Search

Your search keyword '"Jian, Linni"' showing total 189 results
Start Over Author "Jian, Linni" Publication Year Range Last 10 years
189 results on '"Jian, Linni"'

1. Addressing Trust Issues for Vehicle to Grid in Distributed Power Grids Using Blockchains

Author

Chen, Yunwang, Lei, Xiang, and Jian, Linni

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

While blockchain offers inherent security, trust issues among stakeholders in vehicle-to-grid (V2G) applications remain unresolved due to a lack of regulatory frameworks and standardization. Additionally, a tailored decentralized privacy-preserved coordination scheme for blockchain in V2G networks is needed to ensure user privacy and efficient energy transactions. This paper proposes a V2G trading and coordination scheme tailored to the decentralized nature of blockchain as well as the interests of stakeholders utilizing smart charging points (SCPs) and Stackelberg game model. Case studies using real-world data from Southern University of Science and Technology demonstrate the efficacy of proposed scheme in reducing EV charging costs and the potential for supporting auxiliary grid services., Comment: This paper has been accepted by The 14th International Conference on Power and Energy Systems (ICPES 2024)

Published
2024

2. Trustworthy V2G scheduling and energy trading: A blockchain-based framework

Author

Chen, Yunwang, Lei, Xiang, Niu, Songyan, and Jian, Linni

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

The rapid growth of electric vehicles (EVs) and the deployment of vehicle-to-grid (V2G) technology pose significant challenges for distributed power grids, particularly in fostering trust and ensuring effective coordination among stakeholders. Establishing a trustworthy V2G operation environment is crucial for enabling large-scale EV user participation and realizing V2G potential in real-world applications. In this paper, an integrated scheduling and trading framework is developed to conduct transparent and efficacious coordination in V2G operations. In blockchain implementation, a cyber-physical blockchain architecture is proposed to enhance transaction efficiency and scalability by leveraging smart charging points (SCPs) for rapid transaction validation through a fast-path practical byzantine fault tolerance (fast-path PBFT) consensus mechanism. From the energy dispatching perspective, a game-theoretical pricing strategy is employed and smart contracts are utilized for autonomous decision-making between EVs and operators, aiming to optimize the trading process and maximize economic benefits. Numerical evaluation of blockchain consensus shows the effect of the fast-path PBFT consensus in improving systems scalability with a balanced trade-off in robustness. A case study, utilizing real-world data from the Southern University of Science and Technology (SUSTech), demonstrates significant reductions in EV charging costs and the framework potential to support auxiliary grid services.

Published
2024

13. Hull-Compatible Underwater IPT System with Enhanced Electromagnetic–Thermal Performance for USVs.

Author

Zhao, Qingyu, Niu, Songyan, Shao, Ziyun, and Jian, Linni

Subjects
UNDERWATER exploration, WIRELESS power transmission, LIGHTWEIGHT construction, FRESH water, SUBMERSIBLES
Abstract

With the growing use of unmanned surface vehicles (USVs) for underwater exploration, efficient wireless charging solutions like inductive power transfer (IPT) are crucial for addressing power limitations. This paper presents a novel IPT system for USVs and introduces a systematic design approach for optimizing magnetic couplers. The proposed design addresses three critical challenges: misalignment tolerance, lightweight construction, and thermal safety, which are intricately linked through a magnetic field. In terms of misalignment, this paper demonstrates that the coil length is a key factor in determining misalignment tolerance. For a lightweight design, replacing the ferrite plate with ferrite bars can significantly reduce the weight of the coupler without causing core saturation. The design is further validated through a two-way coupled electromagnetic–thermal simulation. The results reveal that, with proper thermal management, the system avoids thermal risks in underwater environments compared to air. Finally, a 3 kW prototype is constructed and tested in fresh water, achieving 55 V and 50 A wireless charging at an 85.7% full-load dc-to-dc efficiency, thus confirming the practicality and performance of the design. [ABSTRACT FROM AUTHOR]

Published
2025
Full Text
View/download PDF

25. An Information Security Solution for Vehicle-to-grid Scheduling by Distributed Edge Computing and Federated Deep Learning

Author

Shang, Yitong, Li, Zekai, Li, Sen, Shao, Ziyun, Jian, Linni, Shang, Yitong, Li, Zekai, Li, Sen, Shao, Ziyun, and Jian, Linni

Abstract

This work proposes an information security vehicleto- grid (V2G) scheduling solution, which combines Federated deep learning with distributed edge computing for V2G operation. In this framework, each charging point is equipped with an intelligent computing module to conduct distributed edge scheduling for the connected electric vehicle (EV), so that not only the computation of inference process is efficient, but also the privacy-preserving of EV users is guaranteed. Besides, the desensitized V2G data of charging points are used to train the deep neural network model in each charging station. Therefore, the accurate future data acquisition problem and the uncertainty handling challenges under traditional optimization methods is avoided. At the same time, the spatial-based and time-based clustering methods are applied to improve the accuracy of prediction. Finally, through federated learning, each charging station uploads the local model to the cloud server, and a stochastic client selection pattern is designed to improve the scalability of model aggregation in the cloud server. In this way, the digital assets of each charging station are protected, and computing and communication costs are reduced. Simulation results on real datasets show that the proposed framework has superior performance in terms of training accuracy, communication burden, and computing performance, while maintaining the privacy of EV users and the digital assets of charging stations. IEEE

Published
2024

28. A Comprehensive Investigation of Thermal Risks in Wireless EV Chargers Considering Spatial Misalignment From a Dynamic Perspective

Author

Niu, Songyan, Zhao, Qingyu, Niu, Shuangxia, and Jian, Linni

Abstract

As wireless electric vehicle charging (WEVC) technology moves toward commercialization, understanding its associated risks is crucial, particularly with the trend toward high-power fast charging. In this article, thermal risks and factors exacerbating the risks in wireless electric vehicle chargers are investigated comprehensively, focusing on the magnetic coupler and foreign objects (FOs) intruding on the charging area. The power losses and temperatures of the coupler and FOs are obtained using a two-way electromagnetic–thermal coupled model, and the coupler will be assigned to a risk level according to the proposed four-temperature-level risk evaluation mechanism. In this model, practical considerations include coil types, misalignments, FO materials, ambient temperatures, etc. Moreover, a dynamic perspective is used to characterize the trend of risks so as to provide predictive insights. To validate the newly identified risks, two 6.6-kW WEVC prototypes are built employing circular coils and DD coils, respectively. The experimental studies confirm the necessity of implementing thermal risk management strategies for magnetic coupler under misaligned conditions, such as enhanced cooling, including a wider range of materials for foreign object detection (FOD), expanding the FOD area, and so forth.

Published
2024
Full Text
View/download PDF

38. Predicting the state of health of VRLA batteries in UPS using data-driven method

Author

Shang, Yitong, Zheng, Weike, Yan, Xiaoyun, Nguyen, Dinh Hoa, Jian, Linni, Shang, Yitong, Zheng, Weike, Yan, Xiaoyun, Nguyen, Dinh Hoa, and Jian, Linni

Abstract

Uninterruptible power battery (UPS) is an important part to ensure the stable operation of data center. Its security is related to the reliability and stability of power system. Among them, the state of health (SOH) prediction is a key issue of the valve regulated lead–acid (VRLA) battery operation and maintenance in data center. In this work, the battery SOH is predicted by the correlation between the nadir voltage value of Coup De Fouet (CDF) phenomenon and SOH. Then, the CDF phenomenon is combined with popular data-driven methods, such as linear regression, regression tree, support-vector machine, gaussian process, neural network, to predict battery SOH through 215 features. Finally, the above method is verified with the real discharge dataset of UPS battery in data center. The experimental results show that the data-driven method combining big data has higher accuracy than the simple prediction of battery SOH based on the nadir voltage value of CDF phenomenon and its variants. © 2023 The Authors

Published
2023

44. Noncooperative Metal Object Detection Using Pole-to-Pole EM Distribution Characteristics for Wireless EV Charger Employing DD Coils

Author

Niu, Songyan, Zhao, Qingyu, Chen, Haibiao, Niu, Shuangxia, and Jian, Linni

Abstract

Metal object detection (MOD) is indispensable for wireless electric vehicle charging (WEVC) systems for safety concerns. Most recently, interests are growing in MOD by electromagnetic (EM) sensing. However, current works based on EM sensing rarely target the systems with DD coils, a promising coil type for commercialization. Additionally, EM sensing schemes are mostly derived from a mindset, viz., cooperation between two sensing coils to obtain the voltage difference as a MOD criterion. Hence, undesired out-of-phase incident becomes an inherent problem jeopardizing the success rates of detection. In this article, noncooperative MOD, which solely relies on the voltages of individual sensing coils rather than cooperative ones, is proposed with absolute confidence in detection results. A unique sensing coil array is devised to suit the field characteristics of DD coils perfectly. Size-modulated patch coils are created to eliminate the undetectable zones with proper polarity configuration. Moreover, virtual double-layer sensing coils are constructed to enhance MOD's reliability. Tested by 13 different positions and three types of MOs using a 3.0-kW WEVC prototype, the induced voltages reach at least four times the threshold value. Moreover, the stability of the proposed method is verified successfully no matter the system operates in light-load or heavy-load conditions.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results