nVent Electric

Rami Zaino, Vian Ahmed, A. Alhammadi, Mohamad Alghoush

This comprehensive systematic review explores the multifaceted impacts of electric vehicle (EV) adoption across technological, environmental, organizational, and policy dimensions. Drawing from 88 peer-reviewed articles, the study addresses a critical gap in the existing literature, which often isolates the impact of EV adoption without considering holistic effects. Technological advancements include innovations in the battery technology and energy storage systems, enhancing EV performance and mitigating range anxiety. The environmental analysis reveals substantial reductions in greenhouse gas emissions, with lifecycle assessments showing significant reductions for EVs compared to internal combustion engine vehicles, particularly when charged with renewable energy sources. Key comparisons include lifecycle emissions between mid-size battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs), and global average lifecycle emissions by powertrain under various policy scenarios. The organizational implications are evident, as businesses adopt new models for fleet management and logistics, leveraging EVs for operational efficiency and sustainability. Policy analysis underscores the crucial role of government incentives, regulatory measures, and infrastructure investments in accelerating EV adoption. The review identifies future research areas such as efficient battery recycling methods, the potential impact of EVs on grid stability, and long-term economic implications. This study offers insights for stakeholders aiming to foster sustainable transportation and achieve global climate goals.

Jahanzeab Hussain, Runmin Zou, P. Pathak, Awais Karni, Samina Akhtar

Zhiwei Xue, Wei Liu, Chang Liu, K. Chau

As the world transitions towards sustainable transportation, the advancement of electric vehicles (EVs) has become imperative. Wireless power transfer (WPT) technology presents a promising solution to enhance the convenience and efficiency of EV charging while alleviating the challenges associated with traditional wired systems. This paper conducts an in-depth exploration of WPT technologies for EVs, focusing on their theoretical foundations, practical implementation, optimization strategies, development trends, and limitations. The theoretical principles of wireless charging are first elucidated, categorizing them into near-field methods, such as inductive and capacitive charging, and far-field methods, including microwave and laser-based charging. A comparative analysis reveals the advantages and limitations inherent to each technology. The implementation section examines various charging strategies, encompassing stationary, dynamic, and quasi-dynamic wireless charging, assessing their feasibility and effectiveness in practical applications. Furthermore, optimization techniques aimed at enhancing WPT system performance are examined in depth, with particular emphasis on coil structure optimizations, anti-misalignment solutions, compensation topology optimizations, modulation strategy optimizations, and parameter identifications. The discussion section outlines current development trends in wireless charging technologies for EVs, highlighting the limitations that hinder the widespread adoption of wireless charging technologies in the EV market. Finally, potential research directions and the implications of wireless charging technology on the development of EVs are summarized. This critical review aims to provide valuable insights for researchers and practitioners dedicated to advancing the field of wireless charging for EVs.

João C. Ferreira, M. Esperança

The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic performance of urban logistics. Through a comprehensive literature review, we examine current trends, technological developments, and implementation challenges at the intersection of smart mobility, green logistics, and digital transformation. We propose an operational framework that leverages AI for route optimization, fleet coordination, and energy management in EV-based delivery networks. This framework is validated through a real-world case study conducted in Lisbon, Portugal, where a logistics provider implemented a city consolidation center model supported by AI-driven optimization tools. Using key performance indicators—including delivery time, energy consumption, fleet utilization, customer satisfaction, and CO₂ emissions—we measure the pre- and post-AI deployment impacts. The results demonstrate significant improvements across all metrics, including a 15–20% reduction in delivery time, a 10–25% gain in energy efficiency, and up to a 40% decrease in emissions. The findings confirm that the synergy between EVs and AI provides a robust and scalable model for achieving sustainable last-mile logistics, supporting broader urban mobility and climate objectives.

Miguel Antonio Pisani Orta, David García Elvira, Hugo Valderrama Blaví

Continuous and accurate state-of-charge estimation is essential for optimal reliability and performance in electric vehicle battery management systems. This work reviews state-of-charge estimation strategies, from straightforward methods like lookup tables and ampere-hour counting to advanced mathematical models, such as electrochemical, observer-assisted equivalent circuit, and impedance-based models that capture cell dynamics. Additionally, data-driven models including fuzzy logic, neural networks, and support vector machines are explored for their ability to leverage large datasets. This review highlights the strengths and limitations of each method, emphasizing the specific contexts in which these strategies can be applied to achieve optimal effectiveness.