy and spiralized learning scheme to enhance the exploitation and solution diversity of the proposed GWO approach to control the power sources in HEVs based on power demand and economy. The recommended GWObased EMS provides economical, pollution-free HEV management in addition to effective power source switching.