## 5 System Implementation  
This study utilizes an open dataset [23] of electric vehicle batteries and employs a specifically designed method based on the EKF to estimate the SoH of the battery, thus exploring its feasibility. We implemented the FELL system using software engineering methods. Figure 4a depicts the homepage of the FELL system website platform implemented in this study. As illustrated, the FELL platform offers users three significant system functions: data analysis, SoH test, and SoC prediction. Figure 4b represents the user interface furnished by the FELL system, facilitating electric vehicle users in inputting fundamental details concerning electric vehicles and batteries. The data supplied by users undergoes secure storage within the system's backend database, facilitating subsequent processes such as data analysis, algorithmic computations, and training of prediction models. Figure 5 displays the web page featuring the user interface for the implemented data analysis function (UI 1). As depicted, the FELL website employs a box-and-whisker plot to present users with pertinent statistical analysis results concerning the utilization of electric vehicle batteries. These results encompass battery operating temperature, voltage, electric vehicle mileage, current, and other relevant metrics. Moreover, below each data analysis graph, users can find data analysis conclusions and insights aimed at facilitating their timely understanding of the critical performance aspects of electric vehicle batteries. Figure 6 depicts another implemented data analysis function user interface web page. As illustrated, the FELL website employs an interactive histogram, enabling users to select from 11 data analysis options related to electric vehicle batteries. Subsequently, the web page dynamically presents corresponding data analysis results to the user in a timely manner, facilitating their comprehension of battery usage patterns. Moreover, users can utilize the outcomes of data analysis as input values for the predictive battery health function (SoH test) on the FELL website. Figure 7 displays the implemented SoH test function user interface web page. Users of the website can input parameters related to electric vehicle batteries, including battery usage days, state of charge, battery voltage, battery current, battery temperature, and the average usage time (in minutes) of electric vehicles per day. Upon clicking the prediction button, the API of the SoH prediction module of the website platform's back-end system will be invoked, returning the predicted health status of the electric vehicle battery. Users can choose between short, medium, and long-term predictions, and the web page will exhibit the results of electric vehicle battery health predictions based on the user's selections. Figure 4: Implemented FELL platform homepage and user input page. (**a**) Implemented FELL website homepage. (**b**) Implemented user input interface for EV users. Figure 5: Implemented data analysis function UI 1 web page on FELL website. Figure 6: Implemented data analysis function UI 2 web page on FELL website. Figure 8 illustrates the user interface webpage of the implemented SoC prediction function. Users can utilize this webpage to forecast electric vehicle battery consumption and plan charging in advance before traveling. Users input the start and destination locations of their trip and click the Predict button. The system invokes the back-end map API and SoC prediction function API, displaying the calculation results on the webpage. This provides the user with the estimated driving distance, driving time, and electric vehicle battery consumption. Importantly, the website system conducts real-time analysis and calculations based on the user's historical data on electric vehicle battery usage, thereby offering users real-time SoC prediction results.