The automotive industry has used plastics almost since the beginning. The lightness, flexibility, and many qualities of plastics make them ideal for the automotive industry, reducing cars’ overall weight and fuel consumption. Engineering plastics in this industry belong to the high-performance segment of non-renewable resources. These plastics exhibit higher properties than commodity plastics. Fortunately, unlike recycled commodity plastics, the super properties and high-performance characteristics make engineering plastics effectively reused after recycling. The substitution of these fossil-fuel-derived plastics adds to the solution of lightweighting, a much-needed solution to waste management, and solves industrial and ecological issues surrounding plastic disposal. All major vehicle manufacturers worldwide use bioplastics and bio-based plastics, including natural-fiber composites and engineering plastics reinforced with natural fibers. Changing the source of plastics to raw materials from renewable resources is the logical approach to sustainability. Thus, high-quality plastics, recycled plastics, bio-based plastics, and biodegradable plastics could be exploited from design, making sustainability an integral concept of mobility development. This review analyzes that switching from fossil-fuel- to renewable-sources-derived plastics is a step toward meeting the current environmental goals for the automotive industry, including electric cars.

This work presents the development of a classification method that can contribute to precise and increased awareness of the situational context of vehicles, for it to be used in autonomous driving applications. This work aims to obtain a method for machine-learning-based driving environment classification that does not involve computer vision but instead makes use of dynamics variables from Inertial-Measurement-Unit (IMU) sensors and instantaneous energy consumption measurements. This article includes details about the data acquisition, the electric vehicle used for the experiments, and the pre-processing methods employed. This explores the viability of a method for classifying a vehicle’s driving environment. The results of such a system can potentially be used to provide precise information for path planning, energy optimization, or safety purposes. Information about the driving context could be also used to decide if the conditions are safe for autonomous driving or if human intervention is recommended or required. In this work, the feature selection process and statistical data pre-processing methods are evaluated. The pre-processed data are used to compare 13 different classification algorithms and then the best three are selected for further testing and data dimensionality reduction. Two approaches for feature selection based on feature importance and final classification scores are tested, achieving a classification mean accuracy of 93 percent with a real testing dataset that included three driving scenarios and eight different drivers. The obtained results and high classification accuracy represent a first approach for the further development of such classification systems and the potential for direct implementation into autonomous driving technology.

It is necessary to reduce the weight of components while maintaining or improving their mechanical properties to withstand dynamic loads in lightweight structures. In this study, heat treatment and a trigger mechanism were implemented for a thin-walled tube of aluminium to increase energy absorption while reducing the peak crushing force. Different geometries and locations were proposed to trigger deformation in a controlled manner, in combination with heat treatments. Experimental designs for each energy absorption mechanism were performed, and designs were tested by quasi-static crushing. Data obtained from experiments were used to calculate energy absorption indicators that were used to compared designs with components without mechanism to analyse performance. By comparing proposed designs with tubes without modification, the best combination of design variables for each trigger mechanism were identified. It was determined that 160 mm from the upper side, 250 mm${}^2$ area and a rectangular trigger shape reduced peak crushing force by 22.03% and increased energy absorption by 37.76%. For heat treatment, the optimal combination was heating in a furnace at 175 ${}^{\circ }$C for 1 h and cooling in water at 70 ${}^{\circ }$C during 10 min while only soaking half of its length. This combination reduced peak crushing force by 19.02% and increased energy absorption 15.08%. When these mechanisms were combined on a single tube, peak crushing force was reduced by 21.63%, and energy absorption increased by 42.53%.

In-wheel motor technology has reduced the number of components required in a vehicle’s power train system, but it has also led to several additional technological challenges. According to kinematic laws, during the turning maneuvers of a vehicle, the tires must turn at adequate rotational speeds to provide an instantaneous center of rotation. An Electronic Differential System (EDS) controlling these speeds is necessary to ensure speeds on the rear axle wheels, always guaranteeing a tractive effort to move the vehicle with the least possible energy. In this work, we present an EDS developed, implemented, and tested in a virtual environment using MATLAB™, with the proposed developments then implemented in a test car. Exhaustive experimental testing demonstrated that the proposed EDS design significantly improves the test vehicle’s longitudinal dynamics and energy consumption. This paper’s main contribution consists of designing an EDS for an in-wheel motor electric vehicle (IWMEV), with motors directly connected to the rear axle. The design demonstrated effective energy management, with savings of up to 21.4% over a vehicle without EDS, while at the same time improving longitudinal dynamic performance.

Until now, the major problem of methane conversion is to break the very strong C-H bond. It is possible by employing some catalytic methods. However, the formation of carbon powder diminishes the catalytic performance. The use of a gliding arc, described in this work, has two objectives: the hydrogen production and other syngas such as acetylene, and the treatment of the green house gas methane. A good approximation model describing the chemical processes concerning the methane decomposition is described, first by the interaction of key radicals such as CH3 and H, followed by chemical reactions involving other hydrocarbons. Furthermore, the experimental results demonstrated the ability of the gliding arc to accelerate chemical reactions at low temperatures while decreasing energetic cost.