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.

Under the global carbon neutrality initiative, carbon emissions from the transportation sector are becoming increasingly prominent due to the growth in vehicle ownership. And electric mobility may be a potentially effective measure to reduce road traffic carbon emissions and achieve a green transformation of transportation. This paper systematically collates the relevant carbon accounting standards for the automotive industry and elaborates the current status of road transport greenhouse gas emissions by combining the data from the International Energy Agency. And by comparing the lifecycle carbon footprint of various energy types of vehicles, the necessity and feasibility of electric mobility to reduce carbon emissions are discussed. However, the comparison of vehicle lifecycle carbon footprints shows that electric vehicles (EVs) are not as environmentally friendly as expected, although they can significantly reduce road traffic carbon emissions. The high carbon emissions from the manufacturing process of the core components of EVs, especially the power battery, reduce the low‐carbon potential of electric mobility. Therefore, the carbon emission reduction strategies and outcomes of automakers in the automotive industry chain have been further reviewed. Finally, focusing on vehicle power batteries, this article reviews the technologies such as refined management and echelon utilization that can make EVs more environmentally friendly and promote carbon neutrality in the transportation sector.

Electric vehicles (EVs) in severe cold regions face the real demand for fast charging under low temperatures, but low-temperature environments with high C-rate fast charging can lead to severe lithium plating of the anode material, resulting in rapid degradation of the lithium-ion battery (LIB). In this paper, by constructing an electrode–thermal model coupling solid electrolyte interphase (SEI) growth and lithium plating, the competition among different factors of capacity degradation under various ambient temperatures and C-rates are systematically analyzed. In addition, the most important cause of rapid degradation of LIBs under low temperatures are investigated, which reveal the change pattern of lithium plating with temperature and C-rate. The threshold value and kinetic law of lithium plating are determined, and a method of lithium-free control under high C-rate is proposed. Finally, by studying the average aging rate of LIBs, the reasons for the abnormal attenuation of cycle life at lower C-rates are ascertained. Through the chromaticity diagram of the expected life of LIBs under various conditions, the optimal fast strategy is explored, and its practical application in EVs is also discussed. This study can provide a useful reference for the development of high-performance and high-safety battery management systems to achieve fine management.

The accurate estimation of the battery state of health (SOH) is crucial for the dependability and safety of battery management systems (BMS). The generality of existing SOH estimation methods is limited as they tend to primarily consider information from single-source features. Therefore, a novel method for integrating multi-feature collaborative analysis with deep learning-based approaches is proposed in this research. First, several battery degradation features are obtained through differential thermal voltammetry (DTV) analysis, singular value decomposition (SVD), incremental capacity analysis (ICA), and terminal voltage characteristic (TVC) analysis. The features highly related to SOH are selected as inputs for the deep learning model based on the results of a Pearson correlation analysis. The SOH estimation is achieved by developing a deep learning framework cored by long short-term memory (LSTM) neural network (NN), which integrates multi-source features as an input. A suggested method is validated using NASA and Oxford Battery Degradation datasets. The results demonstrate that the presented model provides great SOH estimation accuracy and generality, where the maximum root mean square error (RMSE) is less than 1%. Based on a cloud computing platform, the proposed method can be applied to provide a real-time prediction of battery health, with the potential to enhance battery full lifespan management.