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.

The objective of this study was to develop a Fuel Cell Hybrid Electric Vehicle (FCHEV) powertrain with the aim of enhancing battery usage autonomy. The vehicle, which participated in the Eco-Marathon competition as a prototype, incorporates batteries, a Direct Current (DC) electric motor, and a Proton Exchange Membrane (PEM) fuel cell. The design permits the operation of the fuel cell to be conducted in a more efficacious and fuel-efficient manner. The study employs the MATLAB-Advisor software to construct powertrain models that are then validated in laboratory settings. These models are subsequently compared with the performance of the actual FCHEV prototype and adapted for use in automotive applications. The FCHEV power model calculates instantaneous energy consumption using input variables, such as vehicle speed, acceleration, and road gradient. Furthermore, Real Cycle drive was carried out to improve the trade-off between energy consumption, fuel cells, battery State of Charge (SOC) dynamics, and battery power smoothness, while ensuring that all essential limitations were met. The addition of a fuel cell to an electric car model enhances its range by 250%, significantly improving its adoption and usage.

The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, there exists a MFULM controller, an observer, and an intelligent MAOPRL algorithm. Initially, a flexible MFULM controller is created to make adjustments to blood pressure and medication dosages. Following this, an observer is incorporated into the main controller to improve performance and stability by estimating states and disturbances. The controller parameters are optimized using MAOPRL in an adaptive manner, which involves the use of an actor-critic approach in an adaptive fashion. This approach enhances the adaptability of the controller by allowing for dynamic modifications to dosage and blood pressure control parameters. In the presence of disturbances or instabilities, the critic’s feedback aids the actor in adjusting actions to reduce their impact, utilizing a complementary strategy to tackle deficiencies in the primary controller. Lastly, various evaluations, including assessments under normal conditions, adaptability between patients, and stability evaluations against mixed disturbances, have been carried out to confirm the efficiency and viability of the proposed method.

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.

In the face of escalating energy demand, this research proposes a demand-side management (DSM) strategy that focuses on appliance-level load shifting in residential environments. The proposed approach utilizes detailed energy consumption forecasts that are generated by ensemble machine learning models, which predict usage at both whole-household and individual appliance levels. This granular forecasting enables the development of customized load-shifting schedules for controllable devices. These schedules are optimized using a metaheuristic genetic algorithm (GA) with the objectives of minimizing consumer energy costs and reducing peak demand. The iterative nature of GA allows for continuous fine-tuning, thereby adapting to dynamic energy market conditions. The implemented DSM technique yields significant results, successfully reducing the daily energy consumption cost for shiftable appliances. Overall, the proposed system decreases the per-day consumer electricity cost from 237 cents (without DSM) to 208 cents (with DSM), achieving a 12.23% cost saving. Furthermore, it effectively mitigates peak demand, reducing it from 3.4 kW to 1.2 kW, which represents a substantial 64.7% reduction. These promising outcomes demonstrate the potential for substantial consumer savings while concurrently enhancing the overall efficiency and reliability of the power grid.