Electric vehicles (EVs) play a critical role in the transition to sustainable transportation. Despite significant advancements in technology, EVs continue to face major challenges, particularly in terms of limited range, high costs, and insufficient charging infrastructure. This paper presents a comprehensive review that systematically categorizes the multifaceted factors influencing EV range into technical, environmental, user-related, economic, policy, and cultural dimensions. The aim is to offer a holistic view of how these elements interact to shape EV performance, adoption, and usage. Notably, advancements in battery capacity, charging time, vehicle weight, and aerodynamics are identified as key factors that significantly enhance EV range. Environmental factors such as temperature and terrain are shown to drastically impact energy consumption, with cold climates leading to up to a 50% reduction in range. Furthermore, user behaviors, driving patterns, and economic factors like battery costs, charging infrastructure availability, and electricity prices play a crucial role in determining EV efficiency. This review shows the importance of supportive policies, societal attitudes, and infrastructural developments in promoting the widespread adoption of EVs, making it an innovative and timely contribution to the field.

Remaining useful life (RUL) prediction for offshore wind turbines is important for predictive maintenance. However, its practical use is limited by the lack of reliable RUL labels, the small number of fault events, and the complex degradation links among turbine components. Most existing data-driven methods require labeled failure data and cannot fully capture how the degradation of different components is related. To address these limitations, a Dual-Pathway Degradation-Aware Network for label-free RUL prediction is proposed. The proposed method first constructs component-level health indicators (HIs) from SCADA data in a self-supervised manner and generates pseudo-RUL labels through a linear countdown strategy. It then jointly models the global degradation evolution and cross-component interactions using a global temporal branch and a fully connected graph branch, where residual aggregation is adopted to fuse node representations for RUL estimation. Experiments are conducted on the German North Sea Farm B dataset under a leave-one-out cross-validation (LOOCV) protocol. From the obtained results, the proposed method achieves an MAE of 111.3 h and an RMSE of 122.6 h, reducing the average MAE by approximately 19% compared with the strongest baseline. The proposed method achieves the best performance on three of the five turbines with fault events. Furthermore, the proposed method demonstrates superior capability in capturing component interaction patterns and provides an effective label-free solution for offshore wind turbine RUL prediction under limited fault data.