The massive deployment of renewable energies is driving increasing demand for stationary energy storage, whose specific characteristics (cost, safety, durability) differ radically from those of electric mobility. Faced with the limitations of Li-ion batteries (fire risks, criticality of lithium and cobalt, production costs), aqueous zinc-manganese (Zn-MnO2) technology is emerging as a disruptive alternative. Based on abundant, non-toxic, and inherently safe materials, it offers unique potential for long-term storage with a low environmental impact.
However, the industrialization of this technology faces scientific hurdles that limit reversibility and cycle life, notably the formation of zinc dendrites and cathode instability. This doctoral project proposes to overcome these obstacles through a hybrid research strategy combining multiphysics modeling and artificial intelligence.
Initially, a finite element model will be developed and experimentally validated to characterize degradation mechanisms (current density hotspots, concentration gradients). Subsequently, this model will serve as a data generator to train machine learning algorithms. These surrogate models will enable the rapid exploration of a vast design space to identify the most resilient architectures. The ultimate goal is to accelerate the eco-design of high-performance Zn-MnO2 batteries that meet the imperatives of energy sovereignty and the circular economy.