End of life vehicle (ELV) is a significant renewable resource with enormous economic value and environmental value. New regulation promulgated by the State Council of PRC has been implemented since June 2019, allows main assemblies of ELV can be sold to remanufacture enterprises, which dramatically prompts more enterprises to invest in the recycling industry. According to the technological recovery characteristics of ELV, the system layout design (SLP) method is briefly applied to arrange the plant facility layout of ELV recycling and disassembly enterprise. The relationship between logistics and non-logistics was firstly analyzed, and the operating unit correlation diagram of location and area were presented correspondingly. Then, through further modification and adjustment, the feasible layout scheme was obtained, and the reasonable general layout of the plant area was determined by using the weighted factor method. As a critical functional unit of the factory area, the detailed planning and design of the disassembly and shredding workshop were further presented. A detailed facility layout and logistics route of the disassembly and shredding workshop are finally proposed according to the obtained correlation diagram of the operating unit area based SLP, which has been implemented in a recycling company in Changsha, a typical case in China. Combined with the theoretical techniques of statistics, intelligent algorithms, and logistics analysis under the background of modern green production and sustainable manufacturing, this work can provide a new approach for plant layout design and optimization for ELV recycling and disassembly industry.

The need for lithium battery life forecast algorithms has increased due to the global acceptance of electric vehicles. To further enhance the forecast’s precision and resilience about lithium batteries’ remaining life, this study implements quantile regression with in support vector networks to evaluate battery health conditions. Furthermore, it proceeds to integrate self-coding neural networks with temporal convolutional networks for the purpose of processing and extracting battery life data, and finally proposes a novel prediction model. The outcomes of the experiment demonstrate that when the width parameter is 0.75 and the penalty coefficient is 1, the battery health prediction accuracy of this new model is up to 88%, the remaining life prediction accuracy is up to 95.41%, and the number of battery capacity degradation times is up to 340, which is up to 45 times more than the number of the same type of model. In addition, the minimum difference in temperature prediction of battery charging under this model is close to 0.2°C, the minimum difference in temperature prediction during discharge is 0.3°C, and the battery capacity fidelity test findings’ average value is 91.08%. It is evident that the study’s suggested model offers a considerable advantage in estimating lithium battery lifespan for electric vehicles. Additionally, the study’s findings provide a quicker, more precise, and more flexible reference for estimating the lithium batteries’ condition and remaining life.

In order to study the effect of O₂ concentration on the intake gas for combustion and emissions during the cold-start period of the gasoline engine, many parameters were tested in the conditions of 24% oxygen-enriched air and natural air during the initial 60s period after starting, such as the pressure, heat release, the emissions of HC, CO and NO_x, and the exhaust temperature. By comparing two intake conditions the test results show that the oxygen-enriched combustion can optimize combustion, reduce the emissions of HC and CO, increase exhaust temperature, and shorten the light-off time of three-way catalyst.

In this article, a motion model for the liquid center of gravity in a truck’s partially filled tank is established to solve the roll-stability problem that can occur during vehicle maneuvers. A calculation for the overturn moment, together with a liquid-sloshing equivalent mechanical model, was coupled with the partially filled vehicle motion model using an improved optimization method based on a genetic algorithm for optimizing tank geometry. Four optimized geometries were calculated to select the best roll stability, and numerical simulation results showed that the elliptical cross-section tank, with a = 1.6–1.7 m (long axis) and b = 1.15–1.25 m (short axis), exhibits the best stability. These results can provide a valuable benchmark for vehicle design and roll-stability analysis.