###### Abstract
Machine learning-based methods have been employed to determine the battery status, referring to a set of algorithms that learn complex relationships using large datasets and make predictions based on these learnings [9]. These methods analyze large amounts of data and try to identify trends based on the statistical models learned and developed by computers. The process is based on algorithms that recognize patterns in data and use these patterns to predict future events or outcomes. Machine learning methods are generally divided into two main categories as follows: supervised and unsupervised learning. Supervised learning involves the use of labeled data (data in which inputs and outputs are matched) during the training process. Various algorithms, such as classification and regression, fall into this category. Unsupervised learning, on the other hand, performs the learning process using unlabeled datasets and is generally used to identify patterns or structures in the dataset. Algorithms such as clustering and dimensionality reduction are examples of unsupervised learning. These methods enable the collection of large amounts of data and learning from a large dataset regarding the behavior of the battery, providing a highly scalable and flexible approach to determining the state of the battery [10]. Various machine learning techniques have been used in the literature to predict the states of LIBs. These techniques include support vector machines (SVMs) [11], decision trees (DTs) [12], random forests (RFs) [9], artificial neural networks (ANNs), k-means clustering [13], deep learning [14; 15; 16], genetic algorithms [14], gradient boosting machines (GBMs) [17], automatic regression, and nonlinear regression models [11]. SVMs are used to solve classification and regression problems by making a distinction between datasets. In contrast, DT and RF are used to identify patterns in datasets and make predictions, while artificial neural networks are preferred in order to model complex relationships using an architecture similar to the neural networks of the human brain. K-means clustering is used to group datasets based on their similar features, while deep learning is used to learn complex structures and recognize complex patterns pertaining to multilayer artificial neural networks. Genetic algorithms are used as an optimization technique based on evolutionary principles, while GBMs are used to make stronger predictions by combining weak learning algorithms. Automatic regression and nonlinear regression models are used to analyze the relationship between dependent and independent variables and make predictions. These machine learning techniques provide a wide range of tools by which to solve complex problems, such as state of health predictions pertaining to LIBs. In the present study, various statistical and machine learning approaches were employed to predict the states of LIBs in EVs. These approaches include various techniques such as linear regression (LR), DT, SVM, Gaussian process regression (GPR) [18], ensemble models, and neural networks. While basic statistical methods such as linear regression and stepwise linear regression are being used to predict the key states of LIBs and analyze their performance, tree-based approaches such as DT and ensemble models attempt to predict battery states and performance by considering complex situations. GPR has been used to understand the dynamic structure of the battery and optimize its performance of the battery, while deep learning techniques such as neural networks and long short-term memory (LSTM) analyze large datasets to model complex relationships and optimize the use of LIBs in EVs. These various approaches have been combined to improve the performance of LIBs in EVs, support their correct and efficient use, optimize their range in line with predictions, extend battery life, and ensure their safety.