Cascaded Use and Sustainable Management of Lithium-ion Batteries in Mobility and Stationary Power
Bibliographic record
Abstract
The purpose of this thesis is to assess —from a life cycle perspective — the environmental benefits of re-purposing electric vehicle Li-ion batteries to re-use in stationary applications. \nThe thesis consists of three separate papers arranged in as chapters. The main objectives are threefold: to develop and analyze a parameterized life cycle model of Li-ion battery first use in EV and extended usage to incorporate the re-purposing and re-use in grid storage for a utility application (Chapter 3), to evaluate effective factors on the feasibility of re-purposing used EV Li-ion batteries and the effect of factors on the cumulative energy use and greenhouse gas (GHG) emissions of the re-purposed batteries life cycle (Chapter 4)., and to assess potential environmental impacts of re-purposing and re-using of EV Li-ion batteries into stationary applications from a life cycle perspective and compare with natural gas stationary power generation (Chapter 5). \nAccording to the study, it is found that the magnitude of CO2 mitigation associated with battery re-use is similar to that of switching from using a conventional vehicle to an electric vehicle, meaning that the GHG benefits of vehicle electrification could be doubled by extending the life of EV batteries, and better using off-peak low-cost clean electricity. \nthe effects of capacity fade, energy efficiency fade, failure rate, and charge/discharge profile are investigated for Li-ion batteries based on first use in EVs and second-use in ESS. It is estimated that the re-purposed EV battery loses a further 15% of its capacity after its second use in the energy storage system (ESS) over 10 years. As energy efficiency decreases with increased charge/discharge cycles, a capacity fade model is used to approximate the effect of the relationship between cycles and capacity fade over the life of the battery. The performance of the \niiii \nbattery in its second use is represented using a model of degradation modes, assuming a 0.01% cell failure rate and a non-symmetric charge/discharge profile. Finally, an accurate modeling of battery performance is used to examine energy savings and GHG emission reduction benefits from using a Li-ion battery first in an EV and then in an ESS connected to the Ontario electrical grid. \nA cradle-to-grave life cycle assessment (LCA) of the Li-ion battery pack is conducted and six environmental impact categories are assessed including global warming potential, particulate matter formation, freshwater eutrophication, photochemical oxidant formation potential, metal depletion, and fossil depletion. It is concluded that the manufacturing phase of the Li-ion battery has the main environmental impacts during the life cycle of the battery as concluded from. Utilizing the re-purposed Li-ion battery in contrast with natural gas source in the stationary application powering causes more savings from an environmental standpoint. The assessed environmental impacts highlight the importance of electricity mix used in the processes of the product systems. Finally, the effect of the battery degradation is analyzed through energy efficiency fade effect on the battery performance and it is found that the use phase of the battery in the EV during 8 years is more sensitive to this phenomenon than the re-using of the Li-ion batteries in the stationary application during additional 10 years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".