Battery system sensor fault diagnosis method based on state estimation method

Abstract

The invention provides a battery system sensor fault diagnosis method based on a state estimation method. The method comprises the following steps : pr constructing a battery model is constructed, then, carrying out real-time estimation on the state of charge of the battery by adopting a model-based method; determining the capacity of the battery by adopting a ratio of the electric quantity changeto the state of charge change within a certain time period; respectively acquiring a capacity reference value and five groups of capacity estimation values by changing initial moments correspondingto a starting point and an ending point of a used time period; and respectively carrying out difference treatment on the capacity reference value and the estimated value to obtain five groups of capacity residual errors, respectively comparing the five groups of capacity residual errors with a threshold value, and judging that the sensor has a fault when the absolute value of one or more groups ofresidual errors reaches or exceeds the threshold value.

Description

technical field [0001] The invention relates to the field of power battery systems, in particular to a battery system sensor fault diagnosis method based on a state estimation method. Background technique [0002] Sensor fault diagnosis is one of the core functions of the battery management system (BMS), which is of great significance to the safety of the battery system. BMS performs modeling and battery state estimation based on the data collected by sensors, so using battery state changes for sensor fault diagnosis is one of the typical approaches. The state of the battery includes state of charge (State of charge, SOC), state of energy (state of energy, SOE) and state of health (state of health, SOH), and SOH is mainly measured by capacity decline. Therefore, the SOC, SOE and capacity C of the battery can be used n The corresponding residuals are generated for sensor fault diagnosis. In fact, the change rates of the three are different during the charge and discharge c...

AI Technical Summary

Problems solved by technology

In the current research, the starting point data is fixed and the end point data is continuously updated with the increase of sampling time, which will lead to the gradual increase of the power and SOC changes in the later stage of discharge, which will lead to an increase in the failure rate of fault diagnosis.

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