Green reverse logistics network management for electric vehicle batteries under uncertainties: review and future trends
Abstract
The electric vehicle (EV) industry, a pivotal contributor to mitigating the energy crisis and fostering environmental sustainability, is experiencing rapid growth. However, the surge in end-of-life (EOL) batteries from early-generation EVs has made their proper disposal an urgent challenge. Although many countries acknowledge the significance of circular production for solving environmental problems, managing reverse logistics networks for EOL EV batteries remains complicated by various uncertainties, which add to management challenges and decrease system efficiency. This article examines the management of reverse logistics networks to optimise resource efficiency amidst multiple uncertainties in the EV battery industry. A comprehensive analysis of 54 state-of-the-art papers published in ‘Web of Science’ and ‘Scopus’ (2019–2025) was conducted to address three key research questions: (i) What are the sources of uncertainty in this reverse logistics? (ii) How do the uncertainties impact the network management? (iii) How to deal with these uncertainties? Based on a thorough analysis, we identify and categorise the sources of uncertainty into six primary types: quantity, quality, cost, epistemic, environment, and operational uncertainty. Furthermore, our analysis indicates that methodologies have progressed from single-objective models to multi-objective frameworks capable of managing hybrid uncertainties, with fuzzy and stochastic programming being the most predominant approaches. This review offers valuable insights for both academic research and practical applications, contributing to the development of more efficient and sustainable reverse logistics networks for EOL EV batteries.
Fields of Study & Subjects
Sustainable Supply Chain Management
Extraction and Separation Processes
Electric Vehicles and Infrastructure
Publication details
- Journal / source
- International Journal of Production Research
- Volume
- 64
- Issue
- 14
- Pages
- 6146–6176
- Publication date
- 29 January 2026
- Language
- EN