The present investigation studies the lateral stability of vehicles by using a two-degree-of-freedom bicycle model which is implemented based on MATLAB/Simulink. The proposed model considered the driver model as an expert system to mitigate the vehicle's lateral deviation. Rear-wheel steering is incorporated into the typical front-steering vehicle model to represent a Four-Wheel Steering (4WS) system that improves lateral vehicle stability. The linear optimal control theory (LQR) is employed to determine the rear wheel steering angle as the control action to minimize the vehicle lateral responses of the vehicle, such as body sideslip angle, lateral deviation, lateral acceleration, and yaw rate. To validate the effectiveness of the proposed controller, the Particle Swarm Optimization (PSO) algorithm is designed to obtain the optimal gain of the LQR. Three scenarios are utilized to evaluate the proposed model. First, at a lateral deviation of 2.5 m; second, at a front steering wheel angle of 4 degrees; and third, activating both previous scenarios together. The vehicular lateral responses are represented in the time domain and root mean square values. Significant improvements are observed in the lateral stability of the vehicle whereas active four-wheel steering is employed, particularly in terms of lateral acceleration, lateral deviation and yaw rate compared to the typical 2WS vehicle.

Road traffic accidents continue to be a problem across the world and according to statistics cause high mortality and economic losses. This research work conceptualizes an idea that will use open traffic data and machine learning models to forecast accidents on roads in order to promote road safety. Based on the presented literature review, the framework incorporates a step-by-step procedure to analyze risk factors for targeted safety interventions, including data pre-processing and feature selection, application of a chosen model for high-risk zones identification, and improving the result by altering related factors. The findings show the applicability of open data and predictive analysis in traffic safety matters, with special emphasis on temporal, spatial, and environmental features. Resources allocation, urban traffic control, and monitoring are cases used to illustrate the framework's applicability. Although this is a conceptual model, the challenges, such as data quality, data privacy issues, and practical issues with implementation, are also included in the framework, along with suggestions for future research, such as the use of stream data and improved modeling techniques. This investigation contributes to the literature as a robust theoretical model from which practical solutions for road traffic safety interventions can be derived to reduce and ultimately eliminate traffic accidents and fatalities worldwide.

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.

Artificial neural networks (ANNs) have emerged as the technology that provides solutions to key issues arising in the field of automobile engineering regarding autonomous driving, predictive maintenance, energy control, and vehicle protection. This paper aims to present various uses of ANNs in car industry concerning data handling for continuous decision-making and adaptation. Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Generative Adversarial Networks (GANs) are all explored in relation to their ANN specific relevance to automobiles. The identified limitation also responds to issues associated with the integration of ANN such as data dependency, the computational load required, and questions related to the ethical use of AI decision making. This paper compares ANN techniques in an automotive context, explaining where they excel and where they could use improvement in terms of the tasks they are applied to. The strategies for phased implementation of the ANN framework, the performance evaluation for each stage of implementation, and the optimization methodologies are discussed below. Future direction highlights the future development of transformers, energy efficient models and raising concerns of ethical regulatory frameworks with regards to ANN driven systems. Thus, by such barriers overcoming, ANNs have a potential to significantly influence the further development of automotive engineering and make automobiles safer, more efficient and environmentally friendly. This study advances the discussion around intelligent mobility and provides the foundation on which future research in the field can build from.

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.