Heavy-duty trucks are major contributors in NOx and PM2.5 emissions in the transportation sector. Many countries are seriously considering electrification as a pathway towards cleaner freight transport. The advancement of diverse technological solutions has created multiple pathways towards the decarbonisation of heavy-duty trucks, among which battery-electric heavy-duty trucks remain in the early stages of development. This paper presents a comprehensive and systematic review of their electrification from technological, economic, and social perspectives, covering battery installation approaches, electricity refuelling solutions (e.g., charging, battery swapping, wireless charging), and typical application scenarios for short- and long-haul trucking. Key issues including electricity demand, operation scheduling, total cost of ownership, and greenhouse gas emissions are discussed, followed by an identification of current research gaps in empirical data and an examination of grid integration challenges and opportunities across different technology pathways.
The rapid growth of the lithium-ion battery (LIBs) has created an urgent demand for sustainable recycling technologies. Although conventional pyrometallurgical and hydrometallurgical recycling efficiently recover critical metals, their reliance on complete material decomposition inevitably sacrifices the structural and functional value embedded in spent cathodes. This limitation has stimulated growing interest in value-retention strategies, among which direct regeneration and electrocatalytic repurposing have emerged as two promising yet fundamentally different approaches. This review summarizes recent advances in direct regeneration and electrocatalytic repurposing, highlighting their fundamentally different principles, feedstock requirements, and value-retention mechanisms. Direct regeneration restores electrochemical functionality through structural repair, whereas electrocatalytic repurposing converts spent cathodes into functional catalysts by exploiting their intrinsic elemental composition. Analysis of recent progress reveals that these approaches are complementary rather than competing, with fundamentally different requirements for feedstock quality, structural integrity, and compositional traceability. Based on these distinctions, an adaptive value-retention framework is proposed in which pathway selection is guided by cathode chemistry, degradation severity, and recycling scenario. Finally, emerging opportunities enabled by digital battery passports, advanced diagnostics, artificial intelligence, and standardized environmental and techno-economic assessment are highlighted as key enablers of intelligent recycling systems. By integrating these technologies within an adaptive decision-making framework, this Review provides a unified perspective for developing flexible, economically viable, and sustainable LIB recycling systems.
The rapid growth of electric vehicles (EVs) is driving the need for charging infrastructure expansion, which in turn has significant implications for power system operations. Such expansion may also affect the EV-charging demand, making this uncertainty decision-dependent. This paper develops a two-stage robust optimization model that determines EV-charging station expansion decisions in the first stage and power system operational decisions in the second stage, while explicitly capturing decision-dependent uncertainty of charging demands to enhance planning robustness. To efficiently solve the resulting challenging optimization problem, we design a column-and-cut generation algorithm that obtains high-quality solutions quickly. Comprehensive numerical experiments demonstrate that generation availability and line congestion play critical roles in shaping optimal expansion decisions.
The maritime industry is among the key players in global trade, transporting the largest portion of the world’s goods. It connects economies, supports millions of jobs, and enables the movement of raw materials, energy resources, and manufactured products efficiently across continents. However, this industry is also a major source of greenhouse gas emissions. Considering these emissions, this industry is undergoing a transition to maritime electrification with net-zero emissions. In order to achieve that goal, batteries are the main tool, including the development of fully electric and hybrid vessels. To this end, this paper provides a structured overview of battery systems in marine electrification, covering various aspects of this rapidly evolving field. It presents an analysis of different ship types and the power topologies of electric and hybrid vessels, supported by updated examples of real-world commercialized and produced vessels across different regions. Additionally, the evolution of battery technology is demonstrated by detailing both commercialized and early-stage batteries, with a focus on their power rating, energy density, specific energy, cycle life, and management systems. Furthermore, the applications of Artificial Intelligence (AI) and digital twin technologies in marine electrification are presented with a detailed review of recent developments. The Artificial Intelligence (AI) strategies are classified into several categories, and a case study from a representative strategy of each class in battery and marine electrification is presented. It also includes guidelines and standards issued by official organizations to ensure compliance and safety in the sector. Additionally, it highlights recently completed and ongoing funded projects in marine electrification in the U.S. and the EU, as well as the related software tools and industry white papers. Moreover, the correlation between battery systems and marine electrification is provided with the United Nation (UN) Sustainable Development Goals (SDGs) and which SDGs they are effective and aligned. Finally, the updated challenges and future research directions are presented for a forward-looking perspective on the marine electrification sector and its transition to a net-zero industry.
Wire harness assembly exemplifies a non-rigid object assembly task that remains challenging to automate due to object deformability, occlusions, high variability, and workspace constraints. This study introduces a vision-based human–robot collaboration (HRC) framework developed for wire harness assembly in automotive final assembly processes. In this system, the robot performs repetitive, force-intensive tasks, while the human operator handles operations that require dexterity and adaptive decision-making. The proposed approach combines marker-based pose estimation of wire harness components with a hand-triggered control scheme. Detected hand landmarks define a region of interest, enabling intentional, context-aware robot activation. The HRC framework is validated by performing wire harness installation on the vehicle chassis. A two-phase within-subjects experimental study compares manual installation with HRC-assisted installation in both laboratory and industrially relevant environments, corresponding to Technology Readiness Level 4 to 6. The results indicate that robotic assistance significantly reduces localized physical discomfort and physical demand while maintaining a high success rate in wire harness installation. However, overall workload does not differ significantly between HRC and manual conditions. In the HRC condition, both mental demand and average assembly time increase significantly compared to manual assembly. Cycle-time analysis reveals that the robot execution phase accounts for the largest proportion of total time in the collaborative workflow. These findings suggest that vision-based HRC can provide targeted ergonomic benefits for non-rigid object assembly, while also introducing cognitive and temporal trade-offs. Therefore, real-world deployment requires further improvement in interaction fluency and throughput. The proposed method and code are available at https://github.com/HWANG7308/ClampTracking.
