Monitoring and identifying sensor failure in an electric drive system
A method for monitoring and identifying sensor faults in an electric drive system of a vehicle includes collecting corresponding data using sensors in the electric drive system, inputting the collected data to an already-established sensor fault mode identification model, and determining whether a fault mode exists and a fault mode type based on the collected data using the sensor fault mode identification model. The method quickly determines the fault mode caused by a sensor fault in the electric drive system, and the fault mode type of the sensor fault.
1. A method for monitoring and identifying sensor faults in an electric drive system of a vehicle, comprising:
collecting corresponding data using sensors in the electric drive system of the vehicle, the vehicle included in a plurality of vehicles each having a corresponding electric drive system;
transmitting the collected data to a cloud computing system configured to communicate wirelessly with a controller of each vehicle of the plurality of vehicles;
inputting the collected data to an already-established sensor fault mode identification model of the cloud computing system;
determining whether a fault mode exists and a fault mode type based on the collected data using the sensor fault mode identification model;
when it is determined that the fault mode exists, running a corresponding compensation algorithm program stored in the controller of the vehicle, to compensate for data inaccuracy caused by a faulty sensor of the sensors in the electric drive system of the vehicle; and
operating the electric drive system of the vehicle based on compensated data from the faulty sensor,
wherein the sensor fault mode identification model is based on:
S (normal, Flt 1 ,Flt 2 , . . . ,Flt n )=Func( X )
wherein X is an input variable corresponding to a variation of the electric drive system with a time domain or a frequency domain,
wherein Func(X) is the sensor fault mode identification model established based on initial data,
wherein S corresponds to the fault data and the normal data, and
wherein X includes a parameter X(f) corresponding to a variation of the electric drive system with the frequency domain, and is one or more of harmonic information, standard deviation, kurtosis, or skewness acquired from a time domain variable by frequency domain calculation or statistical calculation.
2. The method for monitoring and identifying sensor faults according to claim 1 , further comprising:
after determining that the fault mode exists and the fault mode type, inputting the fault data in a database, the fault data corresponding to the fault mode and/or the fault mode type; or
after determining that the fault mode does not exist, inputting the normal data in the database, the normal data indicating that the fault mode does not exist.
3. The method for monitoring and identifying sensor faults according to claim 2 , further comprising:
optimizing and updating the sensor fault mode identification model based on the fault data and/or the normal data.
4. The method for monitoring and identifying sensor faults according to claim 1 , wherein the sensor fault mode identification model is established based on initial data by a machine learning method.
5. The method for monitoring and identifying sensor faults according to claim 1 , wherein the cloud computing system comprises a database for storing the collected data, and a central processing unit for performing calculations and communication.
6. The method for monitoring and identifying sensor faults according to claim 1 , wherein:
the faulty sensor outputs an inaccurate measurement value, and
the compensated data corresponds to a measurement value in which an inaccuracy has been eliminated.
7. A method for monitoring and identifying sensor faults in an electric drive system of a vehicle, comprising:
collecting corresponding data using sensors in the electric drive system of the vehicle, the vehicle included in a plurality of vehicles each having a corresponding electric drive system;
transmitting the collected data to a cloud computing system configured to communicate wirelessly with a controller of each vehicle of the plurality of vehicles;
inputting the collected data to an already-established sensor fault mode identification model of the cloud computing system;
determining whether a fault mode exists and a fault mode type based on the collected data using the sensor fault mode identification model;
when it is determined that the fault mode exists, running a corresponding compensation algorithm program stored in the controller of the vehicle, to compensate for data inaccuracy caused by a faulty sensor of the sensors in the electric drive system of the vehicle; and
operating the electric drive system of the vehicle based on compensated data from the faulty sensor,
wherein the sensor fault mode identification model is based on:
S (normal, Flt 1 ,Flt 2 , . . . ,Flt n )=Func( X )
wherein X is an input variable corresponding to a variation of the electric drive system with a time domain or a frequency domain as detected by another sensor of the sensors in the electric drive system,
wherein Func(X) is the sensor fault mode identification model,
wherein S corresponds to the fault data and the normal data,
wherein the faulty sensor is an electrical current sensor,
wherein the other sensor is a torque sensor, and
wherein X includes a parameter X(f) corresponding to a variation of the electric drive system with the frequency domain, and is one or more of harmonic information, standard deviation, kurtosis, or skewness acquired from a time domain variable by frequency domain calculation or statistical calculation.
8. The method for monitoring and identifying sensor faults according to claim 7 , wherein;
X further includes a parameter X(t) corresponding to a variation of the electric drive system with the time domain from the other sensor, such that collected data from the other sensor is used to determine that the fault mode exists as caused by the faulty sensor, and
the parameter X(t) includes one or more of a motor power, a motor rotation speed, a motor torque, a DC voltage from a battery pack, a current measurement value, a position sensor measurement value, a stator temperature, an insulated gate bipolar transistor temperature, a direct-axis current, a quadrature-axis current, a direct-axis voltage, and a quadrature-axis voltage.