California has set a goal for all drayage trucks operating in the state to be zero-emitting by 2035. In order to achieve this goal, drayage operators would need to transition 100 % of their fleets to zero-emission vehicles such as battery electric trucks (BETs). This paper presents an intelligently controlled charging model for BETs that minimizes charging costs while optimizing subsequent tour completion. To develop this model, real-world activity data from a drayage truck fleet operating in Southern California was combined with a two-stage clustering technique to identify trip and tour patterns. The energy consumption for each trip and tour was then simulated for BETs with a battery capacity of 565 kWh using a 150 kW charging power level. Home base charging load profiles were generated using the proposed charging model, subject to constraints of the energy needed to complete the next subsequent tour and Time-of-Use energy cost rates. A sensitivity analysis evaluated three scenarios: a passive scenario with a 5% state-of-charge (SOC) constraint after completing the subsequent tour, an average scenario with a 50% SOC constraint, and an aggressive scenario with an 80% SOC constraint. Results indicated that the 80% SOC constraint scenario achieved the lowest charging cost. However, it also yielded the lowest tour completion rate (51%). In contrast, the 5% SOC constraint scenario registered the highest tour completion rate. These results revealed that 96% of the tours could be successfully completed using the intelligently controlled charging model. The remaining tours were infeasible, indicating that the available time at the home base was inadequate for charging the necessary energy for the next tour. In terms of total costs, the scenario with a 5% SOC constraint resulted in an annual cost of approximately $40,000, whereas the 80% SOC scenario nearly doubled that amount.

The proliferation of electric vehicles all around the world offers both challenges and opportunities. Bidirectional charging capabilities at workplace charging facilities (e.g., as part of a microgrid) have now made the overall economic optimization more attractive, but also more complex. This paper investigates the cost optimization problem for bidirectional charging at a workplace microgrid with the goal of determining the optimal framework for a combination of both heavy-duty and light-duty electric vehicles (HDEV and LDEV). A deep learning-based model has been developed to forecast the 15-minute solar generation and building power consumption. Real-time travel profile data is used to represent the temporal uncertainty of electric vehicle charging. The cost optimization problem is formulated as a Mixed Integer Programming (MIP) model which also addresses battery life degradation. Further, a comprehensive cost-benefit analysis has been carried out to analyze the payback period, peak reduction, and cost savings for two different buildings at the same workplace with both on-board and off-board charger configurations. It has been found that HDEVs are a better cost-effective solution in comparison to LDEVs in terms of energy cost reduction and payback periods. Net metering capability leads to higher energy savings and peak reductions in most cases

This paper presents a holistic review of Liquid Organic Hydrogen Carriers (LOHCs), focusing on typology, energy efficiency, techno-economic analyses (TEAs), and life cycle assessments (LCAs). Initially, the study categorizes various LOHC systems documented in existing literature, outlining their chemical structures, catalysts, properties, and main applications. This survey aims to provide a comprehensive understanding of LOHC varieties, making it easier to compare across different types. Next, we explore the efficiency of LOHC systems by reviewing hydrogenation and dehydrogenation energy requirements, catalyst behavior, heat-management constraints, product-separation needs, and net energy storage capabilities. This review also includes reaction stoichiometries, recent catalyst and reactor developments, catalyst-deactivation mechanisms, and heat-integration options for high-temperature dehydrogenation. Finally, our investigation offers a detailed evaluation of TEA and LCA for LOHC systems found in the literature. By exploring economic feasibility and environmental impact, this study presents a complete picture of the sustainability of LOHC deployment. It includes assessments of life-cycle carbon emissions, levelized cost, supply-chain configuration, carrier recyclability, infrastructure compatibility, renewable-electricity capacity factors, and heat-recovery assumptions. Our findings aim to contribute to hydrogen storage and transport research by identifying the most important technical, economic, and environmental trade-offs for LOHC systems. By addressing gaps in recent literature, TEA comparability, and LCA coverage, this paper seeks to advance the development of LOHC technologies and support their broader adoption in a green and sustainable energy landscape.