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V2C is trigonal omega structured and crystallizes in the trigonal P-3m1 space group. The structure is two-dimensional and consists of one V2C sheet oriented in the (0, 0, 1) direction. V2+ is bonded in a distorted T-shaped geometry to three equivalent C4- atoms. All V–C bond lengths are 2.03 Å. C4- is bonded to six equivalent V2+ atoms to form edge-sharing CV6 octahedra.
V2C crystallizes in the hexagonal P6_3/mmc space group. The structure is two-dimensional and consists of two V2C sheets oriented in the (0, 0, 1) direction. V2+ is bonded in a 3-coordinate geometry to three equivalent C4- atoms. All V–C bond lengths are 2.02 Å. C4- is bonded to six equivalent V2+ atoms to form edge-sharing CV6 octahedra.
V2C is beta Vanadium nitride-like structured and crystallizes in the orthorhombic Pbcn space group. The structure is three-dimensional. V2+ is bonded in a distorted trigonal planar geometry to three equivalent C4- atoms. There are a spread of V–C bond distances ranging from 2.01–2.04 Å. C4- is bonded to six equivalent V2+ atoms to form a mixture of corner and edge-sharing CV6 octahedra. The corner-sharing octahedra tilt angles range from 50–51°.
This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.
MXenes offer high metallic conductivity and redox capacitance that are attractive for high-power, high-energy storage devices. However, they operate limitedly under high anodic potentials due to irreversible oxidation. Pairing them with oxides to design asymmetric supercapacitors may expand the voltage window and increase the energy storage capabilities. Hydrated lithium preintercalated bilayered V 2 O 5 ( δ-Li x V 2 O 5 ·nH 2 O) is attractive for aqueous energy storage due to its high Li capacity at high potentials; however, its poor cyclability remains a challenge. To overcome its limitations and achieve a wide voltage window and excellent cyclability, it is combined with V 2 C and Nb 4 C 3 MXenes. Asymmetric supercapacitors employing lithium intercalated V 2 C (Li-V 2 C) or tetramethylammonium intercalated Nb 4 C 3 (TMA-Nb 4 C 3 ) MXenes as the negative electrode, and a δ-Li x V 2 O 5 ·nH 2 O composite with carbon nanotubes as the positive electrode in 5 m LiCl electrolyte operate over wide voltage windows of 2 and 1.6 V, respectively. The latter shows remarkably high cyclability—capacitance retention of ≈95% after 10 000 cycles. Here, this work highlights the importance of selecting appropriate MXenes to achieve a wide voltage window and a long cycle life in combination with oxide anodes to demonstrate the potential of MXenes beyond Ti 3 C 2 in energy storage.