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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.

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.

Vulnerable Road User (VRU) safety has been an important issue throughout the years as corresponding fatality numbers in traffic have been increasing each year. With the developments in connected vehicle technology, there are new and easier ways of implementing Vehicle to Everything (V2X) communication which can be utilized to provide safety and early warning benefits for VRUs. Mobile phones are one important point of interest with their sensors being increased in quantity and quality and improved in terms of accuracy. Bluetooth and extended Bluetooth technology in mobile phones has enhanced support to carry larger chunks of information to longer distances. The work we discuss in this paper is related to a mobile application that utilizes the mobile phone sensors and Bluetooth communication to implement Personal Safety Message (PSM) broadcast using the SAE J2735 standard to create a Pedestrian to Vehicle (P2V) based safety warning structure.

In recent years, many motorsports have been developing competitions based on electric vehicles. The demanding performance requires the battery pack to have the perfect balance between energy, power, and weight. This research paper presents a systematic methodology for the initial design of the battery pack (size and cell chemistry) by statistically analyzing the characteristics of the mission profile. The power profile for the battery pack of a motorsport vehicle can be estimated by considering the duty cycle of a racing car using the technical and sporting regulations and vehicle parameters. In this paper, many statistical metrics correlated to this power profile have been defined and analyzed (such as the max, mean, and standard deviation of the power profile, the total energy consumed, and the expected heat generation). These metrics have been used to estimate the cell energy and power density requirement and the pack sizing considering the weight constraints.

Physics-based electrochemical models and empirical Equivalent Circuit Models (ECMs) are well-established and widely used modeling techniques to predict the voltage behavior of lithium-ion cells. Electrochemical models are typically very accurate and require relatively little experimental data to calibrate, but present high mathematical and computational complexity. Conversely, ECMs are more computationally efficient and mathematically simpler, making them well-suited for applications in controls, diagnosis, and state estimation of lithium-ion battery packs. However, the calibration process requires extensive testing to calibrate the parameters of the model over a wide range of operating conditions. This paper bridges the gap between these two classes of models by developing a method to analytically define the ECM parameters starting from an already-calibrated Extended Single-Particle Model (ESPM).

This paper benchmarks three different lithium-ion (Li-ion) battery voltage modelling approaches, a physics-based approach using an Extended Single Particle Model (ESPM), an equivalent circuit model, and a recurrent neural network. The ESPM is the selected physics-based approach because it offers similar complexity and computational load to the other two benchmarked models. In the ESPM, the anode and cathode are simplified to single particles, and the partial differential equations are simplified to ordinary differential equations via model order reduction. Hence, the required state variables are reduced, and the simulation speed is improved. The second approach is a third-order equivalent circuit model (ECM), and the third approach uses a model based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)). A Li-ion pouch cell with 47 Ah nominal capacity is used to parameterize all the models.

The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications. Soon all the newly produced cars will have Vehicle to Everything (V2X) communication modems alongside the existing Advanced Driver Assistant Systems (ADAS). It is essential to identify the different sensor measurements for the same targets (Data Association) to use connectivity reliably as a safety feature alongside the standard ADAS functionality. Considering the camera is the most common sensor available for ADAS systems, in this paper, we present an experimental implementation of a Mahalanobis distance-based data association algorithm between the camera and the Vehicle to Vehicle (V2V) communication sensors. The implemented algorithm has low computational complexity and the capability of running in real-time. One can use the presented algorithm for sensor fusion algorithms or higher-level decision-making applications in ADAS modules.

The design and development of new biologically inspired technologies based on intelligent materials that are capable of sensing the levels of target biomolecules and, if needed, trigger appropriate countermeasures to regulate biological processes and rhythms of the astronauts is being undertaken in our laboratories. This is accomplished by coupling biologically inspired sensors that monitor the levels of the target biomolecules with intelligent polymeric materials that can regulate the release of a countermeasure. The technology developed here integrates sensors and artificial muscle material into a self-regulating device that can perform with minimal crew intervention. Further, it takes advantage of microfabrication technology to construct lightweight and robust responsive delivery systems. These “intelligent” devices address the need for the control and regulation of biological processes and rhythms under spaceflight conditions.

Batteries are useful in Fuel Cell Hybrid Electric Vehicles (FCHEV) to fulfill transient demands and for regenerative braking. Efficient energy management strategies paired with optimal powertrain design further improves the efficiency. In this paper, a new methodology to simultaneously size the propulsive elements and optimize the power-split strategy of a Range Extended Battery Electric Vehicle (REBEV), using a Polymer Electron Membrane Fuel Cell (PEMFC), is proposed and preliminary studies on the effects of the driving mission profile and the auxiliary power loads on the sizing and optimal performance of the powertrain design are carried out. Dynamic Programming is used to compute the optimal energy management strategy for a given driving mission profile, providing a global optimal solution.

Battery packs used in electrified automotive powertrains support heavy electrical loads resulting in significant heat generation within them. Cooling systems are used to regulate the battery pack temperatures, helping to slow down battery aging. Vehicle-level energy consumption simulations serve as a first step for determining the specifications of a battery cooling system based on the duty cycle and interactions with the rest of the powertrain. This paper presents the development of a battery model that takes into account the energy impact of heating in the battery and demonstrates its use in a vehicle-level energy consumption simulator to set the specifications of a suitable cooling system for a vehicle application. The vehicle application used in this paper is a Class 6 Pickup and Delivery commercial vehicle with a Range-Extended Electric Vehicle (REEV) powertrain configuration.

Current Advanced Driver Assistance Systems (ADAS) often interact with the driver; aiding with either warnings or direct intervention. This work explores the development of an ADAS system to provide lane departure warning, forward collision warning, and a recommended following distance for a custom plug-in hybrid-electric vehicle. The system utilizes off-the-shelf hardware with in-house computer vision and sensor fusion algorithms to create a low-cost SAE Level 0 driver assistance system. The system utilizes a radar sensor as well as a camera to detect, classify, and track target vehicles. This work will illustrate the systems engineering methods used for outlining customer requirements, technical requirements, component selection, software development, simulation, vehicle fitment, and validation. Similar system engineering processes could be implemented for higher level SAE systems.

A machine has been developed to measure the complete inertia matrix; mass, center of gravity (CG) location, and all moments and products of inertia. Among other things these quantities are useful in studying engine vibrations, calculation of the torque roll axis, and in the placement of engine mounts. While the machine was developed primarily for engines it can be used for other objects of similar size and weight, and even smaller objects such as tires and wheels/rims. A key feature of the device is that the object, once placed on the test table, is never reoriented during the test cycle. This reduces the testing time to an hour or less, with the setup time being a few minutes to a few hours depending on the complexity of the shape of the object. Other inertia test methods can require up to five reorientations, separate CG measurement, and up to several days for a complete test.

In recent years, there has been increasing research on automated driving technology. Autonomous vehicle path following performance is one of significant consideration. This paper presents discrete time design of robust PD controlled system with disturbance observer (DOB) and communication disturbance observer (CDOB) compensation to enhance autonomous vehicle path following performance. Although always implemented on digital devices, DOB and CDOB structure are usually designed in continuous time in the literature and also in our previous work. However, it requires high sampling rate for continuous-time design block diagram to automatically convert to corresponding discrete-time controller using rapid controller prototyping systems. In this paper, direct discrete time design is carried out. Digital PD feedback controller is designed based on the nominal plant using the proposed parameter space approach.

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 44 miles of all-electric range. The vehicle features an 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes. This is 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 50 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This paper details three years of modeling and simulation development for the OSU EcoCAR 2 vehicle. Included in this paper are the processes for developing simulation platform and model requirements, plant model and soft ECU development, test development and validation, automated regression testing, and controls and calibration optimization.

Vaporizing Foil Actuators (VFA) are based on the phenomenon of rapid vaporization of thin metallic foils and wires, caused by passage of a capacitor bank driven current on the order of 100 kA. The burst of the conductor is accompanied with a high-pressure pulse, which can be used for working metal at high strain rates. This paper focuses on the use of VFA for collision welding of dissimilar metals, in particular, aluminum and steel. Aluminum alloy 6061 sheets of 1 mm thickness were launched to velocities in excess of 650 m/s with input electrical energy of 8 kJ into 0.0762 mm thick, dog-bone shaped aluminum foil actuators. Target sheets made from dual phase steel (DP780) were impacted with the aluminum flyer sheet, and solid state impact welds were created. During mechanical testing, many samples failed outside the weld area, thereby indicating that the weld was stronger than the parent aluminum.

Advanced Driver Assistance Systems (ADAS) are designed and calibrated rigorously to provide them with the robustness against highly uncertain environments that they usually operate in. Typical calibration procedures for such systems rely extensively on track (controlled environment) testing, which is time-consuming, expensive, and sometimes cannot cover all the critical test scenarios that could be encountered by ADAS in the real world. Therefore, virtual (simulation-based) testing and validation has been gaining more prominence and emphasis for ensuring high coverage along with easier scalability and usage. This paper attempts to provide an alternative approach for calibrating ADAS in the controller validation phase by the aid of simulated test case scenarios. The study executes characterization of the uncertainty in the position and heading of the ego and the obstacle vehicles.

DC-fast charge (DCFC) may be amenable for widespread EV adoption. However, there are potential challenges associated with implementation and operation of the DCFC infrastructures. The integration of energy storage systems can limit the scale of grid installation required for DCFC and enable more efficient grid energy usage. In addition, second-life batteries (SLBs) can find application in DCFC, significantly reducing installation cost when compared to solutions based on new battery packs. However, both system architecture and control strategy require optimization to ensure an optimal use of SLBs, including degradation and thermal aspects. This study proposes an application of automotive SLBs for DCFC stations where high power grid connection is not available or feasible. Several SLBs are connected to the grid by means of low power chargers (e.g. L2 charging station), and a DC/DC converter controls the power to the EV power dispenser.