Since EV batteries lifetime is about 8-10 years, it has been estimated around a million batteries retired from EV applications in 2030 in the EU. The end-of-life management of EV batteries is currently regulated by the EU Battery Directive, and decommissioned EV batteries are 100% collected. As these end-of-1st life batteries may still have up to 80% of their initial capacity, it was estimated that in Europe 2nd life battery, when used for stationary application, would reach 80GWh/year by 2030. Repurposing this attractive large volume of batteries faces however some challenges like the trending price drop of the new batteries, the big spread in the EVs battery pack design, and the lack of second life quality and performance standards.
The main objective of this PhD is to develop novel methods for modelling, identification, and control of large second-life battery packs in view of smart grid application. This is motivated by the potential to significantly enhance the reliability and quality of the electrical grid. In a smart grid, there's a continuous flow of information between utilities and consumers, allowing real-time communication about electricity prices and usage. By seamlessly incorporating these batteries into our advanced control and estimation software for battery management systems, we can harness their latent potential to further optimize grid performance. This dynamic environment offers exciting opportunities, such as energy arbitrage, maintaining electricity reserves, and aligning power supply with fluctuating demand through the deployment of grid-connected energy storage systems.
The aim is to demonstrate that significant performance improvements can be achieved by designing control and estimation algorithms for the battery management system that accounts for variations among the batteries within the pack and including the integration of second-life batteries. It is important to emphasize that the ultimate goal is to experimentally validate the efficiency, performance, and accuracy of the proposed methodologies.
If you have a master's degree in control/electrical engineering and a passion for scientific excellence, you might be the perfect fit for our team. We are looking for someone who can tackle challenging problems with creativity, analytical skills, and effective communication. You should also be proficient in Python programming. Having some background in electrical storage would be a plus.
