The commonly used form of transportation mostly relies on-road vehicles nowadays. A vehicle is a mechanism to commute between places with higher efficiency and less time consumption. A vehicle usually consists of multiple working components, in which the engine is well-known to be one of its most crucial components. Although it can be considered the most vital component in a vehicle, the knowledge gaps for an engine are still unlimited. So much more potential can be explored for an engine to reach its maximum capabilities. Part of the perspective that should be explored is to study the engine’s thermal properties. This paper aims to investigate the effect of varying temperatures at the exhaust manifold under a certain amount of time spans. The temperature varied from low to high and vice versa for 6 cycles. The results revealed that the exhaust manifold had undergone an alteration in the strain of the body’s elasticity. Because of that, the displacement of the exhaust manifold, load distribution, and direction have also been affected.

The development of autonomous vehicle systems has progressed rapidly in recent years. One challenge that persists is the capability of the autonomous system to respond to human drivers. Human behavior is an integral part of driving; thus, driver behavior determines changing lanes and speed adjustments. However, human behavior is unpredictable and immeasurable. Some traffic accidents are caused due to the erratic behavior of the driver. Although, traffic laws, such as in Indonesia, regulate the use of lanes concerning the vehicle’s speed. The drivers’ behavior in the lane is more likely to be influenced by the regulation. This paper proposes a novel method of predicting drivers’ behavior by utilizing the concept of fuzzy Hidden Markov Model (fuzzy HMM). HMM has been proven reliable in predicting human behavior by observing measurable states to determine unmeasurable hidden states. The use of fuzzy logic is to mimic the way that humans perceive the speeds of other vehicles. The fuzzy logic determines the relative observed state of other vehicles according to the measured velocity of an ego vehicle and the observed state of observed vehicles. Observation data is obtained by equipping an ego vehicle with an action camera. The observed data, in the form of a video, is then discretized every 2 seconds. The resulting sequence of images is processed to determine several variables: speed and state of the observed vehicles (lane position and speed) and the time instance of the observation. The fuzzy HMM is generated based on observational data. A predictor created using fuzzy HMM equipped with a training and prediction algorithm successfully predicts the behavior of other drivers on the road.

Wheel rims made of metal alloy considerably impact the vehicle’s overall weight. Consequently, employing alloys in the design of wheels results in higher fuel efficiency and lower carbon dioxide emissions. Weight reduction of vehicles also leads to better acceleration. Lightweight automotive design has been increasingly popular in recent years as a means of conserving energy and protecting the environment. The rim is an essential feature of the vehicle since it bears a substantial portion of its overall weight. A vehicle’s weight can be greatly reduced by using a lightweight rim. However, the impact of a lightweight rim on improved fuel economy and reduced carbon dioxide emissions has not been widely explored. In this study, a wheel rim has been designed, and a finite element model has been developed considering radial load, where tire pressure has also been considered. A practical experiment with identical parameters had also been carried out. The values of equivalent stress, strain, and deformation for a metal and an alloy which is steel and cast aluminum alloy (A356.0), respectively, have been compared. In terms of structural stability, steel and cast aluminum alloy have shown fairly similar results based on equivalent stress and deformation. However, the use of cast aluminum alloy has greatly decreased the rim’s weight as a result of its low density and high specific strength. Additionally, the aluminum alloy rim has shown superior fuel efficiency and lower carbon dioxide emissions. According to the findings, cast aluminum alloy rims are more feasible when building a vehicle wheel rim since they minimize the wheel’s and vehicle’s weight while maintaining structural strength. It leads to less fuel consumption, which can save fuel costs and is important for energy conservation.

The battery system in electric vehicles needs proper monitoring and control to ensure reliable, efficient, and safe operation. Recent advancement in cyber-physical technology has brought the emerging digital twin concept. This concept opens a new possibility of real-time condition monitoring and fault diagnosis of the battery system. Although it sounds promising, the concept implementation still faces many challenges. One of the challenges is the availability of a platform to develop digital twins, which involves data pipelines and modeling tools. The data pipeline will include the acquisition, storing, and extract-transform-load (ETL) with high velocity, volume, value, variety, and veracity data, known as big data. The modeling tools must provide applications to build the high-fidelity model, one of the required elements of the digital twin. Based on those urgencies, this paper proposes a platform that facilitates a digital twinning of the battery system in an electric vehicle. The platform is built on the open-source framework CDAP, equipped with a data pipeline and modeling tools. It has run several performance tests with different computation resource configurations and workloads. Doubling the processing power can reduce 12% of computation time while increasing memory size by four times only reduces 10% of computation time. The result shows that the processing power affects the performance digital twin platform more than the memory size.

In developing a lightweight structure, the density of the structure can be reduced by converting a solid structure to a porous infill structure. Previous research has investigated that a triangular infill structure has the highest impact strength compared to other infill patterns. However, the impact strength of this structure may still be improved by adjusting other parameters without extending the production time. This research goal is to investigate the effect of the build orientation and infill angle on the impact strength and the production time of the triangular infill structure. To achieve that goal, this research uses a 3D printing process to manufacture a triangular infill structure made of Polylactic Acid. Seven levels of infill angle and two levels of build orientation are used to find the effect. In this experiment, twenty-six Charpy Impact specimens are printed using the Fused Deposition Modelling machine. Each specimen is printed according to ISO 179 standards and tested using a GT-7045 impact testing machine to measure the impact strength of the specimen. The results of this experiment indicate that the resistance of the on-edge build orientation to the impact load is better than the flat build orientation. The on-edge build orientation type requires less printing time compared to the flat build orientation type for each infill angle. The use of a 45-degree infill angle exhibits the highest impact resistance. To achieve the shortest printing time, the 0-degree infill angle is recommended.