This paper details the advancements and outcomes of the NEXTCAR (Next-Generation Energy Technologies for Connected and Automated on-Road Vehicles) program, an initiative led by the Advanced Research Projects Agency-Energy (ARPA-E). The program focusses on harnessing the full potential of Connected and Automated Vehicle (CAV) technologies to develop advanced vehicle dynamic and powertrain control technologies (VD&PT). These technologies have shown the capability to reduce energy consumption by 20% in conventional and hybrid electric cars and trucks at automation levels L1-L3 and by 30% L4 fully autonomous vehicles. Such reductions could lead to significant energy savings across the entire U.S. vehicle fleet. This study summarizes the results from Phases I and II of the NEXTCAR program, highlighting the contributions of four teams that participated in both phases: Southwest Research Institute, Michigan Technical University, Ohio State University, and the University of California, Berkeley. The study details the technologies developed by each team, including eco-routing, power-split optimization, cooperative driving, blended mode, speed harmonization, predictive cruise control, charge-sustaining engine on/off optimizer, and eco-approach and departure, among other innovative solutions. It outlines the energy savings achieved by these innovations. These technologies have experimentally demonstrated significant energy savings, ranging from 10-30%, while maintaining travel times. Additionally, the paper examines the challenges in commercializing these technologies and highlights ARPA-E's envisioned actions to provide a unified testing environment for all teams. This environment will allow for the assessment of all developed technologies under similar conditions, aiming to overcome the limitations of standardized Environmental Protection Agency EPA testing cycles and more accurately reflect real-world driving conditions. This approach validates the effectiveness of CAV technologies and supports their commercialization.

Potential energy savings achievable by using isothermal dehumidification instead of conventional air conditioning systems are modeled on a technology-agnostic basis. First, we use simple thermodynamics to model potential device-level energy savings by dehumidifying isothermally instead of by sub-dew-point cooling. A model for dehumidification demand in a prototypical house is then developed, and the device-level model is applied to the house under relevant climactic norms and weather extremes. Finally, we model all U.S. houses by coupling the household model with a statistical model for air infiltration representing all single-family, detached homes in the United States. The results show potential nationwide electricity savings of 8-25 or 5-14% of electricity usage for space cooling in U.S. single-family homes, depending on weather. The results also display large regional differences, with moist hot climates having the greatest potential absolute energy savings, but moist temperate climates having the greatest potential relative energy savings. Individual houses with high balance-point temperatures in moist climate zones are likely to derive the greatest benefit by using isothermal dehumidification over conventional systems. Ultimately, this technology-agnostic analysis provides fundamental, thermodynamics-based insight into where, when, and how isothermal dehumidification devices can reduce the energy and greenhouse gas footprints of space cooling.

When understanding the effect of new technology on any sector, it is essential to have a quantitative understanding of the sector's "turnover rate": the sector-wide inertia based on buying and scrappage rates of existing products. To assess sector-wide inertia in the transportation sector, this paper develops an agent-based stochastic simulation for characterization of the U.S. light-duty (LD) vehicle fleet, applying the output to quantifying the effect of electric vehicles sales on the greenhouse gas (GHG) emissions of the fleet as a whole. Such a model framework allows us to study transportation policies that might affect scrappage rates, vehicle types, and car sharing adoption and comparing them to different exogenous electric vehicle adoption scenarios. Overall, we find that the impact of scrappage rate on emissions is small but could have synergistic interactions with our exogenous electric vehicle adoption scenarios. Furthermore, the impact of restricting vehicle type is negligible. Grid decarbonization has a large effect, roughly the same as doubling EV saturation. Lastly, mandating shared fleet electrification could have the same effect as decarbonization.

Societal and governmental pressures to reduce diesel exhaust emissions are reflected in the existing and projected future heavy-duty certification standards of these emissions. Various factors affect the amount of emissions produced by a heterogeneous charge diesel engine in any given situation, but these are poorly quantified in the existing literature. The parameters that most heavily affect the emissions from compression ignition engine-powered vehicles include vehicle class and weight, driving cycle, vehicle vocation, fuel type, engine exhaust aftertreatment, vehicle age, and the terrain traveled. In addition, engine control effects (such as injection timing strategies) on measured emissions can be significant. Knowing the effect of each aspect of engine and vehicle operation on the emissions from diesel engines is useful in determining methods for reducing these emissions and in assessing the need for improvement in inventory models. The effects of each of these aspects have been quantified in this paper to provide an estimate of the impact each one has on the emissions of diesel engines.

The concept of defining a regulatory standard for the maximum allowable emissions of oxides of nitrogen (NOx) from a heavy-duty diesel engine on an instantaneous basis is presented. The significance of this concept from a regulatory point of view is the possibility to realise a steady brake-specific NOx emissions result independent of the test schedule used. The emissions of oxides of nitrogen from a state-of-the-art direct injection diesel engine have been examined on an integral as well as on an instantaneous basis over the Federal Test Procedure as well as over several other arbitrary transient cycles generated for this study. Three candidate standards of specific NOx emissions have been evaluated on a real-time. continuous basis. These include brake power specific, fuel mass specific, and carbon dioxide mass specific NOx emissions. Retaining the stock engine control module, the carbon dioxide specific emissions of NOx have been shown to be the most uniform, varying only by about 30% of its mean value regardless of the test schedule or engine operation. The instantaneous fuel specific NOx emissions are shown to be relatively less invariant and the least steady are the brake power specific emissions with a coefficient of variation of up to 200%. Advancing injection timing has been shown to have a wide range of authority over the specific emissions of oxides of nitrogen regardless of the units used, when operating at full load in the vicinity of peak torque speeds. The carbon dioxide specific NOx emissions have shown a linear dependence on the power specific emissions, independent of the examined operating conditions. The trade-off between better brake thermal efficiency, lower exhaust gas temperature at advanced timing and lower NOx emissions has also been shown to be independent of the units of the specific standard used.

Internal combustion engines are being required to comply with increasingly stringent government exhaust emissions regulations. Compression ignition (CI) piston engines will continue to be used in cost-sensitive fuel applications such as in heavy-duty buses and trucks, power generation, locomotives and off-highway applications, and will find application in hybrid electric vehicles. Close control of combustion in these engines will be essential to achieve ever-increasing efficiency improvements while meeting increasingly stringent emissions standards. The engines of the future will require significantly more complex control than existing map-based control strategies, having many more degrees of freedom than those of today. Neural network (NN)-based engine modelling offers the potential for a multidimensional, adaptive, learning control system that does not require knowledge of the governing equations for engine performance or the combustion kinetics of emissions formation that a conventional map-based engine model requires. The application of a neural network to model the output torque and exhaust emissions from a modern heavy-duty diesel engine (Navistar T444E) is shown to be able to predict the continuous torque and exhaust emissions from a heavy-duty diesel engine for the Federal heavy-duty engine transient test procedure (FTP) cycle and two random cycles to within 5 per cent of their measured values after only 100 min of transient dynamometer training. Applications of such a neural net model include emissions virtual sensing, on-board diagnostics (OBD) and engine control strategy optimization.

Internal combustion engines are being required to comply with increasingly stringent government exhaust emissions regulations. Compression ignition (CI) piston engines will continue to be used in cost-sensitive fuel applications such as in heavy-duty buses and trucks, power generation, locomotives and off-highway applications, and will find application in hybrid electric vehicles. Close control of combustion in these engines will be essential to achieve ever-increasing efficiency improvements while meeting increasingly stringent emissions standards. The engines of the future will require significantly more complex control than existing map-based control strategies, having many more degrees of freedom than those of today. Neural network (NN)-based engine modelling offers the potential for a multidimensional, adaptive, learning control system that does not require knowledge of the governing equations for engine performance or the combustion kinetics of emissions formation that a conventional map-based engine model requires. The application of a neural network to model the output torque and exhaust emissions from a modern heavy-duty diesel engine (Navistar T444E) is shown to be able to predict the continuous torque and exhaust emissions from a heavy-duty diesel engine for the Federal heavy-duty engine transient test procedure (FTP) cycle and two random cycles to within 5 per cent of their measured values after only 100 min of transient dynamometer training. Applications of such a neural net model include emissions virtual sensing, on-board diagnostics (OBD) and engine control strategy optimization.