With the improvement of lithium-ion battery (LIB) technology, safety is becoming increasingly urgent topic for battery electric vehicles (BEVs). Short circuits, overcharging, high temperatures and overheating can cause thermal runaway reactions and the release of the flammable electrolyte which makes fire suppression very difficult. This study focuses on the mechanism of thermal runaway and fire suppression applications of LIBs. In order to understand this, 10 experiments were carried out. The experiments were divided into as Exp. A and Exp. B. A manual water suppression system was used in Exp. A and an automatic boron-based suppression system (AUT-BOR) was used in Exp. B. LIBs were heated in a controlled manner with a heat source and the effects of thermal runaway and fire suppression were observed. In Exp. A, a large amount of water was required to extinguish the LIB fires. The holes and slits which formed in the LIB after a fire were useful for injecting water. A projectile effect of cylindrical cells was observed in Exp. A. The Exp. B results showed that AUT-BOR mitigates risks effectively and safely. Also, AUT-BOR provides an early fire warning system and spot cooling to prevent thermal runaway reactions while localizing and suppressing the fire. In Exp. B, fire detection and suppression occurred without any explosion.

In this study, emissions of compression ignition engine fueled by diesel fuel with nanoparticle additives was modeled by regression analysis, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) methods. Cetane number (CN) and engine speed (rpm) were selected as input parameters for estimation of carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) emissions. The results of estimation techniques were compared with each other and they showed that regression analysis was not accurate enough for prediction. On the other hand, ANN and ANFIS modelling techniques gave more accurate results with respect to regression analysis; linear and non-linear. Especially ANFIS models can be suggested as estimation method with minimum error compared to experimental results.

Electric vehicles are now recognized as a crucial answer in the worldwide effort to achieve sustainable and environment-friendly transportation. As the automotive industry moves towards using electric power, it is crucial to assess and improve the powertrain configurations in electric vehicles. This study aims to meet this need by conducting an in-depth comparative analysis of single, double, and quad electric vehicle powertrain systems. The performance of these configurations is rigorously simulated by using two widely used platforms: AVL Cruise and MATLAB Simulink. This study mainly covers the analysis of energy efficiency, which is a critical factor in determining the environmental impacts and feasibility of electric vehicles. In order to conduct a thorough comparison, the energy consumption per kilometer is evaluated as a crucial performance measure. Our research primarily focuses on model validation, namely by comparing it with manufacturer data to determine the accuracy and reliability of the simulation results. The findings reveal a compelling narrative in the pursuit of sustainable transportation. The use of a dual motor setup stands out as a prominent example of energy efficiency, demonstrating remarkable outcomes in both simulation platforms. Significantly, these findings closely correspond with the manufacturer's data for the Volvo XC40, confirming the appropriateness and dependability of our simulation models. Furthermore, the quad motor configuration shows significant energy efficiency, providing helpful insight regarding its applicability and performance. The broader implications of this research go beyond powertrain configurations, including the trustworthiness of simulation models in the automotive industry. These findings improve the continuous advancement of electric vehicle design and development, indicating a more environment-friendly and energy-efficient future for the automotive industry. This study offers invaluable insights and benchmarks for the transition to environment-friendly transportation solutions, as the world advances towards sustainable mobility.

Sıvı fotoduyarlı reçine ve lazer ışığı kullanılarak yüksek boyutsal hassasiyetin elde edildiği Stereolitografi (SLA) yöntemi, eklemeli imalat yöntemleri arasında en dikkat çeken tekniklerden birisi olmuştur. Bu çalışmada, SLA cihazı kullanılarak 3B polimer parçalar basılmış, ikincil kürleme süresinin polimer yapıların mekanik (çekme testi, çentik darbe testi) ve termal (diferansiyel taramalı kalorimetre (DSC) analizi) özelliklerine etkisini incelemek için farklı sürelerde (30, 60, 180 ve 300 dk) UV ikincil kürleme işlemi uygulanmıştır. İşlem uygulanmış polimerlerin elastisite modülü değerinde yaklaşık %49’luk bir artış gerçekleşmiş ve 63,71 MPa mertebelerine ulaşılmıştır. DSC analiz sonuçları 180 dakika ve üzeri ikincil kürleme proseslerinin karbon-karbon çift bağlarının oluşması için yeterli olduğunu göstermektedir.