A previous paper has conducted a detailed study on some data of new energy batteries, and introduced the cyclic neural network (RNN) to visualize and warn on battery data management; Ref. proposed a method to analyze battery fault diagnosis of electric vehicles based on short-term and long-term memory networks.
Numerous test cycles (constant and dynamic measurements) were carried out to identify cell abnormalities (so-called deviations). A query and data filtering process was designed to detect defective battery cells. The fault detection procedure is based on several cell voltage interruptions at various loading levels.
However, when relevant faults occur, the battery management system itself cannot analyze the original data generated by the battery. It can only artificially analyze the stored data and the messages in the CAN bus, and it can not find the root cause of the battery faults [14, 15].
To increase the reliability and safety of lithium-ion batteries, researchers are proposing different methods for diagnosing failures, which can be divided into three groups: (i) experience-based, (ii) model-based, and (iii) data-driven methods [26, 27].
Finally, combined with the thermodynamic diagram, as shown in Figure 11, the correlation between these 15 battery data indicators is further intuitively obtained, in which the correlation between minbatterysinglevoltageval, sumvoltage and SOC is 0.98, basically close to 1, showing a high correlation.
The data provided include the message data obtained from the lithium battery, including protocol type, the server receiving time, message time, message type, and the original messages. We mainly extract and analyze the original messages, which include the current vehicle status, vehicle position, battery voltage, battery voltage, and engine status.