Assessment of Products Future Reusability Based on Consumers Usage Behavior: Implications for Lithium-Ion Laptop Batteries

2015 ◽  
Author(s):  
Mostafa Sabbaghi ◽  
Behzad Esmaeilian ◽  
Ardeshir Raihanian Mashhadi ◽  
Willie Cade ◽  
Sara Behdad
Keyword(s):  
High Reliability ◽  
Lithium Ion ◽  
Degradation Process ◽  
Rechargeable Batteries ◽  
Data Set ◽  
Utilization Behavior ◽  
Product Reuse ◽  
Number Of Cycles ◽  
Stability Time ◽  
Product Durability

Product reuse is a recommended action toward sustainability. However, the profitable reusability of End-of-Use or End-of-Life (EoU/L) products depends on how consumers have used them over the initial lifecycles and what are their EoU conditions. In addition to consumers’ behavior, product design features such as product durability has an impact on the future reusability. In this paper, a data set of Lithium-ion laptop batteries has been studied with the aim of investigating the potential reusability of laptop batteries. This type of rechargeable batteries is popular due to their energy efficiency and high reliability. Therefore, understanding the lifetime of these batteries and improving the recycling process is becoming important. In this paper, the reusability assessment is linked to the consumer behavior and degradation process simultaneously through monitoring the performance of batteries over their lifetimes. After capturing the utilization behavior, the performance-based stability time of batteries is approximately derived. Consequently, the Reusability Likelihood of batteries is quantified using the number of cycles that the battery can be charged with the aim of facilitating future remarketing and recovery opportunities.

Reusability Assessment of Lithium-Ion Laptop Batteries Based on Consumers Actual Usage Behavior

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Mostafa Sabbaghi ◽  
Behzad Esmaeilian ◽  
Ardeshir Raihanian Mashhadi ◽  
Willie Cade ◽  
Sara Behdad
Keyword(s):  
Life Cycle ◽  
High Reliability ◽  
Lithium Ion ◽  
Degradation Process ◽  
Rechargeable Batteries ◽  
Significant Drop ◽  
Data Set ◽  
Actual Usage ◽  
The Stability ◽  
Stability Time

In this paper, a data set of Lithium-ion (Li-ion) laptop batteries has been studied with the aim of investigating the potential reusability of laptop batteries. This type of rechargeable batteries is popular due to their energy efficiency and high reliability. Therefore, understanding the life cycle of these batteries and improving the recycling process is becoming increasingly important. The reusability assessment is linked to the consumer behavior and degradation process simultaneously through monitoring the performance of batteries over their life cycle. After capturing the utilization behavior, the stability time of batteries is approximately derived. The stability time represents the interval that a battery works normally without any significant drop in performance. Consequently, the Reusability Likelihood of batteries is quantified using the number of cycles that the battery can be charged with the aim of facilitating future remarketing and recovery opportunities.

scholarly journals Polyether-Based Supramolecular Electrolytes With Two-Dimensional Boroxine Skeleton

2021 ◽  
Vol 9 ◽  
Author(s):  
Masahiro Yoshizawa-Fujita ◽  
Shunsuke Horiuchi ◽  
Tamao Uemiya ◽  
Jun Ishii ◽  
Yuko Takeoka ◽  
...  
Keyword(s):  
Photoelectron Spectroscopy ◽  
Lithium Ion ◽  
Linear Sweep Voltammetry ◽  
Transference Number ◽  
Rechargeable Batteries ◽  
Solid Polymer Electrolytes ◽  
Solid Polymer ◽  
Two Dimensional ◽  
X Ray ◽  
Number Of Cycles

Solid polymer electrolytes mainly based on polyethers have been actively investigated for over 40 years to develop safe, light, and flexible rechargeable batteries. Here, we report novel supramolecular electrolytes (SMEs) composed of polyether derivatives and a two-dimensional boroxine skeleton synthesized by the dehydration condensation of 1,4-benzenediboronic acid in the presence of a polyether with amines on both chain ends. The formation of SMEs based on polyether derivatives and boroxine skeleton was confirmed by Fourier transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), powder X-ray diffraction (PXRD), and thermogravimetric (TG) analysis. Linear sweep voltammetry (LSV) and cyclic voltammetry (CV) were performed to evaluate the electrochemical stability and lithium conductive properties of SMEs with given amounts of lithium bis(trifluoromethylsulfonyl)amide (LiTFSA). The ionic conductivity of SME/LiTFSA composites increased with increasing lithium-salt concentration and reached a maximum value at a higher concentration than those of simple polyether systems. The lithium-ion transference number (tLi+) of SME/LiTFSA was higher than those of polyether electrolytes. This tendency is unusual for a polyether matrix. SME/LiTFSA composite electrolytes exhibited a stable lithium plating/striping process even after 100 cycles. The current density increased with an increasing number of cycles. The combination of ion conductive polymers and a two-dimensional boroxine skeleton will be an interesting concept for developing solid electrolytes with good electrochemical properties.

scholarly journals A State of Health Estimation Framework for Lithium-Ion Batteries Using Transfer Components Analysis

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2524 ◽  
Author(s):  
Jia ◽  
Guan ◽  
Wu
Keyword(s):  
Lithium Batteries ◽  
Lithium Ion ◽  
Degradation Process ◽  
State Of Health ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Fast Training ◽  
Learning Machine ◽  
Hidden Layer ◽  
Set Up

As different types of lithium batteries are increasingly employed in various devices, it is crucial to predict the state of health (SOH) of lithium batteries. There are plenty of methods for SOH estimation of a lithium-ion battery. However, existing technologies often have computational complexity. Furthermore, it is difficult to use least the previous 30% of data of the battery degradation process to predict the SOH variation of the entire degradation process. To address this problem, in this paper, the SOH of the target battery is estimated based on the transfer of different battery data sets. Firstly, according to importance sampling (IS), valid features are extracted from cycles of charging voltage in both the source and target battery. Secondly, transfer component analysis (TCA) is used to map the source data set to the target data set. Moreover, an extreme learning machine (ELM) algorithm is employed to train a single hidden layer feed forward neural network (SLFN) for its fast training speed and facile to set up. Finally, validation experiments and the comparisons on the results are conducted. The results showed that the proposed framework has a good capability of predicting the SOH of lithium batteries.

scholarly journals Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

2021 ◽  
Vol 11 (11) ◽  
pp. 4773
Author(s):  
Qiaoping Tian ◽  
Honglei Wang
Keyword(s):  
Quantile Regression ◽  
Probability Density ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Health State ◽  
Rolling Bearings ◽  
Data Set ◽  
Time Frequency ◽  
Useful Life ◽  
Degradation Indicator

High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

Recent advances in ferromagnetic metal sulfides and selenides as anodes for sodium- and potassium-ion batteries

2021 ◽  
Author(s):  
Yuhan Wu ◽  
Chenglin Zhang ◽  
Huaping Zhao ◽  
Yong Lei
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Potassium Ion ◽  
Sodium Ion ◽  
Metal Sulfides ◽  
Sodium Ion Batteries ◽  
Next Generation ◽  
Cost Competitiveness ◽  
Sodium And Potassium

In next-generation rechargeable batteries, sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs) have been considered as attractive alternatives to lithium-ion batteries due to their cost competitiveness. Anodes with complicated electrochemical mechanisms...

scholarly journals Separation and Efficient Recovery of Lithium from Spent Lithium-Ion Batteries

Metals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1091
Author(s):  
Eva Gerold ◽  
Stefan Luidold ◽  
Helmut Antrekowitsch
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Sodium Phosphate ◽  
Room Temperature ◽  
State Of The Art ◽  
Potassium Phosphate ◽  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Raw Material ◽  
Efficient Recovery

The consumption of lithium has increased dramatically in recent years. This can be primarily attributed to its use in lithium-ion batteries for the operation of hybrid and electric vehicles. Due to its specific properties, lithium will also continue to be an indispensable key component for rechargeable batteries in the next decades. An average lithium-ion battery contains 5–7% of lithium. These values indicate that used rechargeable batteries are a high-quality raw material for lithium recovery. Currently, the feasibility and reasonability of the hydrometallurgical recycling of lithium from spent lithium-ion batteries is still a field of research. This work is intended to compare the classic method of the precipitation of lithium from synthetic and real pregnant leaching liquors gained from spent lithium-ion batteries with sodium carbonate (state of the art) with alternative precipitation agents such as sodium phosphate and potassium phosphate. Furthermore, the correlation of the obtained product to the used type of phosphate is comprised. In addition, the influence of the process temperature (room temperature to boiling point), as well as the stoichiometric factor of the precipitant, is investigated in order to finally enable a statement about an efficient process, its parameter and the main dependencies.

Design and Characteristics of Microfocus X-ray Source with Sealed Tube and Transmissive Target on Diamond Window

2021 ◽  
Vol 79 (6) ◽  
pp. 631-640
Author(s):  
Takaaki Tsunoda ◽  
Takeo Tsukamoto ◽  
Yoichi Ando ◽  
Yasuhiro Hamamoto ◽  
Yoichi Ikarashi ◽  
...  
Keyword(s):  
Power Density ◽  
High Power ◽  
High Speed ◽  
Electronic Device ◽  
High Reliability ◽  
Electronic Devices ◽  
Lithium Ion ◽  
High Power Density ◽  
High Magnification ◽  
X Ray

Electronic devices such as medical instruments implanted in the human body and electronic control units installed in automobiles have a large impact on human life. The electronic circuits in these devices require highly reliable operation. Radiographic testing has recently been in strong demand as a nondestructive way to help ensure high reliability. Companies that use high-density micrometer-scale circuits or lithium-ion batteries require high speed and high magnification inspection of all parts. The authors have developed a new X-ray source supporting these requirements. The X-ray source has a sealed tube with a transmissive target on a diamond window that offers advantages over X-ray sources having a sealed tube with a reflective target. The X-ray source provides high-power-density X-ray with no anode degradation and a longer shelf life. In this paper, the authors will summarize X-ray source classification relevant to electronic device inspection and will detail X-ray source performance requirements and challenges. The paper will also elaborate on technologies employed in the X-ray source including tube design implementations for high-power-density X-ray, high resolution, and high magnification simultaneously; reduced system downtime for automated X-ray inspection; and reduced dosages utilizing quick X-ray on-and-off emission control for protection of sensitive electronic devices.

scholarly journals Applications of Nanoscale Materials in the Fields of Electrochemistry and Photoelectrochemistry

1998 ◽  
Vol 21 (2) ◽  
pp. 123-146 ◽  
Author(s):  
G. Campet ◽  
A. Deshayes ◽  
J. C. Frison ◽  
N. Treuil ◽  
J. Portier
Keyword(s):  
Energy Storage ◽  
Lithium Batteries ◽  
Nanocrystalline Materials ◽  
Lithium Ion ◽  
Electrical Energy ◽  
Rechargeable Batteries ◽  
Photoelectrochemical Cells ◽  
Nanoscale Materials ◽  
Nanocrystalline Films ◽  
Electrical Energy Storage

We have illustrated the important role played by the nanoscale materials in three-up-to-date energy topics.1/The solar-to-electrical energy conversion in photoelectrochemical cells: we have shown two favorable situations for which photoelectrochemical cells using porous nanocrystalline films have high efficiencies.2/The electrical energy storage in rechargeable rocking-chair lithium batteries: these systems, which use nanocrystalline materials, might be the next generation of rechargeable batteries showing higher capacity, cyclability, and safety than conventional lithium ion batteries.3/The energy saving with efficient electrochromic windows using nanocrystalline materials.

Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Sheng Shen ◽  
M. K. Sadoughi ◽  
Xiangyi Chen ◽  
Mingyi Hong ◽  
Chao Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Rechargeable Batteries ◽  
Li Ion Battery ◽  
Deep Convolutional Neural Networks ◽  
Battery Capacity ◽  
Li Ion

Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.

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