Loading...
Electric Vehicle Batteries’ Lifecycle Management
Salem, AbdulRahman
Salem, AbdulRahman
Date
2025-10
Author
Advisor
Type
Thesis
Degree
Citations
Altmetric:
Files
35.232-2025.49a AbdulRahman Salem.pdf
Adobe PDF, 4.34 MB
- Embargoed until 2027-01-15
Description
A Master of Science thesis in Mechanical Engineering by AbdulRahman Salem entitled, “Electric Vehicle Batteries’ Lifecycle Management”, submitted in October 2025. Thesis advisor is Dr. Basil Darras and thesis co-advisor is Dr. Mohammad Nazzal. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
Abstract
The global transition toward clean energy caused the automotive industry to embrace electric vehicle (EV) production. EVs using clean renewable energy are expected to have significantly lower environmental impact compared to conventional internal combustion vehicles. However, the recycling of EV batteries causes several environmental issues. For instance, the disposal of leaching solutions used in leaching lithium from Li-ion batteries causes soil and water eutrophication. Furthermore, pyrolysis of battery packaging and Printed Circuit Boards (PCBs) containing polymers releases toxic polybrominated fumes, dioxins and furans to the environment. Moreover, most EV batteries are disposed of without utilizing their full potential. EV batteries become unfit for EV use once they lose 20% of their original capacity, which leaves 80% of their capacity untapped. To address these issues, this research developed three decision making frameworks. The first framework provides a blueprint to identify Key Performance Indicators (KPIs) and the decision making process for the selection of battery State of Health (SOH) estimation models responsible for determining EV batteries’ viability for second use options. The second framework introduces a general EV battery management system presented a sorting mechanism capable of 98% sorting consistency, superior to contemporary machine learning models (85 – 90%) used in EV battery health monitoring and management frameworks. Additionally, the sorting framework utilizes a QR code-based Matlab program to identify battery KPIs and assign them accordingly to their appropriate end destination in either renewable energy applications or refurbishing and recycling centers. Moreover, this framework outlines the line of interaction between users, manufacturers and governments to facilitate handling and retrieval of end-of-life EV batteries. Lastly, a third framework was developed to facilitate battery material selection for future EV applications based on holistic KPIs identified from scientometric analysis and literature review of battery material technology advance ments. Multiple case studies validated the flexibility, robustness, and effectiveness of the proposed frameworks in decision-making and end-of-life battery management. The frameworks can be adopted by government entities for sorting used and spent EV batteries