In-wheel motors for electric vehicles

PhD ThesisThe in-wheel motor technology as the source of traction for electric vehicles has been researched recently because it is compact and ease-to-integrate. The motor is housed in the wheel. Since the room for the motor is tightly defined by the size of the wheel and there is no gearing system, the motor must have a high torque density to drive the vehicle directly and a high efficiency to keep cool. The existing motor uses a surface-mounted magnet topology. To make it more cost-competitive, the magnet material needs to be reduced while maintaining the torque performance at the rated operating condition. It is the motive of this Ph.D. research. The thesis starts with a brief introduction on the background of the electric vehicle. Then the major challenges of the in-wheel motor technology are summarised. With the derived specifications, an induction machine and a switched reluctance machine are then simulated and analysed. Still, the permanent magnet synchronous machine is proved to have the highest torque density. Change from surface-mounted to interior topology, six new magnet topologies are investigated. The V-shaped interior magnet topology shows superior torque-to-magnet-mass ratio and is easy-to-manufacture. It gives 96% torque while using 56% of the magnet mass compared to the existing motor due to the assist from the additional reluctance torque and the lower magnetic circuit reluctance. The key to use less magnet mass while avoiding the demagnetisation is the front iron shielding effect. The analytical explanation on the better resistance to demagnetisation in the V-shaped motor is provided. The magnet loss mechanism is discussed for proper segmentation. Detailed design adjustments are made to compromise between the torque-to-magnet-mass ratio and the manufactural practicality. Issues regarding to lower mechanical rigidity occurred in initial assembly of the prototype and solutions are proposed. Followed by successful assembly, experimental tests were conducted and results show good agreement with the simulation. A specific form of torque ripple is found in the V-shaped motor and occurs generally in all fractional-slot concentrated-winding machines with saliency. It is explained by an analytical model. This model is also extended to explain the generally lower reluctance torque in vi fractional-slot concentrated-winding machines. Potential design improvements are suggested and simulated for future versions.Protean Electri

Paging and Registration in Cellular Networks: Jointly Optimal Policies and an Iterative Algorithm

This paper explores optimization of paging and registration policies in cellular networks. Motion is modeled as a discrete-time Markov process, and minimization of the discounted, infinite-horizon average cost is addressed. The structure of jointly optimal paging and registration policies is investigated through the use of dynamic programming for partially observed Markov processes. It is shown that there exist policies with a certain simple form that are jointly optimal, though the dynamic programming approach does not directly provide an efficient method to find the policies. An iterative algorithm for policies with the simple form is proposed and investigated. The algorithm alternates between paging policy optimization and registration policy optimization. It finds a pair of individually optimal policies, but an example is given showing that the policies need not be jointly optimal. Majorization theory and Riesz's rearrangement inequality are used to show that jointly optimal paging and registration policies are given for symmetric or Gaussian random walk models by the nearest-location-first paging policy and distance threshold registration policies.Comment: 13 pages, submitted to IEEE Trans. Information Theor

The utilization of paper-level classification system on the evaluation of journal impact

CAS Journal Ranking, a ranking system of journals based on the bibliometric indicator of citation impact, has been widely used in meso and macro-scale research evaluation in China since its first release in 2004. The ranking's coverage is journals which contained in the Clarivate's Journal Citation Reports (JCR). This paper will mainly introduce the upgraded version of the 2019 CAS journal ranking. Aiming at limitations around the indicator and classification system utilized in earlier editions, also the problem of journals' interdisciplinarity or multidisciplinarity, we will discuss the improvements in the 2019 upgraded version of CAS journal ranking (1) the CWTS paper-level classification system, a more fine-grained system, has been utilized, (2) a new indicator, Field Normalized Citation Success Index (FNCSI), which ia robust against not only extremely highly cited publications, but also the wrongly assigned document type, has been used, and (3) the calculation of the indicator is from a paper-level. In addition, this paper will present a small part of ranking results and an interpretation of the robustness of the new FNCSI indicator. By exploring more sophisticated methods and indicators, like the CWTS paper-level classification system and the new FNCSI indicator, CAS Journal Ranking will continue its original purpose for responsible research evaluation

Reverberation Time Control by Acoustic Metamaterials in a Small Room

In recent years, metamaterials have gained considerable attention as a promising material technology due to their unique properties and customizable design, distinguishing them from traditional materials. This article delves into the value of acoustic metamaterials in room acoustics, particularly in small room acoustics that poses specific challenges due to their significant cavity resonant nature. Small rooms usually exhibit an inhomogeneous frequency response spectrum, requiring higher wall absorption with specific spectrum to achieve a uniform acoustic environment, i.e., a constant reverberation time over a wide audible frequency band. To tackle this issue, we developed a design that simultaneously incorporates numerous subwavelength acoustic resonators at different frequencies to achieve customized broadband absorption for the walls of a specific example room. The on-site experimental measurements agree well with the numerical predictions, attesting to the robustness of the design and method. The proposed method of reverse-engineering metamaterials by targeting specific acoustic requirements has broad applicability and unique advantages in small confined spaces with high acoustic requirements, such as recording studios, listening rooms, and car cabins.Comment: 15 pages, 6 figure

Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing

In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks. Nevertheless, GNN's superior performance will suffer from serious damage when the collected node features or structure relationships are partially missing owning to numerous unpredictable factors. Recently emerged graph completion learning (GCL) has received increasing attention, which aims to reconstruct the missing node features or structure relationships under the guidance of a specifically supervised task. Although these proposed GCL methods have made great success, they still exist the following problems: the reliance on labels, the bias of the reconstructed node features and structure relationships. Besides, the generalization ability of the existing GCL still faces a huge challenge when both collected node features and structure relationships are partially missing at the same time. To solve the above issues, we propose a more general GCL framework with the aid of self-supervised learning for improving the task performance of the existing GNN variants on graphs with features and structure missing, termed unsupervised GCL (UGCL). Specifically, to avoid the mismatch between missing node features and structure during the message-passing process of GNN, we separate the feature reconstruction and structure reconstruction and design its personalized model in turn. Then, a dual contrastive loss on the structure level and feature level is introduced to maximize the mutual information of node representations from feature reconstructing and structure reconstructing paths for providing more supervision signals. Finally, the reconstructed node features and structure can be applied to the downstream node classification task. Extensive experiments on eight datasets, three GNN variants and five missing rates demonstrate the effectiveness of our proposed method.Comment: Accepted by 23rd IEEE International Conference on Data Mining (ICDM 2023

Reduced Complexity Mechanisms for Network Resource Allocation

111 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Finally, we show that the VCG-Kelly mechanism can be applied to reduce the complexity of a combinatorial auction if the users' valuation functions satisfy the strong substitute condition. We investigate the properties of substitute functions. The sufficient and necessary condition for a function to be substitute appears quite strong and network users' valuation functions are not substitute generally.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

VCG-Kelly mechanisms for allocation of divisible goods: Adapting VCG mechanisms to one-dimensional signals

Abstract — The VCG-Kelly mechanism is proposed, which is obtained by composing the communication efficient, onedimensional signaling idea of Kelly with the VCG mechanism, providing efficient allocation for strategic buyers at Nash equilibrium points. It is shown that the revenue to the seller can be maximized or minimized using a particular one-dimensional family of surrogate valuation functions. Index Terms— I