Prediction and identification of potential bearing anomalies within an electric motor of an electric vehicle
Systems and methods for predicting an anomaly within an electric motor of an electric vehicle. One method includes receiving sensor information from a knock sensor, wherein the sensor information represents a detected vibration of the electric motor, deriving, from the sensor information, a signal characteristic, determining, based on the signal characteristic, a potential anomaly within the electric motor, and generating an alert to user based on the potential anomaly.
1. A system for predicting an anomaly in an electric vehicle, the system comprising:
an electric motor;
a knock sensor; and
an electronic processor, the electronic processor configured to:
receive sensor information from the knock sensor, wherein the sensor information represents a detected vibration of a bearing of the electric motor;
derive, from the sensor information, a signal characteristic;
determine, based on the signal characteristic, a potential anomaly within the electric motor;
identify, based on the signal characteristic, that the potential anomaly is a torque ripple within the electric motor caused by a source within the electric vehicle other than the bearing of the electric motor, and
generate an alert to a user based on the potential anomaly.
2. The system of claim 1 , wherein determining the potential anomaly within the electric motor is further based on historic data of a plurality of other electric vehicles.
3. The system of claim 2 , wherein determining the potential anomaly within the electric motor includes utilizing an artificial intelligence algorithm.
4. The system of claim 1 , wherein determining the potential anomaly includes identifying a type of bearing fault of the electric motor.
5. The system of claim 4 , wherein the type of bearing fault includes at least one selected from the group consisting of an outer ring damage, an inner ring damage, a ball damage, a rivet damage, a corrosion damage, an electric current damage, a pre-pitting damage, and a dirt level.
6. The system of claim 1 , wherein the electric motor is an electric axle.
7. The system of claim 1 , wherein determining the potential anomaly is further based on at least one selected from the group consisting of a coolant inlet temperature, a coolant outlet temperature, a vehicle speed, and an electric motor current.
8. The system of claim 1 , wherein deriving the signal characteristic includes filtering out a background vibrational component from the sensor information.
9. The system of claim 1 , wherein the electric vehicle is a hybrid vehicle or a fuel cell electric vehicle.
10. A method for predicting an anomaly within an electric motor of an electric vehicle, the method comprising:
receiving sensor information from a knock sensor, wherein the sensor information represents a detected vibration of the electric motor;
deriving, from the sensor information, a signal characteristic;
determining, based on the signal characteristic, a potential anomaly within the electric motor;
identifying, based on the signal characteristic, that the potential anomaly is a torque ripple within the electric motor caused by a source within the electric vehicle other than the bearing of the electric motor, and
generating an alert to a user based on the potential anomaly.
11. The method of claim 10 , wherein determining the potential anomaly within the electric motor is further based on historic data of a plurality of other electric vehicles.
12. The method of claim 11 , wherein determining the potential anomaly within the electric motor includes utilizing an artificial intelligence algorithm.
13. The method of claim 10 , wherein determining the potential anomaly includes identifying a type of bearing fault of the electric motor.
14. The method of claim 13 , wherein the type of bearing fault includes at least one selected from the group consisting of an outer ring damage, an inner ring damage, a ball damage, a rivet damage, a corrosion damage, an electric current damage, a pre-pitting damage, and a dirt level.
15. The method of claim 10 , wherein the electric motor is an electric axle.
16. The method of claim 10 , wherein determining the potential anomaly is further based on at least one selected from the group consisting of a coolant inlet temperature, a coolant outlet temperature, a vehicle speed, and an electric motor current.
17. The method of claim 10 , wherein deriving the signal characteristic includes filtering out a background vibrational component from the sensor information.
18. The method of claim 10 , wherein the electric vehicle is a hybrid vehicle or a fuel cell electric vehicle.