Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies help to better understand and model degradation and to optimize the operating strategy. Nevertheless, there are only a few comprehensive and freely available aging datasets for these applications. To our knowledge, the dataset¹ presented in the following is one of the largest published to date. It contains over 3 billion data points from 228 commercial NMC/C+SiO lithium-ion cells aged for more than a year under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to cells. The dataset¹ includes result data (such as the remaining usable capacity or impedance measured in check-ups) and raw data (i.e., measurement logs with two-second resolution). The data can be used in a wide range of applications, for example, to model battery degradation, gain insight into lithium plating, optimize operating strategies, or test battery impedance or state estimation algorithms using machine learning or Kalman filtering.

Microgrids using renewable energy sources play an important role in providing universal electricity access in rural areas in the Global South. Current methods of system dimensioning rely on stochastic load profile modeling, which has limitations in microgrids with industrial consumers due to high demand side uncertainties. In this paper, we propose an alternative approach considering demand side management during system design which we implemented using a genetic scheduling algorithm. The developed method is applied to a test case system on Idjwi Island, Democratic Republic of the Congo (DRC), which is to be powered by a micro hydropower plant (MHP) in combination with a photovoltaic (PV) system and a battery energy storage system (BESS). The results show that the increased flexibility of industrial consumers can significantly reduce the cost of electricity. Most importantly, the presented method quantifies the trade-off between electricity cost and consumer flexibility. This gives local stakeholders the ability to make an informed compromise and design an off-grid system that covers their electricity needs in the most cost-efficient way.