## 1 Introduction  
As the global demand for electric vehicles continues to grow, ensuring the safety and reliability of electric vehicle (EV) battery systems is critical. As EVs become more prevalent, the performance and longevity of their batteries become crucial factors affecting vehicle efficiency and operating costs [1, 2, 3]. Accurately predicting the State of Health (SoH) and State of Charge (SoC) of EV batteries is essential for maintaining their safe and consistent operation. SoH refers to the overall condition and remaining capacity of the battery, while SoC indicates the current level of charge stored in the battery [4, 5]. Developing precise and dependable models for predicting SoH and SoC is a significant research challenge in the automotive industry [6, 7]. These models must account for various factors such as battery chemistry, temperature, charging cycles, and usage patterns to provide accurate assessments of battery health and charge status. As the automotive industry moves towards electrification, addressing these challenges is crucial for ensuring the sustainable and reliable advancement of EVs as a viable mode of transportation for the future. By overcoming these hurdles, manufacturers can enhance the safety, performance, and longevity of EV batteries, thereby accelerating the adoption of electric vehicles on a global scale.  
There is an urgent imperative to foster the development of accurate and dependable predictive models to address the SoH and SoC of EV batteries [8, 9]. In this context, leveraging deep learning methodologies emerges as a promising avenue. The deployment of deep learning techniques has demonstrated considerable success across diverse prediction tasks, holding the potential to revolutionize SoH and SoC prediction. While numerous studiesdelve into utilizing machine learning and deep learning technologies for forecasting electric vehicle battery health [10; 11; 12], the predominant focus remains on designing algorithmic models, lacking the implementation of system prototypes.  
This study aims to introduce a novel research method and implement a system prototype to address this research gap. This research work is expected to assist the electric vehicle industry in addressing challenges related to the predictive diagnosis of battery failures. The significant research contributions of this study are summarized as follows:  
1. We designed a system model and an algorithm for predicting the SoH of electric vehicle batteries based on the extended Kalman filter (EKF) and implemented it on the network platform.
2. Utilizing software engineering principles, we designed and implemented FELL, an electric vehicle battery diagnosis and prediction system based on machine learning. We provide a detailed description of the design approach.
3. We implemented five representative deep learning prediction models on the FELL system and demonstrated the results of the system prototype implementation. The outcomes of system testing and experiments also validate the functionality and effectiveness of the model and system prototype developed in this study.  
The rest of this paper is organized as follows: In Section 2, we review significant research on predicting the SoH and SoC of EV batteries. Section 3 outlines the mathematical model based on the EKF to predict the battery status of electric vehicles. An EKF-SoH algorithm is presented in this section. Section 4 demonstrates the system design approach. Section 5 presents the implementation results of the system prototype. Section 6 showcases the results of system testing and experiments. Finally, we conclude this study in Section 7.