Effect of Charge-Discharge Depth and Environment Use Conditions on Flexible Power Sources

2019 ◽  
Author(s):  
Pradeep Lall ◽  
Ved Soni ◽  
Amrit Abrol ◽  
Ben Leever ◽  
Scott Miller
Keyword(s):  
High Temperatures ◽  
High Capacity ◽  
Form Factors ◽  
Lithium Ion ◽  
Consumer Electronics ◽  
Cycle Number ◽  
Power Sources ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Charge Discharge

Abstract Recent surge in demand for wearable technology products such as activity tracking smartwatches, and for medical devices has necessitated development of flexible secondary lithium ion batteries which also possess high capacity, robustness and thin form factors. Oftentimes, these power sources are only charged up to a partial state of charge (SoC) before use (shallow charge). Their usage continues until the SoC reaches almost zero, after which they are recharged again. Nowadays, the ‘fast-charge ‘feature used to charge the battery at higher C-rates, is a necessity in consumer electronics rather than an amenity. Also, in everyday use, these batteries are exposed to higher-than-ambient temperatures due to perpetual human body contact and also to the high temperatures resulting from poor thermal management in compact devices. This study investigates the compounded influence of partial charge, high temperatures and high C-rates on the capacity degradation of a flexible Li-ion power source subjected to accelerated life testing. The battery current and terminal voltage were logged for multiple charge-discharge cycles and were used to compute the battery capacity and energy efficiency. Finally, a regression model based on several parameters was developed to estimate the battery capacity as a function of the cycle number.

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

scholarly journals Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

2021 ◽  
Vol 13 (23) ◽  
pp. 13333
Author(s):  
Shaheer Ansari ◽  
Afida Ayob ◽  
Molla Shahadat Hossain Lipu ◽  
Aini Hussain ◽  
Mohamad Hanif Md Saad
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Recurrent Neural Network ◽  
Single Channel ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Channel Input ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life

Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.

scholarly journals A Short Review of Lithium-ion Battery Technology

2020 ◽  
pp. 500-507
Author(s):  
Imran Hussain Sardar ◽  
Souren Bhattacharyya
Keyword(s):  
Power Systems ◽  
Lithium Batteries ◽  
Lithium Battery ◽  
High Efficiency ◽  
Lithium Ion ◽  
Short Review ◽  
Consumer Electronics ◽  
Power Sources ◽  
High Specific Energy ◽  
Battery Technology

Lithium batteries are characterized by high specific energy, high efficiency and long life. These unique properties have made lithium batteries the power sources of choice for the consumer electronics market with a production of the order of billions of units per year. These batteries are also expected to find a prominent role as ideal electrochemical storage systems in renewable energy plants, as well as power systems for sustainable vehicles, such as hybrid and electric vehicles. However, scaling up the lithium battery technology for these applications is still problematic since issues such as safety, costs, wide operational temperature and materials availability, are still to be resolved. This review focuses first on the present status of lithium battery technology, then on its near future development and finally it examines important new directions aimed at achieving quantum jumps in energy and power content.

Two-dimensional SnS2@PANI nanoplates with high capacity and excellent stability for lithium-ion batteries

2015 ◽  
Vol 3 (7) ◽  
pp. 3659-3666 ◽  
Author(s):  
Gang Wang ◽  
Jun Peng ◽  
Lili Zhang ◽  
Jun Zhang ◽  
Bin Dai ◽  
...  
Keyword(s):  
Electron Transport ◽  
Lithium Ion Batteries ◽  
Electrode Materials ◽  
High Capacity ◽  
Lithium Ion ◽  
Two Dimensional ◽  
Volume Changes ◽  
Nanostructured Electrode ◽  
Charge Discharge ◽  
Excellent Stability

Nanostructured electrode materials have been extensively studied with the aim of enhancing lithium ion and electron transport and lowering the stress caused by their volume changes during the charge–discharge processes of electrodes in lithium-ion batteries.

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.

Evaluation of the influence of high-power charging cycles on the capacity degradation of lithium-ion batteries under various temperatures

2021 ◽  
pp. 014233122110585
Author(s):  
Xiaogang Wu ◽  
Yinlong Xia ◽  
Jiuyu Du ◽  
Kun Zhang ◽  
Jinlei Sun
Keyword(s):  
Lithium Ion Batteries ◽  
High Power ◽  
Electrode Materials ◽  
Electrochemical Impedance ◽  
Degradation Mechanism ◽  
Solid Electrolyte Interphase ◽  
Lithium Ion ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Charging Current

High-power-charging (HPC) behavior and extreme ambient temperature not only pose security risks on the operation of lithium-ion batteries but also lead to capacity degradation. Exploring the degradation mechanism under these two conditions is very important for safe and rational use of lithium-ion batteries. To investigate the influence of various charging-current rates on the battery-capacity degradation in a wide temperature range, a cycle-aging test is carried out. Then, the effects of HPC on the capacity degradation at various temperatures are analyzed and discussed using incremental capacity analysis and electrochemical impedance spectroscopy. The analysis results show that a large number of lithium ions accelerate the deintercalation when the HPC cycle rate exceeds 3 C, making the solid electrolyte interphase at the negative surface unstable and vulnerable to destruction, which results in irreversible consumption of active lithium. In addition, the decomposition of electrolyte is significantly promoted when the HPC temperature is more than 30°C, resulting in accelerated consumption of electrode materials and active lithium, which are the main reasons for the capacity degradation of lithium-ion batteries during HPC under various temperatures.

ELECTROCHEMICAL PROPERTIES OF BiFeO3 THIN FILMS PREPARED BY PULSED LASER DEPOSITION

2009 ◽  
Vol 02 (04) ◽  
pp. 163-167 ◽  
Author(s):  
HUI XIA ◽  
FENG YAN ◽  
MAN ON LAI ◽  
LI LU ◽  
WENDONG SONG
Keyword(s):  
Pulsed Laser Deposition ◽  
Steel Substrate ◽  
High Capacity ◽  
Pulsed Laser ◽  
Lithium Ion ◽  
Reversible Capacity ◽  
Laser Deposition ◽  
Potential Range ◽  
Morphology Characterization ◽  
Charge Discharge

BiFeO3 thin film is deposited on the stainless steel substrate by pulsed laser deposition (PLD). Structural characterization using X-ray diffraction indicates that the film is polycrystalline with perovskite structure. Morphology characterization by field emission scanning electron microscopy reveals that the film is composed of grains with wide size distribution with a porous structure. Charge/discharge in the potential range between 0.05 and 3 V shows that the reversible capacity of the first charge/discharge cycle at 280 mA/g current density is about 770 mAh/g with high columbic efficiency about 74%. The reversible capacity after 50 cycles remains 50% of its initial capacity. The preliminary results indicated that BiFeO3 is a promising high capacity anode for lithium-ion batteries.

Corrigendum to ‘Graphite-coated ZnO nanosheets as high-capacity, highly stable, and binder-free anodes for lithium-ion batteries’ [J. Power Sources 320 (2016) 314–321]

2016 ◽  
Vol 333 ◽  
pp. 254 ◽  
Author(s):  
Eliana Quartarone ◽  
Valentina Dall'Asta ◽  
Alessandro Resmini ◽  
Cristina Tealdi ◽  
Ilenia Giuseppina Tredici ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
High Capacity ◽  
Lithium Ion ◽  
Power Sources ◽  
Zno Nanosheets ◽  
Binder Free
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