Method and apparatus for determining series arcing in a hybrid-electric propulsion system
A hybrid-electric propulsion system is provided that includes a thermal engine, an electric motor, a battery, a battery management unit (BMU), an electrical distribution bus, an artificial intelligence (AI) model, and a monitoring system (MS) controller. The electrical distribution bus electrically connects the electric motor and the BMU. The MS controller is in communication with the AI model, the electric motor, the BMU, and a memory storing instructions. The instructions when executed cause the MS controller to: receive a first operational parameter from the BMU unit; control the AI model to produce a predicted first operational parameter utilizing at least one electric motor operating parameter; and determine the presence of series arcing within the electrical distribution bus using the first operational parameter and the predicted first operational parameter.
1 . A hybrid-electric propulsion (HEP) system, comprising:
a thermal engine;
an electric motor;
a battery;
a battery management unit (BMU);
an electrical distribution bus electrically connecting the electric motor and the BMU;
an artificial intelligence (AI) model;
a monitoring system (MS) controller in communication with the AI model, the electric motor, the BMU, and a non-transitory memory storing instructions, which instructions when executed cause the MS controller to:
receive a first operational parameter from the BMU unit;
control the AI model to produce a predicted first operational parameter, wherein the AI model produces the predicted first operational parameter utilizing at least one electric motor operating parameter; and
determine a presence of series arcing within the electrical distribution bus using the first operational parameter and the predicted first operational parameter; and
wherein the instructions when executed cause the MS controller to cause a HEP system portion to shut down upon determination of the presence of series arcing within the electrical distribution bus.
2 . The hybrid-electric propulsion system of claim 1 , wherein the first operational parameter from the BMU unit is a battery output power rate.
3 . The hybrid-electric propulsion system of claim 2 , wherein the battery output power rate is an electrical current value.
4 . The hybrid-electric propulsion system of claim 2 , wherein the electric motor operating parameter is at least one of a torque value representative of torque produced by the electric motor or an electric motor shaft rotational speed value.
5 . The hybrid-electric propulsion system of claim 1 , wherein the AI model produces the predicted first operational parameter further utilizing an aircraft power demand request value.
6 . The hybrid-electric propulsion system of claim 5 , wherein the AI model produces the predicted first operational parameter further utilizing at least one of an aircraft altitude value, an aircraft ambient temperature value, or an aircraft weight value.
7 . The hybrid-electric propulsion system of claim 1 , wherein the AI model is configured for machine learning and the AI Model is trained.
8 . The hybrid-electric propulsion system of claim 1 , wherein the thermal engine and the electric motor are configured to provide motive force to a propulsor.
9 . The hybrid-electric propulsion system of claim 1 , wherein the instructions when executed cause the MS controller to produce an indicator upon determination of the presence of series arcing within the electrical distribution bus.
10 . The hybrid-electric propulsion system of claim 1 , wherein the instructions when executed cause the MS controller to produce a record of the presence of series arcing upon determination of the presence of series arcing within the electrical distribution bus.
11 . A method for determining series arcing in an aircraft hybrid-electric propulsion (HEP) system, comprising:
providing a HEP system having a thermal engine, an electric motor, a battery, a battery management unit (BMU), and an electrical distribution bus electrically connecting the electric motor and the BMU;
providing a first operational parameter from the BMU unit to an artificial intelligence (AI) model;
controlling the AI model to produce a predicted first operational parameter, wherein the AI model produces the predicted first operational parameter utilizing at least one electric motor operating parameter based on an operation of the electric motor; and
determining a presence of series arcing within the electrical distribution bus using the first operational parameter and the predicted first operational parameter; and
further including shutting down a HEP system portion upon determination of the presence of series arcing within the electrical distribution bus.
12 . The method of claim 11 , wherein the first operational parameter from the BMU unit is a battery output power rate.
13 . The method of claim 11 , wherein the electric motor operating parameter is at least one of a torque value representative of torque produced by the electric motor or an electric motor shaft rotational speed value.
14 . The method of claim 11 , wherein the AI model is controlled to produce the predicted first operational parameter further utilizing at least one of an aircraft power demand request value, an aircraft altitude value, an aircraft ambient temperature value, or an aircraft weight value.
15 . The method of claim 11 , wherein the AI model is configured for machine learning and the AI Model is trained.
16 . The method of claim 11 , wherein the thermal engine and the electric motor are configured to provide motive force to a propulsor.
17 . The method of claim 11 , further including producing an indicator upon determination of the presence of series arcing within the electrical distribution bus.
18 . The method of claim 11 , further including producing a record of the presence of series arcing upon determination of the presence of series arcing within the electrical distribution bus.