Life cycle assessment of battery electric vehicles: Implications of future electricity mix and different battery end-of-life management

The environmental performance of battery electric vehicles (BEVs) is influenced by their battery size and charging electricity source. Therefore, assessing their environmental performance should consider changes in the electricity sector and refurbishment of their batteries. This study conducts a scenario-based Life Cycle Assessment (LCA) of three different scenarios combining four key parameters: future changes in the charging electricity mix, battery efficiency fade, battery refurbishment, and recycling for their collective importance on the life-cycle environmental performance of a BEV. The system boundary covers all the life-cycle stages of the BEV and includes battery refurbishment, except for its second use stage. The refurbished battery was modelled considering refurbished components and a 50% cell conversation rate for the second life of 5 years. The results found a 9.4% reduction in climate impacts when future changes (i.e., increase in the share of renewable energy) in the charging electricity are considered. Recycling reduced the BEV climate impacts by approximately 8.3%, and a reduction smaller than 1% was observed for battery refurbishment. However, the battery efficiency fade increases the BEV energy consumption, which results in a 7.4 to 8.1% rise in use-stage climate impacts. Therefore, it is vital to include battery efficiency fade and changes to the electricity sector when estimating the use-stage impacts of BEVs; without this, LCA results could be unreliable. The sensitivity analysis showed the possibility of a higher reduction in the BEV climate impacts for longer second lifespans (>5 years) and higher cell conversation rates (>50%). BEV and battery production are the most critical stages for all the other impact categories assessed, specifically contributing more than 90% to mineral resource scarcity. However, recycling and battery refurbishment can reduce the burden of the different impact categories considered. Therefore, manufacturers should design BEV battery packs while considering recycling and refurbishment.

Development, Retainment and Assessment of the Graphite-Electrolyte Interphase in Li-ion Batteries Regarding the Functionality of SEI-Forming Additives

Formation of a decent solid-electrolyte interphase (SEI) is recognized as an approach to improve the performance of lithium-ion batteries. SEI is a passivation layer generated on the anode during the initial cycles. Characteristics of the graphite SEI depend on the operational parameters, state of the anode, and the content of the electrolyte. Introducing reduction-type additives to the carbonate electrolytes has been one of the most practiced methods to generate an effective SEI on carbonous anodes. To track the role of additives in SEI evolution, first, we have presented a general review on what is currently understood about the SEI formation processes and the impacting parameters. In the second step, the most reported methods to study and analyze the functionality of the SEI-forming additives are classified. As the third part, different reduction-type additives are categorized, and their performances are comparatively reviewed.

A quick battery charging curve prediction by artificial neural network

Battery health prognosis and monitoring require the information of the available battery capacity that Tian et al. (2021) proposes to acquire from a partial 10-min charging curve via a deep neural network.

A hybrid thermal management system for high power lithium-ion capacitors combining heat pipe with phase change materials

Lithium-ion capacitor (LiC) technology is an energy storage system (ESS) that combines the working mechanism of electric double-layer capacitors (EDLC) and lithium-ion batteries (LiB). When LiC is supposed to work under high power applications, the inevitable heat loss threatens the cell's performance and lifetime. Therefore, a proper thermal management system (TMS) can remove the generated heat of the LiC during high cycling conditions. In this paper, a hybrid TMS (HTMS) using phase change materials (PCM) and six flat heat pipes is proposed to maintain the temperature profile below 40 °C under a high current rate of 150 A for 1400 s profile without any pause. Two K-type thermocouples (T1 & T2) are responsible for monitoring the experiments' temperature evolution in the experiments. Numerical analysis is also performed and verified with experimental results to analyze the temperature profile numerically. The experimental and numerical simulation comprises three case studies, including the cell's temperature under natural convection, temperature distribution when using the heat pipe TMS, and temperature distribution when using HTMS. The results reveal that the HTMS is an exceptionally robust cooling system since it reduces the T₁ temperature by 35% compared to the natural convection case study, while the heat pipe TMS can reduce the T₁ temperature by 15% compared to the same case study.

Battery lifetime prediction and performance assessment of different modeling approaches

Lithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a challenge. In this work, a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cell is used to develop and/or train different capacity fade models to compare output responses. The assessment is conducted for semi-empirical modeling (SeM) approach against a machine learning model and an artificial neural network model. Among all, the nonlinear autoregressive network (NARXnet) can predict the capacity degradation most precisely minimizing the computational effort as well. This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior.

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Development of several lifetime models based on long-term cell aging dataset

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Models validation by WLTC cycling and based on realistic scenarios

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Models assessment in terms of accuracy, computational effort, applicability, etc.

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Pros and cons of the best lifetime model toward realistic use