Calendar Degradation and Self-Discharge Occurring During Short- and Long-Term Storage of NMC Based Lithium-Ion Batteries

2021 ◽  
Vol 105 (1) ◽  
pp. 3-11
Author(s):  
Vaclav Knap ◽  
Martin Molhanec ◽  
Alejandro Gismero ◽  
Daniel-Ioan Stroe
Keyword(s):  
Discharge Rate ◽  
Storage Period ◽  
Lithium Ion ◽  
Battery Lifetime ◽  
Long Term Storage ◽  
Capacity Loss ◽  
Degradation Rates ◽  
Battery Capacity ◽  
Short And Long Term

Idling periods are a major part of the Lithium-ion battery operation. Due to parasitic reactions, the battery capacity is decreasing and self-discharge occurs over time. Thus, in order to predict the battery lifetime and optimize its operation, it is required to capture this behavior. In this study, two different storage periods of 2 and 6 months were investigated and used to develop and validate models dedicated to reversible and irreversible capacity loss. It has been observed that while for the shorter storage period, the self-discharge rate does not change significantly, for the longer storage period it decreased during aging. Moreover, the degradation rates vary significantly for various time scales at low temperature, while at medium and high temperatures they are matching closely for 2- and 6-months periodic storage.

scholarly journals Capacity Fade Modeling of Li-Ion Battery using Evolutionary Algorithm

2019 ◽  
Vol 87 ◽  
pp. 01026
Author(s):  
Tamilselvi S ◽  
Karuppiah N ◽  
Rajagopal Reddy B
Keyword(s):  
Evolutionary Algorithm ◽  
Discharge Rate ◽  
Lithium Ion ◽  
Vital Role ◽  
Available Energy ◽  
Capacity Loss ◽  
Life Period ◽  
Internal Parameters ◽  
Battery Capacity ◽  
Rate Discharge

Renewable sources are seasonal and cannot be considered as available energy source as their generation varies with time. The insufficient forecasting techniques lead to thought of storage of energy. Even though many techniques of energy storage are available, batteries play a vital role as the time taken to start delivering the stored energy is very less. The life period of the battery depends upon the charging and discharging characteristics which in turn depend on the internal parameters such as life period, charge rate, discharge rate of the battery. The energy stored in the battery can be calculated by finding these parameters. In this paper these parameters are estimated for a Sony lithium ion battery by evolutionary algorithm CMA-ES under different Charging and discharging rates. As the batteries are charged and discharged there is capacity loss in the battery. This loss is modelled by modified Arrhenius equation on practical conditions. Capacity loss of the sample battery is modelled for five different cycles starting from 50th cycle to 100th cycle in an interval of 10 cycles. The results are validated with those of manufacturer catalogue. The optimized battery capacity loss are found to coincide with the measured values.

scholarly journals Influence of the Calendar Aging on the Cycle Aging of LiNiMnCoO2 Lithium-Ion Batteries

2021 ◽  
Author(s):  
Wen-Feng Cai ◽  
Kuo-Ching Chen
Keyword(s):  
Late Stage ◽  
Lithium Ion ◽  
Storage Conditions ◽  
Cycle Life ◽  
Long Term Storage ◽  
Capacity Loss ◽  
Rest Time ◽  
A Cell ◽  
Semi Empirical

Abstract An experimental and theoretical study of lithium nickel manganese cobalt oxide (NMC) cells with a long rest time under different storage temperatures is carried out. We show that the long-term storage of a cell decisively influences its cycle life, and this influence is more pronounced at the late stage of the battery cycle life. Experimental outcomes demonstrate that the cycle life drops as the storage span lengthens, and the storage under relatively low temperature helps to reduce the cycle fading. Based on the experimental data, we identify the point on the fading trajectory to separate the early-medium stage and the late stage for the cycle aging. By extending the previously proposed semi-empirical model to incorporate the two-stage fading into a single formulation, the cycling capacity loss of the stored NMC cells is predicted. An incremental capacity analysis is further performed to assess the cycle fading of the cells under various storage conditions.

scholarly journals Degradation of chlorobenzene and 2-chlorotoluene by immobilized bacteria strains Comamonas testosterone kt5 and Bacillus subtilis dkt

2019 ◽  
Vol 41 (4) ◽  
Author(s):  
Ha Danh Duc ◽  
Nguyen Thi Oanh
Keyword(s):  
Bacillus Subtilis ◽  
Bacterial Strain ◽  
Immobilized Cells ◽  
Resting Cells ◽  
Long Term Storage ◽  
Degradation Rates ◽  
Contributory Negligence ◽  
Biofilm Bacteria ◽  
Term Storage

Chlorobenzenes and chlorotoluenes have been used to produce a number of industrial products. They are toxic and widely detected in environments due to human contributory negligence. In this article, the mixed culture of a toluenes-degrading bacterial strain, Comamonas testosterone KT5 (a Gram-positive, catalase-positive bacterium) and a chlorobenzenes-degrading bacterial strain, Bacillus subtilis DKT (a Gram-negative soil bacterium) effectively degraded both chemical compounds co-contaminating in liquid media. In addition, the degradations of mixed compounds by biofilm, bacteria immobilized in polyurethane foam (PUF) and alginate were determined. The results showed that the degradation of both compounds by cells in alginate was significantly higher than that by suspended cells. Moreover, cells immobilized in these materials showed lower adverse effects than those of non-immobilized cells for long-term storage. For examples, the degradation rates for chlorobenzine and 2-chlorotoluene by resting cells reduced by 39.5% and 37.3% after storage for 4 months at 4°C, while the degradation rates by immobilized cells decreased by from 16.3% to 19.8% respectively. 

Capacity Loss Estimation For Li-Ion Batteries Based On A Semi-Empirical Model

2021 ◽  
Author(s):  
Mohammed Rabah ◽  
Eero Immonen ◽  
Sajad Shahsavari ◽  
Mohammad-Hashem Haghbayan ◽  
Kirill Murashko ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Empirical Model ◽  
Lithium Ion ◽  
Average Error ◽  
Li Ion Batteries ◽  
Capacity Loss ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Li Ion ◽  
Semi Empirical

Understanding battery capacity degradation is instrumental for designing modern electric vehicles. In this paper, a Semi-Empirical Model for predicting the Capacity Loss of Lithium-ion batteries during Cycling and Calendar Aging is developed. In order to redict the Capacity Loss with a high accuracy, battery operation data from different test conditions and different Lithium-ion batteries chemistries were obtained from literature for parameter optimization (fitting). The obtained models were then compared to experimental data for validation. Our results show that the average error between the estimated Capacity Loss and measured Capacity Loss is less than 1.5% during Cycling Aging, and less than 2% during Calendar Aging. An electric mining dumper, with simulated duty cycle data, is considered as an application example.

scholarly journals Derating Guidelines for Lithium-Ion Batteries

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3295 ◽  
Author(s):  
Yongquan Sun ◽  
Saurabh Saxena ◽  
Michael Pecht
Keyword(s):  
Lithium Ion ◽  
Stress Factors ◽  
Li Ion Batteries ◽  
Degradation Data ◽  
Capacity Loss ◽  
Battery Capacity ◽  
Operational Life ◽  
Battery Degradation ◽  
Li Ion

Derating is widely applied to electronic components and products to ensure or extend their operational life for the targeted application. However, there are currently no derating guidelines for Li-ion batteries. This paper presents derating methodology and guidelines for Li-ion batteries using temperature, discharge C-rate, charge C-rate, charge cut-off current, charge cut-off voltage, and state of charge (SOC) stress factors to reduce the rate of capacity loss and extend battery calendar life and cycle life. Experimental battery degradation data from our testing and the literature have been reviewed to demonstrate the role of stress factors in battery degradation and derating for two widely used Li-ion batteries: graphite/LiCoO2 (LCO) and graphite/LiFePO4 (LFP). Derating factors have been computed based on the battery capacity loss to quantitatively evaluate the derating effects of the stress factors and identify the significant factors for battery derating.

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