Impact of Low and High Congestion Traffic Patterns on a Mild-HEV Performance

Copyright © 2017 SAE International. Driven by stricter mandatory regulations on fuel economy improvement and emissions reduction, market penetration of electrified vehicles will increase in the next ten years. Within this growth, mild hybrid vehicles will become a leading sector. The high cost of hybrid electric vehicles (HEV) has somewhat limited their widespread adoption, especially in developing countries. Conversely, it is these countries that would benefit most from the environmental benefits of HEV technology. Compared to a full hybrid, plug-in hybrid, or electric vehicle, a mild hybrid system stands out due to its maximum benefit/cost ratio. As part of our ongoing project to develop a mild hybrid system for developing markets, we have previously investigated improvements in drive performance and efficiency using optimal gearshift strategies, as well as the incorporation of high power density supercapacitors. In this paper, the fuel and emissions of a baseline conventional vehicle and mild hybrid electric vehicle (MHEV) are compared. The objective of this analysis is to compare the fuel economy and Greenhouse Gas (GHG) emissions of the baseline and MHEV models, using low and high-density traffic patterns chosen for their similarity to traffic density profiles of our target markets. Results demonstrate the benefits of a lower ongoing cost for the HEV architecture. These advantages include torque-hole filling between gear changes, increased fuel efficiency and performance

A system analysis and modeling of a HEV based on ultracapacitor battery

© 2017 IEEE. There is a clear shift toward the implementation of electrified vehicles in the market, influenced by the introduction of stricter mandatory regulations on fuel economy improvement and emissions reduction. Of these vehicles, the penetration of hybrid vehicles in the market has much potential for growth in the next few years. The adoption of these vehicles has been limited by the high cost of HEV's, which have less uptake in developing regions. Considering this point, developing countries would see the greatest benefit in adopting HEV technology. A mild hybrid system has an observable advantage in these markets due to its maximum benefit/cost ratio when compared to a full hybrid, plug-in hybrid or electric vehicles. This paper discusses the development of a mild hybrid system for such markets with a focus on improving drive performance and efficiency. To achieve this, high power density ultracapacitors are used based on their fast charging and discharging characteristics, together with intelligent drivetrain control taking advantage of the ultracapacitors' characteristics to deliver smooth torque delivery during gear change (torque-filling). A comparison and analysis is undertaken, of both conventional powertrain and an otherwise identical powertrain but for the incorporation of components required for the mild hybrid system. Software models simulated the powertrains in specific driving conditions, with observations made of the advantages of MHEV over conventional drivetrains. The model demonstrated increased fuel efficiency and performance

Energy management and shifting stability control for a novel dual input clutchless transmission system

© 2019 Elsevier Ltd A dual input clutchless transmission system based on automated manual transmission (AMT) structure is developed for pure electric vehicles. An energy management strategy (EMS) is proposed to determine the power distribution between two motors and the optimal gear state. A mathematical model is built to minimize the energy consumption of the motors at each instant based on the motor efficiency maps. However, the proposed EMS in line with other energy-oriented strategies often result in excessive gear shifts and compromised drivability. To avoid the undesired gear shift, a shifting stabilizer is built in the EMS objective function to improve the shift quality. Accordingly, to achieve a balance between the energy consumption and the drivability, a multi-objective optimization method is adopted to reduce the unnecessary shift events while minimizing energy consumption. Two driving cycles representing typical daily driving conditions are used to demonstrate the effectiveness of the proposed system in terms of energy efficiency and shifting stability

Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research

Operating cycle representations for road vehicles

This thesis discusses different ways to represent road transport operations mathematically. The intention is to make more realistic predictions of longitudinal performance measures for road vehicles, such as the CO2 emissions. It is argued that a driver and vehicle independent description of relevant transport operations increase the chance that a predicted measure later coincides with the actual measure from the vehicle in its real-world application. This allows for fair comparisons between vehicle designs and, by extension, effective product development. Three different levels of representation are introduced, each with its own purpose and application. The first representation, called the bird\u27s eye view, is a broad, high-level description with few details. It can be used to give a rough picture of the collection of all transport operations that a vehicle executes during its lifetime. It is primarily useful as a classification system to compare different applications and assess their similarity. The second representation, called the stochastic operating cycle (sOC) format, is a statistical, mid-level description with a moderate amount of detail. It can be used to give a comprehensive statistical picture of transport operations, either individually or as a collection. It is primarily useful to measure and reproduce variation in operating conditions, as it describes the physical properties of the road as stochastic processes subject to a hierarchical structure.The third representation, called the deterministic operating cycle (dOC) format, is a physical, low-level description with a great amount of detail. It describes individual operations and contains information about the road, the weather, the traffic and the mission. It is primarily useful as input to dynamic simulations of longitudinal vehicle dynamics.Furthermore, it is discussed how to build a modular, dynamic simulation model that can use data from the dOC format to predict energy usage. At the top level, the complete model has individual modules for the operating cycle, the driver and the vehicle. These share information only through the same interfaces as in reality but have no components in common otherwise and can therefore be modelled separately. Implementations are briefly presented for each module, after which the complete model is showcased in a numerical example.The thesis ends with a discussion, some conclusions, and an outlook on possible ways to continue

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

Cyber-Physical Embedded Systems with Transient Supervisory Command and Control: A Framework for Validating Safety Response in Automated Collision Avoidance Systems

The ability to design and engineer complex and dynamical Cyber-Physical Systems (CPS) requires a systematic view that requires a definition of level of automation intent for the system. Since CPS covers a diverse range of systemized implementations of smart and intelligent technologies networked within a system of systems (SoS), the terms “smart” and “intelligent” is frequently used in describing systems that perform complex operations with a reduced need of a human-agent. The difference between this research and most papers in publication on CPS is that most other research focuses on the performance of the CPS rather than on the correctness of its design. However, by using both human and machine agency at different levels of automation, or autonomy, the levels of automation have profound implications and affects to the reliability and safety of the CPS. The human-agent and the machine-agent are in a tidal lock of decision-making using both feedforward and feedback information flows in similar processes, where a transient shift within the level of automation when the CPS is operating can have undesired consequences. As CPS systems become more common, and higher levels of autonomy are embedded within them, the relationship between human-agent and machine-agent also becomes more complex, and the testing methodologies for verification and validation of performance and correctness also become more complex and less clear. A framework then is developed to help the practitioner to understand the difficulties and pitfalls of CPS designs and provides guidance to test engineering design of soft computational systems using combinations of modeling, simulation, and prototyping

Next Generation HEV Powertrain Design Tools: Roadmap and Challenges

Hybrid electric vehicles (HEVs) represent a fundamental step in the global evolution towards transportation electrification. Nevertheless, they exhibit a remarkably complex design environment with respect to both traditional internal combustion engine vehicles and battery electric vehicles. Innovative and advanced design tools are therefore crucially required to effectively handle the increased complexity of HEV development processes. This paper aims at providing a comprehensive overview of past and current advancements in HEV powertrain design methodologies. Subsequently, major simplifications and limits of current HEV design methodologies are detailed. The final part of this paper defines research challenges that need accomplishment to develop the next generation HEV architecture design tools. These particularly include the application of multi-fidelity modeling approaches, the embedded design of powertrain architecture and on-board control logic and the endorsement of multi-disciplinary optimization procedures. Resolving these issues may indeed remarkably foster the widespread adoption of HEVs in the global vehicle market

EVALUATION OF PLUG-IN PARALLEL HEV TOPOLOGIES USING OPTIMAL CONTROL METHODS AND VEHICLE DYNAMICS SIMULATION

Hybrid electric vehicle (HEV) powertrains with parallel topologies are among the frequently used layouts, because of their easy applicability on an existing conventional powertrain, by the addition of hybrid modules with mild, full, or a plug-in capability. This paper investigates three of such parallel HEV topologies: P2, P3, and P4; all in a plug-in variant, to find-out which one performs best. Apart from the topology consideration, one of the other common questions or challenges in HEV development is the ICE concept selection. To address it, the paper combines the three HEV topologies with three technologically different internal combustion engines, all with the same power outputs. Then, all the powertrain and ICE combinations are tested in homologation driving cycles and vehicle dynamics simulation test – different acceleration tests – giving a holistic methodology suitable for thorough HEV topology evaluation, identifying all possible hybridization benefits. To find the maximum CO2 potential, it is convenient to exclude the effect of the energy management control strategy on the CO2 result in a charge sustaining driving cycle; therefore, to use some optimal control method. For this task, the paper compares the Equivalent Consumption Minimization Strategy, that realizes a Pontryagin’s minimum principle against the Dynamic Programming optimal control method, which is based on Bellman’s principle of optimality. Both control methods are available as a part of GT-Suite 0D/1D/3D multi-physics CAE simulation software, that is used in our whole study