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Title of the thesis: Optimization of electrochemical discharge for efficient Lithium-Ion battery recycling

Thesis defender: Hassan Rouhi
Opponent: Prof. Julia Amici, Politecnico di Torino, Italy 
Custos: Prof. Annukka Santasalo-Aarnio,Aalto University School of Engineering

Making Battery Recycling Safer and Faster

Every year, millions of lithium-ion batteries reach the end of their life. Before they can be safely recycled, they must be fully discharged — a process that currently takes up to 400 hours and represents one of the biggest bottlenecks in battery recycling worldwide. 
This doctoral research investigated how electrochemical discharge — immersing spent batteries in conductive salt solutions to safely drain their remaining energy — can be made significantly faster and more efficient. The study focused on understanding which electrolyte solutions work best, how discharge behaves in real conditions, and how to find the optimal point to stop the process without sacrificing safety or energy recovery. 
A key finding was that conventional sodium chloride (NaCl) solutions, widely assumed in earlier literature to be effective, cause corrosion that distorts measurements and reduces performance. Potassium carbonate (Kâ‚‚CO₃) solutions proved superior, recovering 75–76% of residual battery energy while eliminating corrosion entirely. Using a physics-based electrochemical model validated against experimental data, combined with three optimization methods — Pareto analysis, Bayesian optimization, and the NSGA-II evolutionary algorithm — the research showed that discharge time can be reduced by 50–71% without compromising safety. 
These results provide battery recyclers with concrete, data-driven guidance on electrolyte selection and process termination criteria. As the demand for battery recycling grows alongside electric vehicle adoption, the findings offer a practical pathway to higher throughput, lower costs, and safer recycling operations.

Keywords: Lithium-ion battery, electrochemical discharge, battery recycling, multi-objective optimization, P2D modeling, Bayesian optimization 

Thesis available for public display 7 days prior to the defence at . 

Contact information: 
Hassan Rouhi, hassan.rouhi@aalto.fi