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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.

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions.

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.

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.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

The U.S. Department of Energy’s Co-Optimization of Fuels and Engines initiative (Co-Optima) aims to simultaneously transform both transportation fuels and engines to maximize performance and energy efficiency. Researchers from across the DOE national laboratories are working within Co-Optima to develop merit functions for evaluating the impact of fuel formulations on the performance of advanced engines. The merit functions relate overall engine efficiency to specific measurable fuel properties and will serve as key tools in the fuel/engine co-optimization process. This work focused on developing a term for the Co-Optima light-duty boosted spark ignition (SI) engine merit function that captures the effects of fuel composition on emissions control system performance. For stoichiometric light-duty SI engines, the majority of NOx, NMOG, and CO emissions occur during cold start, before the three-way catalyst (TWC) has reached its “light-off” temperature.

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.

Aluminum alloys containing cerium have excellent castability and retain a substantial fraction of their room temperature strength at temperatures of 200°C and above. High temperature strength is maintained through a thermodynamically trapped, high surface energy intermetallic. Dynamic load partitioning between the aluminum and the intermetallic increases mechanical response. Complex castings have been produced in both permanent mold and sand castings. This versatile alloy system, using an abundant and inexpensive co-product of rare earth mining, is suitable for parts that need to maintain good properties when exposed to temperatures between 200 and 315°C.

For renewable fuels to displace petroleum, they must be compatible with emissions control devices. Pure biodiesel contains up to 5 ppm Na + K and 5 ppm Ca + Mg metals, which have the potential to degrade diesel emissions control systems. This study aims to address these concerns, identify deactivation mechanisms, and determine if a lower limit is needed. Accelerated aging of a production exhaust system was conducted on an engine test stand over 1001 h using 20% biodiesel blended into ultra-low sulfur diesel (B20) doped with 14 ppm Na. This Na level is equivalent to exposure to Na at the uppermost expected B100 value in a B20 blend for the system full-useful life. During the study, NOx emissions exceeded the engine certification limit of 0.33 g/bhp-hr before the 435,000-mile requirement.

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.

Rapid vehicle powertrain development has become a technological breakthrough for the design and implementation of vehicles that meet and exceed the fuel efficiency, cost, and performance targets expected by today’s consumer. Recently, advances in large scale additive manufacturing have provided the means to bridge hardware-in-the-loop with preproduction mule chassis testing. This paper details a case study from Oak Ridge National Laboratory bridging the powertrain-in-the-loop development process with vehicle systems implementation using big area additive manufacturing (BAAM). For this case study, the use of a component-in-the-loop laboratory with math-based models is detailed for the design of a battery electric powertrain to be implemented in a printed prototype mule. The ability for BAAM to accelerate the mule development process via the concept of computer-aided design to part is explored.

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.

Small impurities in the fuel can have a significant impact on the emissions control system performance over the lifetime of the vehicle. Of particular interest in recent studies has been the impact of sodium, potassium, and calcium that can be introduced either through fuel constituents, such as biodiesel, or as lubricant additives. In a collaboration between the National Renewable Energy Laboratory and the Oak Ridge National Laboratory, a series of accelerated aging studies have been performed to understand the potential impact of these metals on the emissions control system. This paper explores the effect of the rate of accelerated aging on the capture of fuel-borne metal impurities in the emission control devices and the subsequent impact on performance. Aging was accelerated by doping the fuel with high levels of the metals of interest. Three separate evaluations were performed, each with a different rate of accelerated aging.

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.

Plug-in hybrid electric vehicles (PHEV) operate predominantly as electric vehicles (EV) with intermittent assist from the engine. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current conventional vehicles, the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts. ORNL, in collaboration with the University of Tennessee, developed an Engine-In-the-Loop (EIL) test platform to investigate cold start emissions on a 2.0l Gasoline Turbocharged Direct Injection (GTDI) Ecotec engine coupled to a virtual series hybrid electric vehicle.