An analysis of track mechanics to improve the simulation of the ride dynamics of tracklaying vehicles; final report,

"Performed under the technical supervision of the Dynamic Simulations Laboratory, Ordnance Tank-Automotive Command, Detroit Arsenal."

USSR: about to enter the automotive age?

At head of title: Intelligence report.

Interstate relationships in the taxation of commercial vehicles; a preliminary report prepared for the Board of Managers of the Council of State Governments.

Cover title.

Green technologies in the automotive industry

In most of the discussions about environmental issues and policies, transportation is highlighted as one of the main sources of pollutant emissions and energy consumption. The attention given to the automotive industry is understandable in this context due to its size, expansion, presence in our daily lives, and of course its environmental impact. If we scrutinize the “greenness” of car manufacturers we will find issues of concern from the raw material use, production processes, use, and end-of-life of vehicles. The main issues for production are high consumption of energy, raw materials, water and the waste stream, which contains the four substances of concern (cadmium, lead, hexavalent chromium, mercury). In respect of carbon emissions and energy use the use of cars is the main phase of its life-cycle due to the combination of internal combustion engines with fossil fuels. The most recent pressure is aimed at the end-of-life vehicles (ELV). In addition to the pollution from vehicle use, traffic jams and car accidents continue to be part of the downside of a car culture. Landfills sites are becoming scarce and the contamination of soil and aquifers completes the picture.

Challenges and opportunities for reverse logistics initiatives in the automotive industry

This paper investigates the main strategies automotive companies adopt to address the issue of dealing with end-of-life vehicles and spare parts. Furthermore, it investigates the reasons behind take-back strategies, i.e how and why automotive companies undertake initiatives in reverse logistics. The research findings indicate that companies are trying to respond to the end-of-life legislation based on cost-effective approaches as well as corporate environmental responsibility. Outsourcing is used when expertise is found to extract value from scrap and there is cooperation with suppliers and vendors to facilitate the dismantling of cars and recycling of parts.

Hybrid Power System Intelligent Operation and Protection Involving Plug-in Electric Vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.

Investigation of the effects of flow conditions at rotor inlet on mixed flow turbine performance for automotive applications

Current trends in the automotive industry have placed increased importance on engine downsizing for passenger vehicles. Engine downsizing often results in reduced power output and turbochargers have been relied upon to restore the power output and maintain drivability. As improved power output is required across a wide range of engine operating conditions, it is necessary for the turbocharger to operate effectively at both design and off-design conditions. One off-design condition of considerable importance for turbocharger turbines is low velocity ratio operation, which refers to the combination of high exhaust gas velocity and low turbine rotational speed. Conventional radial flow turbines are constrained to achieve peak efficiency at the relatively high velocity ratio of 0.7, due the requirement to maintain a zero inlet blade angle for structural reasons. Several methods exist to potentially shift turbine peak efficiency to lower velocity ratios. One method is to utilize a mixed flow turbine as an alternative to a radial flow turbine. In addition to radial and circumferential components, the flow entering a mixed flow turbine also has an axial component. This allows the flow to experience a non-zero inlet blade angle, potentially shifting peak efficiency to a lower velocity ratio when compared to an equivalent radial flow turbine.
This study examined the effects of varying the flow conditions at the inlet to a mixed flow turbine and evaluated the subsequent impact on performance. The primary parameters examined were average inlet flow angle, the spanwise distribution of flow angle across the inlet and inlet flow cone angle. The results have indicated that the inlet flow angle significantly influenced the degree of reaction across the rotor and the turbine efficiency. The rotor studied was a custom in-house design based on a state-of-the-art radial flow turbine design. A numerical approach was used as the basis for this investigation and the numerical model has been validated against experimental data obtained from the cold flow turbine test rig at Queen’s University Belfast. The results of the study have provided a useful insight into how the flow conditions at rotor inlet influence the performance of a mixed flow turbine.

Autonomous Vehicles and Commercial Real Estate

Hardly a day goes by without the release of a handful of news stories about autonomous vehicles (or AVs for short). The proverbial “tipping point” of awareness has been reached in the public consciousness as AV technology is quickly becoming the new focus of firms from Silicon Valley to Detroit and beyond. Automation has, and will continue to have far-reaching implications for many human activities, but for driving, the technology is here. Google has been in talks with automaker Ford (1), Elon Musk has declared that Tesla will have the appropriate technology in two years (2), GM is paired-up with Lyft (3), Uber is in development-mode (4), Microsoft and Volvo have announced a partnership (5), Apple has been piloting its top-secret project “Titan” (6), Toyota is working on its own technology (7), as is BMW (8). Audi (9) made a splash by sending a driverless A7 concept car 550 miles from San Francisco to Las Vegas just in time to roll-into the 2016 Consumer Electronics Show. Clearly, the race is on.

A long-term energy efficiency prediction model for the Brazilian automotive industry

According to law number 12.715/2012, Brazilian government instituted guidelines for a program named Inovar-Auto. In this context, energy efficiency is a survival requirement for Brazilian automotive industry from September 2016. As proposed by law, energy efficiency is not going to be calculated by models only. It is going to be calculated by the whole universe of new vehicles registered. In this scenario, the composition of vehicles sold in market will be a key factor on profits of each automaker. Energy efficiency and its consequences should be taken into consideration in all of its aspects. In this scenario, emerges the following question: which is the efficiency curve of one automaker for long term, allowing them to adequate to rules, keep balancing on investment in technologies, increasing energy efficiency without affecting competitiveness of product lineup? Among several variables to be considered, one can highlight the analysis of manufacturing costs, customer value perception and market share, which characterizes this problem as a multi-criteria decision-making. To tackle the energy efficiency problem required by legislation, this paper proposes a framework of multi-criteria decision-making. The proposed framework combines Delphi group and Analytic Hierarchy Process to identify suitable alternatives for automakers to incorporate in main Brazilian vehicle segments. A forecast model based on artificial neural networks was used to estimate vehicle sales demand to validate expected results. This approach is demonstrated with a real case study using public vehicles sales data of Brazilian automakers and public energy efficiency data.

Analysis of Automotive Cybersecurity - Attack surfaces and users’ privacy concerns

Modern automobiles are no longer just mechanical tools. The electronics and computing services they are shipping with are making them not less than a computer. They are massive kinetic devices with sophisticated computing power. Most of the modern vehicles are made with the added connectivity in mind which may be vulnerable to outside attack. Researchers have shown that it is possible to infiltrate into a vehicle’s internal system remotely and control the physical entities such as steering and brakes. It is quite possible to experience such attacks on a moving vehicle and unable to use the controls. These massive connected computers can be life threatening as they are related to everyday lifestyle. First part of this research studied the attack surfaces in the automotive cybersecurity domain. It also illustrated the attack methods and capabilities of the damages. Online survey has been deployed as data collection tool to learn about the consumers’ usage of such vulnerable automotive services. The second part of the research portrayed the consumers’ privacy in automotive world. It has been found that almost hundred percent of modern vehicles has the capabilities to send vehicle diagnostic data as well as user generated data to their manufacturers, and almost thirty five percent automotive companies are collecting them already. Internet privacy has been studies before in many related domain but no privacy scale were matched for automotive consumers. It created the research gap and motivation for this thesis. A study has been performed to use well established consumers privacy scale – IUIPC to match with the automotive consumers’ privacy situation. Hypotheses were developed based on the IUIPC model for internet consumers’ privacy and they were studied by the finding from the data collection methods. Based on the key findings of the research, all the hypotheses were accepted and hence it is found that automotive consumers’ privacy did follow the IUIPC model under certain conditions. It is also found that a majority of automotive consumers use the services and devices that are vulnerable and prone to cyber-attacks. It is also established that there is a market for automotive cybersecurity services and consumers are willing to pay certain fees to avail that.

A future dynamically reconfigurable automotive software system

Embedded software systems in vehicles are of rapidly increasing commercial importance for the automotive industry. Current systems employ a static run-time environment; due to the difficulty and cost involved in the development of dynamic systems in a high-integrity embedded control context. A dynamic system, referring to the system configuration, would greatly increase the flexibility of the offered functionality and enable customised software configuration for individual vehicles, adding customer value through plug-and-play capability, and increased quality due to its inherent ability to adjust to changes in hardware and software. We envisage an automotive system containing a variety of components, from a multitude of organizations, not necessarily known at development time. The system dynamically adapts its configuration to suit the run-time system constraints. This paper presents our vision for future automotive control systems that will be regarded in an EU research project, referred to as DySCAS (Dynamically Self-Configuring Automotive Systems). We propose a self-configuring vehicular control system architecture, with capabilities that include automatic discovery and inclusion of new devices, self-optimisation to best-use the processing, storage and communication resources available, self-diagnostics and ultimately self-healing. Such an architecture has benefits extending to reduced development and maintenance costs, improved passenger safety and comfort, and flexible owner customisation. Specifically, this paper addresses the following issues: The state of the art of embedded software systems in vehicles, emphasising the current limitations arising from fixed run-time configurations; and the benefits and challenges of dynamic configuration, giving rise to opportunities for self-healing, self-optimisation, and the automatic inclusion of users’ Consumer Electronic (CE) devices. Our proposal for a dynamically reconfigurable automotive software system platform is outlined and a typical use-case is presented as an example to exemplify the benefits of the envisioned dynamic capabilities.

MODELING AND HARDWARE-IN-THE-LOOP SIMULATION OF POWER-SPLIT HYBRID ELECTRIC VEHICLES

Conventional vehicles are creating pollution problems, global warming and the extinction of high density fuels. To address these problems, automotive companies and universities are researching on hybrid electric vehicles where two different power devices are used to propel a vehicle. This research studies the development and testing of a dynamic model for Prius 2010 Hybrid Synergy Drive (HSD), a power-split device. The device was modeled and integrated with a hybrid vehicle model. To add an electric only mode for vehicle propulsion, the hybrid synergy drive was modified by adding a clutch to carrier 1. The performance of the integrated vehicle model was tested with UDDS drive cycle using rule-based control strategy. The dSPACE Hardware-In-the-Loop (HIL) simulator was used for HIL simulation test. The HIL simulation result shows that the integration of developed HSD dynamic model with a hybrid vehicle model was successful. The HSD model was able to split power and isolate engine speed from vehicle speed in hybrid mode.

COMPARATIVE ANALYSIS OF EFFICIENCY AND OPERATING CHARACTERISTICS OF AUTOMOTIVE POWERTRAIN ARCHITECTURES THROUGH CHASSIS DYNAMOMETER TESTING

The thesis "COMPARATIVE ANALYSIS OF EFFICIENCY AND OPERATING CHARACTERISTICS OF AUTOMOTIVE POWERTRAIN ARCHITECTURES THROUGH CHASSIS DYNAMOMETER TESTING" was completed through a collaborative partnership between Michigan Technological University and Argonne National Laboratory under a contractual agreement titled "Advanced Vehicle Characterization at Argonne National Laboratory". The goal of this project was to investigate, understand and document the performance and operational strategy of several modern passenger vehicles of various architectures. The vehicles were chosen to represent several popular engine and transmission architectures and were instrumented to allow for data collection to facilitate comparative analysis. In order to ensure repeatability and reliability during testing, each vehicle was tested over a series of identical drive cycles in a controlled environment utilizing a vehicle chassis dynamometer. Where possible, instrumentation was preserved between vehicles to ensure robust data collection. The efficiency and fuel economy performance of the vehicles was studied. In addition, the powertrain utilization strategies, significant energy loss sources, tailpipe emissions, combustion characteristics, and cold start behavior were also explored in detail. It was concluded that each vehicle realizes different strengths and suffers from different limitations in the course of their attempts to maximize efficiency and fuel economy. In addition, it was observed that each vehicle regardless of architecture exhibits significant energy losses and difficulties in cold start operation that can be further improved with advancing technology. It is clear that advanced engine technologies and driveline technologies are complimentary aspects of vehicle design that must be utilized together for best efficiency improvements. Finally, it was concluded that advanced technology vehicles do not come without associated cost; the complexity of the powertrains and lifecycle costs must be considered to understand the full impact of advanced vehicle technology.

Hybrid power system intelligent operation and protection involving plug-in electric vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. ^ The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. ^ The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.^

Modelling and Optimization of Energy Management Strategies for Hybrid Vehicles

In the last decades the automotive sector has seen a technological revolution, due mainly to the more restrictive regulation, the newly introduced technologies and, as last, to the poor resources of fossil fuels remaining on Earth. Promising solution in vehicles’ propulsion are represented by alternative architectures and energy sources, for example fuel-cells and pure electric vehicles. The automotive transition to new and green vehicles is passing through the development of hybrid vehicles, that usually combine positive aspects of each technology. To fully exploit the powerful of hybrid vehicles, however, it is important to manage the powertrain’s degrees of freedom in the smartest way possible, otherwise hybridization would be worthless. To this aim, this dissertation is focused on the development of energy management strategies and predictive control functions. Such algorithms have the goal of increasing the powertrain overall efficiency and contextually increasing the driver safety. Such control algorithms have been applied to an axle-split Plug-in Hybrid Electric Vehicle with a complex architecture that allows more than one driving modes, including the pure electric one. The different energy management strategies investigated are mainly three: the vehicle baseline heuristic controller, in the following mentioned as rule-based controller, a sub-optimal controller that can include also predictive functionalities, referred to as Equivalent Consumption Minimization Strategy, and a vehicle global optimum control technique, called Dynamic Programming, also including the high-voltage battery thermal management. During this project, different modelling approaches have been applied to the powertrain, including Hardware-in-the-loop, and diverse powertrain high-level controllers have been developed and implemented, increasing at each step their complexity. It has been proven the potential of using sophisticated powertrain control techniques, and that the gainable benefits in terms of fuel economy are largely influenced by the chose energy management strategy, even considering the powerful vehicle investigated.