A systematic review on sustainability assessment of internal combustion engines

Internal combustion engines (ICEs) have served as the primary powertrain for mobile sources since the 1890s and also recognized as significant contributors to CO2 emissions in the transportation sector. In order to achieve "carbon neutrality" for transportation sectors, ICE vehicles (ICEVs) are facing substantial challenges in meeting CO2 regulations and intense competition from battery electric vehicles and fuel cell vehicles. Consequently, new technologies of ICEs are continually emerging to enhance competitiveness in reducing environmental impacts. However, the limited studies on the life cycle assessment (LCA) of ICEs make it difficult to evaluate the actual contributions of the emerging technologies from a life cycle perspective. Conducting a systematic review of ICEs LCA studies could identify weaknesses and gaps in these studies for new scenarios. Therefore, this article presents the first systematic review of the LCA of ICEs to provide an overview of the current state of knowledge. A total of 87 life cycle assessment studies between 2017 and 2023 using the Scopus database were identified after searching for the keywords "Sustainability assessment" OR "Life cycle assessment" AND "Internal combustion engine*" OR "ICE*" and carefully screening, and then classified and analyzed by six aspects including sustainability indicators, life cycle phases, life cycle inventories, ICE technologies (including alternative fuels), types of mobile sources and powertrain systems. It is concluded that there are quite limited studies solely focusing on LCA of ICEs, and the LCA assessment lacks consideration of: 1. environmental pollution, human health and socio-economic aspects, 2. fuel production process and maintenance & repair phase, 3. small and developing countries, 4. the emerging ICE technologies and zero carbon/carbon-neutral fuels, 5. large and high-power mobile sources and heavy-duty hybrid technologies

Development of a bio-inspired transformable robotic fin

Fish swim by oscillating their pectoral fins forwards and backwards in a cyclic motion such that their geometric parameters and aspect ratios change according to how fast or slow a fish wants to swim; these complex motions result in a complicated hydrodynamic response. This paper focuses on the dynamic change in the shape of a fin to improve the underwater propulsion of bio-inspired mechanism. To do this, a novel transformable robotic fin has been developed to investigate how this change in shape affects the hydrodynamic forces acting on the fin. This robotic fin has a multi-link frame and a flexible surface skin where changes in shape are activated by a purpose designed multilink mechanism driven by a transformation motor. A drag platform has been designed to study the performance of this variable robotic fin. Numerous experiments were carried out to determine how various controlling modes affect the thrust capability of this fin. The kinematic parameters associated with this robotic fin include the oscillating frequency and amplitude, and the drag velocity. The fin has four modes to control the cyclic motion; these were also investigated in combination with the variable kinematic parameters. The results will help us understand the locomotion performance of this transformable robotic fin. Note that different controlling modes influence the propulsive performance of this robotic fin, which means its propulsive performance can be optimized in a changing environment by adapting its shape. This study facilitates the development of bio-inspired unmanned underwater vehicles with a very high swimming performance

Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking Frequency

Speed judgment is a vital component of autonomous driving perception systems. Automobile drivers were able to evaluate their speed as a result of their driving experience. However, driverless automobiles cannot autonomously evaluate their speed suitability through external environmental factors such as the surrounding conditions and traffic flows. This study introduced the parameter of overtaking frequency (OTF) based on the state of the traffic flow on both sides of the lane to reflect the difference between the speed of a driverless automobile and its surrounding traffic to solve the above problem. In addition, a speed evaluation algorithm was proposed based on the long short-term memory (LSTM) model. To train the LSTM model, we extracted OTF as the first observation variable, and the characteristic parameters of the vehicle’s longitudinal motion and the comparison parameters with the leading vehicle were used as the second observation variables. The algorithm judged the velocity using a hierarchical method. We conducted a road test by using real vehicles and the algorithms verified the data, which showed the accuracy rate of the model is 93%. As a result, OTF is introduced as one of the observed variables that can support the accuracy of the algorithm used to judge speed

Moving-Vehicle Identification Based on Hierarchical Detection Algorithm

The vehicle detection method plays an important role in the driver assistance system. Therefore, it is very important to improve the real-time performance of the detection algorithm. Nowadays, the most popular method is the scanning method based on sliding window search, which detects the vehicle from the image to be detected. However, the existing sliding window detection algorithm has many drawbacks, such as large calculation amount and poor real-time performance, and it is impossible to detect the target vehicle in real time during the motion process. Therefore, this paper proposes an improved hierarchical sliding window detection algorithm to detect moving vehicles in real time. By extracting the region of interest, the region of interest is layered, the maximum and minimum values of the detection window in each layer are set, the flashing frame generated by the layering is eliminated by the delay processing method, and a method suitable for the motion is obtained: the real-time detection algorithm of the vehicle, that is, the hierarchical sliding window detection algorithm. The experiments show that the more layers are divided, the more time is needed, and when the number of detection layers is greater than 7, the time change rate increases significantly. As the number of layers decreases, the detection accuracy rate also decreases, resulting in the phenomenon of a false positive. Therefore, it is determined to meet the requirements of real time and accuracy when the image is divided into 7 layers. It can be seen from the experiment that when the images to be detected are divided into 7 layers and the maximum and minimum values of detection windows are 30 × 30 and 250 × 250, respectively, the number of sub-windows generated is one thirty-seventh of the original sliding window detection algorithm, and the execution time is only one-third of the original sliding window detection algorithm. This shows that the hierarchical sliding window detection algorithm has better real-time performance than the original sliding window detection algorithm