Wind turbine drivetrains:State-of-the-art technologies and future development trends

This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and digitalization are also a focal point in this study. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator and power converter. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. The main challenges in drivetrain research identified in this paper include drivetrain dynamic responses in large or floating turbines, aerodynamic and farm control effects, use of rare-earth material in generators, improving reliability through prognostics, and use of advances in digitalization. These challenges illustrate the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration

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

Design and Validation of a High-Level Controller for Automotive Active Systems

Active systems, from active safety to energy management, play a crucial role in the development of new road vehicles. However, the increasing number of controllers creates an important issue regarding complexity and system integration. This article proposes a high-level controller managing the individual active systems - namely, Torque Vectoring (TV), Active Aerodynamics, Active Suspension, and Active Safety (Anti-lock Braking System [ABS], Traction Control, and Electronic Stability Program [ESP]) - through a dynamic state variation. The high-level controller is implemented and validated in a simulation environment, with a series of tests, and evaluate the performance of the original design and the proposed high-level control. Then, a comparison of the Virtual Driver (VD) response and the Driver-in-the-Loop (DiL) behavior is performed to assess the limits between virtual simulation and real-driver response in a lap time condition. The main advantages of the proposed design methodology are its simplicity and overall cooperation of different active systems, where the proposed model was able to improve the vehicle behavior both in terms of safety and performance, giving more confidence to the driver when cornering and under braking. Some differences were discovered between the behavior of the VD and the DiL, especially regarding the sensitivity to external disturbances

Predictive maintenance using digital twins: A systematic literature review

Context: Predictive maintenance is a technique for creating a more sustainable, safe, and profitable industry. One of the key challenges for creating predictive maintenance systems is the lack of failure data, as the machine is frequently repaired before failure. Digital Twins provide a real-time representation of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use. Since 2018, scientific literature on the utilization of Digital Twins for predictive maintenance has accelerated, indicating the need for a thorough review. Objective: This research aims to gather and synthesize the studies that focus on predictive maintenance using Digital Twins to pave the way for further research. Method: A systematic literature review (SLR) using an active learning tool is conducted on published primary studies on predictive maintenance using Digital Twins, in which 42 primary studies have been analyzed. Results: This SLR identifies several aspects of predictive maintenance using Digital Twins, including the objectives, application domains, Digital Twin platforms, Digital Twin representation types, approaches, abstraction levels, design patterns, communication protocols, twinning parameters, and challenges and solution directions. These results contribute to a Software Engineering approach for developing predictive maintenance using Digital Twins in academics and the industry. Conclusion: This study is the first SLR in predictive maintenance using Digital Twins. We answer key questions for designing a successful predictive maintenance model leveraging Digital Twins. We found that to this day, computational burden, data variety, and complexity of models, assets, or components are the key challenges in designing these models. 2022Scopus2-s2.0-8513459995

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154104/1/kampfner1996.pd

Electric vehicles and smart grids: impacts, challenges and opportunities

Electric vehicles and smart grids: impacts,challenges, opportunitie

Long-term research challenges in wind energy – a research agenda by the European Academy of Wind Energy

The European Academy of Wind Energy (eawe), representing universities and institutes with a significant wind energy programme in 14 countries, has discussed the long-term research challenges in wind energy. In contrast to research agendas addressing short- to medium-term research activities, this eawe document takes a longer-term perspective, addressing the scientific knowledge base that is required to develop wind energy beyond the applications of today and tomorrow. In other words, this long-term research agenda is driven by problems and curiosity, addressing basic research and fundamental knowledge in 11 research areas, ranging from physics and design to environmental and societal aspects. Because of the very nature of this initiative, this document does not intend to be permanent or complete. It shows the vision of the experts of the eawe, but other views may be possible. We sincerely hope that it will spur an even more intensive discussion worldwide within the wind energy community

Intelligent energy management in hybrid electric vehicles

The modelling and simulation approach is employed to develop an intelligent energy management system for hybrid electric vehicles. The aim is to optimize fuel consumption and reduce emissions. An analysis of the role of drivetrain, energy management control strategy and the associated impacts on the fuel consumption with combined wind/drag, slope, rolling, and accessories loads are included.<br /

Economic Payback Time of Battery Pack Replacement for Hybrid and Plug-in Hybrid Electric Vehicles

This paper investigates the economic viability of replacing the high-voltage battery pack of a power-split hybrid electric vehicle (HEV) and a plug-in hybrid electric vehicle (PHEV) by estimating the impact of battery ageing on the fuel economy, drivability capability and electric range. The HEV is modelled first, an optimal energy management strategy based on dynamic programming is then implemented, and experimental characterization data for the battery cell is presented. The batteries are tested to a heavily aged state, with up to an 84% loss of capacity. The battery pack payback period is estimated by assessing the vehicle operative costs in terms of fuel and electricity as obtained through numerical simulations as a function of battery ageing. Replacing the battery pack at the conventional end-of-life limit of 80% residual capacity is suggested not to be convenient from an economic standpoint for both the HEV and the PHEV. On the other hand, acceptable payback periods (i.e. 2 to 5 years) can be achieved for the battery pack when being replaced at 20% to 40% residual capacity. The proposed methodology can be implemented to advise an HEV or PHEV user regarding the benefit of replacing the battery pack due to excessive ageing

Increased understanding of hybrid vehicle design through modeling, simulation, and optimization

2010 Fall.Includes bibliographical references.Vehicle design is constantly changing and improving due to the technologically driven nature of the automotive industry, particularly in the hybridization and electrification of vehicle drive trains. Through enhanced design vehicle level design constraints can result in the fulfillment of system level design objectives. These constraints may include improved vehicle fuel economy, all electric range, and component costs which can affect system objectives of increased national energy independence, reduced vehicle and societal emissions, and reduced life-cycle costs. In parallel, as computational power increases the ability to accurately represent systems through analytical models improves. This allows for systems engineering which is commonly quicker and less resource consuming than physical testing. As a systems engineering technique, optimization has shown to obtain superior solutions systematically, in opposition to trial-and-error designs of the past. Through the combination of vehicle models, computer numerical simulation, and optimization, overall vehicle systems design can greatly improve. This study defines a connection between the system level objectives for advanced vehicle design and the component- and vehicle-level design process using a multi-level design and simulation modeling environment. The methods and information pathways for vehicle system models are presented and applied to dynamic simulation. Differing vehicle architecture simulations are subjected to a selection of proven optimization algorithms and design objectives such that the performance of the algorithms and vehicle-specific design information and sensitivity is obtained. The necessity of global search optimization and aggregate objective functions are confirmed through exploration of the complex hybrid vehicle design space. Whether the chosen design space is limited to available components or expanded to academic areas, studies can be performed for numerous design objectives and constraints. The combination of design criteria into quantifiable objective functions allows for direct optimization comparison based on any number of design goals. Integrating well-defined objective functions into high performing global optimization search methods provides increased probability of obtaining solutions which represent the most germane designs. Additionally, key interactions between different components in the vehicular system can easily be identified such that ideal directions for gain relative to minimal cost can be achieved. Often times vehicular design processes require lower order representations or consist of time and resource consuming iterations. Through the formulation presented in this study, more details, objectives, and methods become available for comparing advanced vehicles across architectures. The main techniques used for setting up the models, simulations and optimizations are discussed along with results of test runs based on chosen vehicle objectives. Utility for the vehicular design efforts are presented through comparisons of available simulation and future areas of research are suggested