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Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

Adoption of fuel cell electric vehicles (FCEV) or battery electric vehicles (BEV) in heavy-duty (HD) commercial freight transportation is hampered by difficult technoeconomic obstacles. To enable widespread deployment of electrified powertrains, fleet and operational logistics need high uptime and parity with diesel system productivity/total cost of ownership (TCO), while meeting safety compliance. Due to a mix of comparatively high powerplant and energy storage costs, high energy costs (more so for FCEV), greater weight (more so for BEV), slow refueling / recharging durations, and limited supporting infrastructure, FCEV and BEV powertrains have not seen significant uptake in the HD freight transport market. The use of dynamic wireless power transfer (DWPT) systems, consisting of inductive electrical coils on the vehicle and power source transmitting coils embedded in the roadways, may address several of these challenges.

The phenomena of global warming and climate change are encouraging more and more countries, local communities, and companies to establish carbon neutrality targets, which has very significant implications for the US trucking industry. Truck electrification helps fleets to achieve zero tailpipe emissions and macro-scale decarbonization while allowing continued business growth in response to the rapid expansion of e-commerce and shipping related to increased globalization. This paper presents an analysis of Class 8 long-haul truck electrification using a commercial vehicle electrification evaluation tool and Fleet DNA drive data. The study provides new insight into the impacts of streamlined chassis, battery energy density, and superfast charging on battery capacity needs as well as implications for payload, energy consumption, and greenhouse gas emissions for electric long-haul trucks. The study also identifies a pathway for achieving optimal long-haul truck electrification.

China's plug-in electric vehicle (PEV) market with stocks at 7.8 million is the world's largest in 2021, and it accounts for half of the global PEV growth in 2021. The PEV market in China has dramatically evolved since the pandemic in 2020: over 20% of all new PEV sales are from China by mid-2022. Recent features of PEV market dynamics, consumer acceptance, policies, and infrastructure have important implications for both the global energy market and manufacturing stakeholders. From the perspective of demand pull-supply push, this study analyzes China's PEV industry with a market dynamics framework by reviewing sales, product and brand, infrastructure, and government policies from the last few years and outlooking the development of the new government’s 14th Five-Year Plan (2021-2025).

The use of wireless power transfer systems, consisting of inductive electrical coils on the vehicle and the power source may be designed for dynamic operations where the vehicle will absorb energy at highway speeds from transmitting coils in the road. This has the potential to reduce the onboard energy storage requirements for vehicles while enabling significantly longer missions. This paper presents an approach to architecting a dynamic wireless power transfer corridor for heavy duty battery electric commercial freight vehicles. By considering the interplay of roadway power capacity, roadway and vehicle coil coverage, seasonal road traffic loading, freight vehicle class and weight, vehicle mobility energy requirements, on-board battery chemistry, non-electrified roadway vehicle range requirements, grid capacity, substation locations, and variations in electricity costs, we minimize the vehicle TCO by architecting the electrified roadway and the vehicle battery simultaneously.

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance.

Battery electric vehicles (BEV) in heavy duty (HD) commercial freight transport face challenging technoeconomic barriers to adoption. Specifically, beyond safety and compliance, fleet and operational logistics require both high up-time and parity with diesel system productivity/Total Cost of Ownership (TCO) to enable strong adoption of electrified powertrains. At present, relatively high energy storage prices coupled with the increased weight of BEV systems limit the practicality of HD commercial freight transport to shorter range applications, where smaller batteries will suffice for the mission energy requirements (single operational shift). This paper presents an approach to extend the feasibility of BEV HD trucking for a broad range of applications.

Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems.

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency.

It is widely recognized in the automotive industry that, in very cold climatic conditions, the driving range of an Electric Vehicle (EV) can be reduced by 50% or more. In an effort to minimize the EV range penalty, a novel thermal energy storage system has been designed to provide cabin heating in EVs and Plug-in Hybrid Electric Vehicles (PHEVs) by using an advanced phase change material (PCM). This system is known as the Electrical PCM-based Thermal Heating System (ePATHS) [1, 2]. When the EV is connected to the electric grid to charge its traction battery, the ePATHS system is also “charged” with thermal energy. The stored heat is subsequently deployed for cabin comfort heating during driving, for example during commuting to and from work. The ePATHS system, especially the PCM heat exchanger component, has gone through substantial redesign in order to meet functionality and commercialization requirements.

Modeling and simulation of batteries, in conjunction with theory and experiment, are important research tools that offer opportunities for advancement of technologies that are critical to electric motors. The development of data from the application of these tools can provide the basis for managerial and technical decision-making. Together, these will continue to transform batteries for electric vehicles.

A major driving force for change in light-duty vehicle design and technology is the National Highway Traffic Safety Administration (NHTSA) and the U.S. Environmental Protection Agency (EPA) joint final rules concerning Corporate Average Fuel Economy (CAFE) and greenhouse gas (GHG) emissions for model years 2017 (MY17) through 2025 (MY25) passenger cars and light trucks. The chief goal of this current study is to compare the already rapid pace of fuel economy improvement and technological change over the previous decade to the required rate of change to meet regulations over the next decade. EPA and NHTSA comparisons of the model year 2005 (MY05) US light-duty vehicle fleet to the model year 2015 (MY15) fleet shows improved fuel economy (FE) of approximately 26% using the same FE estimating method mandated for CAFE regulations. Future predictions by EPA and NHTSA concerning ensemble fleet fuel economy are examined as an indicator of required vehicle rate-of-change.

Without the waste heat available from the engine of a conventional automobile, electric vehicles (EVs) must provide heat to the cabin for climate control using energy stored in the vehicle. In current EV designs, this energy is typically provided by the traction battery. In very cold climatic conditions, the power required to heat the EV cabin can be of a similar magnitude to that required for propulsion of the vehicle. As a result, the driving range of an EV can be reduced very significantly during winter months, which limits consumer acceptance of EVs and results in increased battery costs to achieve a minimum range while ensuring comfort to the EV driver. To minimize the range penalty associated with EV cabin heating, a novel climate control system that includes thermal energy storage has been designed for use in EVs and plug-in hybrid electric vehicles (PHEVs). The system uses the stored latent heat of an advanced phase change material (PCM) to provide cabin heating.

Alternative propulsion technologies are becoming increasingly important with the rise of stricter regulations for vehicle efficiency, emission regulations, and concerns over the sustainability of crude oil supplies. The fuel cell is a critical component of alternative propulsion systems, and as such has many aspects to consider in its design. Fuel cell electric vehicles (FCEVs) powered by proton-exchange membrane fuel cells (PEFC) and fueled by hydrogen, offer the promise of zero emissions with excellent driving range of 300-400 miles, and fast refueling times; two major advantages over battery electric vehicles (BEVs). FCEVs face several remaining major challenges in order to achieve widespread and rapid commercialization. Many of the challenges, especially those from an FCEV system and subsystem cost and performance perspective are addressed in this book.

In the current work, a series-hybrid vehicle has been constructed that utilizes a dual-fuel, Reactivity Controlled Compression Ignition (RCCI) engine. The vehicle is a 2009 Saturn Vue chassis and a 1.9L turbo-diesel engine converted to operate with low temperature RCCI combustion. The engine is coupled to a 90 kW AC motor, acting as an electrical generator to charge a 14.1 kW-hr lithium-ion traction battery pack, which powers the rear wheels by a 75 kW drive motor. Full vehicle testing was conducted on chassis dynamometers at the Vehicle Emissions Research Laboratory at Ford Motor Company and at the Vehicle Research Laboratory at Oak Ridge National Laboratory. For this work, the US Environmental Protection Agency Highway Fuel Economy Test was performed using commercially available gasoline and ultra-low sulfur diesel. Fuel economy and emissions data were recorded over the specified test cycle and calculated based on the fuel properties and the high-voltage battery energy usage.

It is well known that spark ignited engine performance and efficiency is closely coupled to fuel octane number. The present work combines historical and recent trends in spark ignition engines to build a database of engine design, performance, and fuel octane requirements over the past 80 years. The database consists of engine compression ratio, required fuel octane number, peak mean effective pressure, specific output, and combined unadjusted fuel economy for passenger vehicles and light trucks. Recent trends in engine performance, efficiency, and fuel octane number requirement were used to develop correlations of fuel octane number utilization, performance, specific output. The results show that historically, engine compression ratio and specific output have been strongly coupled to fuel octane number.

This paper utilizes GPS tracked multiday travel activities to estimate the temporal distribution of electricity loads and assess battery electric vehicle (BEV) grid impacts at a significant market penetration level. The BEV load and non-PEV load vary by time of the day and day of the week. We consider two charging preferences: home priority assumes BEV drivers prefer charging at home and would not charge at public charging stations unless the state of charge (SOC) of the battery is not sufficient to cover the way back to home; and charging priority does not require drivers to defer charging to home and assumes drivers will utilize the first available charging opportunity. Both home and charging priority scenarios show an evening peak demand. Charging priority scenario also shows a morning peak on weekdays, possibly due to workplace charging.

Driver is a key component in vehicle simulation. An ideal driver model simulates driving patterns a human driver may perform to negotiate road profiles. There are simulation packages having the capability to simulate driver behavior. However, it is rarely documented how they work with road profiles. This paper proposes a new truck driver model for vehicle simulation to imitate actual driving behavior in negotiating road grade and curvature. The proposed model is developed based upon Gipps' car-following model. Road grade and curvature were not considered in the original Gipps' model although it is based directly on driver behavior and expectancy for vehicles in a stream of traffic. New parameters are introduced to capture drivers' choice of desired speeds that they intend to use in order to negotiating road grade and curvature simultaneously. With the new parameters, the proposed model can emulate behaviors like uphill preparation for different truck drivers.

As energy storage system (ESS) technology advances, vehicle testing in both laboratory and on-road settings is needed to characterize the performance of state-of-the-art technology and also identify areas for future improvement. The Idaho National Laboratory (INL), through its support of the U.S. Department of Energy's (DOE) Advanced Vehicle Testing Activity (AVTA), is collaborating with ECOtality North America and Oak Ridge National Laboratory (ORNL) to conduct on-road testing of advanced ESSs for the Electric Drive Advanced Battery (EDAB) project. The project objective is to test a variety of advanced ESSs that are close to commercialization in a controlled environment that simulates usage within the intended application with the variability of on-road driving to quantify the ESS capabilities, limitations, and performance fade over cycling of the ESS.

To provide useful information for automakers to design successful plug-in hybrid electric vehicle (PHEV) products and for energy and environmental analysts to understand the social impact of PHEVs, this paper addresses the question of how many of the U.S. consumers, if buying a PHEV, would prefer what electric ranges. The Market-oriented Optimal Range for PHEV (MOR-PHEV) model is developed to optimize the PHEV electric range for each of 36,664 sampled individuals representing U.S. new vehicle drivers. The optimization objective is the minimization of the sum of costs on battery, gasoline, electricity and refueling hassle.