A reliable power grid system based on renewable energy sources is a crucial step to restrict the climate crisis. Stationary battery energy storage systems (BESS) offer a great potential to repel power fluctuations in the grid at different timescales. However, for a reliable operation and cost estimation, the degradation in the batteries needs to be understood. We present an accelerated battery degradation study, on single as well as multi-service applications, of NCM532/Gr lithium-ion battery cells. Frequency regulation (FR) was the least harmful for the battery, with an expected lifetime of 12 years, while peak shaving (PS) resulted in an expected lifetime of 8 years. The combined cycle (FRPS) accelerated the capacity loss, and degradation of the positive electrode was induced from the start of cycling, causing power limitations after only 870 equivalent full cycles (EFC). Tracking the 1C-rate discharge capacity was proven to be a good indication of the accelerated cell polarization, and it can serve as a useful method to evaluate the internal battery state of health (SOH).

Li-ion batteries are a key enabling technology for electric vehicles and determining their properties precisely is an essential step in improving utilization and performance. Batteries are highly complex electrochemical sys-tems, with processes occurring in parallel on many time-and length-scales. Models describing these mechanisms require extensive parametrization efforts, conventionally using a combination of ex-situ characterization and systems identification. We present a methodology that algorithmically designs current input signals to optimize parameter identifiability from voltage measurements. Our approach uses global sensitivity analysis based on the generalized polynomial chaos expansion to map the entire parameter uncertainty space, relying on minimal prior knowledge of the system. Parameter specific optimal experiments are designed to maximize sensitivity and simultaneously minimize interactions and unwanted contributions by other parameters. Experiments are defined using only three design variables making our approach computationally efficient. The methodology is demon-strated using the Doyle-Fuller-Newman battery model for eight parameters of a 2.6 Ah 18,650 cell. Validation confirms that the proposed approach significantly improves model performance and parameter accuracy, while lowering experimental burden.

Lithium-ion battery degradation is complex, and many mechanisms occur concurrently. In-depth degradation is traditionally investigated by post-mortem characterization in lab-settings. If mechanisms could instead be identified in-operando, utilization could be adjusted, and battery lifetime extended. We investigate changes in electrochemical model parameters during battery testing and their correlation with degradation observed in a traditional post-mortem characterization. Commercial batteries are cycle-aged using different stationary storage service cycles and a novel reference performance test is applied intermittently. This test is based on current profiles optimally designed with respect to maximized sensitivity for individual electrochemical parameters and embedded within a charging procedure. Usage dependency of parameter trajectories over the course of ageing is demonstrated and coupled to observed micro-structural changes. Subsequently, the parameter trajectories are extrapolated using Gaussian Process Regression for physics-based state-of-health estimation and remaininguseful-life prediction. We demonstrate and validate estimation of full cell performance under constant load at a later state in life.

Lithium-ion batteries have a great potential in stationary energy storage, both for first- and second life, but the understanding and tools to evaluate cell degradation needs to be improved. In this study, the degradation of batteries subjected to three types of stationary services, as well as the repurposing of cells from more demanding to a milder application is investigated. The milder cycle is frequency regulation with a maximum C-rate of 1.5C (FR1.5C) and the more demanding cycles peak shaving with a C-rate of 1C (PS1C) and FR and PS combined (FRPS2C). The main driver for accelerated capacity loss was identified as the state-of-charge (SOC) change during operation, increasing the rate of degradation for PS and FRPS. The cell impedance was measured and fitted to a physics-based model to deconvolute the sources of polarization increase. A tortuosity increase in the negative electrode was seen for all cells, as well as a resistance increase. FRPS2C and PS1C further showed a decrease in the electrolyte mass transport properties. When repurposed to the milder FR1.5C application, PS1C showed a clear decrease in capacity loss rate while more heterogeneous degradation might be the reason for a higher rate of degradation for the repurposed FRPS2C cell.

To prolong the lifetime of lithium-ion batteries and successfully implement repurposed cells in milder applications, several challenges need to be overcome. First, the degradation and sources of performance loss needs to be better understood, as a sudden rapid drop in capacity sometimes is observed at a later stage in the battery life. Reliable methods to characterize the battery state of health (SOH) based on the in situ measurable signals (voltage, current and surface temperature) are needed as well, especially in the case when the cell history is unknown. Finally, path-dependent degradation should be explored to match batteries with the most suitable application. In this study we analyse the performance and degradation of Tesla (LiNixCoyAlzO2 (NCA)/Gr-SiOx) lithium-ion batteries in 2nd applications. The cells are initially characterized through different electrochemical techniques and later subjected to two different cycling protocols. Characterization methods are evaluated and cell suitability for different services explored. Mass transport limitations are found to accelerate cell degradation, and an improved SOH evaluation metrics is suggested.