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Power split in Fuel Cell Hybrid Electric Vehicles (FCHEVs) has been controlled using different strategies ranging from rule-based to optimal control. Dynamic Programming (DP) and Model Predictive Control (MPC) are two common optimal control strategies used in optimization of the power split in FCHEVs with a trade-off between global optimality of the solution and online implementation of the controller. In this paper, both control strategies are developed and tested on a FC/battery vehicle model, and the results are compared in terms of total energy consumption. In addition, the effects of the MPC prediction horizon length on the controller performance are studied. Results show that by using the DP strategy, up to 12% less total energy consumption is achieved compared to MPC for a charge sustaining mode in the Urban Dynamometer Driving Schedule (UDDS) drive cycle.

This investigation was aimed at measuring the relative performance of two spray-guided, single-cylinder, spark-ignited direct-injected (SIDI) engine combustion system designs. The first utilizes an outwardly-opening poppet, piezo-actuated injector, and the second a conventional, solenoid operated, inwardly-opening multi-hole injector. The single-cylinder engine tests were limited to steady state, warmed-up conditions. The comparison showed that these two spray-guided combustion systems with two very different sprays had surprisingly close results and only differed in some details. Combustion stability and smoke emissions of the systems are comparable to each other over most of the load range. Over a simulated Federal Test Procedure (FTP) cycle, the multi-hole system had 15% lower hydrocarbon and 18% lower carbon monoxide emissions.

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Speed Harmonization, Eco Approach and Departure and in-situ vehicle parameter characterization.

Turbocharging, in addition to increasing an engine's power output, can be effectively used to maintain exhaust emission levels while improving fuel economy. This paper presents the emission and performance results obtained from a turbocharged multicylinder spark ignition engine with thermal reactors and exhaust gas recirculation (EGR) operated at steady-state, part-load conditions for four engine speeds. When comparing a turbocharged engine to a larger displacement naturally aspirated engine of equal power output, the emissions expressed in grams per mile were relatively unchanged both with and without EGR. However, turbocharging provided an average of 20% improvement in fuel economy both with and without EGR. When comparing the turbocharged and nonturbocharged versions of the same engine without EGR at a given load and speed, turbocharging increased the hydrocarbon (HC) and carbon monoxide (CO) emissions and decreased oxides of nitrogen (NOx) emissions.

The successful implementation of a clean, quiet, high-performance four-stroke motorcycle engine into an existing snowmobile chassis has been achieved. The snowmobile is easy to start, easy to drive, and environmentally friendly. The following paper describes the conversion process in detail with actual dynamometer and field test data. The vehicle meets the proposed 2010 EPA snowmobile emissions regulations and is quieter than a stock snowmobile. The snowmobile not only addresses environmental concerns, it is economical as well, with an approximate cost of $5874.

Future fuel economy targets represent a significant challenge to the automotive industry. While a range of technologies are in research and development to address this challenge, they all bring additional cost and complexity to future products. The most cost effective solutions are likely to be combinations of technologies that in isolation might have limited advantages but in a systems approach can offer complementary benefits. This presentation describes work carried out at Ricardo to explore Intelligent Electrification and the use of Stratified Charge Lean Combustion in a spark ignition engine. This includes a next generation Spray Guided Direct Injection SI engine combustion system operating robustly with highly stratified dilute mixtures and capable of close to 40% thermal efficiency with very low engine-out NOx emissions.

An accurate estimation of cycle-by-cycle in-cylinder mass and the composition of the cylinder charge is required for spark-ignition engine transient control strategies to obtain required torque, Air-Fuel-Ratio (AFR) and meet engine pollution regulations. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) sensors have been utilized in different control strategies to achieve these targets; however, these sensors have response delay in transients. As an alternative to air flow metering, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold.

As part of the U.S. Environmental Protection Agency (U.S. EPA) “Midterm Evaluation of Light-duty Vehicle Standards for Model Years 2022-2025 [1]”, the U.S. EPA is evaluating engines and assessing the effectiveness of future engine technologies for reducing CO2 emissions. Such assessments often require significant development time and resources in order to optimize intake and exhaust cam variable valve timing (VVT), exhaust gas recirculation (EGR) flow rates, and compression ratio (CR) changes. Mazda SkyActiv-G spark-ignition (SI) engines were selected by EPA for an internal engine development program based upon their high geometric compression ratio (14:1 in Europe and Japan, 13:1 in North America) and their use of a flexible valve train configuration with electro-mechanical phasing control on the intake camshaft. A one-dimensional GT-Power engine model was calibrated and validated using detailed engine dynamometer test data [2] from 2.0L and 2.5L versions of the SkyActiv-G engine.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

Computational fluid dynamics of gas-fueled large-bore spark ignition engines with pre-chamber ignition can speed up the design process of these engines provided that 1) the reliability of the results is not affected by poor meshing and 2) the time cost of the meshing process does not negatively compensate for the advantages of running a computer simulation. In this work a flame propagation model that runs with arbitrary hybrid meshes was developed and coupled with the KIVA4-MHI CFD solver, in order to address these aims. The solver follows the G-Equation level-set method for turbulent flame propagation by Tan and Reitz, and employs improved numerics to handle meshes featuring different cell types such as hexahedra, tetrahedra, square pyramids and triangular prisms. Detailed reaction kinetics from the SpeedCHEM solver are used to compute the non-equilibrium composition evolution downstream and upstream of the flame surface, where chemical equilibrium is instead assumed.

Charge motion is known to accelerate and stabilize combustion through its influence on turbulence intensity and flame propagation. The present work investigates the effect of charge motion generated by intake runner blockages on combustion characteristics and emissions under part-load conditions in SI engines. Firing experiments have been conducted on a DaimlerChrysler (DC) 2.4L 4-valve I4 engine, with spark range extending around the Maximum Brake Torque (MBT) timing. Three blockages with 20% open area are compared to the fully open baseline case under two operating conditions: 2.41 bar brake mean effective pressure (bmep) at 1600 rpm, and 0.78 bar bmep at 1200 rpm. The blocked areas are shaped to create different levels of swirl, tumble, and cross-tumble. Crank-angle resolved pressures have been acquired, including cylinders 1 and 4, intake runners 1 and 4 upstream and downstream of the blockage, and exhaust runners 1 and 4.

Homogenous charge compression ignition (HCCI) engines offer a great potential in achieving high thermal efficiency and extremely low NOx at the same time. However, control of combustion phasing over a wide speed and load range has been a challenge, especially during transient operations. This paper describes work conducted at the National Vehicle and Fuel Emissions Laboratory, which explores the potential use of an HCCI engine as a power plant for a hybrid vehicle. A four-cylinder, 1.9 L commercial diesel engine was modified to operate with port-injected regular grade gasoline in HCCI mode. The combustion phasing is controlled by a combination of boost, EGR and thermal management as a function of engine speed and load. As a stand-alone unit, the engine has demonstrated a wide operation range with efficiency like that of a diesel engine and NOx below 0.2 g/kWh. At room temperature, the engine starts in SI mode and then transitions to HCCI in about 25 seconds.

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. The engine’s limited operating range can be improved by taking advantage of electrification in the powertrain. In this study, a multi-mode LTC-SI engine is integrated with a parallel hybrid electric configuration, where the engine operation modes include Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations.

Combustion knock detection and control in internal combustion engines continues to be an important feature in engine management systems. In spark-ignition engine applications, the frequency of occurrence of combustion knock and its intensity are controlled through a closed-looped feedback system to maintain knock at levels that do not cause engine damage or objectionable audible noise. Many methods for determination of the feedback signal for combustion knock in spark-ignition internal combustion engines have been employed with the most common technique being measurement of engine vibration using an accelerometer. With this technique single or multiple piezoelectric accelerometers are mounted on the engine and vibrations resulting from combustion knock and other sources are converted to electrical signals. These signals are input to the engine control unit and are processed to determine the signal strength during a period of crank-angle when combustion knock is expected.

In light-duty vehicles, there is a fundamental trade-off between fuel consumption and acceleration performance, if other vehicle attributes are held fixed. Earlier econometric studies have estimated the magnitude of this trade-off - the elasticity of fuel consumption with respect to performance - based on historical vehicle data. The majority of these studies assume, a priori, that elasticity is constant across the model year, vehicle power, and technology content. However, there is evidence that the content in the underlying powertrain technology packages is shifting in a way that reduces the value of the elasticity of fuel consumption with respect to performance, such that historical trends would not predict future behavior. This paper presents an alternative strategy for studying vehicle fuel consumption versus performance trade-off.

Increasing regulatory demand to reduce CO2 emissions has led to an industry focus on electrified vehicles while limiting the development of conventional internal combustion engine (ICE) and hybrid powertrains. Hybrid electric vehicle (HEV) powertrains rely on conventional SI mode IC engines that are optimized for a narrow operating range. Advanced combustion strategies such as Gasoline Compression Ignition (GCI) have been demonstrated by several others including the authors to improve brake thermal efficiency compared to both gasoline SI and Diesel CI modes. Soot and NOx emissions are also reduced significantly by using gasoline instead of diesel in GCI engines due to differences in composition, fuel properties, and reactivity. In this work, an HEV system was proposed utilizing a multi-mode GCI based ICE combined with a HEV components (e-motor, battery, and invertor).

Hydrogen is widely considered a promising fuel for future transportation applications for both, internal combustion engines and fuel cells. Due to their advanced stage of development and immediate availability hydrogen combustion engines could act as a bridging technology towards a wide-spread hydrogen infrastructure. Although fuel cell vehicles are expected to surpass hydrogen combustion engine vehicles in terms of efficiency, the difference in efficiency might not be as significant as widely anticipated [1]. Hydrogen combustion engines have been shown capable of achieving efficiencies of up to 45 % [2]. One of the remaining challenges is the reduction of nitric oxide emissions while achieving peak engine efficiencies. This paper summarizes research work performed on a single-cylinder hydrogen direct injection engine at Argonne National Laboratory.

This paper describes a multi-sensor data set, suitable for testing algorithms to detect and track pedestrians and cyclists, with an autonomous vehicle’s sensor suite. The data set can be used to evaluate the benefit of fused sensing algorithms, and provides ground truth trajectories of pedestrians, cyclists, and other vehicles for objective evaluation of track accuracy. One of the principal bottlenecks for sensing and perception algorithm development is the ability to evaluate tracking algorithms against ground truth data. By ground truth we mean independent knowledge of the position, size, speed, heading, and class of objects of interest in complex operational environments. Our goal was to execute a data collection campaign at an urban test track in which trajectories of moving objects of interest are measured with auxiliary instrumentation, in conjunction with several autonomous vehicles (AV) with a full sensor suite of radar, lidar, and cameras.

Heavy fuel engines have typically been limited to large, heavy compression ignition engines. However, with the push by the US military to use a common fuel (JP5/JP8/diesel) there is a need to develop small, lightweight, high performance engines that are also capable of operating on heavy fuel. Recent advancements in air assisted direct injection technologies have improved fuel atomization to the level necessary to overcome the poor physical properties of heavy fuel. This has permitted the operation of small two-stroke engines which retain the advantage of a lightweight design with high power output. This paper discusses the process of benchmarking a two-stroke heavy fuel spark ignited engine with an integrated air-assist direct injection system. The setup and commissioning phases of the testing are outlined, including specific techniques for quantifying scavenging, burn rate, and heat release characteristics with the objective of validating a 1-D performance simulation model.

The growing competition of the automotive market makes more and more necessary the reduction of development time and consequently, the increase of the capacity to quickly respond to the launching of the competitors. One of the most costly phases on the vehicle development process is the field durability test, both in function of the number of prototypes employed and the time needed to its execution. More and more diffused, the fatigue life prediction methods have played an important part in the durability analysis via CAE. Nevertheless, in order they can be reliable and really being able to reduce the development time and cost, they need to be provided with load cases that can accurately represent the field durability tests. This work presents a CAE approach used for light trucks in order to get a reasonable understanding of component durability behavior due to payload increase. In general, road load data is not available for a new payload condition.