# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 2 Literature Review  
The automotive industry plays a vital role in the global economy and remains a central focus of technological research. With the dramatic rise in the number of vehicles, many developed countries are promoting the adoption of electric vehicles (EVs), which offer significant advantages in terms of reduced air pollution, enhanced reliability, and improved energy efficiency. Lithium-ion (Li-ion) batteries are the predominant choice for EVs due to their high energy density, lightweight components, and excellent charge/discharge cycling performance [5]. These batteries also offer a favorable balance between power and energy density, decreasing costs due to increased demand, extended lifespan, and low self-discharge rates [6,7]. Li-ion batteries come in various chemistries and configurations, each with distinct properties and performance characteristics [5]. In this context, monitoring the battery condition and accurately estimating its internal states are crucial for ensuring safe and efficient operation. The performance of a Li-ion battery directly influences an EV's driving range, safety, and overall battery lifespan [7]. These tasks are managed by the Battery Management System (BMS), which is responsible for monitoring and estimating the battery's states to prevent potential failures or misuse [6]. Due to the non-linear behavior of Li-ion batteries, the BMS must be highly effective to maintain safe operating conditions [8]. The core functionalities of a BMS include cell balancing, state monitoring, data acquisition, thermal regulation, and energy management [1,8]. Cell balancing mitigates voltage discrepancies among battery cells during charge/discharge cycles, while thermal management ensures that the battery operates within an optimal temperature range. Energy management guarantees the correct energy supply for each operation phase. BMSs collect key data--such as voltage, current, and temperature--via embedded sensors [6,8]. These data are crucial not only for monitoring but also for estimating internal battery states [8]. Battery state refers to a set of quantifiable indicators that describe the internal condition and performance characteristics of a battery at a given time. In this sense, battery state estimation provides insights into the battery's current condition through parameters like State of Charge (SoC), State of Power (SoP), State of Energy (SoE), State of Function (SoF), State of Balance (SoB), State of Temperature (SoT), State of Health (SoH), and Remaining Useful Life (RUL) [1,7]. These indicators collectively inform battery performance, remaining capacity, and degradation levels [7]. Each of these states serves a specific function:  
* State of Charge (SoC) reflects the battery's remaining capacity (Ah) and is vital for controlling charging and discharging cycles. Due to its dependence on internal chemistry, a battery's SoC cannot be directly measured, necessitating estimation through indirect methods [8]. * State of Energy (SoE) indicates the battery's available energy (Wh) and supports the energy management functions of the BMS. Accurate SoE estimation is essential for reliable energy delivery [8]. * State of Power (SoP) represents the battery's capacity to deliver power (W) under specific constraints related to voltage, temperature, and SoC. SoP is crucial for safe energy transfer to the EV's components [8]. * State of Function (SoF) evaluates the battery's ability to deliver power under defined SoC and SoH conditions, providing insight into performance capabilities [9]. * State of Temperature (SoT) pertains to the thermal condition of the battery. Proper thermal control is imperative as excessive heat can cause degradation or even hazardous events like fire or an explosion [1]. * State of Balance (SoB) measures the uniformity of parameters such as voltage and temperature across battery cells, which is essential for optimal battery pack performance [1]. * State of Health (SoH) is defined as the ratio between the current and initial capacities of the battery.