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An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

While machine learning in autonomous vehicles development has increased significantly in the past few years, the use of reinforcement learning (RL) methods has only recently been applied. Convolutional Neural Networks (CNNs) became common for their powerful object detection and identification and even provided end-to-end control of an autonomous vehicle. However, one of the requirements of a CNN is a large amount of labeled data to inform and train the neural network. While data is becoming more accessible, these networks are still sensitive to the format and collection environment which makes the use of others’ data more difficult. In contrast, RL develops solutions in a simulation environment through trial and error without labeled data. Our research expands upon previous research in RL and Proximal Policy Optimization (PPO) and the application of these algorithms to 1/18th scale cars by expanding the application of this control strategy to a full-sized passenger vehicle.

Light duty vehicle emission standards are getting more stringent than ever before as stipulated by US EPA Tier 2 Standards and LEV III regulations proposed by CARB. The research in this paper sponsored by US DoE is focused towards developing a Tier 2 Bin 2 Emissions compliant light duty pickup truck with class leading fuel economy targets of 22.4 mpg “City” / 34.3 mpg “Highway”. Many advanced technologies comprising both engine and after-treatment systems are essential towards accomplishing this goal. The objective of this paper would be to discuss key engine technology enablers that will help in achieving the target emission levels and fuel economy. Several enabling technologies comprising air-handling, fuel system and base engine design requirements will be discussed in this paper highlighting both experimental and analytical evaluations.

Current production heavy duty diesel engines have a brake thermal efficiency (BTE) between 43-46% [1]. In partnership with the United States Department of Energy (DOE) as part of the Supertruck 2 program, Cummins has undertaken a research program to develop a new heavy-duty diesel engine designed to deliver greater than 50% BTE without the use of waste heat recovery. A system level optimization focused on: increased compression ratio, higher injection rate, carefully matched highly efficient turbocharging, variable lube oil pump, variable cooling components, and low restriction after treatment designed to deliver 50% BTE at a target development point. This work will also illustrate the system level planning and understanding of interactions required to allow that same 50% BTE heavy duty diesel engine to be integrated with a waste heat recovery (WHR) system to deliver system level efficiency of 55% BTE at a single point.

The recent interest to use Alternative Fuels for power generation and or mobile power is mainly driven by the serious environmental concerns and energy security of the nation. In this context, the utilization of the landfill gases which are generally low heating valve (Btu) content fuels and normally produced from municipal and city solid waste such as in landfills or garbage dump sites has been increasingly used due to the two fold benefits; firstly, the proper utilization of the gas will avoid the green house gas effect and other environmental concerns and secondly it can produce revenue for the local governments or industries at the same time. The paper describes the results of the investigation of the utilization of landfill gases in a spark ignition (SI) engine. Methane and landfill gas compositions were tested and their performance and combustion characteristics are studied and compared.

Global emission legislations for diesel engines are becoming increasingly stringent. While the exhaust gas composition requirements for prior iterations of emission legislation could be met with improvements in the engine's combustion process, the next issue of European, North American and Japanese emission limits greater than 2005 will require more rigorous measures, mainly employment of exhaust gas aftertreatment systems. As a result, many American diesel OEMs are considering NOx adsorbers as a means to achieve 2007+ emission standards. Since the efficacy of a NOx adsorber over its lifetime is significantly affected by sulfur (“sulfur poisoning”), forthcoming reductions in diesel fuel sulfur (down to 15 ppm), have raised industry concerns regarding compatibility and possible poisoning effects of sulfur from the lubricant.

With increasing energy prices and concerns about the environmental impact of greenhouse gas (GHG) emissions, a growing number of national governments are putting emphasis on improving the energy efficiency of the equipment employed throughout their transportation systems. Within the U.S. transportation sector, energy use in commercial vehicles has been increasing at a faster rate than that of automobiles. A 23% increase in fuel consumption for the U.S. heavy duty truck segment is expected from 2009 to 2020. The heavy duty vehicle oil consumption is projected to grow while light duty vehicle (LDV) fuel consumption will eventually experience a decrease. By 2050, the oil consumption rate by LDVs is anticipated to decrease below 2009 levels due to CAFE standards and biofuel use. In contrast, the heavy duty oil consumption rate is anticipated to double. The increasing trend in oil consumption for heavy trucks is linked to the vitality, security, and growth of the U.S. and global economies.

Prediction based optimal energy management systems are a topic of high interest in the automotive industry as an effective, low-cost option for improving vehicle fuel efficiency. With the continuing development of connected and autonomous vehicle (CAV) technology there are many data streams which may be leveraged by transportation stakeholders. The Suite of CAVs-derived data streams includes advanced driver-assistance (ADAS) derived information about surrounding vehicles, vehicle-to-vehicle (V2V) communications for real time and historical data, and vehicle-to-infrastructure (V2I) communications. The suite of CAVs-derived data streams have been demonstrated to enable improvements in system-level safety, emissions and fuel economy.

Sulfur poisoning from engine fuel and lube is one of the most recognizable degradation mechanisms of a NOx adsorber catalyst system for diesel emission reduction. Even with the availability of 15 ppm sulfur diesel fuel, NOx adsorber will be deactivated without an effective sulfur management. Two general pathways are currently being explored for sulfur management: (1) the use of a disposable SOx trap that can be replaced or rejuvenated offline periodically, and (2) the use of diesel fuel injection in the exhaust and high temperature de-sulfation approach to remove the sulfur poisons to recover the NOx trapping efficiency. The major concern of the de-sulfation process is the many prolonged high temperature rich cycles that catalyst will encounter during its useful life. It is shown that NOx adsorber catalyst suffers some loss of its trapping capacity upon high temperature lean-rich exposure.

The steam reforming of CH4 plays a crucial role in the high-temperature activity of natural gas three-way catalysts. Despite existing reports on sulfur inhibition in CH4 steam reforming, there is a limited understanding of sulfur storage and removal dynamics under various lambda conditions. In this study, we utilize a 4-Mode sulfur testing approach to elucidate the dynamics of sulfur storage and removal and their impact on three-way catalyst performance. We also investigate the influence of sulfur on CH4 steam reforming by analyzing CH4 conversions under dithering, rich, and lean reactor conditions. In the 4-Mode sulfur test, saturating the TWC with sulfur at low temperatures emerges as the primary cause of significant three-way catalyst performance degradation. After undergoing a deSOx treatment at 600 °C, NOx conversions were fully restored, while CH4 conversions did not fully recover.

Changing diesel engine emission requirements for 2002 have led many diesel engine manufacturers to incorporate cooled Exhaust Gas Recirculation, EGR, as a means of reducing NOx. This has resulted in higher levels of soot being present in used oils. This paper builds on earlier work with fresh oils and describes a study of the effect of highly sooted oils on the low temperature pumpability in diesel engines. Four experimental diesel engine oils, of varying MRV TP-1 viscosities, were run in a Mack T-8 engine to obtain a soot level ranging between 6.1 and 6.6%. These sooted oils were then run in a Cummins M11 engine installed in a low temperature cell. Times to lubricate critical engine components were measured at temperatures ranging between -10 °C and -25 °C. A clear correlation was established between the MRV TP-1 viscosity of a sooted oil and the time needed to lubricate critical engine components at a given test temperature.

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

Public awareness of center city bus emissions is high, especially on transit ways where at peak load times a bus passes every 15 seconds. To investigate the potential reduction in smoke and odor levels, a typical bus with a 6V71 engine was equipped with a Catalytic Trap Oxidizer. An in-service test procedure was developed and the reductions in measured smoke and subjective odor were significant. The bus was returned to revenue service in Philadelphia at an average speed of 7.5 mph. The effects of the CTO on smoke, smell, fuel consumption and maintenance during service are reported.

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors.

In April 2003, a small field study was initiated to evaluate the effect of lube oil formulations on ash accumulation in heavy-duty diesel DPFs. Nine (9) Fuel Delivery Trucks were retrofitted with passive diesel particulate filters and fueled with ultra low sulfur diesel which contains less than 15 ppm sulfur. Each vehicle operated in the field for 18 months or approximately 160,000 miles (241,401 km) using one of three lube oil formulations. Ash accumulation was determined for each vehicle and compared between the three differing lube oil formulations. Ash analyses, used lube oil analysis and filter substrate evaluations were performed to provide a complete picture of DPF operations. The evaluation also examined some of the key parameters that allows for the successful implementation of the passive DPF in this heavy-duty application.

Plug flow reactors, simulating engine exhaust gas, are widely used in emissions control research to gain insight into the reaction mechanisms and engineering aspects that controls activity, selectivity, and durability of catalyst components. The choice of relevant hydrocarbon (HC) species is one of the most challenging factor in such laboratory studies, given the variety of compositions that can be encountered in different application scenarios. Furthermore, this challenge is amplified by the experimental difficulties related to introducing heavier and multi-component HCs and analyzing the reaction products.

A technique for rapid in situ measurement of the fuel dilution of oil in a diesel engine is presented. Fuel dilution can occur when advanced in-cylinder fuel injection techniques are employed for the purpose of producing rich exhaust for lean NOx trap catalyst regeneration. Laser-induced fluorescence (LIF) spectroscopy is used to monitor the oil in a Mercedes 1.7-liter engine operated on a dynamometer platform. A fluorescent dye suitable for use in diesel fuel and oil systems is added to the engine fuel. The LIF spectra are monitored to detect the growth of the dye signal relative to the background oil fluorescence; fuel mass concentration is quantified based on a known sample set. The technique was implemented with fiber optic probes which can be inserted at various points in the engine oil system. A low cost 532-nm laser diode was used for excitation.

Evaluation of the platooning legislative space suggests a limited near-term opportunity for autonomous vehicles as currently only nine states have platooning and autonomous favorable legislations. An extensive closed-loop vehicle model simulation was conducted to quantify two-truck platooning fuel economy entitlement benefits across all United States (US) interstate routes (I-xx) spanning over 40,000 miles as compared to a single truck. A simultaneous study was carried out to identify the density of Class 8 heavy-duty trucks on these interstates, using the Freight Analysis Framework (FAF) 4 database. These two studies were combined to ascertain interstates that foresee the least fuel consumption due to platooning and thus identifying states with the most platooning benefits. Identification of states with most platooning benefits provides realistic data to push for autonomous driving and platooning legislations.