Al-Hussein Matar

Thermal management remains a key challenge for lithium-ion batteries in electric vehicles, especially under transient driving and charging conditions. This study develops a coupled thermo-hydraulic model for a liquid-cooled battery thermal management system and uses it to compare four coolants with different thermophysical properties: water, ethylene glycol–water, propylene glycol–water, and Jet-A aviation fuel. Unlike studies that focus mainly on temperature reduction, the present work evaluates battery temperature, hydraulic pump power, and cooling load/heat rejection demand within the same framework. The coolants are tested under the FTP-75 driving cycle and a high-rate charging case while pump speed is varied between 1500 and 4500 rpm. Water provides the strongest cooling performance, reducing the battery temperature during FTP-75 from about 30 °C to 21.2 °C at 1500 rpm and 20.6–20.8 °C at 4500 rpm. During charging, water maintains the battery temperature near 23 °C at 1500 rpm, whereas ethylene glycol–water and Jet-A reach about 46–47 °C. Increasing pump speed improves thermal regulation, particularly for weaker-performing coolants, but it also increases auxiliary demand; for example, the RMS pump power of water during charging rises from 0.039 to 0.735 kW. Overall, the results show that coolant selection in liquid-cooled BTMS requires a balanced assessment of heat removal capability, pumping demand, and heat rejection requirements.

Thermal control represents one of the most important parameters influencing the safety and reliability of lithium-ion batteries, especially at high rates required for modern electric vehicles. The present paper investigates the thermal and electrothermal performance of a lithium iron phosphate (LiFePO₄) battery pack using a combination of experimental, statistical, and numerical methods. The 8S5P module was assembled and examined under load tests of 200, 400, and 600 W with and without active air-based cooling. The findings indicate that cooling reduced cell surface temperature by up to 10 °C and extended discharge time by 7–16% under various load conditions, emphasizing the effect of thermal management on battery performance and safety. In order to more systematically investigate the impact of ambient temperature and load, a RSM study with a central composite design (CCD; 13 runs) was performed, resulting in two very significant quadratic models (R² > 0.98) for peak temperature and discharge duration prediction. The optimum conditions are estimated at a 200 W load and an ambient temperature of 20 °C. Based on experimentally determined parameters, a finite-element simulation model was established, and its predictions agreed well with the measured results, which verified the analysis. Integrating measurements, statistical modeling, and simulation provides a tri-phase methodology to date for determining and optimizing battery performance under the electrothermal dynamics of varied environments.

This paper introduces a detailed approach to boosting the functioning and finances of public transport in Greater Cairo. The research depends on multicriteria analysis, econometric forecasting, mathematical optimization, and comparison with other countries to judge how efficiently standard buses, minibuses, and special services make money, reduce costs, and fill seats. When ARIMA was boosted with Fourier terms, it forecasted revenue trends with an error of less than 5%. Both Monte Carlo simulations and Sobol sensitivity indices pointed out that changes in fuel prices had the highest impact on uncertainty. It was shown through optimization that a slight fare raise and adjustment in a few trips could increase net revenue by 6.2% while still respecting capacity and equity. The results encourage changing prices for special services, maintenance improvement based on forecasts, development of updated passenger information services, and better coordination between different types of transport. The research proposes a roadmap that can be applied to cities lacking data but with intense travel needs and boosts global focus on urban sustainability in developing countries.

This study presents a machine learning framework for predicting traffic accident occurrence and congestion intensity using artificial neural networks (ANNs) trained on real-world traffic data collected from a central urban corridor in Egypt. The research aims to enhance proactive traffic management by providing reliable, data-driven forecasts derived from temporal and environmental road features. Sixty-seven traffic observations were recorded over three months, capturing variations across vehicle flow, speed, weather, holidays, and road conditions. Two predictive models were developed: a binary accident detection classifier and a multi-class congestion level estimation classifier. Both models employed Bayesian optimization for hyperparameter tuning and were evaluated under three validation strategies—5-fold cross-validation, 10-fold cross-validation, and resubstitution—combined with different train/test splits. The results demonstrated that the model using 10-fold cross-validation and a 75/25 split achieved the highest accuracy in accident prediction (93.8% on test data), with minimal variance between validation and testing phases. In contrast, resubstitution validation yielded artificially high training accuracy (up to 100%) but lower generalization performance, confirming overfitting risks. Congestion prediction showed similarly strong classification trends, with the optimized model effectively distinguishing between congestion levels under dynamic traffic conditions. These findings validate the use of ANN-based prediction in real-world traffic scenarios and highlight the critical role of validation design in developing robust forecasting models. The proposed approach holds promise for integrating intelligent transportation systems, enabling anticipatory interventions, and enhancing road safety.

Transportation planning is a complex process that aims to achieve the maximum level of effectiveness in terms of costs, usage of transport resources, reliability of deliveries, and minimizing the negative impact on the environment. Most traditional models focus on cost minimization at the expense of risk, road dynamics, and emissions constraints. In contrast, the current paper presents a mixed-integer linear programming (MILP) model for scheduling fleets, selecting transportation modes in multiple modes of transportation, and meeting emissions regulation requirements according to dynamic transportation requirements. Risk-aware routing and taking the factor of congestion and CO₂ emission limits proposed by the government into consideration, this model can offer a more efficient and flexible optimization strategy. From the case study, we observe the significant result that the proposed model achieves, a 23% reduction in transport costs, a 25% improvement in fleet use, a 33.3% decrease in the delivery delay, and a 24.6% decrease in CO₂ emissions. The model dynamically delivers shipments utilizing both road and rail transportation and improves mode choice by minimizing idle vehicle time. This is confirmed through sensitivity analysis which addresses factors such as traffic congestion, changing fuel prices, and changing environmental standards.