###### Abstract  
The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM ( bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods' performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies. machine learning; state of health; Li-ion; battery; electrical vehicle +
Footnote †: journal:  
and performance of LIBs have led to constraints in meeting the requirements for higher energy density and long-lasting batteries [3]. Security concerns are other factors that have hindered the development of LIBs. Safety issues pertaining to these batteries, such as overheating, explosion, or fire risks, have revealed that battery technology needs further development. Additionally, the recycling and waste management of these batteries are important issues in terms of reducing their environmental impact. Technological challenges and costs in terms of recycling LIBs have made it difficult to effectively manage and recycle waste batteries [4]. These obstacles are seen as limiting factors for the wider acceptance of LIB technology. However, research in these areas in the literature and technological advances enable important steps that need to be taken to overcome these obstacles. In order to minimize the effects of the abovementioned limiting factors, the current status of LIBs used in EVs must be accurately predicted and managed. The accurate estimation of the basic parameters of the battery, such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP), will facilitate the safe and efficient use of these batteries [5; 6]. However, the accuracy and reliability of these predictions still face some challenges in terms of existing technologies. In particular, the precise estimation of the SoC and SoH is necessary in order to fully understand and optimize the real-time state of LIBs. However, the complex structure of the battery, its sensitivity to external factors, and differences between cells make it difficult to accurately predict these situations. In addition to the traditional methods, significant advances have been made in terms of the conditions for predicting the use of model-based and data-driven techniques [7]. However, more precise, reliable, and faster methods are needed to accurately estimate battery use under different operating conditions and over longer periods of time. Research in this area has focused on efforts to overcome this obstacle through the development of better algorithms, the use of more sensitive sensors, and more comprehensive modeling to understand the complexity of the battery [8]. These developments constitute important steps for the more reliable, efficient, and long-lasting use of LIBs.