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Predicting fuel economy during early stages of concept development or feasibility study for a new type of powertrain configuration is an important key factor that might affect the powertrain configuration decision to meet CAFE standards. In this paper an efficient model has been built in order to evaluate the fuel economy for a new type of charge sustaining series hybrid vehicle that uses a Genset assembly (small 2 cylinders CNG fueled engine coupled with a generator). A first order mathematical model for a Li-Ion polymer battery is presented based on actual charging /discharging datasheet. Since the Genset performance data is not available, normalized engine variables method is used to create powertrain performance maps. An Equivalent Consumption Minimization Strategy (ECMS) has been implemented to determine how much power is supplied to the electric motor from the battery and the Genset.

This manuscript provides a review of different types and categorization of the chassis dynamometer systems. The review classifies the chassis dynamometers based on the configuration, type of rollers and the application type. Additionally the manuscript discusses several application examples of the chassis dynamometer including: performance and endurance mileage accumulation tests, fuel efficiency and exhaust emissions, noise, vibration and harshness testing (NVH). Different types of the vehicle attachment system in the dynamometer cell and its influences on the driving force characteristics and the vehicle acoustic signature is also discussed. The text also highlights the impact of the use of the chassis dynamometer as a development platform and its impact on the development process. Examples of using chassis dynamometer as a development platform using Vehicle Hardware In-the-Loop (VHiL) approach including drivability assessment and transmission calibrations are presented.

The challenge of meeting the Corporate Average Fuel Economy (CAFE) standards of 2025 has led to major developments in the transportation sector, among which is the attempt to utilize clean energy sources. To date, use of solar energy as an auxiliary source of on-board fuel has not been extensively investigated. This paper is the first study at undertaking a comprehensive analysis of using solar energy on-board by means of photovoltaic (PV) technologies to enhance automotive fuel economies, extend driving ranges, reduce greenhouse gas (GHG) emissions, and ensure better economic value of internal combustion engine (ICE) -based vehicles to meet CAFE standards though 2025. This paper details and compares various aspects of hybrid solar electric vehicles with conventional ICE vehicles.

This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc. This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices. After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results.

The US Code of Federal Regulations (CFR) Title 40 Part 1065 and 1066 require gravimetric determination of automobile Particulate Matter (PM) collected onto filter media from the diluted exhaust. PM is traditionally collected under simulated driving conditions in a laboratory from a full flow Constant Volume Sampler (CVS) system, where the total engine exhaust is diluted by HEPA filtered air. This conventional sampling and measurement practice is facing challenges in accurately quantifying PM at the upcoming 2025-2028 CARB LEVIII 1 mg/mi PM emissions standards. On the other hand, sampling a large amount of PM emitted from large size high power engines introduces additional challenges. Applying flow weighting, adjusting the Dilution Ratio (DR) and Filter Face Velocity (FFV) are proposed options to overcome these challenges.

Spark-assisted compression ignition (SACI) leverages flame propagation to trigger autoignition in a controlled manner. The autoignition event is highly sensitive to several parameters, and thus, achieving SACI in production demands a high tolerance to variations in conditions. Limited research is available to quantify the combustion response of SACI to these variations. A simulation study is performed to establish trends, limits, and control implications for SACI combustion over a wide range of conditions. The operating space was evaluated with a detailed chemical kinetics model. Key findings were synthesized from these results and applied to a 1-D engine model. This model identified performance characteristics and potential actuator positions for a production-viable SACI engine. This study shows charge preparation is critical and can extend the low-load limit by strengthening flame propagation and the high-load limit by reducing ringing intensity.

It has been extensively evidenced that modern diesel engines generate a considerable amount of soot nanoparticles. Existing soot sensors are not suitable for such nanoparticles. Current standard gravimetric techniques are extremely insensitive to fine soot particles. Soot diagnostics developed for research purposes, e.g., laser induced-incandescence, do not provide quantitative characterization, and expanded practical applications of these techniques are hardly conceivable. This paper addresses this emerging need for monitoring nano-sized soot emissions. Here, we investigated the use of polarization modulated scattering (PMS) for soot sensing in engine environments. The technique involves 1) measuring laser scattering by soot particles at multiple angles while varying the polarization states of the incident laser beam, 2) determining multiple elements of the Mueller matrix from the measured signals, and 3) inferring properties of the soot particles from these elements.

Low pressure (LP) and cooled EGR systems are capable of increasing fuel efficiency of turbocharged gasoline engines, however they introduce control challenges. Accurate exhaust pressure modeling is of particular importance for real-time feedforward control of these EGR systems since they operate under low pressure differentials. To provide a solution that does not depend on physical sensors in the exhaust and also does not require extensive calibration, a coupled temperature and pressure physics-based model is proposed. The exhaust pipe is split into two different lumped sections based on flow conditions in order to calculate turbine-outlet pressure, which is the driving force for LP-EGR. The temperature model uses the turbine-outlet temperature as an input, which is known through existing engine control models, to determine heat transfer losses through the exhaust.

Knock-limited engine operation is one of the most important constraints on fuel efficiency and performance that must be considered during the design, control algorithm development and calibration of spark-ignition engines. This research evaluates the accuracy of model-based knock prediction routines and their applicability for control-oriented applications over various engine operating conditions using commercial fuels. Two common methods of knock prediction, a generalized chemical kinetics model and an empirical induction-time correlation, are evaluated and compared against experimental data. The experimental investigation is conducted using a naturally aspirated 3.6L V6 engine, retrofitted with cooled Exhaust Gas Recirculation (EGR). Data are acquired from spark timing sweeps under knocking conditions at different engine speeds and loads in an engine dynamometer cell.

The automotive industry is aggressively pursuing fuel efficiency improvements through hybridization of production vehicles, and there are an increasing number of racing series adopting similar architectures to maintain relevance with current passenger car trends. Hybrid powertrains offer both performance and fuel economy benefits in a motorsport setting, but they greatly increase control complexity and add additional degrees of freedom to the design optimization process. The increased complexity creates opportunity for performance gains, but simulation based tools are necessary since hybrid powertrain design and control strategies are closely coupled and their optimal interactions are not straightforward to predict. One optimization-related advantage that motorsports applications have over production vehicles is that the power demand of circuit racing has strong repeatability due to the nature of the track and the professional skill-level of the driver.

A safe driver program has been established through a public-private partnership. This program targets novice drivers and uses a combination of classroom and in-vehicle training exercises to address critical driver errors known to lead to crashes. Students participate in four modules: braking to learn proper stopping technique, obstacle avoidance / reaction time to facilitate proper lane selection and collision avoidance, tailgating to learn about following distances, and loss of control to react appropriately when a vehicle is about to become laterally unstable. Knowledge pre and posttests are also administered at the start and end of the program. Students' in-vehicle driving performance are evaluated by instructors as well as recorded by onboard data acquisition units. The data has been evaluated with objective and subjective grading rubrics. The 70 participants in three classes used as a case study achieved an average skill score of 83.93/100.

Electrified transportation has received significant interest recently because of sustainable and clean energy goals. However, the degradation of electrical components such as energy storage systems raises system reliability and economic concerns. In this paper, a prognostic-based control strategy is proposed for hybrid electric vehicles (HEVs) to abate the degradation of energy systems. Degradation forecasting models of electrical components are developed to predict their degradation paths. The predicted results are then used to control HEVs in order to reduce the degradation of components.

As powertrain hybridization technologies are becoming popular, their application for heavy-duty military vehicles is drawing attention. An intelligent design and operation of the energy management system (EMS) is important to ensure that hybrid military vehicles can operate efficiently, simultaneously maximize fuel economy and minimize monetary cost, while successfully completing mission tasks. Furthermore, an integrated EMS framework is vital to ensure a functional vehicle power system (VPS) to survive through critical missions in a highly stochastic environment, when needed. This calls for situational awareness and dynamic system reconfiguration capabilities on-board of the military vehicle. This paper presents a new energy management and control (EMC) framework based on holistic situational awareness (SA) and dynamic reconfiguration of the VPS.

Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.

With recent advances in electric vehicles, there is a plethora of powertrain topologies and components available in the market. Thus, the performance of electric vehicles is highly sensitive to the choice of various powertrain components. This paper presents a multi-objective optimization model that can optimally select component sizes for batteries, supercapacitors, and motors in regular passenger battery-electric vehicles (BEVs). The BEV topology presented here is a hybrid BEV which consists of both a battery pack and a supercapacitor bank. Focus is placed on optimal selection of the battery pack, motor, and supercapacitor combination, from a set of commercially available options, that minimizes the capital cost of the selected power components, the fuel cost over the vehicle lifespan, and the 0-60 mph acceleration time. Available batteries, supercapacitors, and motors are from a market survey.

Diesel engine exhaust poses an ongoing threat to human health as well as to the environment. Automotive exhaust treatment systems have been developed over the years to reduce the large amount of diesel particulate matter (DPM) released to the atmosphere. Current systems can be categorized as selective catalytic reduction, catalytic converters, and diesel particulate filters. This study presents an emission system that focuses on the removal of exhaust particles using Brownian diffusion of DPM toward fog drops followed by cyclonic separation of DPM rich fog drops. The experimental system consisted of a 13.2 kW diesel engine, heat exchanger to cool the exhaust to saturation temperature, ultrasonic fogger, cyclone separator, and recovery of waste particulate. Representative emission tests have been performed at five different diesel engine speeds and corresponding crankshaft loads.

Knock control systems are fundamentally stochastic, regulating some aspect of the distribution from which observed knock intensities are drawn. Typically a simple threshold is applied, and the controller regulates the resultant knock event rate. Recent work suggests that the choice of threshold can have a significant impact on closed loop performance, but to date such studies have been performed only in simulation. Rigorous assessment of closed loop performance is also a challenging topic in its own right because response trajectories depend on the random arrival of knock events. The results therefore vary from one experiment to the next, even under identical operating conditions. To address this issue, stochastic simulation methods have been developed which aim to predict the expected statistics of the closed loop response, but again these have not been validated experimentally.

Capacitance sensing is the technology that detects the presence of nearby objects by measuring the change in capacitance. A change in capacitance is triggered either by a change in dielectric constant, area of overlap or distance of separation between the electrodes of the capacitor. It is a technology that finds wide use in applications such as touch screens, proximity sensing etc. Drawing motivation from such applications, this paper investigates how capacitive sensing can be employed to detect the presence and posture of occupants inside vehicles. Compared to existing solutions, the proposed approach is low-cost, easy to deploy and highly efficient. The sensing system consists of a capacitance-sensing mat that is embedded with copper foils and an associated sensing circuitry. Inside the mat the foils are arranged in rows and columns to form several touch-nodes across the surface of the mat.

With the increasing attention towards electric vehicles (EV), power electronics technology has become more prominent on vehicular systems. EV requires compact energy conversion and control technology to improve system efficiency and optimize the sizing of power components. Therefore, it is important to reduce thermal losses, while supplying an adequate amount of power to different EV devices. Silicon carbide (SiC)-based power semiconductors provide performance improvements such as lower power losses, higher junction temperature and higher switching frequency compared to the conventional silicon (Si)-based switching devices. High-frequency switching is preferred for power converters to minimize the necessity of passive filters; however, high-frequency switching causes additional thermal stress on semiconductor switches due to the increase in switching losses. The degradation of switching devices in power converters are primarily related to the junction temperature.

Spark ignition engines have low tailpipe criteria pollutants due to their stoichiometric operation and three-way catalysis and are highly controllable. However, one of their main drawbacks is that the compression ratio is low due to knock, which incurs an efficiency penalty. With a global push towards low-lifecycle-carbon renewable fuels, high-octane alternatives to gasoline such as ethanol are attractive options as fuels for spark ignition engines. Under premixed spark ignition operating conditions, ethanol can enable higher compression ratios than regular-grade gasoline due to its high octane number. The high cooling potential of high-ethanol content gasolines, like E85, or of ethanol-water blends, like hydrous ethanol, can be leveraged to further reduce knock and enable higher compression ratios as well as further downsizing and boosting to reduce frictional and throttling losses.