Analyzing the Environmental Burden of Electric Vehicle Batteries: A Life Cycle Assessment Synthesis

Abstract

Introduction: The rapid growth of the EV market and the associated surge in battery demand raise environmental concerns regarding battery production and disposal. While EVs have zero tailpipe emissions, battery production contributes significantly to their overall environmental burden. Life Cycle Assessment (LCA) is a crucial tool for evaluating this impact, but inconsistencies in methodologies across different LCA studies hinder effective comparison and progress towards cleaner battery technologies.

Objectives: The research aims to analyze numerous existing LCA studies on electric vehicle batteries using a standardized approach based on ISO 14040 guidelines. The goal is to ensure consistent comparisons and identify areas for environmental improvement throughout the battery life cycle.

Methods: This review examines 20 relevant LCA studies on single battery models published between 2018 and 2023. It critically appraises these studies based on the ISO 14040 framework, focusing on the four key stages of LCA: defining objectives and scope, conducting inventory analysis, performing impact assessment, and interpreting the results.

Results: The review found significant variability in methodologies, system boundaries (cradle-to-gate vs. cradle-to-grave), functional units, data sources (primary vs. secondary), and impact assessment methods used in the analyzed LCA studies. The production phase and the product use phase were identified as having the most significant environmental impacts. Global warming was the most frequently studied impact category.

Conclusions: The inconsistency in LCA studies on EV batteries necessitates a more standardized approach. The author recommends using a cradle-to-grave system boundary, a consistent functional unit (like "kilometer traveled" or "kilowatt-hour of energy delivered over the battery's lifetime"), and a core set of nine relevant impact categories. Transparency in methodology, assumptions, and data quality is crucial for ensuring reliable and comparable results, ultimately informing efforts to improve the environmental performance of EV batteries.