Capacity Degradation Model Research Articles

A capacity degradation model under interferences for sectorized cellular networks with fractional frequency reuse

AbstractThis paper presents a capacity degradation model under interferences for sectorized cellular networks with fractional frequency reuse. In a sectorized fractional frequency reuse network, allocated bandwidth consists of a number of frequency partitions. To avoid intra‐cell interferences, each frequency partition is then assigned to two different groups, the super group (Sup‐G) and the regular group. However, because a Sup‐G in a cell employs the same frequency partition as the other Sup‐Gs in the neighboring cells, inter‐cell interferences may become serious when the radius of the Sup‐G is largely increased. In the worse case, the largely increasing inter‐cell interferences will eventually degrade the overall system capacity. Additionally, different density distributions of mobile stations may have different levels of impact on the capacity degradation. In this paper, both folded normal distribution and uniform distribution are considered in building the capacity degradation model. Numerical simulations show that by carefully adjusting the ratio between the radius of the Sup‐G and the radius of a cell, (i) an mobile station could receive the same capacity no matter where it is residing, and (ii) the maximum system capacity can be therefore achieved. Copyright © 2013 John Wiley & Sons, Ltd.

Research on Evolution Model of Inconsistency of Power Battery Pack

In order to know how inconsistency of power battery pack evolves during work, capacity range of cells was studies. The characteristic of operating circuit of power battery pack both in parallel connecting and in serial connecting was analyzed. Then, ratio models with parameters such as charging/discharging rate and depth of discharging of each cell in pack were studied. Remaining capacity of a cell was calculated according to the capacity degradation model. Evolution model of capacity range of cells in pack was established. Based on that of parallel or serial connecting, evolution models of capacity and capacity range in hybrid connecting were established too. Models established here would be helpful to determine cycle life of power battery pack.

Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model

Lithium-ion batteries are widely used as power sources in commercial products, such as laptops, electric vehicles (EVs) and unmanned aerial vehicles (UAVs). In order to ensure a continuous power supply, the functionality and reliability of lithium-ion batteries have received considerable attention. In this paper, a battery capacity prognostic method is developed to estimate the remaining useful life of lithium-ion batteries. This capacity prognostic method consists of a relevance vector machine and a conditional three-parameter capacity degradation model. The relevance vector machine is used to derive the relevance vectors that can be used to find the representative training vectors containing the cycles of the relevance vectors and the predictive values at the cycles of the relevance vectors. The conditional three-parameter capacity degradation model is developed to fit the predictive values at the cycles of the relevance vectors. Extrapolation of the conditional three-parameter capacity degradation model to a failure threshold is used to estimate the remaining useful life of lithium-ion batteries. Three instance studies were conducted to validate the developed method. The results show that the developed method is able to predict the future health condition of lithium-ion batteries.

An experimentally validated capacity degradation model for Li-ion batteries in PHEVs applications

This paper proposes an experimentally validated capacity degradation model for Li-Ion batteries deployed in plug-in hybrid electric vehicle (PHEV) applications. An aging and characterization campaign aimed to mimic the real battery usage at low state of charge during charge sustaining operation was conducted on six Lithium iron phosphate (LiFePO4) battery cells. We present an analytical model, driven by experimental data, that relates the main aging factors under which the battery cells have been tested to capacity fade. State-of-health (SOH) and prediction of battery end-of-life (EOL) algorithms can be designed with the proposed model.

Modeling the capacity degradation of LiFePO 4/graphite batteries based on stress coupling analysis

A coupling-analysis-based model to predict the capacity degradation of LiFePO 4 batteries under multi-stress accelerated conditions has been developed. In this model, the joint effect on the battery capacity degradation of any 2 out of 5 stress factors, which include ambient temperature, end of discharge and charge voltage (EODV and EOCV), and discharge and charge rate, is studied through coupling validation tests. Coupling generally exists among these 5 stress factors, and the coupling intensity has a certain relationship with the stress levels. There is a critical stress level at which the coupling can be considered negligible, and when the stress level goes higher, coupling aggravates battery degradation exponentially. Additionally, the study also indicates that battery life shows stronger sensitivity to discharge rate and EOCV than to charge rate and EODV. The developed capacity degradation model based on the input of real operating conditions and coupling intensity calibration achieves error less than 15% when the cycling goes into the stable decay period, and the error converges gradually as the cycling continues.