The proposed project will address the technical issues of the railway system including train components, rail infrastructure and power signaling systems, which are critical from the viewpoints of safety, punctuality and ride comfort. New technologies are to be developed by the research team with multidisciplines in structural health monitoring, sensing technology, vibration and acoustic control, electrical power supply, smart materials and energy harvesting. The direct research outputs of this project are new technological systems, smart devices, material designs and potential applications that go beyond the current practice and standards. To achieve this target, the proposed research will develop
This project will improve the local railway operation safety and serviceability in the short term and will enhance the sustainability for future railway development and benefit the whole society in the long run.
1 Jun 2019 – 31 May 2023
RIF
In the proposed research, we will focus on developing interdisciplinary fusion-based approaches that can flexibly incorporate multi-source data streams and advanced forecasting techniques. It is expected that various spatiotemporal forecasting problems can significantly benefit from these newly developed methodologies, models, and algorithms to further improve the prediction accuracy in real applications.
1 Jan 2020 – 31 Dec 2022
GRF
The project aims to extend the advantage of Hong Kong by establishing it as a center of expertise in the safety, reliability, and efficient management of complex networking systems.
In this project, the following deliverables are anticipated:
1 Jan 2016 – 31 Dec 2020
TBRS
The ubiquitous use of battery-powered electronic devices has created a strong demand of sophisticated battery management systems (BMSs) to maintain battery safety and reliability. Prognostic and health management (PHM), a framework offering comprehensive yet individualized solutions for managing system health, has been successfully applied in BMSs. Nevertheless, the increasingly complex battery systems pose significant barriers to existing PHM methods for battery status evaluation, due to the fact that these methods are often empirical and population based. As a consequence, the estimation and prediction of battery status might be highly biased and thus lead to safety hazard and other problems in practical operations.
Motivated by the new challenges encountered in existing BMSs, the proposed research develops a radically new approach for monitoring and evaluating battery health status by incorporating the advances in PHM modeling and analysis techniques. The proposed research approach focuses on effective and efficient estimation of battery health status based on integration of empirical knowledge and real-time data, and heterogeneous information on each individual, including the actual field operating conditions and ambient environment of a battery system. It is a worldwide trend in PHM research to shift the traditional paradigm from empirical to data fusion and from population based to individual based. The proposed research is promising across a wide range of battery-powered applications.
The health status of rechargeable battery is mainly characterized by two key parameters: state of health (SOH) and state of charge (SOC). SOH denotes the remaining performance of a battery over its whole life cycle, which is usually quantified by remaining useful life (RUL), while SOC quantifies the remaining usable energy at the present cycle. The main objective of this research is to develop innovative modeling methods for estimating SOH and SOC by incorporating additional information and PHM advances into the modeling process.
In particular, we will
1 Jan 2018 – 31 Dec 2020
GRF
