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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.

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.

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).

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.

Accurate in-cylinder air charge estimation is important for engine torque determination, controlling air-to-fuel ratio, and ensuring high after-treatment efficiency. Spark ignition (SI) engine technologies like variable valve timing (VVT) and exhaust gas recirculation (EGR) are applied to improve fuel economy and reduce pollutant emissions, but they increase the complexity of air charge estimation. Increased air-path complexity drives the need for cost effective solutions that produce high air mass prediction accuracy while minimizing sensor cost, computational effort, and calibration time. A large number of air charge estimation techniques have been developed using a range of sensors sets combined with empirical and/or physics-based models. This paper provides a technical review of research in this area, focused on SI engines.

Low-pressure cooled EGR (LP-cEGR) systems can provide significant improvements in spark-ignition engine efficiency and knock resistance. However, open-loop control of these systems is challenging due to low pressure differentials and the presence of pulsating flow at the EGR valve. This research describes a control structure for Low-pressure cooled EGR systems using closed loop feedback control along with internal model control. A Smith Predictor based PID controller is utilized in combination with an intake oxygen sensor for feedback control of EGR fraction. Gas transport delays are considered as dead-time delays and a Smith Predictor is one of the conventional methods to address stability concerns of such systems. However, this approach requires a plant model of the air-path from the EGR valve to the sensor.

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.

The research objective was to measure and understand the preferred seat position of older drivers and younger drivers within their personal vehicles to influence recommended practices and meet the increased safety needs of all drivers. Improper selection of driver’s seat position may impact safety during a crash event and affect one’s capacity to see the roadway and reach the vehicle’s controls, such as steering wheel, accelerator, brake, clutch, and gear selector lever. Because of the stature changes associated with ageing and the fact that stature is normally distributed for both males and females, it was hypothesized that the SAE J4004 linear regression would be improved with the inclusion of gender and age terms that would provide a more accurate model to predict the seat track position of older drivers. Participants included 97 older drivers over the age of 60 and 20 younger drivers between the ages of 30 to 39.

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.

This study was driven by the prevalence of older drivers’ overrepresentation in crashes caused by pedal application errors. Previous research has shown tasks prone to pedal errors, which include emergency braking, parking lot maneuvers and reaching out of the driver’s window. However, pedal usage characteristics of older drivers while performing on-road driving tasks are unknown. The objective of this research was to understand pedal usage characteristics of older drivers during on-road driving tasks in an instrumented vehicle. Twenty-six drivers over the age of 60 completed 10 stopping tasks as the baseline for stopping performance, a startle-braking task, two forward parking tasks and two reaching out of the vehicle tasks. Results for this instrumented vehicle study showed significantly positive correlations between stature and the percent of foot pivoting, and between shoe length and percent of foot pivoting in the baseline stopping tasks.

The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only.

Improper fit in a vehicle will affect a driver’s ability to reach the steering wheel and pedals, view the roadway and instrument gauges, and allow vehicle safety features to protect the driver during a crash. CarFit® is a community outreach program to educate older drivers on proper “fit” within their personal vehicle. A subset of measurements from CarFit® were used to quantify the “fit” of 97 older drivers over 60 and 20 younger drivers, ages 30-39, in their personal vehicles. Binary, logistic regression was used to assess the likelihood of drivers meeting the CarFit® measurement criteria prior to and after CarFit® education. The results showed older drivers were five times more likely than younger drivers to meet the CarFit® criteria for line of sight above the steering wheel, suggesting that younger drivers would also benefit from CarFit® education.

The ability to independently transfer into and out of a vehicle is essential for many wheelchair users to achieve driving independence. The purpose of the current study is to build upon the previous exploratory study that investigated the transfer strategies of wheelchair users by observing YouTube videos. This observational study videotaped five wheelchair users transferring from their wheelchairs into two research vehicles, a small and mid-size sedan that were equipped with a 50mm grid. The goal of this study was to use these videos and vehicle grids to precisely identify ingress and egress motions as well as “touch points” in a controlled setting with a small sample of five male wheelchair users. Using the videos from multiple different camera perspectives, the participants’ ingress and egress transfers were coded, documenting the touch points and step-by-step action sequences.

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 explain the interior concept that offers a flexible interior utility/activity space for Generation Z (Gen Z) users. 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 technical specifications, which formed the foundation of the Unique Selling Points (USPs) of the concept.

Since 1989, ISO has published procedures for developing and testing public information symbols (ISO 9186), while the SAE standard for in-vehicle icon comprehension testing (SAE J2830) was first published in 2008. Neither testing method was designed to evaluate the comprehension of symbols in modern vehicles that offer digital instrument cluster interfaces that afford new levels of flexibility to further improve drivers’ understanding of symbols. Using a driving simulator equipped with an eye tracker, this study investigated drivers’ understanding of six automotive symbols presented on in-vehicle displays. Participants included 24 teens, 24 adults, and 24 senior drivers. Symbols were presented in a symbol-only, symbol + short text descriptions, and symbol + long text description conditions. Participants’ symbol comprehension, driving performance, reaction times, and eye glance times were measured.