Reliability and Degradation Modelling for Rechargeable Battery

Project: Research

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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 (i) develop degradation models and estimation methods for reliable RUL prediction of rechargeable batteries; (ii) develop effective and robust methods that provide accurate estimation for SOC of rechargeable batteries, particularly under a dynamic ambient environment; (iii) evaluate and test the proposed methods using both experimental and field data, and compare their performance with the state of art methods.?


Project number9042490
Effective start/end date1/01/18 → …