As global initiatives to reduce greenhouse gas emissions and combat climate change expand, electric vehicles (EVs) powered by fuel cells and lithium-ion batteries are gaining global recognition as solutions for sustainable transportation due to their high energy conversion efficiency. Considering the driving range limitations of battery electric vehicles (BEVs) and the low efficiency of internal combustion engines (ICEs), fuel cell hybrid vehicles offer a compelling alternative for long-distance, low-emission driving with less refuelling time. To facilitate their wider scale adoption, it is essential to understand their energy performance through models that consider external weather effects, driving styles, road gradients, and their simultaneous interaction. This paper presents a microlevel, multicriteria assessment framework to investigate the performance of BEVs, fuel cell electric vehicles (FCEVs), and hybrid electric vehicles (HEVs), with a focus on energy consumption, drive systems, and emissions. Simulation models were developed using MATLAB 2021a Simulink environment, thus enabling the integration of standardized driving cycles with real-world wind and terrain variations. The results are presented for various trip scenarios, employing quantitative and qualitative analysis methods to identify the most efficient vehicle configuration, also validated through the simulation of three commercial EVs. Predictive modelling approaches are utilized to estimate a vehicle’s performance under unexplored conditions. Results indicate that trip conditions have a significant impact on the performance of all three vehicles, with HEVs emerging as the most efficient and balanced option, followed by FCEVs, making them strong candidates compared with BEVs for broader adoption in the transition toward sustainable transportation.

This paper presents robust speed-control techniques for permanent-magnet synchronous motors (PMSMs) by integrating model-predictive and sliding-mode strategies. Two controllers are developed: a Sliding-Mode Speed Control–based Model Predictive Current Controller (SMSC-MPCC) using an Adaptive Sliding Mode Surface (ASMS) and an Adaptive Improved Exponential Reaching Law (AIERL), and a Model Predictive Direct Speed Controller (MPDSC) incorporating a Sliding-Mode Load Torque Observer (SMLTO) and a Sliding-Mode Disturbance Observer (SMDO) for sensorless operation and delay compensation. The methods are analyzed under explicit disturbance and saturation assumptions, and their stability is established via Lyapunov arguments. Extensive simulations were conducted, encompassing low-speed operation, frequency-sweep tracking, and Monte Carlo–based robustness evaluation under parameter variations. Performance is assessed using rise time, settling time, overshoot, steady-state error, ITAE, chattering index, and closed-loop bandwidth. Simulated tests confirm statistically significant improvements in mean ITAE and stability margin for the proposed SMSC-MPCC across operating conditions, while MPDSC achieves the fastest disturbance recovery with sensorless capability. The results provide a practical pathway to robust, low-chattering speed control under uncertainty, and clarify the trade-offs among predictive sliding-mode designs for PMSM drives.