Due to global warming, ozone depletion and their ramifications on the Arctic and Antarctic snowscapes, there has been an incentivized drive towards net zero-carbon emission policies by several countries. These policies extend to several sectors, including several manufacturing and processing industries and transportation, which are a few of their notable stakeholders. In the transportation sector, this journey towards net zero-carbon emissions is aided by the adoption of battery electric vehicles (BEVs) due to their zero-carbon emissions during operation. However, they might have zero running emissions, but they do have emissions when charging through conventional sources. This research paper looks at the carbon emissions produced by both electric vehicles (EVs) and internal combustion engine (ICE) vehicles during their operational stages and compares them based on a 200,000 km driving range, battery manufacturing emissions and different power production alternatives to draw up some very important recommendations. The analysis presented in this paper helps in drawing conclusions and proposes ideas which, when included in transport policies, will help curb global warming and eventually lead to the sustainable development of the transport sector. The analysis in this study shows that the emissions needed to produce a single battery unit have increased by approximately 258.7% with the change in battery production locations. Furthermore, charging EVs with a fossil-fuel-dominated grid has shown an increase in emissions of 17.98% compared to the least emissive ICE car considered in the study. Finally, policy update recommendations which are essential for the sustainable development of the transport sector are discussed.

This paper presents a novel hybrid control strategy for a specific class of nonlinear systems based on interval Type 3 fuzzy logic systems (IT3‐FLSs), and its effectiveness in the management of Type 1 diabetes is demonstrated. The main achievements of this work can be summarized as the following main points. First, an online system identification framework whose inputs are only from the measurable signals of the system, including glucose concentration and administered insulin, is introduced using a newly developed IT3‐FLS. Second, the system identification method is independent of the type of specific model or a predefined model and aims to estimate the measurable output of the system to predict the system behavior. Third, a novel hybrid controller consisting of a final control signal steady‐state controller, a proportional controller, a step‐forward predictive controller, and an adaptive compensator is developed. Fourth, through Lyapunov‐based stability analysis, adaptive learning rules are derived for both IT3‐FLS identifier and adaptive compensator, and finally the asymptotic stability of the closed‐loop system will be guaranteed. The proposed method is validated on a large range of virtual Type 1 diabetes patients modeled by the modified Bergman model (MBM) under significant uncertainties, noisy glucose measurements, and perturbations such as food intake. Comparative simulation results confirm the superior performance and robustness of the proposed hybrid strategy over existing insulin–glucose control approaches.

The finite element model (FEM) for induction motors (IM) was developed and validated through experimental testing. The validated FEM provides a reliable basis for further optimization of the electric machine. A strong sliding mode technique, in conjunction with field-oriented control (FOC), is proposed for speed control of the IM. The sliding mode controller ensures steady functioning in the face of ambiguities and disruptions, while FOC enables precise control of the motor’s magnetic field. This combination enhances both the efficiency and accuracy of speed control in IM, making it a valuable tool for industrial applications. The proposed sliding mode control (SMC) was fine-tuned using the advantages produced by the ant colony optimization algorithm. This approach aids in resolving issues and delivers optimal speed and field responses. Simulation and experimental results demonstrate the effectiveness of the proposed approach. The optimized induction motor achieved a 28% reduction in rotor Joule losses, resulting in improved energy efficiency. Additionally, using Ant Colony Optimization to adjust the SMC parameters led to a 99.74% reduction in speed tracking error and a 99.59% reduction in flux error compared to traditional manual tuning. These substantial improvements confirm the superiority of the proposed method for high-performance and energy-efficient electric vehicle applications.