Designing Power Converter-Based Energy Management Systems with a Hierarchical Optimization Method
| dc.contributor.author | Li, Qian | en |
| dc.contributor.committeechair | Boroyevich, Dushan | en |
| dc.contributor.committeemember | Raj, Pradeep | en |
| dc.contributor.committeemember | Canfield, Robert Arthur | en |
| dc.contributor.committeemember | Burgos, Rolando | en |
| dc.contributor.committeemember | Stilwell, Daniel J. | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2024-06-11T08:02:01Z | en |
| dc.date.available | 2024-06-11T08:02:01Z | en |
| dc.date.issued | 2024-06-10 | en |
| dc.description.abstract | This dissertation introduces a hierarchical optimization framework for power converter-based energy management systems, with a primary focus on weight minimization. Emphasizing modularity and scalability, the research systematically tackles the challenges in optimizing these systems, addressing complex design variables, couplings, and the integration of heterogeneous models. The study begins with a comparative evaluation of various metaheuristic optimization methods applied to power inductors and converters, including genetic algorithm, particle swarm optimization, and simulated annealing. This is complemented by a global sensitivity analysis using the Morris method to understand the impact of different design variables on the design objectives and constraints in power electronics. Additionally, a thorough evaluation of different modeling methods for key components is conducted, leading to the validation of selected analytical models at the component level through extensive experiments. Further, the research progresses to studies at the converter level, focusing on a weight-optimized design for the thermal management systems for silicon carbide (SiC) MOSFET-based modular converters and the development of a hierarchical digital control system. This stage includes a thorough assessment of the accuracy of small-signal models for modular converters. At this point, the research methodically examines various design constraints, notably thermal considerations and transient responses. This examination is critical in understanding and addressing the specific challenges associated with converter-level design and the implications on system performance. The dissertation then presents a systematic approach where design variables and constraints are intricately managed across different hierarchies. This strategy facilitates the decoupling of subsystem designs within the same hierarchy, simplifying future enhancements to the optimization process. For example, component databases can be expanded effortlessly, and diverse topologies for converters and subsystems can be incorporated without the need to reconfigure the optimization framework. Another notable aspect of this research is the exploration of the scalability of the optimization architecture, demonstrated through design examples. This scalability is pivotal to the framework's effectiveness, enabling it to adapt and evolve alongside technological advancements and changing design requirements. Furthermore, this dissertation delves into the data transmission architecture within the hierarchical optimization framework. This architecture is not only critical for identifying optimal performance measures, but also for conveying detailed design information across all hierarchy levels, from individual components to entire systems. The interrelation between design specifications, constraints, and performance measures is illustrated through practical design examples, showcasing the framework's comprehensive approach. In summary, this dissertation contributes a novel, modular, and scalable hierarchical optimization architecture for the design of power converter-based energy management systems. It offers a comprehensive approach to managing complex design variables and constraints, paving the way for more efficient, adaptable, and cost-effective power system designs. | en |
| dc.description.abstractgeneral | This dissertation introduces an innovative approach to designing energy control systems, inspired by the creativity and adaptability of a Lego game. Central to this concept is a layered design methodology. The journey begins with power components, the fundamental 'Lego bricks'. Each piece is meticulously optimized for compactness, forming the robust foundation of the system. Like connecting individual Lego bricks into a module, these power components come together to form standardized power converters. These converters offer flexibility and scalability, similar to how numerous structures can be built from the same set of Lego pieces. The final layer involves assembling these power converters in order to construct comprehensive energy control systems. This mirrors the process of using Lego subassemblies to build larger, more intricate structures. At this system-level design, the standardized converters are integrated to optimize overall system performance. Key to this dissertation's methodology is an emphasis on modularity and scalability. It enables the creation of diverse energy control systems of varying sizes and functionalities from these fundamental units. The research delves into the intricacies of design variables and constraints, ensuring that each 'Lego piece' contributes optimally to the bigger picture. This includes exploring the scalability of the architecture, allowing it to evolve with technological advancements and design requirements, as well as examining data transmission within the system to ensure efficient data communication across all levels. In essence, this dissertation is about recognizing the potential in the smallest components and understanding their role in the grand scheme of the system. It is akin to playing a masterful game of Lego, where building something greater from small, well-designed parts leads to more efficient, adaptable, and cost-effective energy control system designs. This approach is particularly relevant for applications in transportation systems and renewable energy in remote locations, showcasing the universal applicability of this 'Lego game' to energy management. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:38634 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/119388 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | hierarchical optimization | en |
| dc.subject | energy management systems | en |
| dc.subject | weight minimization | en |
| dc.subject | modeling | en |
| dc.subject | modular converters | en |
| dc.subject | SiC MOSFET | en |
| dc.subject | genetic algorithm | en |
| dc.title | Designing Power Converter-Based Energy Management Systems with a Hierarchical Optimization Method | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
Files
Original bundle
1 - 1 of 1