Remaining Useful Life Prediction for Lithium-Ion Batteries Based on the Partial Voltage and Temperature

Abstract

Remaining useful life (RUL) prediction is vital to provide accurate decision support for a safe power system. In order to solve capacity measurement difficulties and provide a precise and credible RUL prediction for lithium-ion batteries, two health indicators (HIs), the discharging voltage difference of an equal time interval (DVDETI) and the discharging temperature difference of an equal time interval (DTDETI), are extracted from the partial discharging voltage and temperature. Box-Cox transformation, which is data processing, is used to improve the relation grade of HIs. In addition, the Pearson correlation is employed to evaluate the relationship degree between HIs and capacity. On this basis, a local Gaussian function and a global sigmoid function are utilized to improve the multi-kernel relevance vector machine (MKRVM), whose weights are optimized by applying a whale optimization algorithm (WOA). The availability of the extracted HIs as well as the accuracy of the RUL prediction are verified with the battery data from NASA.

Fabrication of samarium doped MOF-808 as an efficient photocatalyst for the removal of the drug cefaclor from water

“…This could be resolved by reduction of particle dimensions making it suitable for catalytic reactions. 59,60…”

“…This could be resolved by reduction of particle dimensions making it suitable for catalytic reactions. 59,60 The absorbance of MOF-808(Ce) was less than doped Sm/ MOF-808(Ce) as HOMO-LUMO gap of pristine MOF-808(Ce) (1.81 eV) is large. MOFs photocatalyst possess the metallic chains that help in capturing visible photons and generate electron-holes in the process.…”

“…Small band gaps enable visible light excitation in MOFs.Less electrons and hole recombination will limit the generation of oxidizing species/radicals. This could be resolved by reduction of particle dimensions making it suitable for catalytic reactions 59,60. The absorbance of MOF-808(Ce) was less than doped Sm/ MOF-808(Ce) as HOMO-LUMO gap of pristine MOF-808(Ce) (1.81 eV) is large.…”

A Novel Fine-Tuning Model Based on Transfer Learning for Future Capacity Prediction of Lithium-Ion Batteries

“…The mobile devices' service time depends on the battery's State of Health (SOH), which can represent the batteries' working available period, long or short [1]. The ability to estimate how much energy a Li-ion battery will be able to store and deliver over time, based on various factors such as usage patterns, temperature, charging and discharging rates, and the age of the battery has become an important issue [2][3][4][5][6]. Forecasting the future capacity of batteries and their Remaining Useful Life (RUL) represents a complex issue within the domain of battery health diagnosis and management applications [7,8].…”