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The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

Connectivity and autonomy in vehicles promise improved efficiency, safety and comfort. The increasing use of embedded systems and the cyber element bring with them many challenges regarding cyberattacks which can seriously compromise driver and passenger safety. Beyond penetration testing, assessment of the security vulnerabilities of a component must be done through the design phase of its life cycle. This paper describes the development of a benchtop testbed which allows for the assurance of safety and security of components with all capabilities from Model-in-loop to Software-in-loop to Hardware-in-loop testing. Environment simulation is obtained using the AV simulator, CARLA which provides realistic scenarios and sensor information such as Radar, Lidar etc. MATLAB runs the vehicle, powertrain and control models of the vehicle allowing for the implementation and testing of customized models and algorithms.

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections.

Typically, when one thinks of advanced driver assistance systems (ADAS), systems such as Forward Collision Warning (FCW) and Collision Imminent Braking (CIB) come to mind. In these systems driver assistance is provided based on knowledge about the subject vehicle and surrounding objects. A new class of these systems is being implemented. These systems not only use information on the surrounding objects but also use information on the driver's response to an event, to determine if intervention is necessary. As a result of this trend, an advanced level of understanding of driver braking behavior is necessary. This paper presents an alternate method of analyzing driver braking behavior. This method uses a frequency content based approach to study driver braking and allows for the extraction of significantly more data from driver profiles than traditionally would have been done.

The transient vibration phenomenon in a vehicle powertrain system during the start-up (or shut-down) process is studied with focus on the development and experimental validation of the nonlinear powertrain models. First, a new nonlinear four-degree-of-freedom torsional powertrain model for this transient event, under instantaneous flywheel motion input, is developed and then validated with a vehicle start-up experiment. Second, the interactions between the clutch damper and the transmission transients are established via transient metrics. Third, a single-degree-of-freedom nonlinear model, focusing on the multi-staged clutch damper, is developed and its utility is then verified.

The effect of aerodynamically induced pre-swirl on the acoustic and performance characteristics of an automotive centrifugal compressor is studied experimentally on a steady-flow turbocharger facility. Accompanying flow separation, broadband noise is generated as the flow rate of the compressor is reduced and the incidence angle of the flow relative to the leading edge of the inducer blades increases. By incorporating an air jet upstream of the inducer, a tangential (swirl) component of velocity is added to the incoming flow, which improves the incidence angle particularly at low to mid-flow rates. Experimental data for a configuration with a swirl jet is then compared to a baseline with no swirl. The induced jet is shown to improve the surge line over the baseline configuration at all rotational speeds examined, while restricting the maximum flow rate. At high flow rates, the swirl jet increases the compressor inlet noise levels over a wide frequency range.

Ported shroud compressor covers recirculate low momentum air near the inducer blade tips, and the use of these devices has traditionally been confined to extending the low-flow operating region at elevated rotational speeds for compressors on compression-ignition (CI) engines. Implementation of ported shrouds on compressors for spark-ignition (SI) engines has been generally avoided due to operation at pressure ratios below the region where ported shrouds improve low-flow range, the slight efficiency penalty, and the perception of increased noise. The present study provides an experimental investigation of performance and acoustics for a SI engine turbocharger compressor both with a ported shroud and without (baseline). The objective of implementing the ported shroud was to reduce mid-flow range broadband whoosh noise of the baseline compressor over 4-12 kHz.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

With the current drive of automotive and technology companies towards producing vehicles with higher levels of autonomy, it is inevitable that there will be an increasing number of SAE level L4-L5 autonomous vehicles (AVs) on roadways in the near future. Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways. The roadways used in these simulations were replicas of real roadways in and around Columbus, Ohio, including an AV shuttle routes in operation.

Dynamic programming (DP) has been used for optimal control of hybrid powertrain and vehicle speed optimization particularly in design phase for over a couple of decades. With the advent of autonomous and connected vehicle technologies, automotive industry is getting closer to implementing predictive optimal control strategies in real time applications. The biggest challenge in implementation of optimal controls is the limitation on hardware which includes processor speed, IO speed, and random access memory. Due to the use of autonomous features, modern vehicles are equipped with better onboard computational resources. In this paper we present a comparison between multiple hardware options for dynamic programming. The optimal control problem considered, is the optimization of travel time and fuel economy by tuning the torque split ratio and vehicle speed while maintaining charge sustaining operation.

In order to increase the efficiency of automotive turbochargers at low speed without compromising the performance at maximum boost conditions, variable geometry compressor (VGC) systems, based on either variable inlet guide vanes or variable geometry diffusers, have been recently considered as a future design option for automotive turbochargers. This work presents a modeling, analysis and optimization study for a Diesel engine equipped with a variable geometry compressor that help understand the potentials of such technology and develop control algorithms for the VGC systems,. A cycle-averaged engine system model, validated on experimental data, is used to predict the most important variables characterizing the intake and exhaust systems (i.e., mass flow rates, pressures, temperatures) and engine performance (i.e., torque, BMEP, volumetric efficiency), in steady-state and transient conditions.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

The EcoCAR 2: Plugging into the Future team at the Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 50 miles of all-electric range. The vehicle features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 75 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report details the rigorous design process followed by the Ohio State team during Year 1 of the competition. The design process includes identifying the team customer's needs and wants, selecting an overall vehicle architecture and completing detailed design work on the mechanical, electrical and control systems. This effort was made possible through support from the U.S.

An appropriate fault diagnosis and Isolation (FDI) strategy is very useful to prevent system failure. In this paper, a model-based fault diagnosis strategy is developed for an internal combustion engine (ICE) under speed control. Engine throttle fault and the manifold pressure sensor fault are detected and isolated. A nonlinear observer based residual generation approach is proposed. Manifold pressure and throttle are observed. Fault codes are designed with redundancy to prevent bit error. Performance of fault diagnosis strategy has been evaluated with simulations.

The design of a performance hybrid electric vehicle includes a wide range of architecture possibilities. A large part of the design process is identifying reasonable vehicle architectures and vehicle performance capabilities. The Ohio State University EcoCAR 3 team designed a plug-in hybrid electric vehicle (PHEV) post-transmission parallel 2016 Chevrolet Camaro. With the end-goal of reducing the environmental impact of the vehicle, the Ohio State Camaro has been designed with a 44-mile all-electric range. It also features an 18.9 kWh Li-ion energy storage system, a 119 kW 2.0L GDI I4 engine that runs on 85% ethanol (E85) fuel, a 5-speed automated manual transmission, and a 150 kW peak electric machine. This report details the design and modeling process followed by the Ohio State team during Year 1 of the competition. The process included researching the customer needs of the vehicle, determining team design goals, initial modeling, and selecting a vehicle architecture.

The Ohio State University EcoCAR 3 team is designing a plug-in hybrid electric vehicle (PHEV) post-transmission parallel 2016 Chevrolet Camaro. With the end-goal of reducing the environmental impact of the vehicle, the Ohio State Camaro has been designed to have a 44-mile all-electric range. The vehicle is to consist of an 18.9 kWh Li-ion energy storage system, a 119 kW 2.0L GDI I4 engine that runs on 85% ethanol (E85) fuel, a 5-speed automated manual transmission, and a 150 kW peak-power electric machine. This report details the model and controls development process followed by the Ohio State team during Year 1 of the EcoCAR 3 competition. The focus of the paper will be on overall development of a vehicle model, initial simulation results, and supervisory controls development. Finally, initial energy consumption results from the model and future improvements will be discussed.

Future aircraft will demand a significant amount of electrical power to drive primary flight control surfaces. The electrical system architecture needed to source these flight critical loads will have to be resilient, autonomous, and fast. Designing and ensuring that a power system architecture can meet the load requirements and provide power to the flight critical buses at all times is fundamental. In this paper, formal methods and linear temporal logic are used to develop a contactor control strategy to meet the given specifications. The resulting strategy is able to manage multiple contactors during different types of generator failures. In order to verify the feasibility of the control strategy, a real-time simulation platform is developed to simulate the electrical power system. The platform has the capability to test an external controller through Hardware in the Loop (HIL).

Energy management plays a key role in achieving higher fuel economy for plug-in hybrid electric vehicle (PHEV) technology; the state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining the fuel consumed. The energy management algorithm should be designed to meet all driving scenarios while achieving the best possible fuel economy. The knowledge of the power requirement during a driving trip is necessary to achieve the best fuel economy results; performance of the energy management algorithm is closely related to the amount of information available in the form of road grade, velocity profiles, trip distance, weather characteristics and other exogenous factors. Intelligent transportation systems (ITS) allow vehicles to communicate with one another and the infrastructure to collect data about surrounding, and forecast the expected events, e.g., traffic condition, turns, road grade, and weather forecast.

Experimentally derived brain response envelopes are needed to evaluate and validate existing finite element (FE) head models. Motion of the brain relative to the skull during rotational input was measured using high-speed biplane x-ray. To generate repeatable, reproducible, and scalable data, methods were developed to reduce experimental variance. An “extreme-energy” device was developed to provide a controlled input that is unaffected by specimen characteristics. Additionally, a stereotactic frame was used to deploy radiopaque markers at specific, pre-determined locations within the brain. One post-mortem human surrogate (PMHS) head specimen was subjected to repeat tests of a half-sine rotational speed pulse in the sagittal plane. The desired pulse had a peak angular speed of 40 rad/s and duration of 30 ms. Relative motion of the brain was quantified using radiopaque targets and high-speed biplane x-ray. Frontal and occipital intracranial pressure (ICP) were also measured.

Thoracic injury criteria have been previously developed to predict thoracic injury for vehicle occupants as a function of biomechanical response. Historically, biomechanical testing of post-mortem human surrogates (PMHS) for injury criteria development has primarily been focused on mid-sized males. Response targets and injury criteria for other demographics, including small females, have been determined by scaling values from mid-sized males. The objective of this study was to explore the applicability of scaled injury criteria to their representative population. Two PMHS were subjected to a side-impact loading condition which replicates a near-side, MDB-to-vehicle impact for the driver. This was accomplished using the Advanced Side Impact System, or ASIS, on a HYGE sled. The sled acceleration matched the acceleration profile of an impacted vehicle, while the four pneumatic cylinders of the ASIS produced realistic door intrusion.