The number of Stationary Battery Systems (SBS) connected to various power distribution networks across the world has increased drastically. The increase in the integration of renewable energy sources is one of the major contributors to the increase in the number of SBS. SBS are also used in other applications such as peak load management, load-shifting, voltage regulation and power quality improvement. Accurately modeling the charging/discharging characteristics of such SBS at various instances (charging/discharging profile) is vital for many applications. Capacity loss due to the aging of the batteries is an important factor to be considered for estimating the charging/discharging profile of SBS more accurately. Empirical modeling is a common approach used in the literature for estimating capacity loss, which is further used for estimating the charging/discharging profiles of SBS. However, in the case of SBS used for renewable integration and other grid related applications, machine-learning (ML) based models provide extreme flexibility and require minimal resources for implementation. The models can even leverage existing smart meter data to estimate the charging/discharging profile of SBS. In this paper, an analysis on the performance of different ML approaches that can be applied for lithium iron phosphate battery systems and vanadium redox flow battery systems used as SBS is presented for the scenarios where the aging of individual cells is non-uniform.

The need for grid scale energy storage is inevitable in the future. Among all the available battery technologies, Redox Flow Battery (RFB) is one of the most feasible options for large-scale energy storage. Different species of vanadium ions in the battery diffuses at different rates across the membrane and this limits the capacity of the battery over long period of operation. In this paper, the concentration change of different species of vanadium ion is modeled with temperature influence for each of these vanadium ion variants. The concentration change of different vanadium ions due to diffusion is simulated for different temperatures. The change in capacity of the battery is calculated for different temperature profiles and the total loss was partially contributed by intrinsic loss and partly due to diffusion. It was also observed that the difference in the loss at different temperatures is higher during the initial 50% of the total lifecycle and stabilizes as it proceeds. At the end of 200 cycles, the capacity loss at 40°C was higher (14.23%) than that of 30°C (13.37%) and 20°C (12.67%) of which a major part was contributed due to diffusion in the first 100 cycles of operation.