As part of the BRIDGES - German-Brazilian Research Network, a recent workshop brought together emerging research at the intersection of battery technology, energy systems, and sustainability. The session featured presentations from two master's students, highlighting both technical innovation and economic analysis, the two essential pillars for advancing the energy transition.
Advancing Battery Diagnostics with Data-Driven Methods
Maximilian Kerscher from the Technische Hochschule Ingolstadt presented his work on an advanced battery diagnostic framework integrating Direct Current Internal Resistance (DCIR), Electrochemical Impedance Spectroscopy (EIS), and Machine Learning for improved State of Health (SoH) estimation.
His research addresses a key challenge in battery systems: accurately assessing battery health without relying on time-intensive full-cycle testing. The proposed framework combines automated testing protocols with advanced analytical methods, including Distribution of Relaxation Times (DRT), which allows for the decomposition of electrochemical processes such as charge transfer, diffusion, and degradation mechanisms.
A central aspect of the work is the integration of machine learning models, trained on large datasets from battery testing, to enable scalable and precise SoH prediction. By linking physical diagnostics with data-driven approaches, this research contributes to more efficient battery monitoring - an important step for both electric vehicles and stationary energy storage systems.
Evaluating the Economic Viability of Second-Life Batteries
Tim Seeland from the Universität Duisburg-Essen focused on the economic dimension of battery reuse, addressing the question:
Can second-life electric vehicle batteries become economically viable for stationary energy storage applications in Brazil under future market conditions?
His work develops a comprehensive analytical framework that combines market-level (top-down) analysis of economic and regulatory conditions, technical (bottom-up) evaluation of battery performance and degradation, and scenario-based financial modeling to assess viability under different assumptions.
A key metric used in the analysis is the Levelized Cost of Storage (LCOS), enabling comparison between second-life batteries and alternative storage technologies. Initial findings suggest that while second-life applications are generally feasible, a significant portion of scenarios remains economically challenging, highlighting the importance of factors such as battery costs, degradation rates, and market conditions.
This research underscores that the success of second-life battery systems depends not only on technical feasibility but also on market dynamics, policy frameworks, and system integration.
Bridging Technology and Economics in the Energy Transition
Together, the two presentations highlight the importance of balancing the integration of engineering innovation with economic and systemic analysis to successfully advance sustainable energy systems.
For improving battery diagnostics through machine learning to evaluating the real-world feasibility of second-life applications, these projects demonstrate the multidisciplinary approach at the core of the BRIDGES network.