Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression
University of Science and Technology of China
Abstract
Accurate remaining useful life (RUL) prediction and state-of-health (SOH) diagnosis are of extreme importance for safety, durability, and cost of energy storage systems based on lithium-ion batteries. It is also a crucial challenge for energy storage systems to predict RUL and diagnose SOH of batteries due to the complicated aging mechanism. In this paper, a novel method for battery RUL prediction and SOH estimation is proposed. First, a novel support vector regression-based battery SOH state-space model is established to simulate the battery aging mechanism, which takes the capacity as the state variable and takes the representative features during a constant-current and constant-voltage protocol as the input…
Citation impact
- FWCI
- 23.39
- Percentile
- 100%
- References
- 32
Authors
3Topics & keywords
- State of health
- Particle filter
- Robustness (evolution)
- Control theory (sociology)
- Battery (electricity)
- State of charge
- Voltage
- Constant current