Battery (electricity)

Quantum mechanics

Voltage

Engineering

State of health

Lithium (medication)

Reliability engineering

Statistics

Support vector machine

Power (physics)

Computer science

Battery capacity

Regression analysis

Physics

Organic chemistry

Regression

Mathematics

Feature selection

Internal medicine

Interval (graph theory)

Combinatorics

Medicine

Chemistry

Prognostics

Machine learning

Electrical engineering

Microelectronics Reliability

Traditional approaches to lithium-ion battery health management mostly focus on the state of charge (SOC) estimation issues, whereas the state of health (SOH) estimation is also critical to lithium-ion batteries for safe operation. For online battery prognostics, it is critical to make timely and accurate response to SOH. The loss of rated capacity of a battery is usually used to determine the battery SOH, whereas the measurement of the capacity of an operating battery is quite challenging. Normally, the rated capacity fading largely relies on laboratory measurements and offline analysis. In this paper, two real-time measurable health indicators (HI) - one is the time interval of an equal charging voltage difference (TIECVD), and the other is the time interval of an equal discharging voltage difference (TIEDVD) - are extracted. A novel method which combines feature vector selection (FVS) with SVR is utilized to model the relationship between these two HIs and capacity, then the online capacity can be evaluated, more accurate prognostics of SOH and remaining useful life (RUL) can be made. Besides, compared to standard SVR, the proposed method takes FVS to cut down the training data size, which improves the efficiency of model training and prediction. In the end, two datasets demonstrated this approach performs both well in accuracy and efficiency.

A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries