Bin Yu
Research Subjects
Bin Yu has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
Lei Yao
College of System Engineering
1 shared publicationTao Zhang
College of System Engineering
1 shared publication- Tianyu Liu
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
1 shared publication
Latest Publications
- Open Access
- 6 December 2018
- Published by Wiley in Mathematical Problems in Engineering
- Vol. 2018, 1-12
Reliability Evaluation and In-Orbit Residual Life Prediction for Satellite Lithium-Ion Batteries
As a new type of secondary battery, lithium-ion battery is widely used in the aerospace industry with the advantages of long lifetime, high energy density and low pollution, etc. In this paper, we focus on the problem of offline and online life prediction for satellite lithium-ion batteries. Firstly, based on the NASA laboratory battery dataset, a Wiener process with time-scale transformation is used to capture battery capacity fading, and then the battery reliability point and interval estimation equation are derived, respectively. Secondly, by analyzing the charge and discharge profiles of the batteries in orbit environments, an accurate capacity prediction model is proposed based on the partial charging curves. Finally, the Bayesian framework is used to perform capacity degradation model online updating, and the analytical expression of residual life distribution is derived to achieve RL prediction for in-orbit satellite lithium-ion batteries.
- 6 December 2018
- Published by Wiley in Mathematical Problems in Engineering
- Vol. 2018, 1-12
Open AccessReliability Evaluation and In-Orbit Residual Life Prediction for Satellite Lithium-Ion Batteries
As a new type of secondary battery, lithium-ion battery is widely used in the aerospace industry with the advantages of long lifetime, high energy density and low pollution, etc. In this paper, we focus on the problem of offline and online life prediction for satellite lithium-ion batteries. Firstly, based on the NASA laboratory battery dataset, a Wiener process with time-scale transformation is used to capture battery capacity fading, and then the battery reliability point and interval estimation equation are derived, respectively. Secondly, by analyzing the charge and discharge profiles of the batteries in orbit environments, an accurate capacity prediction model is proposed based on the partial charging curves. Finally, the Bayesian framework is used to perform capacity degradation model online updating, and the analytical expression of residual life distribution is derived to achieve RL prediction for in-orbit satellite lithium-ion batteries.