## 2 State of the Art
The model uses a linear combination of the sum of the resistance of electrodes and charge transfer and the double-layer capacitance to predict the end-of-life values. In [11], a model consisting of a resistance in series an inductance, a Warburg element, \(R/\)/L, and 3 RCs is used. The series resistance is found to be the most correlated with the SoH. In [12], an exponential regression model based on the SEI layer resistance from an ECM fitting with 3 RQ in series with Warburg element and resistance is depicted. Ref. [13] contains a detailed review of SoH prediction methods based on impedance and impedance spectroscopy. This review shows that most literature directly uses the impedance spectrum, or a direct value of the ECM, which is then correlated with the capacity or number of cycles. Most of the state-of-the-art methods based on impedance spectroscopy have demonstrated a good correlation with the SoH, which gives high accuracy greater or equal to 95% [5]. However, they use cell-dependent values for the correlation or linearized with future values, making generalizing the method even for the same battery types unfeasible. Adaptive methods use mathematical models based on current and voltage measurements during operation to estimate the capacity or the impedance. They can be classified into capacity-fade estimation methods and power-fade methods. Adaptive capacity-fade estimation methods are inferred by an online current and voltage measurement and mainly involve estimation theory (e.g., filters-based approaches). For example, the Kalman filter and its derivatives are used to either estimate the total capacity in steady white noise filtering configuration [6; 14; 15] or to estimate the power decay by estimating the internal resistance from running signals [16]. Least-square methods, such as Ordinary Least Squares, Weighted Ordinary Least Squares, and Total Least Squares, have also been used for capacity-fade detection. One possible implementation is estimating a Coulombic efficiency-like quantity over time and aging, which is compensated inside the total capacity [6]. Adaptive methods are argued to have outstanding accuracy [5; 6], but they require a long calibration/estimation time for reaching high accuracy. They also have considerable risks of deviation after a long-running time as their principle is mainly incremental, and deviations are therefore accumulated. Experimental methods are often thought to be limited due to measurement deviations or difficulties of interpretation and end-of-life estimation. For example, HPPC does not provide accurate results if the batteries were not at rest before measurement or when the cells are affected by environmental factors, e.g., temperature and noise. Impedance-spectroscopy-based methods are more reliable, as they rely on several measurement points. Although they are not prone to noise, it is possible to use curve fitting to filter them out. Moreover, the impedance spectrum is very sensitive to SoH. However, a significant problem is a dependency on the cell's individual properties. The measurements tend to be very individual and may even fail to provide a generalizable solution to be applied to cells from the same type or same manufacturer. Although other solutions have investigated the linearization of the fitted circuit elements to future end-of-life values, such as in [4], these solutions may not be realizable in reality. In this paper, we investigate the possibility of providing a linearization function to calculate the SoH that is less affected by the cell's individual properties. The proposed solution aimed to work individually but can also be incorporated as a basis for adaptative solutions [1; 5; 6].