As the energy density of lithium-ion batteries continues to increase, battery safety issues characterized by thermal runaway have become increasingly severe. Battery safety issues have severely restricted the large-scale application of power batteries. Among them, the flammable liquid organic electrolyte is one of the main reasons for the safety hazards of battery thermal runaway. This article reviews the flame-retardant mechanism and research progress of phosphorus-based flame-retardant additives, nitrogen-based flame-retardant additives, and halogen-based flame-retardant additives. The design strategies of conventional flame-retardant additives and intelligent flame-retardant additives in lithium-ion batteries are summarized. Finally, a development direction and research prospects of flame-retardant additives in lithium-ion battery electrolytes are prospected.

Being one of the core power units of electric vehicles, the lithium-ion batteries (LIBs) are broadly concerned. However, in the cases of abuses, LIBs may counter thermal runaway, threatening the personal and property safety of users. In order to avoid the occurrence of thermal runaway, the battery thermal management system (BTMS) has been introduced to improve the safety, optimize the efficiency and prolong the service life of lithium-ion batteries. In this review, feasible thermal management schemes of LIBs system were summarized chronically, different thermal management schemes were evaluated, and case studies were made. The schemes of controlling the internal reaction control in the battery are highlighted as well. This review offers a comprehensive view of BTMS and proposes a promising future for the employment of lithium-ion batteries.

Brain-like intelligent decision-making is a prevailing trend in today’s world. However, inspired by bionics and computer science, the linear neural network has become one of the main means to realize human-like decision-making and control. This paper proposes a method for classifying drivers’ driving behaviors based on the fuzzy algorithm and establish a brain-inspired decision-making linear neural network. Firstly, different driver experimental data samples were obtained through the driving simulator. Then, an objective fuzzy classification algorithm was designed to distinguish different driving behaviors in terms of experimental data. In addition, a brain-inspired linear neural network was established to realize human-like decision-making and control. Finally, the accuracy of the proposed method was verified by training and testing. This study extracts the driving characteristics of drivers through driving simulator tests, which provides a driving behavior reference for the human-like decision-making of an intelligent vehicle.

In this article we used simulator experiments to explore the intelligent mechanisms of human decision-making. Three types of typical emergency scenarios were used in the experiment, in which Scenario 1 was used to analyze the driver’s choice to protect themselves or to protect pedestrians in emergency situations. Scenario 2 was compared with Scenario 1 to verify whether the driver’s avoidance behavior to protect pedestrians was instinctive or selective. Scenario 3 was to verify whether the driver would follow the principle of damage minimization. The driver’s decisions and actions in emergency situations, from the cumulative frequency of time to collision (TTC) to the maximum steering wheel angle rate during the experiments, were recorded. The results show that the driver was not just instinctively avoiding the immediate obstacle, but more selectively protecting pedestrians. At the same time, the time taken up by the driver’s instinctive avoidance response also had a negative impact on decision-making. The actual decisions of the driver were analyzed to provide a basis for building up the ethical decision-making of autonomous vehicles.