Ilya Kuangaliyev, a PhD student at Chalmers University of Technology, has recently completed his secondment at the Institute for Power Electronics and Electric Drives, RWTH Aachen, where he focused on Control and Machine Learning for Sustainable Battery Recycling. Ilya spent 2 months at the “Artificial Intelligence and Digitalization for Batteries” research group, where he explored aspects of modelling batteries’ deep discharge, including using machine learning methods.

Global demand for lithium-ion (Li-ion) batteries is increasing rapidly, largely driven by the growth of electric mobility and renewable energy storage. By 2030, worldwide demand is projected to reach around 4,700 GWh, with the transportation sector accounting for the majority. As the number of batteries produced and used continues to rise, the quantity of end-of-life batteries will also grow substantially. Greenpeace estimates that between 2021 and 2030, approximately 12.85 million tons of electric vehicle (EV) Li-ion batteries will reach the end of their service life. This surge in decommissioned batteries makes it essential to expand recycling capacity in order to meet new sustainability requirements, such as the European Union regulation that mandates an increase in the recycling rate of Li-ion batteries from 50% today to 65% by 2026 and 70% by 2031.

Recycling plays a crucial role in ensuring the sustainable use of raw materials, whose extraction carries significant environmental, economic, and social consequences. Lithium, one of the main elements used in Li-ion batteries, illustrates this issue well: its global consumption by battery manufacturers is expected to be nearly 30 times higher in 2030 than in 2018. However, around 60% of global lithium reserves are concentrated in just two countries—Australia and Chile. Similarly, cobalt reserves are projected to decline by 30% by 2030, with roughly 60% of cobalt mining currently taking place in the Democratic Republic of Congo. This geographic concentration places major battery-producing nations, such as China and the United States, in a vulnerable position with respect to raw material supply chains.

One of the key bottlenecks in increasing recycling capacity lies in the pretreatment process, particularly the deep discharge of batteries to a safe voltage before dismantling. This step is essential for preventing fires and explosions during mechanical processing such as crushing or shredding. Existing pretreatment methods—like short-circuiting or discharging in salt solutions—are often inefficient, time-consuming, or unsafe, limiting their industrial applicability.

My project aims to address this challenge through a controlled electrical discharge system that employs power electronics to maintain a stable discharge current. This approach improves both safety and efficiency, while enabling the recovery of residual energy from used batteries rather than dissipating it as heat.

The secondment at the Institute for Power Electronics and Electrical Drives, RWTH Aachen, was a valuable and inspiring experience that deepened my understanding of battery modeling, particularly in the context of deep discharge. Our discussions on modeling techniques and parameterization revealed several promising directions for future collaboration and research development.

Ilya Kuangaliyev

Chalmers University of Technology – RWTH Aachen