# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 2 Literature Review
A battery reaches its End of Life (EOL) when capacity falls to 80% of its initial value, beyond which it is unsuitable for EV applications [8]. * Remaining Useful Life (RUL) estimates the remaining charge/discharge cycles before the battery reaches its EOL. This indicator is key for long-term battery usage planning and safety [8]. The aging of Li-ion batteries is driven by various mechanisms, including capacity fade, increased internal resistance, and safety deterioration caused by chemical decomposition, structural damage, and electrolyte breakdown. Factors like environmental temperature and frequent cycling accelerate these aging processes [9]. Moreover, rapid the charging and discharging of the battery and extreme environmental temperatures drastically affect the lifespan of the battery. For this reason, there is a great effort in temperature controlling and monitoring through Battery Temperature Management Systems (BTMSs) alongside ML techniques [10]. A deep understanding of these mechanisms, combined with accurate state estimation, is critical to enhancing battery longevity, improving performance, and ensuring operational safety [8,11]. Data informs decisions throughout a battery's entire lifecycle--from speeding up material discovery and manufacturing optimization during design to predicting and classifying battery longevity for sales and deployment under varying stresses (e.g., charge/discharge rates, temperatures, and depths of discharge) [12]. Thus, the collection of adequate and appropriate variables is crucial to the analysis. As illustrated in [12], there is a large variety of public datasets regarding Li-ion batteries that demonstrate a wide range of test data and parameters. Furthermore, most of these datasets illustrate the lifetime or a certain part of the lifetime of batteries at the cell level, since they refer to experimental processes on a cell level in a laboratory environment. They depict a variety of cell chemistries, testing conditions, and procedures that evolved, reflecting the wide range of characteristics in Li-ion batteries [12]. However, at the same time, datasets describing the behavior of Li-ion batteries in real-world conditions, i.e., in the BMS of EVs, are not available due to confidentiality and ownership issues, thus making them inaccessible to the research community [13]. Although voltage, current, and temperature can be directly measured, most internal states cannot. This limitation complicates the accurate prediction of battery behavior, particularly under dynamic operating conditions and aging influences [14]. Consequently, various estimation approaches have been developed, including experimental techniques, observer- and filter-based methods, model-based methods, and data-driven approaches leveraging machine learning algorithms [8]. Figure 1 depicts a classification of the methods used in the reviewed related works per function. The first category of these methods describes the common ones for state estimation; it refers to the experimental methods that have been developed for each one of these states. The second category refers to the model-based methods, including the filter-based ones, which rely on mathematical formulas or models that describe the underlying system's behavior. These approaches often incorporate domain knowledge and system dynamics since they contain information about the physical characteristics of the system. An example of these methods is the extended Kalman filter (EKF) method which linearizes  
the non-linear system of the battery. Nevertheless, the battery's parameters are changing easily and quickly due to non-linear behavior, and hence it is necessary for this method to be combined with other model-based or data-driven methods [1]. The third category refers to the data-driven approaches, which are based on a large amount of historical data to uncover patterns and make predictions without requiring explicit knowledge of the underlying system's dynamics.