SERVICING SCHEDULE METHOD BASED ON PREDICTION OF DEGRADATION IN ELECTRIFIED VEHICLES

• US 20190035170A1
• Filed: 07/27/2017
• Published: 01/31/2019
• Est. Priority Date: 07/27/2017
• Status: Active Grant

• Current Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Original Assignee: Toyota Motor Engineering & Manufacturing North America Incorporated (Toyota Motor Corporation)
• Inventors: DEDE, Ercan M., JOSHI, Shailesh N.
• Granted Patent: US 10,692,302 B2
• Time in Patent Office: Days
• CPC Class Codes: G06Q 10/1095 Meeting or appointment
• CPC Class Codes: G07C 5/006 Indicating maintenance
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

A method includes collecting, via processing circuitry, a first set of multi-load data from a power control unit of a vehicle and usage data at a first servicing of the vehicle, computing, via the processing circuitry, a Mahalanobis Distance (MD) using the first set of multi-load data, defining, via the processing circuitry, a healthy state and an anomaly threshold based on the MD, correlating, via the processing circuitry, the first set of usage data to the anomaly threshold; generating, via the processing circuitry, a usage based servicing schedule for a vehicle; collecting, via the processing circuitry, a next set of multi-load data and usage data at a next servicing of the vehicle; updating, via the processing circuitry, the MD, a servicing schedule and evaluating the performance of the vehicle; determining, via the processing circuitry, whether the MD crosses the anomaly threshold; and transmitting, via a network, a servicing alert.

Claims

1. A method for generating a servicing schedule of an electrified vehicle, the method comprising:

collecting, via processing circuitry, a first set of multi-load data from a power control unit of a vehicle and usage data at a first servicing of the vehicle;

computing, via the processing circuitry, a Mahalanobis Distance (MD) using the first set of multi-load data;

defining, via the processing circuitry, a healthy state and an anomaly threshold based on the MD;

correlating, via the processing circuitry, the first set of usage data to the anomaly threshold;

generating, via the processing circuitry, a usage based servicing schedule for a vehicle;

collecting, via the processing circuitry, a next set of multi-load data and usage data at a next servicing of the vehicle;

updating, via the processing circuitry, the MD, a servicing schedule and evaluating the performance of the vehicle;

determining, via the processing circuitry, whether the MD crosses the anomaly threshold; and

transmitting, via a network, a servicing alert upon exceeding the anomaly threshold to the vehicle.

2. The method of claim 1, wherein the healthy state is a statistical distribution having a mean and a standard deviation computed for more than one vehicle at the first servicing.

3. The method of claim 2, wherein the anomaly threshold is three standard deviations away from the average of the MD.

4. The method of claim 1, wherein the multi-load data refers to values of parameters including a temperature, a vibration, a power, a voltage and a current.

5. A system for generating a servicing schedule of an electrified vehicle, the system comprising:

one or more vehicle including an electric vehicle or a hybrid vehicle;

one or more service stations where the one or more vehicle is serviced; and

processing circuitry configured to

collect a first set of multi-load data from a power control unit of the one or more vehicle and usage data brought in for a first servicing at the one or more service stations,

compute a Mahalanobis Distance (MD) using the first set of multi-load data,

define a healthy state and an anomaly threshold based on the MD,

correlate the first set of usage data to the anomaly threshold,

generate a usage based servicing schedule for the one or more vehicles serviced at the one or more service stations,

collect a next set of multi-load data and usage data at a next servicing of the one or more vehicles,

update the MD, a servicing schedule and evaluating the performance of the one or more vehicles,

determine whether the MD crosses the anomaly threshold, and

transmit, via a network, a servicing alert upon exceeding the anomaly threshold to the one or more vehicles.

6. The system of claim 5, wherein the healthy state is a statistical distribution having a mean and a standard deviation computed for more than one vehicle at the first servicing.

7. The system of claim 6, wherein the anomaly threshold is three standard deviations away from the average of the MD.

8. The system of claim 5, wherein the multi-load data refers to values of parameters including a temperature, a vibration, a power, a voltage and a current.

9. A non-transitory computer-readable medium storing instructions which when executed by a computer, cause the computer to perform a method recognizing a digital document, the method comprising:

collecting a first set of multi-load data from a power control unit of a vehicle and usage data at a first servicing of the vehicle;

computing a Mahalanobis Distance (MD) using the first set of multi-load data;

defining a healthy state and an anomaly threshold based on the MD;

correlating the first set of usage data to the anomaly threshold;

generating a usage based servicing schedule for a vehicle;

collecting a next set of multi-load data and usage data at a next servicing of the vehicle;

updating the MD, a servicing schedule and evaluating the performance of the vehicle;

determining whether the MD crosses the anomaly threshold; and

transmitting, via a network, a servicing alert upon exceeding the anomaly threshold to the vehicle.