The development of hybrid electric vehicles (HEVs) is rapidly gaining traction as a viable solution for reducing carbon emissions and improving fuel efficiency. One type of HEV that is gaining significant interest is the fuel cell/battery/supercapacitor HEV (FC/Bat/SC HEV), which combines fuel cell, battery, supercapacitor, AC, and DC generators. These FC/B/SC HEVs are particularly appealing because they excel at efficiently managing energy and cater to a wide range of driving requirements. This study presents a novel approach for exploiting the kinetic energy of a sensorless HEV. The vehicle has a primary fuel cell resource, a supercapacitor, and lithium-ion battery energy storage banks, where each source is connected to a special converter. The obtained hybrid system allows the vehicle to enhance autonomy, support the fuel cell during low production moments, and improve transient and steady-state load requirements. The exploitation of kinetic energy is performed by the DC and AC generators that are linked to the electric vehicle front wheels to transfer the HEV’s wheel rotation into power, contributing to the overall power balance of the vehicle. The energy management system for electric vehicles determines the FC setpoint power through the classical state machine method. At the same time, a robust speed controller-based artificial intelligence algorithm reduces power losses and enhances the supply efficiency for the vehicle. Furthermore, we evaluate the performance of a robust controller with a speed estimator, specifically using the adaptive neuro-fuzzy inference system (ANFIS) and the model reference adaptive system (MRAS) estimator in conjunction with the direct torque control-support vector machine (DTC-SVM), to enhance the torque and speed performance of HEVs. The results demonstrate the feasibility and reliability of the vehicle while utilizing the additional DC and AC generators to extract free kinetic energy, both of which contributed to 28% and 24% of the total power for the vehicle, respectively. This approach leads to a vehicle supply efficiency exceeding 96%, reducing the burden on fuel cells and batteries and resulting in a significant reduction in fuel consumption, which is estimated to range from 25% to 35%.

Among issues facing the transportation sector today is the limited autonomy of electric vehicles, which are highly reliant upon energy storage systems. Considering this issue as the current research gap, researchers seek to prolong vehicle dependability through renewable-free and sustainable energy that tackles negative environmental impacts. This research exploits the electric vehicle’s kinetic energy to improve its performance and reliability. It uses fuel-cell resources and supercapacitors hybridized with lithium-ion batteries, in addition to DC generators connected to front wheels that convert their rotations into energy contributing to the vehicle’s overall power balance. A state machine-based energy management strategy computes fuel-cell setpoint power, while a dual-loop structure uses a super-twisting controller for DC bus voltage regulation and recovery, in addition to tracking banks’ setpoint currents. A speed controller-based artificial intelligence is proposed to reduce power losses and enable accurate tracking of running trajectory to improve vehicle mechanisms. The simulation results using Matlab Simulink software proved the proposed vehicle’s feasibility by adopting the free kinetic energy of additional DC generators that provided 28% of its total power requirements, resulting in superior supply efficiency reaching 98%. Thus, the stress on FC and battery was minimized by 21% and 10%, respectively, in addition to reducing fuel consumption by 39%, so the vehicle autonomy was extended, and its reliability was enhanced and supported, as targeted.

The field of energy is of great interest for development, especially in the transportation industry. This paper investigates a hybrid electric vehicle (HEV) with two-wheel drives powered by a fuel cell, battery, DC generators, and supercapacitors. Each energy source is connected to a specific controllable converter. The authors compared the energy management strategies of the Adaptive Neuro-Fuzzy Inference System (ANFIS) with classical energy management strategies. The proposed ANFIS method reduced hydrogen consumption by 8% compared to the classical approach, and improved efficiency to over 98%. The primary objective of this work is to demonstrate the impact of artificial intelligence in renewable energy management strategies (EMSs), with the aim of improving system performance as much as possible by comparing it with classical methods such as state machine (SM) and PI strategies.

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