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The proliferation of electric vehicles (EVs) is resulting in a big transition in the automotive industry, with the goal of reducing greenhouse gas emissions and improving energy efficiency. There are a variety of different architectural configurations and power distribution strategies that can be optimized for drivability performance, all-electric range, and overall efficiency. This paper describes the efforts of the research team in exploring different EV architectures to better understand their impacts on system performance in terms of energy efficiency and vehicle drivability. In search for an ideal powertrain architecture for a shared-use EV, the research team conducted a comprehensive analysis of a various EV architectures (including RWD and AWD) with different motor parameters, considering a spectrum of targeted vehicle technology specifications such as acceleration and braking performance, and fuel economy.

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.

Highway on-ramp merging is considered as one of the main factors that causes traffic congestion on highways. The drivers along the on-ramp need to adjust vehicle speeds and positions to enter the highway, while the drivers on the highway should also carefully accommodate vehicle speeds and positions to avoid collision with the merging vehicles from the on-ramp, which heavily affects upstream traffic flows. In congested traffic conditions, such maneuvers if inefficiently performed will lead to high risks of accidents and excessive energy consumption and pollutant emissions. In this work, we present an innovative approach to this scenario, where distributed consensus protocol is developed for Connected and Automated Vehicles (CAV) to cooperate with each other by using Vehicle-to-X (V2X) communications.

Quantifying Hybrid Electric Vehicle (HEV) emissions and fuel consumption is a difficult problem for a number of different reasons: 1) HEVs can be configured in significantly different ways (e.g., series or parallel); 2) the Auxiliary Power Unit (APU) can consist of a wide variety of engines, fuel types, and sizes; and 3) the APU can be operated very differently depending on the energy management system strategy and the type of driving that is performed (e.g., city vs. highway driving). With the future increase of HEV penetration in the vehicle fleet, there is an important need for government agencies and manufacturers to determine HEV emissions and fuel consumption. In this paper, several critical issues associated with HEV emissions and fuel consumption are identified and analyzed, using a sophisticated set of HEV and emission simulation modeling tools.

A vehicle's energy consumption and emissions are extremely sensitive to the operating modes of that vehicle. The LA4 test cycle in the Federal Test Procedure (FTP) is the current basis for evaluating a vehicle's energy consumption and emissions, but it was developed more than 20 years ago and does not represent today's typical driving patterns. In this paper, we describe a set of computer simulation models to evaluate energy consumption and emissions of internal combustion engine (ICE) vehicles, electric vehicles (EVs), and hybrid-electric vehicles (HEVs) under a variety of driving cycles. Using these models, two real-world vehicles -- a 92 Ford Taurus and a 97 GM EV1, -- and a hypothetical rangeextender type HEV, are modeled and analyzed under five different driving cycles. We focus our analysis on vehicle performance characteristics such as driving range, equivalent fuel economy, EV and HEV system efficiency, pure electric drive range, and tailpipe emissions.

A methodology is established to assess total life-cycle exhaust emissions for light-duty motor vehicles. The focus is to model a vehicle's carbon monoxide (CO), hydrocarbon (HC), and oxides of nitrogen (NOx) exhaust emissions over its lifetime in the state of California. The data used for this analysis is based on California Air Resources Board's (CARB) Light-Duty Vehicle Surveillance Program (LDVSP), series 12, from March 1993 to March 1994. Bag-based emission data of 165 light duty vehicles from model years (MY) 1984 - 1992 driven under the LA4 and Unified Cycles are analyzed. Vehicle exhaust emissions are estimated based on the following operating conditions: hot-stabilized operation, cold and hot starts, enrichment conditions, and emission control device deterioration/malfunctions.