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.

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.

A life cycle assessment of hard carbon anodes for sodium-ion batteries

2021 ◽  
Vol 379 (2209) ◽  
pp. 20200340
Author(s):  
Haoyu Liu ◽  
Zhen Xu ◽  
Zhenyu Guo ◽  
Jingyu Feng ◽  
Haoran Li ◽  
...  
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Changes ◽  
Lithium Ion ◽  
Hydrothermal Carbonization ◽  
Rechargeable Batteries ◽  
Sodium Ion ◽  
Advanced Materials ◽  
Sodium Ion Batteries ◽  
Carbon Anodes

Waste management is one of the biggest environmental challenges worldwide. Biomass-derived hard carbons, which can be applied to rechargeable batteries, can contribute to mitigating environmental changes by enabling the use of renewable energy. This study has carried out a comparative environmental assessment of sustainable hard carbons, produced from System A (hydrothermal carbonization (HTC) followed by pyrolysis) and System B (direct pyrolysis) with different carbon yields, as anodes in sodium-ion batteries (SIBs). We have also analysed different scenarios to save energy in our processes and compared the biomass-derived hard carbons with commercial graphite used in lithium-ion batteries. The life cycle assessment results show that the two systems display significant savings in terms of their global warming potential impact (A1: −30%; B1: −21%), followed by human toxicity potential, photochemical oxidants creation potential, acidification potential and eutrophication potential (both over −90%). Possessing the best electrochemical performance for SIBs among our prepared hard carbons, the HTC-based method is more stable in both environmental and electrochemical aspects than the direct pyrolysis method. Such results help a comprehensive understanding of sustainable hard carbons used in SIBs and show an environmental potential to the practical technologies. This article is part of the theme issue ‘Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 2)’.

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.

N-doped porous hard-carbon derived from recycled separators for efficient lithium-ion and sodium-ion batteries

2019 ◽  
Vol 3 (3) ◽  
pp. 717-722 ◽  
Author(s):  
Yong Wang ◽  
Yong Li ◽  
Samuel S. Mao ◽  
Daixin Ye ◽  
Wen Liu ◽  
...  
Keyword(s):  
Life Cycle ◽  
Environmental Impacts ◽  
Lithium Ion ◽  
Rechargeable Batteries ◽  
Sodium Ion ◽  
Sodium Ion Batteries ◽  
Hard Carbon

Minimizing life-cycle environmental impacts of rechargeable batteries has been attracting tremendous interest recently.

scholarly journals Advances of 2nd Life Applications for Lithium Ion Batteries from Electric Vehicles Based on Energy Demand

2021 ◽  
Vol 13 (10) ◽  
pp. 5726
Author(s):  
Aleksandra Wewer ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
Life Cycle ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Energy Demand ◽  
Lithium Ion ◽  
Life Cycles ◽  
Future Research ◽  
Research Areas ◽  
Study Results ◽  
The Impact

Electromobility is a new approach to the reduction of CO2 emissions and the deceleration of global warming. Its environmental impacts are often compared to traditional mobility solutions based on gasoline or diesel engines. The comparison pertains mostly to the single life cycle of a battery. The impact of multiple life cycles remains an important, and yet unanswered, question. The aim of this paper is to demonstrate advances of 2nd life applications for lithium ion batteries from electric vehicles based on their energy demand. Therefore, it highlights the limitations of a conventional life cycle analysis (LCA) and presents a supplementary method of analysis by providing the design and results of a meta study on the environmental impact of lithium ion batteries. The study focuses on energy demand, and investigates its total impact for different cases considering 2nd life applications such as (C1) material recycling, (C2) repurposing and (C3) reuse. Required reprocessing methods such as remanufacturing of batteries lie at the basis of these 2nd life applications. Batteries are used in their 2nd lives for stationary energy storage (C2, repurpose) and electric vehicles (C3, reuse). The study results confirm that both of these 2nd life applications require less energy than the recycling of batteries at the end of their first life and the production of new batteries. The paper concludes by identifying future research areas in order to generate precise forecasts for 2nd life applications and their industrial dissemination.

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.

scholarly journals Current cancer driver variant predictors learn to recognize driver genes instead of functional variants

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Daniele Raimondi ◽  
Antoine Passemiers ◽  
Piero Fariselli ◽  
Yves Moreau
Keyword(s):  
Data Sets ◽  
Precision Oncology ◽  
Complex Task ◽  
Excellent Performance ◽  
Driver Genes ◽  
Significant Drop ◽  
Data Set ◽  
Gene Effects ◽  
Cancer Driver ◽  
Functional Variants

Abstract Background Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenesis and precision oncology. Various bioinformatics methods have attempted to solve this complex task. Results In this study, we investigate the assumptions on which these methods are based, showing that the different definitions of driver and passenger variants influence the difficulty of the prediction task. More importantly, we prove that the data sets have a construction bias which prevents the machine learning (ML) methods to actually learn variant-level functional effects, despite their excellent performance. This effect results from the fact that in these data sets, the driver variants map to a few driver genes, while the passenger variants spread across thousands of genes, and thus just learning to recognize driver genes provides almost perfect predictions. Conclusions To mitigate this issue, we propose a novel data set that minimizes this bias by ensuring that all genes covered by the data contain both driver and passenger variants. As a result, we show that the tested predictors experience a significant drop in performance, which should not be considered as poorer modeling, but rather as correcting unwarranted optimism. Finally, we propose a weighting procedure to completely eliminate the gene effects on such predictions, thus precisely evaluating the ability of predictors to model the functional effects of single variants, and we show that indeed this task is still open.

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