Recent research emphasizes the growing use of advanced composite materials in modern transportation, highlighting their superior weight-to-strength ratio. These materials are increasingly replacing steel and aluminium in housings to enhance sustainability, improve efficiency, and reduce emissions. Considering these advancements, this article reviews recent studies on composite materials, focusing on fatigue life assessment models. These models, which include performance degradation, progressive damage, and S–N curve models, are essential for ensuring the reliability of composite materials. It is noted that the fatigue damage process in composite materials is complex, as failure can occur in the matrix, reinforcement, or transitions such as interlaminar and intralaminar delamination. Additionally, the article critically examines the integration of artificial intelligence techniques for predicting the fatigue life of composite materials, offering a comprehensive analysis of methods used to indicate the mechanical properties of battery shell composites. Incorporating neural networks into fatigue life analysis significantly enhances prediction reliability. However, the model’s accuracy depends heavily on the comprehensive data it includes, including material properties, loading conditions, and manufacturing processes, which help to reduce variability and ensure the precision of the predictions. This research underscores the importance of continued advancements and their significant scientific contributions to transportation sustainability, especially in the context of emerging artificial intelligence technologies.

Composite materials, known for their enhanced mechanical strength through fiber reinforcement, are increasingly used in industrial applications. However, like metals, they suffer strength degradation from cyclic loading, making fatigue failure a critical concern. The fatigue behavior of polymer composites is strongly influenced by factors such as stress amplitude, stress ratio, and loading frequency. Conventional stress-life approaches often treat these factors independently, which limits their predictive accuracy. This study introduces a novel frequency–stress–ratio fatigue index, integrated into a residual-strength degradation approach, to predict fatigue life under constant amplitude, tension-tension loading conditions. To the best of the authors’ knowledge, the majority of models available in the literature require substantial experimental data to calibrate the parameters essential for their application in real-world scenarios. The proposed model requires only two material parameters and assumes failure occurs when the residual strength degrades to the level of the applied maximum stress. The results demonstrate that the proposed formulation provides a unified representation of fatigue behavior influenced by both cycle-dominated and time-dependent mechanisms, offering robust predictive capabilities. This approach not only addresses a critical need for reliable and practical fatigue prediction methods in composite materials but also contributes significantly to the optimization of engineering design processes.