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Energy efficient HVAC system is becoming increasingly important as higher Corporate Average Fuel Economy (CAFE) standards are required for future vehicle products. The present study is a preliminary attempt at designing energy efficient HVAC system by introducing localized heating/cooling concepts without compromising occupant thermal comfort. In order to achieve this goal of reduced energy consumption while maintaining thermal comfort it is imperative that we use an analytical model capable of predicting thermal comfort with reasonable accuracy in a non-homogenous enclosed thermal environment such as a vehicle's passenger cabin. This study will primarily focus on two aspects: (a) energy efficiency improvements in an HVAC system through micro-cooling/heating strategies and (b) validation of an analytical approach developed in GM that would support the above effort.

Recent advancements in simulation software and computational hardware make it realizable to simulate a full vehicle system comprised of multiple sub-models developed in different modeling languages. The so-called, co-simulation allows one to develop a control strategy and evaluate various aspects of a vehicle system, such as fuel efficiency and vehicle drivability, in a cost-effective manner. In order to study the feasibility of the synchronized parallel processing in co-simulation this paper presents two co-simulation frameworks for a complete vehicle system with multiple heterogeneous subsystem models. In the first approach, subsystem models are co-simulated in a serial configuration, and the same sub-models are co-simulated in a parallel configuration in the second approach.

Spot, or distributed, cooling and heating is an energy efficient way of delivering comfort to an occupant in the car. This paper describes an approach to distributed cooling in the vehicle. A two passenger CFD model of an SUV cabin was developed to obtain the solar and convective thermal loads on the vehicle, characterize the interior thermal environment and accurately evaluate the fluid-thermal environment around the occupants. The present paper focuses on the design and CFD analysis of the energy efficient HVAC system with spot cooling. The CFD model was validated with wind tunnel data for its overall accuracy. A baseline system with conventional HVAC air was first analyzed at mid and high ambient conditions. The airflow and cooling delivered to the driver and the passenger was calculated. Subsequently, spot cooling was analyzed in conjunction with a much lower conventional HVAC airflow.

Lean-burn SIDI engine technology offers improved fuel economy; however, the reduction of NOx during lean-operation continues to be a major technical hurdle in the implementation of energy efficient technology. There are several aftertreatment technologies, including the lean NOx trap and active urea SCR, which have been widely considered, but they all suffer from high material cost and require customer intervention to fill the urea solution. Recently reported passive NH₃-SCR system - a simple, low-cost, and urea-free system - has the potential to enable the implementation of lean-burn gasoline engines. Key components in the passive NH₃-SCR aftertreatment system include a close-coupled TWC and underfloor SCR technology. NH₃ is formed on the TWC with short pulses of rich engine operation and the NH₃ is then stored on the underfloor SCR catalysts.

To realize better fuel economy benefits from transmissions, car makers have started the application of torque converter clutch control in second gear and beyond, resulting in greater demand on the torque converter clutch (TCC) and its control system. This paper focuses on one aspect of the control of the torque converter clutch to improve fuel economy and faster response of the transmission. A TCC is implemented to control the slip between the pump and turbine of the torque converter, thereby increasing its energy transfer efficiency and increasing vehicle fuel economy. However, due to the non-linear nature of the torque converter fluid coupling, the slip feedback control has to be very active to handle different driver inputs and road-load conditions, such as different desired slip levels, changes in engine input torques, etc. This non-linearity requires intense calibration efforts to precisely control the clutch slip in all the scenarios.

With a tighter regulatory environment, reduction of hydrocarbon (HC) and NOx emissions during cold-start has emerged as a major challenge for diesel engines. In the complex diesel aftertreatment system, more than 90% of engine-out NOx is removed in the underfloor SCR. However, the combination of low temperature exhaust and heat sink over DOC delays the SCR light-off during the cold start. In fact, the first 350 seconds during the cold light-duty FTP75 cycle contribute more than 50% of the total NOx tailpipe emission due to the low SCR temperature. For a fast SCR light-off, electrically heated catalyst (EHC) technology has been suggested to be an effective solution as a rapid warm-up strategy. In this work, the EHC, placed in front of DOC, utilizes both electrical power and hydrocarbon fuel. The smart energy management during the cold-start was crucial to optimize the EHC integrated aftertreatment system.

Today, nearly half of the world population lives in urban areas. As the world population continues to migrate to urban areas for increased economic opportunities, addressing personal mobility challenges such as air pollution, Greenhouse Gases (GHGs) and traffic congestion in these regions will become even a greater challenge especially in rapidly growing nations. Road transportation is a major source of air pollution in urban areas causing numerous health concerns. Improvements in automobile technology over the past several decades have resulted in reducing conventional vehicle tailpipe emissions to exceptionally low levels. This transformation has been attained mainly through advancements in engine and transmission technologies and through partial electrification of vehicles. However, the technological advancements made so far alone will not be able to mitigate the issues due to increasing GHGs and air pollution in urban areas.

As part of standardizing the global portfolio, General Motors (GM) created an electrical architecture that will support the GM global product feature set. Introduced in 2009, this common electrical architecture is already being applied to multiple platforms in GM's regional engineering centers. The electrical architecture will be updated regularly to address the needs of new features in the automotive market and to take advantage of the latest technology advancements. The functional requirements of these new features result in technology challenges. In addition, many new features may result in challenges to the vehicle electrical architecture or the vehicle development process. The challenges have been evaluated so that needs and initiatives can be better understood.

In designing vehicles with significant electric driving range, optimizing vehicle energy efficiency is a key requirement to maximize the limited energy capacity of the onboard electrochemical energy storage system. A critical factor in vehicle energy efficiency is the vehicle mass. Optimizing mass allows for the possibility of either increasing electric driving range with a constant level of electrochemical energy storage or holding the range constant while reducing the level of energy storage, thus reducing storage cost. In this paper, a methodology is outlined to study the tradeoff between the battery cost savings achieved by vehicle mass reduction for a constant electric driving range and the cost associated with lightweighting a vehicle. This methodology enables informed business decisions about the available engineering options for lightweighting early in the vehicle development process. The methodology was applied to a compact extended-range electric vehicle (EREV) concept.

Roof racks are designed for carrying luggage during customers' travels. These rails need to be strong enough to be able to carry the luggage weight as well as be able to withstand aerodynamic loads that are generated when the vehicle is travelling at high speeds on highways. Traditionally, roof rail gage thickness is increased to account for these load cases (since these are manufactured by extrusion), but doing so leads to increased mass which adversely affects fuel efficiency. The current study focuses on providing the guidelines for strategically placing lightening holes and optimizing gage thickness so that the final design is robust to noise parameters and saves the most mass without adversely impacting wind noise performance while minimizing stress. The project applied Design for Six Sigma (DFSS) techniques to optimize roof rail parameters in order to improve the load carrying capacity while minimizing mass.

In 2007 the electrical power consumption of 180 rural Alaska villages was 370,000 MW-h, generated using isolated diesel gensets. From a stationary diesel engine considerable amount of heat energy at an elevated temperature is released into the atmosphere from engine jacket liquid and exhaust gases. In rural Alaska, due to the infrastructure, economic impact and needs of the villages, many of village gensets may not be appropriate for applying heat recovery for the purposes other than electrical power generation. Other appropriate types of heat recovery applications in Alaska may include desalination, refrigeration, and district heating. Also due to the varying sizes and electrical loads of most of the diesel gensets (from 100 kw to 1 MW); small-sized heat recovery power systems (80 kW or less) are preferred instead of industrial scale systems.

Scientific understanding and technological capabilities developed by NASA and the NSF to support life in remote regions on Earth or in planetary stations can be transferred to communities to enhance economic development and improve the quality of life. High efficiency Controlled Environment Agriculture (CEA) production is the centerpiece. Extreme waste remediation and sanitation, health, and nutrition problems prevelant in small, remote communities in the arctic are the focus of technology transfer. Alaska's nearly 200 rural villages typify arctic conditions. Early work in CEA in Alaska demonstrated food production in CEA facilities has potential. NASA's improvements to CEA crop production and energy efficiency shows positive impact. Production per unit area and efficiency of energy conversion are increased by a factor of 3; system efficiency by a factor of 10. Knowledge from early work coupled with NASA advances is an ideal combination for success of CEA centers.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.

Controller Area Network (CAN), the de facto standard for in-vehicle networks, has insufficient security features and thus is inherently vulnerable to various attacks. To protect CAN bus from attacks, intrusion detection systems (IDSs) based on advanced deep learning methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have been proposed to detect intrusions. However, those models generally introduce high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory requests, we exploit the use of Binarized Neural Network (BNN) and hardware-based acceleration for intrusion detection in in-vehicle networks. As BNN uses binary values for activations and weights rather than full precision values, it usually results in faster computation, smaller memory cost, and lower energy consumption than full precision models.

The number of EVs are increasing in power distribution systems every day. This research analyses different penetration levels of electric vehicles in power distribution systems to provide stable energy for smart devices and observes its impacts on operational costs and environmental emissions. The supply of EV power is determined based on smart device consumption by optimal energy management of EV batteries so that both the utilities and the car owner get benefits. Utilities can save energy by reducing system loss, while EV owners can earn money by selling it to utilities at their convenient time for smart device operations. The PG&E 69-bus distribution system is used for the simulation and case studies. Case studies in this research show how the power management of EV's batteries charging and discharging characteristics benefits both utilities and EV owners. The uncertainty of the driving pattern of EVs is also considered in the research to get more accurate results.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

This paper presents nonlinear system identification of a variable oil pump for model-based controls and diagnostics of advanced internal combustion engines. The variable oil pump offers great benefits over the conventional fixed displacement oil pump in terms of fuel efficiency and functional optimality. However, to fully benefit from the variable oil pump, an accurate mathematical model that describes its dynamic behavior is foundational to develop an accurate and robust oil pressure control and diagnostic. Toward this end, Hammerstein and Wiener models that consist of a nonlinear static block followed by a linear dynamic block and a linear dynamic block followed by a nonlinear static block, respectively are developed. Under different operating conditions (oil temperature and engine speed), the oil pressure (output) is measured with the multilevel duty cycle (input) of the flow control valve.

The heat generation rate of a lithium ion cell was estimated using a reversible heat generation rate equation. Because the equation is based on the energy conservation law, the influence of kinetically slow processes should be considered. In this analysis, the influence of kinetically slow processes is present but it is small within the domain of the test measurements. This approximation can be of significant usefulness for modeling the thermal response of single cells and multi-cell batteries.

This paper presents an optimal control co-design framework of a parallel electric-hydraulic hybrid powertrain specifically tailored for heavy-duty vehicles. A pure electric powertrain, comprising a rechargeable lithium-ion battery, a highly efficient electric motor, and a single or double-speed gearbox, has garnered significant attention in the automotive sector due to the increasing demand for clean and efficient mobility. However, the state-of-the-art has demonstrated limited capabilities and has struggled to meet the design requirements of heavy-duty vehicles with high power demands, such as a class 8 semi-trailer truck. This is especially evident in terms of a driving range on one battery charge, battery charging time, and load-carrying capacity. These challenges primarily stem from the low power density of lithium-ion batteries and the low energy conversion efficiency of electric motors at low speeds.