Silvia Colnago

Lithium-ion batteries are increasingly used in stationary energy storage and electric mobility, making reliable degradation assessment essential for improving lifetime, safety, and performance. Since aging is governed by coupled electrochemical processes influenced by temperature, state of charge (SOC), and cycling conditions, this work investigates the diagnostic information that can be extracted directly from experimental data without relying on predefined battery model fitting. A structured testing campaign was conducted on a commercial lithium nickel cobalt aluminum oxide cell at different temperature, SOC, and aging conditions. Charge/discharge voltage curve analysis, incremental capacity analysis (ICA), differential voltage analysis (DVA), electrochemical impedance spectroscopy (EIS) analysis, distribution of relaxation times (DRT) analysis, and voltage relaxation analysis were comparatively evaluated. The results show that charge/discharge voltage curve analysis provides global indicators of degradation, while ICA and DVA highlight aging-related changes in characteristic voltage features. EIS, DRT, and voltage relaxation analyses provide complementary information on resistive, interfacial, transport, and relaxation processes. Overall, the different techniques exhibit consistent aging trends and, when considered jointly, support the qualitative interpretation of degradation modes and aging mechanisms. The combined diagnostic approach provides complementary insights into battery aging directly from experimentally observable features while avoiding the uncertainties associated with predefined battery model fitting.

Lithium-ion batteries are widely used in applications ranging from portable electronics to electric vehicles and grid energy storage, owing to their high energy density, efficiency, and long lifetime. However, their performance degrades over time due to aging mechanisms such as solid electrolyte interface growth, lithium plating, and electrolyte decomposition, leading to capacity fade and reduced power capability. Accurate state of charge (SOC) estimation is therefore essential for ensuring safe and efficient battery operation, particularly within battery management systems. While many existing methods rely on the direct relationship between open circuit voltage (OCV) and SOC, practical applications require the inverse mapping, i.e., the estimation of SOC from measured OCV values. This inversion is not always straightforward: analytical solutions are only available for simple models, whereas more accurate formulations often require computationally intensive numerical methods. Direct analytical SOC–OCV relationships (inverse OCV–SOC models) provide an effective alternative, enabling simplified SOC estimation without numerical inversion. Previous work proposed a direct generalized Gaussian analytical relationship expressing the absolute state of charge as a function of OCV, thereby simplifying SOC estimation and avoiding numerical inversion, developed and validated on a lithium cobalt oxide battery cycled in the linear region of the OCV curve at constant battery temperature. Building upon this study, the proposed approach was extended to investigate the effects of cycle aging across a wider operating range, considering low, medium, and high voltage/SOC conditions. The model was experimentally validated, at constant battery temperature, on the same type of lithium cobalt oxide batteries through an extensive testing campaign, demonstrating its effectiveness in capturing battery behavior under different operating conditions.

In recent years, lithium-ion batteries have been extensively used across various applications. To ensure safety and limit degradation, it is important to develop accurate models for implementation in the battery management system. Equivalent circuit models used to reproduce electrical behavior generally consist of a voltage source with a series impedance. This impedance is composed of resistances and capacitances, both of which depend on several factors, such as temperature, state of charge, and aging. A previous study demonstrated that resistances exhibit hysteresis, meaning they differ depending on whether they are measured after charging or discharging. Several studies in the literature focus on the dependence of other elements of battery impedance on temperature and aging. However, the occurrence of hysteresis in these elements has not been thoroughly analyzed. This paper investigates the presence of hysteresis in battery impedance elements and its dependence on temperature.

Achieving climate neutrality before 2050 is a central objective of the European Green Deal. This transition involves phasing out non-renewable power plants and installing numerous renewable energy sources, resulting in non-dispatchable and intermittent energy production. Additionally, the transition entails an increase in energy consumption due to greater electrification, particularly in the heating and mobility sectors. In this context, there may be a growing need for the deployment of energy storage systems. This paper proposes a methodology to optimize the required capacity of energy storage systems to support the achievement of climate neutrality. The methodology is then applied to the case of the Province of Bolzano.

Lithium-ion batteries are extensively used across various industries, making accurate state of charge (SOC) estimation crucial for optimal battery management. One of the most common techniques for estimating SOC involves using the relationship between the open circuit voltage (OCV) and SOC. Specifically, given an OCV measurement, the SOC can be determined by inverting the OCV-SOC relationship, which can be complex and challenging. Building on a previously proposed inverse OCV-SOC function based on a double Gaussian function, which provides a more efficient and reliable approach to SOC estimation at different temperatures, this paper adapts and extends the model by accounting for variation in the relationship as a function of the cycling level.