Projects per year
Abstract
Lithium-ion batteries are widely used in hybrid electric vehicles, consumer electronics, etc. As of today, given a room temperature, many battery prognostic methods working at a constant discharge rate have been proposed to predict battery remaining useful life (RUL). However, different discharge rates (DDRs) affect both usable battery capacity and battery degradation rate. Consequently, it is necessary to take DDRs into consideration when a battery prognostic method is designed. In this paper, we propose a discharge-rate-dependent battery prognostic method that is able to track usable battery capacity affected by DDRs in the process of battery degradation and to predict RUL at DDRs. An experiment was designed to collect accelerated battery life testing data at DDRs, which are used to investigate how DDRs influence usable battery capacity, to design a discharge-rate-dependent state space model and to validate the effectiveness of the proposed battery prognostic method. Results show that the proposed battery prognostic method can work at DDRs and achieve high RUL prediction accuracies at DDRs.
| Original language | English |
|---|---|
| Pages (from-to) | 212-219 |
| Journal | Microelectronics Reliability |
| Volume | 78 |
| Online published | 14 Sept 2017 |
| DOIs | |
| Publication status | Published - Nov 2017 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Research Keywords
- Different discharge rates
- Lithium-ion batteries
- Particle filter
- Prognostics and health management
- Remaining useful life
Fingerprint
Dive into the research topics of 'Battery remaining useful life prediction at different discharge rates'. Together they form a unique fingerprint.Projects
- 1 Finished
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GRF: Integrated Modeling for Remaining Useful Life Prediction and System Health Management
TSUI, K. L. (Principal Investigator / Project Coordinator) & Zhou, Q. (Co-Investigator)
1/01/15 → 12/12/18
Project: Research
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