Charging infrastructure for commercial electric vehicles: Challenges and future works

The journey towards transportation electrification started with small electric vehicles (i.e., electric cars), which have enjoyed an increasing level of global interest in recent years. Electrification of commercial vehicles (e.g., trucks) seems to be a natural progression of this journey, and many commercial vehicle manufacturers have shifted their focus on medium- and heavy-duty vehicle electrification over the last few years. In this paper, we present a comprehensive review and analysis of the existing works presented in the literature on commercial vehicle charging. The paper starts with a brief discussion on the significance of commercial vehicle electrification, especially heavy- and medium-duty vehicles. The paper then reviews two major charging strategies for commercial vehicles, namely the return-to-base model and the on route charging model. Research challenges related to the return-to-base model are then analysed in detail. Next, different methods to charge commercial vehicles on route during their driving cycles are summarized. The paper then analyzes the challenging issues related to charging commercial vehicles at public charging stations. Future works relevant to these challenges are highlighted. Finally, the possibility of accommodating vehicle to grid technology for commercial vehicles is discussed

An evaluation of energy consumption and emissions from intermodal freight operations on the eastern seaboard: A GIS network analysis approach

As global trade continues to increase, the energy and environmental impacts of freight movement in the US have become more of a concern. As such, the freight transport system needs to consider opportunities to meet customer objectives, while also meeting social goals. In the US there has been legislation enacted to address the growing impact that freight movement has on the environment, but there are limited tools to assist in the implementation of those polices. This research sets forth a process for creating a geospatial intermodal freight transportation (GIFT) model within ArcGIS that can be used to analyze freight movement under different economic and environmental scenarios. The GIFT model uses an intermodal network that connects various modes (rail, truck, and ship) via intermodal terminals. ArcGIS Network Analyst is used to create the intermodal network and conduct optimal route analysis for various network attributes. Routes along the network are characterized not only by temporal and distance attributes, but also by cost, energy, and emissions attributes. Decision makers can use the model to explore tradeoffs among alternative route selection across different modal combinations, and to identify optimal routes for objectives that feature energy and environmental parameters (e.g., least carbon dioxide intensive route). The research illustrates the use of this network using a case study that analyzes freight traffic along the US Eastern Seaboard

Walmart\u27s Sustainability Journey: Elizabeth Fretheim and the Search for Sustainable Trucking

In early 2014 Elizabeth Fretheim, Director, Business Strategy and Sustainability for Logistics with Walmart, was reviewing with other members of the transportation team data on the fuel efficiency of their private fleet, which included over 6,000 trucks and 61,000 trailers operated by over 7,000 drivers. The logistics and transportation groups were vigilant in their basic goals of (1) loading trucks efficiently, (2) driving fewer miles, and (3) investing in fuel efficient equipment. But this issue concerned the drivers’ impact on mpg. In particular, the data showed that idling had increased over the past calendar year (2013) compared to the year before. Although some months showed small decreases, increases predominated. Given the company’s sustainability goal of doubling fleet efficiency over 2005 baselines by 2015, this was an issue of some concern

Supervisory control for emission compliance of heavy-duty vehicles

Heavy freight trucks globally contribute to a significant proportion of transport-related air pollution. The dominant air pollutants from heavy freight trucks with diesel engine and exhaust aftertreatment system (EATS) are CO2, hydrocarbons (HC), CO, particulate matter (PM), NOX (NO and NO2), and NH3. The greenhouse gas emission legislation limits the amount of CO2 emission, and Euro VI emission legislation limits the other dominant air pollutants. Emission legislation is gradually becoming more and more stringent to reach the long term goal of near-zero-emission. Several parties are working together to reduce the emissions, keeping both the short and long term goal in mind. Any step which results in ICE downsizing contributes to the reduction of all dominant emissions. But, with size and type of the ICE decided, there is a trade-off between NOX emission and other emissions: reduced NOX emission means reduced fuel efficiency (i.e. increased CO2, PM, and HC emissions). It is a challenge to fulfil the current emission legislation—especially real-driving NOX emission legislation—with existing control functionalities in the engine management system (EMS). However, better control of NOX emission is possible by exploiting predictive driving information and considering the coupling between the engine system and EATS. This work pursues this idea and concludes that fulfilling real-driving NOX emission legislation is possible, considering the coupling between the engine system and EATS while using predictive information. The work provides a mathematical formulation of the concept and then develops, evaluates, and implements an engine-EATS supervisor which optimizes total fuel consumption and fulfils both the world harmonized transient cycle (WHTC) based and real-driving NOX emission legislation. The developed supervisor is a distributed economic nonlinear model predictive controller (E-NMPC). This work develops and analyzes two different versions of the distributed E-NMPC based supervisory control algorithm. The more efficient one of the two is again compared for three variants. Considering the computation time of the three algorithms and processing speed of the existing EMS, one algorithm is selected for implementation. The supervisor performs much better compared to a baseline controller (optimized offline). Simulation results show that the supervisory controller has 1.7% less total fuel consumption and 88.4% less NH3 slip, compared to the baseline controller, to achieve the same real-driving NOX emission

Developing a Sustainable Freight Transportation Framework with the Consideration of Improving Safety and Minimizing Carbon Emissions

Despite the difficulties of the American economy in recent years the transportation sector continues to expand. Freight transportation alone has been projected to increase enormously even if the economy as a whole only manages a very moderate growth. Not only does freight transportation use a large percentage of resources but it contributes significantly to America’s share of carbon emissions and affects the safety of the transportation system and all its users. These problems are only expected to increase as the volume of freight transportation is already reaching the limit of the American transportation infrastructure’s capacity and demand continues to increase. The primary objective of this research was to compile a list of technologies and practices that should be included in the sustainable freight transportation frameworks of government agencies and commercial fleets to reduce their carbon footprint and increase their safety by providing recommendations on promising legislation, research, technologies, and practices. Data was gathered through a literature review of available materials and a survey of the state Departments of Transportation. The success of this research project provides the needed knowledge for the development of a sustainable freight transportation framework

Long driving hours and health of truck drivers

In recent time, the health of truck drivers has become a concern for regulatory agencies and safety professionals all over the world. Fatal and non-fatal injury rates for truck drivers are among the highest of all occupations. Truck driving is an important and tedious job. Driving for long hours, drivers are confined to a small space, sit with static lower and upper extremities posture, mentally focus and absorb vibrations. This thesis provides an in-depth review of the literature related to the problems of long distance truck drivers and commercial motor vehicle operators. The Literature suggests that continuous exposure of truck drivers to risk factors has led to such illnesses as musculoskeletal disorders, obesity, hypertension, cardiovascular disease, stroke, sleep disorders and psychological distress. Prolonged sitting, whole-body vibration, physical and psychological fatigues were found to be the main risk factors that are related to the occupational health problems of truck drivers. These occupational risk factors were analyzed in detail to understand the physiological pathways that cause the risk factors to affect truck drivers\u27 health. Based on these analyses, a set of suggestions on continuous improvement was made in areas of rest break, physical exercise, health monitoring, and psychological well being