The lithium-ion batteries of an electric vehicle belong to a high-voltage direct-current system. The high-voltage insulation performance of electric vehicles is very important for their safe operation. To solve the problems of slow response and the poor estimation accuracy of the insulation resistance under complex vehicle working conditions, a real-time insulation resistance detection method based on the variable forgetting factor least squares algorithm is proposed in this paper. Based on the low-frequency signal injection method and considering the influence of the Y capacitor, the corresponding circuit model and the mathematical model of the reflected wave voltage are established, and the mathematical model is linearized by a first-order Taylor expansion. By analyzing the influence of the forgetting factor on model parameter identification and setting appropriate shutdown criteria, the least squares algorithm with a variable forgetting factor is designed to quickly and accurately estimate the insulation resistance and Y capacitance. The experimental test results show that the proposed method can quickly track the changes in the insulation resistance and Y capacitance under the condition of noise interference and that the root mean square error of the estimation resistor is within 0.012.

How to drive safely in complex real-world traffic settings has long been a question and challenge for autonomous vehicles (AVs). Decision-making systems (DecSs) are the core of AVs, and their safety and rationality are crucial. Several decision-making techniques and algorithms have been applied to AVs; however, they are still subject to limitations and deficiencies, making it impossible to fully guarantee safety. Thus, it is necessary to conduct a safety assessment of the state performance of the DecSs of AVs to reduce driving risks and implement immediate safety measures. This article reviews relevant research on safety assessment methods of AVs’ DecSs. The insufficiency of the current DecSs in terms of safety is analyzed, and the importance and definition of decision-making safety assessment (DMSA) are established. On this basis, assessment indexes related to DMSA are analyzed. From the perspectives of methodology and industrialization development, the existing safety assessment methods are summarized categorically, their applicability and limitations are discussed, and corresponding constructive strategies for improvement are proposed. Additionally, the deficiencies of the existing DMSA methods and the areas that need improvement are analyzed. In this regard, the future challenges and development opportunities of DMSA are proposed. This article provides a useful reference for the perfect DMSA method.

Nowadays, the noise pollution of internal combustion engines is a very important factor influencing human health and is the main noise source of urban environmental noise. Additionally, the main source of gasoline engine noise consists of combustion noise in the cylinder, where the combustion noise is influenced by the combustion processes within the combustion chamber, especially the cyclic variation in the engine combustion. Thus, the inter-relationship between engine noise, cyclic variation and combustion is of great interest to be explored. Moreover, despite the environmental advantages of clean energy, the impact of different fuels on the internal combustion engine’s noise emissions cannot be ignored. As a result, in this work, three blends were prepared and used as test fuels, namely pure gasoline (E0), 10% hydrous ethanol (E10W) and 20% hydrous ethanol (E20W) by volume, accompanied by engine operating at a steady speed of 2000 r/min under various loads. The experimental results show that lower engine noise was observed for both E10W and E20W compared to E0. Upon the addition of hydrous ethanol, the peak in-cylinder pressure increased while the maximum pressure rise rate ((dp/dφ)_max) decreased at the low and medium loads. Furthermore, the coefficients of variation in indicated mean effective pressure (COV_imep) and COV_(dp/dφ)max for the two blended fuels were higher than those for pure gasoline. Compared with those of E20W, E10W has lower COV_imep and COV_(dp/dφ)max at low and medium loads. The (dp/dφ)_max and noise emission have a positive relation when the engine is fueled with the hydrous ethanol–gasoline blends, whereas the cyclic variation parameters vary in the opposite direction of the noise emission level for all the blend mixtures. Moreover, (dp/dφ)_max has an essential effect on the combustion noise from a gasoline engine.

With the increasingly prominent environmental and energy issues, emission regulations are becoming more stringent. Ammonia diesel dual fuel (ADDF) engine is one of the effective ways to reduce carbon emissions. This study investigated the effect of multiple injection strategy on the combustion and emission characteristics of liquid ammonia/diesel dual direct injection (DI) engines through numerical simulation. The results showed that under the condition of maintaining the same pre injection diesel fuel and high ammonia energy ratio (80%), with the introduction of multiple injection, the peak cylinder pressure decreased and the peak phase advanced, the combustion start angle (CA10) advanced, the heat release showed a multi-stage pattern. The times of injection (TSOI) has a significant effect on combustion and emissions. As TSOI increased, ignition delay decreased, the combustion duration is shortened, and the combustion is accelerated. Notably, overall emissions of NOx and N₂O have decreased, but the emissions of unburned NH₃ have increased. Optimized the state of ammonia injection (SOAI) timing and ammonia injection pressure (AIP), showed that advancing SOAI timing and increasing AIP improved combustion. Advanced the SOAI timing to −8 °CA ATDC, resulted in a significant NOx emissions decrease with an increase in TSOI, reaching over 50%. Although increasing injection pressure can improve combustion, it also results in higher N₂O emissions.