As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology.Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy.HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle.A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle.Optimal strategies can be implemented when driving conditions are known a prior.The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque.This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints.When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor.The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development.The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles.Results show a maximum fuel econ-

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles.With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control.The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions.Methods exist by which the globally optimal control strategy may be found.However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle.Real-time con-

The increased concern over global climate change and lack of long-term sustainability of fossil fuels in the projected future has prompted further research into advanced alternative fuel vehicles to reduce vehicle emissions and fuel consumption. One of the primary advanced vehicle research areas involves electrification and hybridization of vehicles. As hybrid-electric vehicle technology has advanced, so has the need for more innovative control schemes for hybrid vehicles, including the development and optimization of hybrid powertrain transmission shift schedules. The hybrid shift schedule works in tandem with a cost function-based torque split algorithm that dynamically determines the optimal torque command for the electric motor and engine. The focus of this work is to develop and analyze the benefits and limitations of two different shift schedules for a position-3 (P3) parallel hybrid-electric vehicle. a traditional two-parameter shift schedule that operates as a function of vehicle accelerator position and vehicle speed (state of charge (SOC) independent shift schedule), and a three-parameter shift schedule that also adapts to fluctuations in the state of charge of the high voltage batteries (SOC dependent shift schedule). The shift schedules were generated using an exhaustive search coupled with a fitness function to evaluate all possible vehicle operating points. The generated shift schedules were then tested in the software-in-the-loop (SIL) environment and the vehicle-in-the-loop (VIL) environment and compared to each other, as well as to the stock 8L45 8-speed transmission shift schedule. The results show that both generated shift schedules improved upon the stock transmission shift schedule used in the hybrid powertrain comparing component efficiency, vehicle efficiency, engine fuel economy, and vehicle fuel economy.

Hybrid electric vehicles (HEV) have been recognized as a viable technology to significantly improve the fuel economy of on-road vehicles operated in urban areas featuring frequent stop-and-go operation. The optimization of the HEV control strategy is a dynamic optimal problem involving a nonlinear cost function and several nonlinear constraints. To take advantage of the global optimality of dynamic programming (DP) and the real-time capability of the equivalent consumption minimization strategy (ECMS) in optimizing the HEV operation strategy, a model that combines ECMS with DP is proposed in this paper, which is defined as ECMSwDP. Using this approach, the optimal equivalent factor λ for ECMS is derived using DP under various driving conditions and the different grades of synthetic driving cycle. The fuel economy simulated using the ECMSwDP control strategy is comparable to the optimal solution derived using DP. The potential of the real-time control strategy developed using ECMSwDP to improve fuel economy is demonstrated using a transit hybrid electric bus as an example.

The addition of hydrogen (H-2) into the intake air of a diesel engine was found to significantly increase the emissions of nitrogen dioxide (NO2). Previous research demonstrated a strong correlation between the emissions of NO2 and unburned H-2 in exhaust gas. However, the mechanism whereby H-2 addition in increasing NO2 formation in a H-2-diesel dual fuel engine. Previously has not been investigated. This research numerically verified the hypothesis that the increased NO2 emissions observed with the addition of H-2 was formed through the conversion from NO to NO2 during the post combustion oxidation process of the unburned H-2 when mixed with the hot NO-containing combustion products. A variable volume single zone model with detailed chemistry was applied to simulate post-combustion oxidation process of the unburned H-2 and its effect on NO2 emissions. The mixing of the unburned H-2 with the NO-containing hot combustion products was found to convert NO to NO2. Such a conversion is promoted by the hydroperoxyl (HO2) radical formed during the oxidation process of the H-2. The factors affecting the NO2 formation and its destruction include the concentration of NO, H-2, O-2, and the temperature of the bulk mixture. When H-2 and hot NO-containing combustion products mixed during the early stage of expansion stroke, the NO2 formed during H-2 oxidation was later dissociated to NO after the complete consumption of H-2. The complete combustion of H-2 exhausted the source of HO2 necessary for the conversion from NO to NO2. The mixing of H-2 with combustion products during the last part of the expansion stroke was not able to convert NO to NO2 since the temperature was too low for H-2 to oxidize and to provide the HO2 needed. The bulk mixture temperature range suitable for meaningful conversion from NO to NO2 aided by HO2 produced during the oxidation of H-2 was examined and presented. (C) 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Information obtainable from intelligent transportation systems (ITS) provides the possibility of improving safety and efficiency of vehicles at different levels. In particular, such information also has the potential to be utilized for the prediction of driving conditions and traffic flow, which allows Hybrid Electric Vehicles (HEVs) to run their powertrain components in corresponding optimum operating regions. This paper proposes to improve the performance of one of the most promising realtime powertrain control strategies, called adaptive equivalent consumption minimization strategy (AECMS), using predicted driving conditions. In this paper, three real-time powertrain control strategies are proposed for HEVs, each of which introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. These factors are proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy, number of engine transients (ON/OFF), and charge sustainability of the battery.

Today's heavy-duty natural gas-fueled fleet is estimated to represent less than 2% of the total fleet. However, over the next couple of decades, predictions are that the percentage could grow to represent as much as 50%. Although fueling switching to natural gas could provide a climate benefit relative to diesel fuel, the potential for emissions of methane (a potent greenhouse gas) from natural gas-fueled vehicles has been identified as a concern. Since today's heavy-duty natural gas-fueled fleet penetration is low, today's total fleet-wide emissions will be also be low regardless of per vehicle emissions. However, predicted growth could result in a significant quantity of methane emissions. To evaluate this potential and identify effective options for minimizing emissions, future growth scenarios of heavy-duty natural gas-fueled vehicles, and compressed natural gas and liquefied natural gas fueling stations that serve them, have been developed for 2035, when the populations could be significant. The scenarios rely on the most recent measurement campaign of the latest manufactured technology, equipment, and vehicles reported in a companion paper as well as projections of technology and practice advances. These "pump-to-wheels"(PTW) projections do not include methane emissions outside of the bounds of the vehicles and fuel stations themselves and should not be confused with a complete wells-to-wheels analysis. Stasis, high, medium, and low scenario PTW emissions projections for 2035 were 1.32%, 0.67%, 0.33%, and 0.15% of the fuel used. The scenarios highlight that a large emissions reductions could be realized with closed crankcase operation, improved best practices, and implementation of vent mitigation technologies. Recognition of the potential pathways for emissions reductions could further enhance the heavy-duty transportation sectors ability to reduce carbon emissions.

This research investigated the combustion process of an AVL Model LEF/Volvo 5312 single cylinder engine configured to simulate the operation of a heavy-duty spark ignition (SI) natural gas (NG) engine operated on stoichiometric mixture. The factors affecting the combustion process that were examined include intake pressure, spark timing (ST), and the addition of diluents including nitrogen (N2) and carbon dioxide (CO2) to the NG to simulate low British thermal unit (BTU) gases. The mixing of diluents with NG is able to slow down the flame propagation speed, suppress the onset of knock, and allow the engine to operate on higher boost pressure for higher power output. The addition of CO2 was more effective than N2 in suppressing the onset of knock and slowing down the flame propagation speed due to its high heat capacity. Boosting intake pressure significantly increased the heat release rate (HRR) evaluated on J/°CA basis which represents the rate of mass of fuel burning. However, its impact on the normalized HRR evaluated on %/°CA basis, representing the flame propagation rate, was relatively mild. Boosting the intake pressure from 1.0 to 1.8 bar without adding diluents increased the peak HRR to 1.96 times of that observed at 1.0 bar. The increase was due to the burning of more fuel (about 1.8 times), and the 12.9% increase in the normalized HRR. The latter was due to the shortened combustion duration from 23.6 to 18.2 °CA, a 22.9% reduction. The presence of 40% CO2 or N2 in their mixture with NG increased the peak cylinder pressure (PCP) limited brake mean effective pressure (BMEP) from 17.2 to about 20.2 bar. The combustion process of a turbocharged SI NG engine can be approximated by referring to the HRR measured under a naturally aspirated condition. This makes it convenient for researchers to numerically simulate the combustion process and the onset of knock of turbocharged SI NG engines using combustion process data measured under naturally aspirated conditions as a reference.

Pump-to-wheels (PTW) methane emissions from the heavy-duty (HD) transportation sector, which have climate change implications, are poorly documented. In this study, methane emissions from HD natural gas fueled vehicles and the compressed natural gas (CNG) and liquefied natural gas (LNG) fueling stations that serve them were characterized. A novel measurement system was developed to quantify methane leaks and losses. Engine related emissions were characterized from twenty-two natural gas fueled transit buses, refuse trucks, and over-the-road (OTR) tractors. Losses from six LNG and eight CNG stations were characterized during compression, fuel delivery, storage, and from leaks. Cryogenic boil-off pressure rise and pressure control venting from LNG storage tanks were characterized using theoretical and empirical modeling. Field and laboratory observations of LNG storage tanks were used for model development and evaluation. PTW emissions were combined with a specific scenario to view emissions as a percent of throughput. Vehicle tailpipe and crankcase emissions were the highest sources of methane. Data from this research are being applied by the authors to develop models to forecast methane emissions from the future HD transportation sector.

An optimal power management strategy is the key to benefit from hybridization of a vehicle powertrain. Designing such a strategy requires knowledge of the vehicle energy requirements during its drive cycle. Therefore, information available through Intelligent Transportation Systems (ITS) can play a critical role in designing such an optimal powertrain management. To avoid the implementation and practical issues of global optimal solutions, sub-optimal methods such as Equivalent Consumption Minimization Strategy (ECMS) have been introduced for the power distribution in Hybrid Electric Vehicles (HEV). However, the dependency of ECMS on prior knowledge about the driving cycle is a deterring effect for real-time implementation. Accordingly, on-line decision making about the equivalent factor value used in ECMS, which translates the electrical energy into the equivalent fuel energy, is the challenge captivating researches attention. In this paper, an adaptive method for enhancement of the ECMS based on the prediction of driving conditions is proposed. Using the approximated future energy requirement of the vehicle over the prediction time horizon, the sub-optimal value of equivalent factor is updated. Simulation results validate the effectiveness of the proposed method to decrease fuel consumption while charge sustainability is satisfied.

Correlation analysis was performed to investigate the effects of drive cycle characteristics on distance-specific emissions (g/mile) and fuel economy (mpg) and consequently determine the most influential cycle metrics for modeling. A detailed analysis of linear and non-linear correlations was performed among cycle metrics to avoid collinearity and reduce the number of variables. The order of importance of the selected cycle metrics was determined. Results show that average speed with idle, number of stops per mile, percentage idle, and kinetic intensity were the most important cycle metrics affecting emissions and fuel economy. Preliminary regression analysis reinforced their importance for emissions modeling purposes.

Transit agencies are subject to both nationwide and local emissions regulations. The Westchester County Department of Transportation (WCDOT) is required to bring its older buses into compliance with county regulations. This paper quantifies emissions reductions resulting from actions taken by WCDOT through an emissions reduction program starting in 2005, with projections up to 2009. Selected buses were tested by the West Virginia University (WVU) Transportable Chassis-Dynamometer Emissions Laboratory over the OCTA cycle and a custom WCDOT driving schedule, the Bee-Line cycle. Based on measured results, future fleet-wide emissions were estimated for two scenarios: a baseline scenario in which the county requirements are met just in time, and a proactive scenario which reflects early actions taken by WCDOT. The proactive approach of WCDOT towards emissions reduction was shown to save, over the five-year period, 112.5 tons (53%) of carbon monoxide (CO), 23.3 tons (1%) of nitrogen oxides (NOx), 11.3 tons (30%) of hydrocarbons (HC), 7.3 tons (44%) of particulate matter (PM), 1,119 tons (1%) of carbon dioxide (CO2), and 114,000 gallons (1%) of diesel fuel.

The oxides of nitrogen (NOx) emissions of diesel engines consist of nitric oxide (NO) and nitrogen dioxide (NO2). Although emitted at small amounts, NO2 has higher toxicity and causes more health and environmental issues than NO. This research investigates the impact of the addition of hydrogen (H-2), natural gas (NG), and engine load on NO2 emissions from a heavy-duty diesel engine converted to operate using dual fuel combustion mode. The substitution of a small amount of H-2 or NG for the diesel fuel substantially increased NO2 emissions, but had a very mild impact on the combustion process. In comparison, the substitution of a large amount of H-2 and NG for the diesel fuel dramatically altered the combustion process and produced more NO2 than the diesel-only operation, but produced less NO2 than the addition of a small amount of H-2 and NG. A preliminary analysis revealed a firm correlation between NO2 emissions and the emissions of the unburned H-2 or CH4, and their relative emissions. The importance of the unburned fumigation fuels in enhancing NO2 formation in dual fuel engines was also supported by the data reported in the literature. The portion of supplemental fuels entrained into the diesel spray plume and simultaneously burned with the diesel fuel may not contribute to the increased NO2 emissions from dual fuel engines.

Distance-specific fuel economy (FE) and emissions of carbon monoxide (CO), hydrocarbons (HC), oxides of nitrogen (NOx), and particulate matter (PM) from transit buses representing diesel, retrofitted diesel, hybrid-electric diesel, and lean-burn natural gas technologies are presented in this paper. Emissions were collected from these buses at the Washington Metropolitan Area Transport Authority (WMATA) test site in Landover, Maryland. In this program, one bus each from diesel, retrofitted diesel, hybrid-electric diesel, and natural gas technologies was tested on 17 chassis cycles and the other buses were tested on a subset of these cycles. Data show that the test cycle has a profound effect on distance-specific emissions and FE, and relative emissions performance of technology is also cycle dependant. Lean-burn natural gas buses demonstrated their low PM output, diesel engines showed low HC output, benefit of exhaust filtration was evident, and the positive effect of hybrid-electric drive technology was most pronounced for low-speed transient cycles.

The new General Motors 2-mode hybrid transmission for front-wheel-drive vehicles has been incorporated into a 2009 Saturn Vue by the West Virginia University EcoCAR team. The 2-mode hybrid transmission can operate in either one of two electrically variable transmission modes or four fixed gear modes although only the electrically variable modes were explored in this paper. Other major power train components include a GM 1.3L SDE turbo diesel engine fueled with B20 biodiesel and an A123 Systems 12.9 kWh lithium-ion battery system. Two additional vehicle controllers were integrated for tailpipe emission control, CAN message integration, and power train hybridization control. Control laws for producing maximum fuel efficiency were implemented and include such features as engine auto-stop, regenerative braking and optimized engine operation. The engine operating range is confined to a high efficiency area that improves the overall combined engine and electric motor efficiency. Simulation results using Powertrain System Analysis Toolkit (PSAT) indicate fuel economy of 28.4/29.4 mpgge over the customized Morgantown Urban Drive Schedule (MUDS)/Route 19 Highway (R19HW), 14.6 second 0-60 mph and 8.6 second 50-70 mph acceleration time. The on-road test results indicated fuel economy of 24.5/31.5 mpgge over the MUDS/R19HW cycles, 16 seconds 0-60 mph and 10 seconds 50-70 mph acceleration time.

Alternative fuels, emissions control technologies and advanced propulsion technologies offer great potential for reducing emissions from, and increasing fuel economy of, buses employed in public transportation.The use of fuels such as Compressed Natural Gas (CNG) and biodiesel, emissions controls such as diesel particulate filters (DPF) and diesel oxidation catalysts (DOC), and the use of advanced propulsion systems such as hybrid-electric diesel have great potential for decreasing emissions from public transit vehicles and potentially increasing fuel economy.The focus of this paper is to assess the environmental benefits of alternative fuels and advanced hybrid drive technologies in transit vehicles through experimental testing and analysis.Results show that hybrid-electric diesel and CNG buses yield significant reductions in CO 2 emissions, approximately 10-20% lower than conventional diesel.Stoichiometric CNG buses demonstrated extremely low emissions of NO x , while conventional, lean-burn CNG had the highest NO x emissions, approximately twice that of hybrid technologies and conventional diesel engines.The hybrid-electric technology demonstrated the highest fuel economy, while CNG has the lowest fuel economy.The use of a 20% biodiesel blend (B20) demonstrated no discernable differences in fuel economy, while showing slightly higher NO x emissions levels and significantly lower PM compared to conventional diesel.