## 1 Introduction
Nevertheless, as the contribution of side reactions and other potential mechanisms varies compared to those in the first life, diagnosing aging during the second life can be more challenging. When exploring, developing, and implementing diagnosis approaches in LiBs, researchers focus on quantifying aging mechanisms, predicting the state of health (SOH) as capacity fades, and accounting for cell spreading evolution [5]. Unique and complex challenges arise in second life applications due to the battery's history, where the pack has been subjected to different user patterns and operating temperatures during its first life, as well as the coupling of several degradation mechanisms with added interaction effects, and cell spreading, which introduces uncertainty when forecasting aging at the module and pack levels. Moreover, while by definition, the window of the second life lies between the first and second knee points of the capacity curve over time, estimating these points remains a challenge [11; 12]. Thus, pathways and aging trajectories associated with knee points have different implications for modelling and prediction, and they are impacted by interactions and heterogeneity [12]. Knees-onsets have been predicted using different techniques, including electrochemical, simple data-based (regression), and machine learning techniques [12]. Several approaches have been proposed to predict aging in second life. Thus, Electrochemical Impedance Spectroscopy (EIS) has been used in combination with statistical and machine learning methods, such as Support Vector Machine and K-Nearest Neighbour Regression, to predict the SOH of batteries [13; 14], thereby accurately predicting their SOH in the first and second lives. EIS has proven to be effective in clustering aging mechanisms due to side reactions, though it is mostly limited to offline applications [14; 15]. Standaloneor combined machine learning methods, on the other hand, have been extensively been applied to predict aging in second life as well as remaining useful life, including feature generation and filtering, bagging, deep neural networks, recurrent neural networks, and Long Short-Term Memory (LSTMN) models using online and accelerated test data for different cell chemistries and combinations of stress factors [13; 16; 17; 18]. The limitations of these works are attributed to relying on extensive amounts of data, reduction in accuracy as complexity increases, and computationally expensive models [16; 17; 18]. The complexity of existing aging prediction models, the inherent limitations of data-driven models, and the need for an understanding of the contribution of different aging mechanisms have led to the conceptualization of an aging diagnosis approach that could offer a trade-off to close these gaps while maintaining accuracy, interpretability, scalability (from cell to pack level), adaptability to different user patterns and stress conditions, and feasibility for real-time or online implementation. An attempt to tackle these challenges included a hybrid aging diagnosis framework in first life, combining kinetically based correlations, time series similarity analysis with side reaction modelling, and remaining useful life (RUL) estimation features [19]. This approach proved reliable in predicting aging contributions and stress factor values, accurate in estimating the contributions of SEI growth and LiP to capacity fade over time, and effective in forecasting RUL [19]. The limitations of the cited work primarily included data dependency, noise sensitivity, and scalability. Nevertheless, the trade-off of this hybrid conceptual approach can be successfully adapted to second life as the principles of aging are retained. At the same time, scalability, or integration into a battery management system, may not be feasible in second life applications. Data dependency will remain a constraint that must be addressed as we gather more aging data.