# Machine Learning-Based Lithium Battery State of Health Prediction Research
## 1 Introduction  
With the worsening global energy crisis and environmental pollution, electric vehicles have become a key industry that countries around the world are actively developing, as they help reduce dependence on traditional energy sources. Lithium-ion batteries, with their high energy density, long life, and low self-discharge rate, have emerged as a reliable power source for electric vehicles. However, as the number of charge and discharge cycles increases, lithium batteries gradually age. If their health status is not monitored in a timely manner, it may result in issues such as reduced driving range, decreased performance, and even safety hazards. Therefore, accurately assessing the state of health (SOH) of lithium batteries is crucial for the safe operation of electric vehicles [1]. The most common definition of SOH is the percentage of the battery's rated capacity that can be discharged from a fully charged state to the cut-off voltage under specific conditions [2]. Currently, SOH estimation methods for lithium-ion batteries can be broadly categorized into three types: direct measurement methods, model-based methods, and data-driven methods [3]. Direct measurement methods estimate SOH by directly measuring aging characteristic parameters such as battery capacity and internal resistance under laboratory conditions. For instance, [PERSON] et al. [4] analyzed impedance data and established a mathematical model correlating impedance spectrum directional element parameters with SOH, achieving rapid SOH estimation with prediction errors within 2%. Similarly, [PERSON] et al. [5] combined the coulomb counting method with differential voltage analysis to estimate battery SOH, demonstrating high accuracy with a mean absolute error controlled within 0.4%, thereby enabling precise SOH prediction. However, direct measurement methods are often not prioritized for practical applications due to their high dependency on specialized testing equipment, time-consuming measurement processes, and limited applicability [6]. Model-based estimation methods typically require the establishment of an electrochemical model (EM) or an equivalent circuit model (ECM) to identify parameters related to battery performance degradation and failure [7]. These parameters are then updated using adaptive filtering techniques or nonlinear state observers to more accurately predict changes in the SOH of the battery. [PERSON] et al. [8] utilized a simplified EM to describe the distribution of lithium ions within the battery and linked SOH to the irreversible loss of lithium ions. Recursive least squares was employed to online identify model parameters, while an unscented Kalman filter was used to estimate the SOH. The experimental data revealed that the method's average prediction inaccuracy was around 2%. [PERSON] et al. [9] proposed a method based on a fractional-order battery EM combined with an iterative model observer technique to adaptively estimate both internal resistance and capacity degradation. This approach avoided the high computational cost caused by limited calibration parameters while maintaining high estimation accuracy. Similarly, [PERSON] et al. [10] developed a 2-RC model to estimate SOH, where the open-circuit voltage was regarded as a function of the state of charge (SOC). Nonlinear least squares curve fitting was used to approximate the model parameters, enabling the model to adapt to and capture the dynamic changes in SOH. The results demonstrate that this method achieved SOH estimation with low RMSE. [PERSON] et al. [11] applied recursive least squares to identify the parameters of a Thevenin model and implemented an extended Kalman filter to achieve joint estimation of SOC and SOH at different time scales. While model-based methods can achieve high estimation accuracy under stable external experimental conditions, they still face significant challenges in accurately modeling batteries under complex operating environments.