scholarly journals Recoverability Analysis of Critical Materials from Electric Vehicle Lithium-Ion Batteries through a Dynamic Fleet-Based Approach for Japan

2019 ◽  
Vol 12 (1) ◽  
pp. 147 ◽  
Author(s):  
Fernando Enzo Kenta Sato ◽  
Toshihiko Nakata
Keyword(s):  
Lithium Ion Batteries ◽  
End Of Life ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
Raw Material ◽  
Dynamics Modeling ◽  
Environmentally Sustainable ◽  
Wide Scale ◽  
Size Type ◽  
Critical Materials

This study aims to propose a model to forecast the volume of critical materials that can be recovered from lithium-ion batteries (LiB) through the recycling of end of life electric vehicles (EV). To achieve an environmentally sustainable society, the wide-scale adoption of EV seems to be necessary. Here, the dependency of the vehicle on its batteries has an essential role. The efficient recycling of LiB to minimize its raw material supply risk but also the economic impact of its production process is going to be essential. Initially, this study forecasted the vehicle fleet, sales, and end of life vehicles based on system dynamics modeling considering data of scrapping rates of vehicles by year of life. Then, the volumes of the critical materials supplied for LiB production and recovered from recycling were identified, considering variations in the size/type of batteries. Finally, current limitations to achieve closed-loop production in Japan were identified. The results indicate that the amount of scrapped electric vehicle batteries (EVB) will increase by 55 times from 2018 to 2050, and that 34% of lithium (Li), 50% of cobalt (Co), 28% of nickel (Ni), and 52% of manganese (Mn) required for the production of new LiB could be supplied by recovered EVB in 2035.

scholarly journals Tackling xEV Battery Chemistry in View of Raw Material Supply Shortfalls

2020 ◽  
Vol 8 ◽  
Author(s):  
Duygu Karabelli ◽  
Steffen Kiemel ◽  
Soumya Singh ◽  
Jan Koller ◽  
Simone Ehrenberger ◽  
...  
Keyword(s):  
End Of Life ◽  
Electric Vehicle ◽  
Circular Economy ◽  
Raw Materials ◽  
Lithium Ion ◽  
Raw Material ◽  
Performance Expectations ◽  
Potential Market ◽  
Critical Materials ◽  
The Impact

The growing number of Electric Vehicles poses a serious challenge at the end-of-life for battery manufacturers and recyclers. Manufacturers need access to strategic or critical materials for the production of a battery system. Recycling of end-of-life electric vehicle batteries may ensure a constant supply of critical materials, thereby closing the material cycle in the context of a circular economy. However, the resource-use per cell and thus its chemistry is constantly changing, due to supply disruption or sharply rising costs of certain raw materials along with higher performance expectations from electric vehicle-batteries. It is vital to further explore the nickel-rich cathodes, as they promise to overcome the resource and cost problems. With this study, we aim to analyze the expected development of dominant cell chemistries of Lithium-Ion Batteries until 2030, followed by an analysis of the raw materials availability. This is accomplished with the help of research studies and additional experts’ survey which defines the scenarios to estimate the battery chemistry evolution and the effect it has on a circular economy. In our results, we will discuss the annual demand for global e-mobility by 2030 and the impact of Nickel-Manganese-Cobalt based cathode chemistries on a sustainable economy. Estimations beyond 2030 are subject to high uncertainty due to the potential market penetration of innovative technologies that are currently under research (e.g. solid-state Lithium-Ion and/or sodium-based batteries).

scholarly journals Recycling End-of-Life Electric Vehicle Lithium-Ion Batteries

Joule ◽  
2019 ◽  
Vol 3 (11) ◽  
pp. 2622-2646 ◽  
Author(s):  
Mengyuan Chen ◽  
Xiaotu Ma ◽  
Bin Chen ◽  
Renata Arsenault ◽  
Peter Karlson ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
End Of Life ◽  
Electric Vehicle ◽  
Lithium Ion

scholarly journals Parameter optimization and yield prediction of cathode coating separation process for direct recycling of end-of-life lithium-ion batteries

RSC Advances ◽  
2021 ◽  
Vol 11 (39) ◽  
pp. 24132-24136
Author(s):  
Liurui Li ◽  
Tairan Yang ◽  
Zheng Li
Keyword(s):  
Lithium Ion Batteries ◽  
End Of Life ◽  
Lithium Ion ◽  
Separation Process ◽  
Yield Prediction ◽  
Li Ion Batteries ◽  
Treatment Efficiency ◽  
Control Factors ◽  
Li Ion ◽  
Pre Treatment

The pre-treatment efficiency of the direct recycling strategy in recovering end-of-life Li-ion batteries is predicted with levels of control factors.

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 Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7521
Author(s):  
Shaheer Ansari ◽  
Afida Ayob ◽  
Molla Shahadat Hossain Lipu ◽  
Aini Hussain ◽  
Mohamad Hanif Md Saad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Systematic Sampling ◽  
Channel Input ◽  
Useful Life ◽  
Artificial Neural

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.

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