Bayesian Monte Carlo-assisted life cycle assessment of lithium iron phosphate batteries production for electric vehicles under uncertainty
Year: 2024
Type: article
Abstract: The environmental performance of electric vehicles (EVs) largely depends on their batteries. However, the extraction and production of materials for these batteries present considerable environmental and social challenges. Traditional environmental assessments of EV batteries often lack comprehensive uncertainty analysis, resulting in evaluations that may not be sufficiently accurate or reliable. To address this issue and quantify uncertainties in the evaluation of EV battery production, based on the foreground data of the lithium-iron-phosphate battery pack manufacturing process, the ReCiPe midpoint methodology was adopted to quantify the lifecycle environmental impacts using eleven environmental indicators. Given the parametric uncertainties in the manufacturing process of lithium-iron-phosphate, a Bayesian Monte Carlo analytical method was developed to determine the probability distribution of global warming potential and acidification potential. Local sensitivity analysis was conducted to identify the influential factors of selected environmental indicators. Results indicated that battery cell production is the largest contributor to the entire emissions and resource utilization (comprising 63.38% of the production of each battery pack), in which cathode electrode paste and anode current collector manufacturing processes were the two predominant components with the highest environmental burdens. Sensitivity analysis showed that environmental indicators were quite sensitive to different substances in the battery pack. The application of Bayesian Monte Carlo uncertainty analysis effectively reduces the uncertainties in life cycle assessment. This study would contribute to uncertainty quantification towards solid life cycle assessment.
Source: Green Manufacturing Open
Authors: Baoyun Gao, Shoulan Yang, Shitong Peng
Institution: Shantou University
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405378182