scholarly journals Calendar Ageing Model for Li-Ion Batteries Using Transfer Learning Methods

2021 ◽  
Vol 12 (3) ◽  
pp. 145
Author(s):  
Markel Azkue ◽  
Mattin Lucu ◽  
Egoitz Martinez-Laserna ◽  
Iosu Aizpuru
Keyword(s):  
Transfer Learning ◽  
Laboratory Testing ◽  
Operating Conditions ◽  
C Cells ◽  
Li Ion Batteries ◽  
Baseline Model ◽  
Testing Data ◽  
Ageing Model ◽  
Experimental Laboratory ◽  
Li Ion

Getting accurate lifetime predictions for a particular cell chemistry remains a challenging process, largely dependent on time and cost-intensive experimental battery testing. This paper proposes a transfer learning (TL) method to develop LIB ageing models, which allow for the leveraging of experimental laboratory testing data previously obtained for a different cell technology. The TL method is implemented through Neural Networks models, using LiNiMnCoO2/C laboratory ageing data as a baseline model. The obtained TL model achieves an 1.01% overall error for a broad range of operating conditions, using for retraining only two experimental ageing tests of LiFePO4/C cells.

Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

Energy ◽  
2015 ◽  
Vol 86 ◽  
pp. 638-648 ◽  
Author(s):  
Junfu Li ◽  
Lixin Wang ◽  
Chao Lyu ◽  
Liqiang Zhang ◽  
Han Wang
Keyword(s):  
Particle Filter ◽  
Discharge Capacity ◽  
Operating Conditions ◽  
Li Ion Batteries ◽  
Capacity Estimation ◽  
Li Ion

Some Constraints on the Reuse of Li-ion Batteries in Data Centers

2016 ◽  
Vol 0 (0) ◽  
Author(s):  
C. Cepisca ◽  
G.C. Seritan ◽  
S.D. Grigorescu ◽  
Sabina Potlog ◽  
S. Ganatsios
Keyword(s):  
Data Centers ◽  
Operating Conditions ◽  
State Of Charge ◽  
Li Ion Batteries ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Electric Cars ◽  
Electrical Vehicles ◽  
New Ideas ◽  
Li Ion

AbstractThe development of rechargeable electrochemical sources, especially those based on Li-ion technology, has opened the way for their use in various fields ranging from electronics and IT to electric cars. New ideas appear, such as using replaced the batteries from electrical vehicles in the development of energy storage devices for critical consumers such as Data Centers. New uses impose detecting the unfavorable operating conditions which may endanger the power supply of Data Centers. This paper, by modeling and simulation, analyzes some particular problems of Li-ion batteries that appear due to the differences in state of charge of the cells.

An Investigation of Temperature Measurement Granularity Towards Improving Li-Ion Battery Management System Design

2019 ◽  
Author(s):  
Mehrdad Zandigohar ◽  
Nima Lotfi
Keyword(s):  
System Design ◽  
Management System ◽  
Operating Conditions ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion Battery ◽  
Fault Detection And Identification ◽  
Li Ion

Abstract Li-ion batteries have gained increased popularity in the past few decades as the main source in various mobile and stationary energy storage applications. Battery management system design, especially fault diagnosis, however, is still a challenge regarding Li-ion batteries. Traditional Li-ion BMSs rely on measurements from current, voltage, and temperature sensors sparsely located throughout the battery pack. Such a BMS is not capable of predicting battery behavior under various operating conditions; moreover, it cannot account for internal discrepancies among battery cells, incipient faults, the distributed nature of battery parameters and states, and the propagation effects inside a battery pack. Although majority of these effects have already been observed and reported, they are either studied in electrochemistry laboratories using in-situ techniques and detailed theoretical analysis or in practical manufacturing settings by engineers and technicians, which are typically considered proprietary information. The aim of this paper is to bridge the gap between these two domains. In other words, a detailed electrochemical/thermal simulation of a Li-ion battery cell under healthy and faulty conditions is performed to provide a better understanding of the exact spatial requirements for an efficient and reliable thermal management system for Li-ion batteries. The results of this study are specifically of great importance for battery fault detection and identification, mainly due to the recent advancements in distributed sensing technologies such as fiber optics.

scholarly journals Lifetime Degradation Cost Analysis for Li-Ion Batteries in Capacity Markets using Accurate Physics-Based Models

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2816 ◽  
Author(s):  
Ahmed Gailani ◽  
Maher Al-Greer ◽  
Michael Short ◽  
Tracey Crosbie ◽  
Nashwan Dawood
Keyword(s):  
Energy Storage ◽  
Active Material ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Storage Device ◽  
Li Ion Batteries ◽  
Degradation Model ◽  
Capacity Markets ◽  
Degradation Models ◽  
Li Ion

Capacity markets (CM) are energy markets created to ensure energy supply security. Energy storage devices provide services in the CMs. Li-ion batteries are a popular type of energy storage device used in CMs. The battery lifetime is a key factor in determining the economic viability of Li-ion batteries, and current approaches for estimating this are limited. This paper explores the potential of a lithium-ion battery to provide CM services with four de-rating factors (0.5 h, 1 h, 2 h, and 4 h). During the CM contract, the battery experiences both calendar and cycle degradation, which reduces the overall profit. Physics-based battery and degradation models are used to quantify the degradation costs for batteries in the CM to enhance the previous research results. The degradation model quantifies capacity losses related to the solid–electrolyte interphase (SEI) layer, active material loss, and SEI crack growth. The results show that the physics-based degradation model can accurately predict degradation costs under different operating conditions, and thus can substantiate the business case for the batteries in the CM. The simulated CM profits can be increased by 60% and 75% at 5 °C and 25 °C, respectively, compared to empirical and semiempirical degradation models. A sensitivity analysis for a range of parameters is performed to show the effects on the batteries’ overall profit margins.

scholarly journals Electrochemical and Mechanical Failure of Graphite-Based Anode Materials in Li-Ion Batteries for Electric Vehicles

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Cheng Lin ◽  
Aihua Tang ◽  
Ningning Wu ◽  
Jilei Xing
Keyword(s):  
Electron Microscopy ◽  
Anode Materials ◽  
Operating Conditions ◽  
Mechanical Failure ◽  
Ion Concentration ◽  
Electrochemical Reactions ◽  
Particle Model ◽  
Mechanical Degradation ◽  
Li Ion Batteries ◽  
Li Ion

Graphite-based anode materials undergo electrochemical reactions, coupling with mechanical degradation during battery operation, can affect or deteriorate the performance of Li-ion batteries dramatically, and even lead to the battery failure in electric vehicle. First, a single particle model (SPM) based on kinetics of electrochemical reactions was built in this paper. Then the Li-ion concentration and evolution of diffusion induced stresses (DISs) within the SPM under galvanostatic operating conditions were analyzed by utilizing a mathematical method. Next, evolution of stresses or strains in the SPM, together with mechanical degradation of anode materials, was elaborated in detail. Finally, in order to verify the hypothesis aforementioned surface and morphology of the graphite-based anode dismantled from fresh and degraded cells after galvanostatic charge/discharge cycling were analyzed by X-ray diffraction (XRD), field-emission scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The results show that large volume changes of anode materials caused DISs during Li-ion insertion and extraction within the active particles. The continuous accumulations of DISs brought about mechanical failure of the anode eventually.

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