Battery operating data of electric vehicles is becoming increasingly quantified and complicated. A data analysis platform is necessary to excavate high-value battery status information for more efficient battery management. This paper proposes a Flask framework and Pyecharts-based lithium-ion data analysis and visualization platform. The design processes including the front-end and back-end frameworks, data preprocessing, data visualization, and data storage are elaborated. In the proposed data platform, a case study of battery state of charge estimation using different machine learning methods is demonstrated, and most of the estimation errors are less than 2.0%, highlighting the effectiveness of the platform.

With the rapid improvement of China's economic level and the rapid development of e-commerce, the demand for logistics warehousing, which is one of the most important links in the logistics transportation system, has also greatly increased. The dispatching management system applied in the warehouse has a wide range of applications in the logistics storage system. Improving the operational efficiency of the dispatching management system can effectively improve the efficiency of the automated warehouse system and reduce the cost of logistics transportation. Therefore, this paper designs a set of dispatching systems with the tracking car as the control target and realizes the positioning and path planning functions of the tracking car in the system. At present, the tracking car can completely receive the commands sent by the system, accurately follow the established path. At the same time, the system software can also obtain the video images taken by the camera on the car and play them smoothly.