Lithium‐ion batteries (LIBs) have become the main choice for electric vehicles (EVs). However, the thermal runaway problems of LIBs largely limit the wider promotion of EVs. To provide background and insight for the improvement of battery safety, the general working mechanism of LIBs is described in this review, followed by a discussion of the thermal runaway process, including the trigger conditions and material factors. Moreover, advances made to improve battery safety are examined from the perspective of battery materials and management systems. Thus, this review provides a general picture of the thermal runaway risks of LIBs and corresponding solutions with the aim of facilitating safer battery designs.

In the research on energy management methods of plug-in hybrid electric vehicles, it is expected that a future trend will be to optimize energy management using the information provided by the global positioning system (GPS) and intelligent transportation system (ITS), which is relatively scarce in current research. This study proposes a PHEV energy management method based on travel characteristic prediction. Firstly, this study processes the historical travel data of a certain driver obtained by GPS and ITS and uses the established Markov trajectory prediction model based on key points to predict the trajectory and mileage. Then, on the basis of characteristics analysis of historical travel data, while considering traffic information to form a target cycle, the driving cycles are classified and identified based on traffic information predictions. Then, according to the reasonable SOC allocation range of the four typical cycles, the planning algorithm of the SOC reference trajectory is determined and verified. Finally, based on the previous work, an A-ECMS energy management method based on travel characteristic prediction is established. By comparing different energy management methods, the developed energy management method based on travel characteristic prediction can reasonably utilize power batteries. The fuel saving is about 8.95% higher than the rule-based energy management method, which can effectively improve the whole vehicle’s fuel economy and optimization ability.

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.

SDP (Stochastic Dynamic Programming) control strategy can mine the travel data of drivers, so that the energy-saving potential of vehicles could be improved. However, there are two problems need to be solved in the current SDP: firstly, the analysis and construction method of driver’s typical driving cycle is not clear; secondly, the adaptability of SDP algorithm to a typical driving cycle is insufficient. To solve the above problems, a driving cycle construction method for off-line SDP solution is proposed, which is based on “analysis, dimension reduction and clustering” process. In addition, a coupled control strategy (ECMS-SDP) based on driving conditions identification is developed. Because it is difficult to predict the driving conditions in real time, the working part of the coupled control strategy is calculated by the method of improved random forest. The simulation results show that ECMS-SDP control strategy can save 8%–15% fuel on average compared with CD-CS control strategy, and can save 4%–7% fuel on average compared with ECMS control strategy. The results prove that the ECMS-SDP coupled control strategy can respond well to the changing driving environment, and the fuel economy of vehicles is enhanced.