Environmental and Economic Sustainability of Sodium-Ion Battery Technologies
Sodium-ion (Na-ion) batteries are emerging as a promising alternative to conventional lithium-ion technologies, with the potential to reduce dependence on critical materials and support the continued electrification of transportation and energy systems. However, the environmental and economic performance of different Na-ion battery technologies and manufacturing pathways is not yet fully understood.
This PhD project will conduct a multi-objective assessment of the environmental and economic sustainability of sodium-ion battery technologies. Using life cycle assessment (LCA) and techno-economic analysis (TEA), the research will compare alternative battery materials, manufacturing pathways, and end-of-life strategies.
The project will take both cradle-to-grave and cradle-to-cradle perspectives, with particular attention to the role of recycling and material recovery. The research will identify environmental process hotspots and economic bottlenecks, and investigate potential trade-offs between environmental and economic performance.
Key objectives include:
- develop environmental and techno-economic models of sodium-ion battery production pathways;
- identify the processes, materials, and design choices that drive environmental impacts and costs;
- evaluate the environmental and economic implications of recycling and alternative end-of-life pathways;
- explore trade-offs and synergies between environmental and economic performance; and
- contribute to the development of more sustainable battery technologies and supply chains.
The project will be conducted collaboratively between École de technologie supérieure (ÉTS) in Montréal, Québec, and Dalhousie University in Halifax, Nova Scotia, as part of a multidisciplinary research program on emerging battery technologies
Required knowledge
Applicants must hold an MSc degree in Chemical Engineering, Environmental Engineering, or a related field.
Structured thinking and analytical rigor, creativity in problem solving, ability to work independently and as part of a team, and excellent oral and written communication skills are required. Specific quantitative skills can be developed during the project. Experience with modeling and data management in Excel, Python, or similar environments is an important asset. Previous experience with LCA, TEA, process modeling, Aspen, openLCA, SimaPro, or related tools is also an asset but is not required.
Applicants must meet the applicable English-language proficiency requirements. Where language test results are required, the expected minimum scores are TOEIC ≥ 725, TOEFL iBT ≥ 75, or IELTS ≥ 6.5.