## 1 Introduction  
Due to the benefits of high energy density, low self-discharge characteristics, fast charging, high-capacity, low-pollution, and long-life lithium-ion batteries (LIB) have recently received much attention in research and application. As a result, their market share is steadily growing [1, 2, 3]. These exceptional characteristics have drawn several scientists to research battery power management in recent years [4, 5]. In particular, the battery's state of health (SOH) is utilized to gauge its aging and anticipate how long it will last during the storm's charge and discharge cycles. Through the design of materials and the evaluation of battery life, this approach may speed up the development of novel electrode materials with increased capacity and energy [6]. Physical modeling and data collection are used to forecast universal batteries' capacity and capacity loss [7]. Math is used in model-based approaches. To accurately represent the battery's decay law, the model is chosen by the battery's physical or experimental deterioration process [8]. The data generated by impedance were analyzed using fuzzy logic in reference [9]. The integral of the discharge or charge current is used to determine the battery's remaining power using the Coulomb (CC) technique of calculating capacity over time, which is frequently used for straightforward computations [10]. Based on a modified [PERSON] circuit model, the authors of [11] created a non-linear model of the battery by employing circuit characteristics such as resistors, capacitors, and inductors to forecast the depletion of battery capacity. However, interference from other integrated system components impacts measurement accuracy [12]. Additionally, according to reference [13], for analogous circuit models with several complicated characteristics, the lifespan prediction model, in conjunction with innovative filtering technology, will forecast the reduction in battery capacity. The battery cell is complex, varied, and mysterious. An empirical exponential and polynomial regression model were also suggested in reference [14; 15; 16] to track the trend of battery cell degradation throughout the battery cell's life based on the analysis of experimental data and utilize a filtering approach to tweak the model parameters live. According to [17], a novel model using a Kalman filter and regression vector was created to forecast the battery's short-term capacity and cycle life. Additionally, the literature has established a novel prediction technique based on multiple filtering interaction models [18] to estimate battery cycle life. Many battery capacity models employ the multi-model interaction modeling technique for various state equations. Here, it is discovered that these model-based techniques have made significant strides toward high performance. Experimental evidence supports this finding. The battery-degrading accuracy of the physical model, however, limits the accuracy and robustness of these models [19; 20]. Artificial intelligence (AI) techniques are now often used in state prediction, voice recognition, natural language processing, and picture and image processing. Machine learning (ML) uses a traditional neural network, but algorithms follow the system. To predict the SOH of Li-ion battery packs, scientists also coupled the effectiveness of ML with training schemes and clever tweaks. As a result, the suggested technique is paired with the Gaussian network process (NGP) model [21; 22; 23; 24; 25] in this study, which considers the battery's degradation under various operating situations. The regression model is used to predict the target values as continuous values. This model also has far-reaching applications, from house price prediction, e-commerce valuation systems, weather forecasts, and stock market predictions to image resolution conversion, super-high automatic encoder learning features, and image compression [26; 27; 28; 29; 30]. In this paper, we used linear regression, bagging regressor, and random forest regressor models.