Intelligent networked automobile BMS sensing array failure cloud diagnosis method based on drifting twinborn bodies

Abstract

The invention discloses a cloud diagnosis method for failure of a BMS (Battery Management System) sensing array of an intelligent networked automobile based on a drifting twinborn body, which comprises the following steps of: extracting power system data and BMS sensing array data of a target vehicle at a typical moment through V2I (Vehicle to Infrastructure) communication, and establishing an instantaneous power matrix and a power factor matrix; the method comprises the following steps: constructing a drift twin network model to carry out feature recombination and vector reconstruction to obtain a power factor standard drift output matrix, compensating and biasing the power factor matrix, and then calculating a BMS sensor array drift failure positioning vector by using normalization processing and a base point average coefficient. And automatically diagnosing a sensor set with drift failure in the sensor array, and finally outputting the failed sensor set through the cloud server. According to the invention, the drifting failure existing in the sensing array in the BMS of the electric vehicle can be diagnosed and the failed sensor set can be positioned in an intelligent network connection environment, so that an important guarantee is provided for the reliability and the effectiveness of a control strategy of the battery management system.

Description

technical field [0001] The invention belongs to the field of intelligent networked vehicles, in particular to a cloud diagnosis method for BMS sensor array failures of intelligent networked vehicles based on drift twins; Background technique [0002] With the gradual popularization of advanced technologies such as 5G, digital twin, and artificial intelligence, the automotive industry has begun to undergo profound changes, and driverless and pure electric vehicles are competing to become research hotspots. Intelligent and connected new energy vehicles have become the main direction of the transformation and development of the global automobile industry. As the core of pure electric vehicles, BMS (Battery Management System) needs to collect technical parameters such as voltage, current and internal temperature of power battery packs through sensor arrays, and control important processes such as battery charging and discharging processes. Therefore, the sensing array greatly f...

AI Technical Summary

Problems solved by technology

At present, there are certain deficiencies in the diagnostic methods for sensing array drift failure. For example, the diagnosis is only for the sensor drift, which leads to the inability to accurately grasp the fault distribution of the sensing array; the appropriate treatment method for environmental interference has not been established; the establishment of a model The key parameters of the method are not clear, so that it cannot achieve high accuracy; it is impossible to locate the failure location of the sensor array; when these methods are applied to the BMS sensor array failure diagnosis of intelligent networked vehicles, the diagnostic accuracy Inevitably low, not accurate diagnosis

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