State of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications. Recently, electrochemical impedance spectroscopy (EIS)-based technique has been proven to be effective for SOH estimation of LIB. However, existing EIS-based methods failed to consider the impact of ambient temperature and battery state of charge (SOC), leading to the limited flexibility of these methods under dynamic environments. In this work, a novel EIS-based method is proposed for battery SOH estimation considering variations of temperature and SOC. An equivalent circuit model (ECM) is first introduced, in which the solid electrolyte interface (SEI) resistance and charge transfer resistance are employed to map their relationship with SOH under variant temperature and SOC. Subsequently, a probabilistic model, taking charge transfer resistance, temperature, and SOC as input variables, is developed for LIB SOH estimation. Experimental study indicates that the estimation error of the proposed method is around 4% when simultaneously considering the temperature and SOC effects. Moreover, the estimation error can reach 1.29% under certain conditions (e.g., 80% SOC at 30 °C). Both results of estimation error are better than the existing EIS-based methods, which indicates that the proposed method is more flexible for SOH estimation with higher precision.

Fault tree analysis (FTA) is one of the most popular failure analysis techniques that reveal the potential pathways leading to systems or components failure. It has been widely employed in numerous sectors to understand how a system fails and is improved. However, the conventional FTA has been criticized due to a series of inherent shortcomings in the FTA state of the arts. Accordingly, scholars, engineers, and practitioners made their attempts to improve the FTA by dealing with its critical deficiencies over the last decade. However, a few works have been performed to review and synthesize the relevant studies on FTA improvement topics. Thus, the present study is aimed to carry out a systematic literature review of the state‐of‐the‐art theoretical and empirical findings concerning FTA improvement from 2011 to 2021 using the Scopus database collection. In this sense, an in‐depth investigation is conducted using statistical metadata analysis. This subject discusses frontier directions and development trends to reveal the research status. In addition, a bibliometric study is undertaken to ascertain the most productive and influential researchers, research centers, and hotspot fields. It also sheds light on the FTA shortcomings in the existing literature, the evolution in FTA improvement topics, and research opportunities. The outcomes of the present work highlighted that the annual publications on FTA improvement topics are significantly growing, especially after 2019. Besides, Jianxiu Wang, Yan‐Feng Li, and Yihuan Wang are the most productive, prolific, and highly cited authors worldwide; and Asia, particularly China, is the leading contributor in the FTA area. According to Bradford's law, one‐third of all publications (7995) in the field of FTA improvement have been published by 40 sources. Finally, “Decision‐making,” “Risk analysis,” “Uncertainty Analysis,” and “Bayesian Networks” are the four major hot topics integrated into improving the conventional FTA.