The powertrain system is critical to the reliability of a battery electric vehicle (BEV). However, the BEV powertrain is a complex system; it includes the motor, motor controller, power distribution unit, battery system, etc. The failure of any of these components may result in the failure of the entire powertrain system and eventually cause serious traffic accidents on the road. However, how much does each component affect the reliability of the entire system, and which components are the most vulnerable in the entire system? These questions are still unanswered today. To develop a reliability design for a BEV powertrain system, it is essential to conduct detailed research by investigating the most vulnerable component parts of the entire powertrain. In the present study, a fault-tree model of the entire powertrain and its subsystems was developed. Based on this model, the failure rates of all components were calculated first. Then, trends in the reliability indices for the entire powertrain and its components were estimated against BEV service life. From the estimation results, we learned that with increased service time, the reliability of the entire powertrain system is indeed much lower than that of its individual subsystems. Moreover, through comparative research, we found that the battery module is the most unreliable component not only of the battery system, but the entire powertrain system. Additionally, it was interesting to find that the reliability of the motor components was higher than that of other subsystem components, but that the reliability indices for the entire motor were not the highest among all the powertrain subsystems studied in this paper. We believe the findings of the present study will be of great significance to an improved understanding of the reliability design and maintenance of BEVs.

Sodium-ion batteries (SIBs) have emerged as a promising alternative to lithium-ion batteries (LIBs) due to the abundant availability of sodium, lower costs, and comparable electrochemical performance characteristics. A thorough understanding of their performance features is essential for the widespread adoption and application of SIBs. Therefore, in this study, we investigate the output characteristics and electrochemical impedance spectroscopy (EIS) features of sodium-ion batteries (SIBs) under various states. The research results show that, unlike conventional lithium iron phosphate (LFP) batteries, SIBs exhibit a strong linear relationship between state of charge (SOC) and open-circuit voltage (OCV) across various SOC and temperature conditions. Additionally, the discharge capacity of the battery remains relatively stable within a temperature range of 15 °C to 35 °C; when the temperatures are outside this range, the available capacity of the sodium-ion battery reduces significantly. Moreover, the EIS profiles in the high-frequency region are predominantly influenced by the ohmic internal resistance, which remains largely unaffected by SOC variations. In contrast, the low-frequency region demonstrates a significant correlation between SOC and impedance, with higher SOC values resulting in reduced impedance, indicated by smaller semicircle radii in the EIS curves. This finds highlights that EIS profiling can effectively monitor SOC and state of health (SOH) in SIBs, offering a clear correlation between impedance parameters and the battery’s operational state. The research not only advances our understanding of the electrochemical properties of SIBs but also provides a valuable reference for the design and application of sodium-ion battery systems in various scenarios.

Proton exchange membrane fuel cell (PEMFC) technology is vital for a cleaner and more sustainable future, offering high efficiency, quiet operation, fuel flexibility, and zero‐emission energy production with versatile applications. The drawbacks of PEMFC include its high cost, susceptibility to degradation, and challenges in scaling up production for commercial use. The gas diffusion layer (GDL), the catalyst layer (CL), and the polymer electrolyte are all very important parts of fuel cell technology. Through the optimization of these components, researchers may strive to develop fuel cells that are more efficient, cost‐effective, durable, and high‐performing, therefore enabling the provision of clean and sustainable energy. In this article, we have reviewed the various components of GDL, CL, and polymer electrolyte materials and their impact on the performance of PEMFC. The various characteristics of the GDL such as porosity, hydrophobic/hydrophilic, polytetrafluoroethylene (PTFE), and carbon materials, are fully discussed in this review article. These topics also cover the CL’s properties, including the catalyst, carbon support, ionomer, and solvent. Explore the different polymer electrolytes that are based on aliphatic Nafion and aromatic hydrocarbons‐based membrane. Finally, this review paper gives suggestions for the way forward in regard to creating electrodes and electrolytes for PEMFCs that are cost‐effective, efficient, and durable.

Lithium-ion batteries (LIBs) are increasingly used in electric vehicles and energy storage systems because of their high energy density and extended cycle life. However, the nonlinear and time-varying nature of their degradation processes severely hampers accurate real-time reliability assessment. This study systematically investigates the degradation behavior of LIBs via controlled cycling tests. According to the experimental results, a novel real-time reliability assessment framework utilizing the inverse Gaussian process as the core modeling approach is developed. The proposed model incorporates a Bayesian updating strategy to dynamically capture the progression of degradation. Moreover, it applies the Expectation–Maximization algorithm to achieve efficient and adaptive parameter estimation. Experimental validation using 18650-type LIBs confirms the model’s effectiveness in capturing capacity degradation trends and reflecting the evolution of decay rates over time. The findings demonstrate that the presented approach offers a reliable and practical solution for the real-time reliability management of LIBs in dynamic application scenarios.