scholarly journals Transportation of electric vehicle lithium-ion batteries at end-of-life: A literature review

2021 ◽  
Vol 174 ◽  
pp. 105755
Author(s):  
Margaret Slattery ◽  
Jessica Dunn ◽  
Alissa Kendall
2019 ◽  
Vol 12 (1) ◽  
pp. 147 ◽  
Author(s):  
Fernando Enzo Kenta Sato ◽  
Toshihiko Nakata

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.


Joule ◽  
2019 ◽  
Vol 3 (11) ◽  
pp. 2622-2646 ◽  
Author(s):  
Mengyuan Chen ◽  
Xiaotu Ma ◽  
Bin Chen ◽  
Renata Arsenault ◽  
Peter Karlson ◽  
...  

RSC Advances ◽  
2021 ◽  
Vol 11 (39) ◽  
pp. 24132-24136
Author(s):  
Liurui Li ◽  
Tairan Yang ◽  
Zheng Li

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


Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7521
Author(s):  
Shaheer Ansari ◽  
Afida Ayob ◽  
Molla Shahadat Hossain Lipu ◽  
Aini Hussain ◽  
Mohamad Hanif Md Saad

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.


Author(s):  
Tao Wang ◽  
Xiaoping Chen ◽  
Gang Chen ◽  
Hongbo Ji ◽  
Ling Li ◽  
...  

Abstract The crush safety of lithium-ion batteries (LIBs) has recently become one of the hottest topics as the electric vehicle (EV) market is growing rapidly. In this study, mechanical properties of prismatic lithium-ion batteries under compression loading are investigated. Batteries with different values of state of charge (SOC) under different loading directions are compared. Results show LIB cells with different SOCs under same loading direction exhibit similar response curve profile, however, the load capacities of LIBs can be influenced by SOCs. The stiffness and stress have approximately linear relationship with the SOC values. Based on experimental results, finite element models are established which can predict the mechanical properties of prismatic LIBs. The proposed models can be utilized to evaluate the crush behaviors of LIBs, providing guidance for electric vehicle safety design.


2020 ◽  
Vol 264 ◽  
pp. 110500 ◽  
Author(s):  
Elena Mossali ◽  
Nicoletta Picone ◽  
Luca Gentilini ◽  
Olga Rodrìguez ◽  
Juan Manuel Pérez ◽  
...  

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