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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.

The highly transient operational nature of passenger car engines makes cylinder pressure based feedback control of combustion phasing difficult. The problem is further complicated by cycle-to-cycle combustion variation. A method for fast and accurate differentiation of normal combustion variations and true changes in combustion phasing is addressed in this research. The proposed method combines the results of a feed forward combustion phasing prediction model and “noisy” measurements from cylinder pressure using an iterative estimation technique. A modified version of an Extended Kalman Filter (EKF) is applied to calculate optimal estimation gain according to the stochastic properties of the combustion phasing measurement at the corresponding engine operating condition. Methods to improve steady state CA50 estimation performance and adaptation to errors are further discussed in this research.

Abundant supply of Natural Gas (NG) is U.S. and cost-advantage compared to diesel provides impetus for engineers to use alternative gaseous fuels in existing engines. Dual-fuel natural gas engines preserve diesel thermal efficiencies and reduce fuel cost without imposing consumer range anxiety. Increased complexity poses several challenges, including the transient response of an engine with direct injection of diesel fuel and injection of Compressed Natural Gas (CNG) upstream of the intake manifold. A 1-D simulation of a Cummins ISX heavy duty, dual-fuel, natural gas-diesel engine is developed in the GT-Power environment to study and improve transient response. The simulated Variable Geometry Turbine (VGT)behavior, intake and exhaust geometry, valve timings and injector models are validated through experimental results. A triple Wiebe combustion model is applied to characterize experimental combustion results for both diesel and dual-fuel operation.

Substantial fuel economy improvements for light-duty automotive engines demand novel combustion strategies. Low temperature combustion (LTC) demonstrates potential for significant fuel efficiency improvement; however, control complexity is an impediment for real-world transient operation. Spark-assisted compression ignition (SACI) is an LTC strategy that applies a deflagration flame to generate sufficient energy to trigger autoignition in the remaining charge. Operating a practical engine with SACI combustion is a key modeling and control challenge. Current models are not sufficient for control-oriented work such as calibration optimization, transient control strategy development, and real-time control. This work describes the process and results of developing a fast-running control-oriented model for the autoignition phase of SACI combustion. A data-driven model is selected, specifically artificial neural networks (ANNs).

In the U.S., the teenage driving population is at the highest risk of being involved in a crash. Teens often demonstrate poor vehicle control skills and poor ability to identify hazards, thus proper understanding of automotive indicators and warnings may be even more critical for this population. This research evaluates teen drivers’, between 15 to 17 years of age, understanding of symbols from vehicles featuring advanced driving assistant systems and multiple powertrain configurations. Teen drivers’ (N=72) understanding of automotive symbols was compared to three other groups with specialized driving experience and technical knowledge: automotive engineering graduate students (N=48), driver rehabilitation specialists (N=16), and performance driving instructors (N=15). Participants matched 42 symbols to their descriptions and then selected the five symbols they considered most important.

As engines are equipped with an increased number of control actuators to meet fuel economy targets, they become more difficult to control and calibrate. The additional complexity created by a larger number of control actuators motivates the use of physics-based control strategies to reduce calibration time and complexity. Combustion phasing, as one of the most important engine combustion metrics, has a significant influence on engine efficiency, emissions, vibration and durability. To realize physics-based engine combustion phasing control, an accurate prediction model is required. This research introduces physics-based control-oriented laminar flame speed and turbulence intensity models that can be used in a quasi-dimensional turbulent entrainment combustion model. The influence of laminar flame speed and turbulence intensity on predicted mass fraction burned (MFB) profile during combustion is analyzed.

The use of Low Pressure - Exhaust Gas Recirculation (EGR) is intended to allow displacement reduction in turbocharged gasoline engines and improve fuel economy. Low Pressure EGR designs have an advantage over High Pressure configurations since they interfere less with turbocharger efficiency and improve the uniformity of air-EGR mixing in the engine. In this research, Low Pressure (LP) cooled EGR is evaluated on a turbocharged direct injection gasoline engine with variable valve timing using both simulation and experimental results. First, a model-based calibration study is conducted using simulation tools to identify fuel efficiency gains of LP EGR over the base calibration. The main sources of the efficiency improvement are then quantified individually, focusing on part-load de-throttling of the engine, heat loss reduction, knock mitigation as well as decreased high-load fuel enrichment through exhaust temperature reduction.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

Combustion phasing of Spark Ignition (SI) engines is traditionally regulated with map-based spark timing (SPKT) control. The calibration time and effort of this feed forward SPKT control strategy becomes less favorable as the number of engine control actuators increases. This paper proposes a model based combustion phasing control frame work. The feed forward control law is obtained by real time numerical optimization utilizing a high-fidelity combustion model that is based on flame entrainment theory. An optimization routine identifies the SPKT which phases the combustion close to the target without violating combustion constraints of knock and excessive cycle-by-cycle covariance of indicated mean effective pressure (COV of IMEP). Cylinder pressure sensors are utilized to enable feedback control of combustion phasing. An Extended Kalman Filter (EKF) is applied to reject sensor noise and combustion variation from the cylinder pressure signal.

As engines are equipped with an increased number of control actuators to meet fuel economy targets they become more difficult to control and calibrate. The large number of control actuators encourages the investigation of physics-based control strategies to reduce calibration time and complexity. Of particular interest is spark timing control and calibration since it has a significant influence on engine efficiency, emissions, vibration and durability. Spark timing determination to achieve a desired combustion phasing is currently an empirical process that occurs during the calibration phase of engine development. This process utilizes a large number of stored surfaces and corrections to account for the wide range of operating environments and conditions that a given engine will experience. An obstacle to realizing feedforward physics-based combustion phasing control is the requirement for an accurate and fast combustion model.

The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the 6th generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to describe the development of a multimaterial lightweight Body-in-White (BiW) structure to support an all-electric powertrain combined with an interior package that maximizes volume to enable a variety of interior configurations and activities for Generation Z users. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics.

The Deep Orange [1] initiative is an integral part of the automotive graduate program at Clemson University International Center for Automotive Research. The initiative was developed to provide the graduate students with hands-on experience of the knowledge attained in the various engineering disciplines and related disciplines (such as marketing and human factors psychology). For the 3rd edition of Deep Orange, the goal was to develop a blank sheet hybrid mainstream sports car concept targeted towards the Generation Y (Gen Y) market segment. The objective of this paper is to explain the unique interior-seating concept that was derived from extensive analyses of the Generation Y market segment based on surveys completed by owners of new cars and light trucks in the United States. The survey data clearly indicated that a significant portion of Gen Y would prefer a vehicle with 5 or more seating positions.

With the dramatic increase in vehicle technology, the availability of a wide range of powertrains, and the development of advanced driver assistant systems (ADAS), instrument cluster interfaces have become more complex, increasing the demand on drivers. Understanding the needs and preferences of a diverse group of drivers is essential for the development of digital instrument cluster interfaces that improve driver’s understanding of critical information about the vehicle. This study investigated drivers’ understanding and preferences related to powertrain and ADAS symbols presented on instrument clusters. Participants answered questions that evaluated nine symbol’s comprehension, familiarity, and helpfulness. Then, participants were presented with information from the owner’s manual for each symbol and responded if the information changed their understanding of the symbol.

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.

The Deep Orange [1] initiative is an integral part of the automotive graduate program at Clemson University International Center for Automotive Research. The initiative was developed to provide the graduate students with hands-on experience of the knowledge attained in the various engineering disciplines and related disciplines (such as marketing and human factors psychology). For the 3rd edition of Deep Orange, the goal was to develop a blank sheet hybrid mainstream sports car concept targeted towards the Generation Y (Gen Y) market segment. The objective of this paper is to explain the unique body-in-white (BiW) concept that offers space for 6-passengers and includes a dual-mode hybrid all-wheel drive powertrain. An additional objective of the project was to develop and showcase a body-in-white concept that will eliminate metal stamping and high capital investments associated with this technology (such as dies and stamping tools).

Exhaust gas turbo-charging helps exploit the improved fuel efficiency of downsized engines by increasing the possible power density from these engines. However, turbo-charged engines exhibit poor transient performance, especially when accelerating from low speeds. In addition, during low-load operating regimes, when the exhaust gas is diverted past the turbine with a waste-gate or pushed through restricted vanes in a variable geometry turbine, there are lost opportunities for recovering energy from the enthalpy of the exhaust gas. Similar limitations can also be identified with mechanical supercharging systems. This paper proposes an electrical supercharging and turbo-generation system that overcomes some of these limitations. The system decouples the activation of the air compression and exhaust-energy recovery functions using a dedicated electrical energy storage buffer. Its main attributes fast speed of response to load changes and flexibility of control.

This paper will address the basic requirements for realizing a stop and go cruise control system. Issues discussed comprise: functional, sensor and basic HMI requirements, primary characterization of naturalistic stop and go driving, and the basic approach of the transformation of situational knowledge in an elementary controller.

The ability to independently transfer into and out of a vehicle is essential for many wheelchair users to achieve driving independence. This paper presents the results of an exploratory study that investigated the transfer strategies of wheelchair users who drive from their driver’s seat and not from their wheelchair. The goal of this study was to identify typical ingress and egress motions as well as “touch points” of wheelchair users transferring into and out of the driver’s seat. While motion databases exist for the ingress and egress of able-bodied drivers, this study provides insight on drivers with physical disabilities. Twenty-five YouTube videos of wheelchair users who transferred into and out of their own sedans were analyzed.

The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the sixth generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to describe the development and implementation of a dual-purpose powertrain system enabling vehicle propulsion as well as stationary activities of the Deep Orange 6 vehicle concept. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics. The resulting market research, benchmarking, and brand essence studies were then converted to consumer needs and wants, to establish vehicle target and subsystem requirement, which formed the foundation of the Unique Selling Points (USPs) of the concept.

Fuel economy regulations have forced the automotive industry to implement transmissions with an increased number of gears and reduced parasitic losses. The objective of this research is to develop a high fidelity and a computationally efficient model of an automatic transmission, this model should be suitable for controller development purposes. The transmission under investigation features a combination of positive clutches (interlocking dog clutches) and conventional wet clutches. Simulation models for the torque converter, lock-up clutch, transmission gear train, interlocking dog clutches, wet clutches, hydraulic control valves and circuits were developed and integrated with a 1-D vehicle road load model. The integrated powertrain system model was calibrated using measurements from real-world driving conditions. Unknown model parameters, such as clutch pack clearances, compliances, hydraulic orifice diameters and clutch preloads were estimated and calibrated.