Predictive energy management system for hybrid electric vehicles

• US 20050274553A1
• Filed: 06/09/2004
• Published: 12/15/2005
• Est. Priority Date: 06/09/2004
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (Motors Liquidation Co. GUC Trust)
• Inventors: Salman, Mutasim A., Chen, Jyh-Shin, Chang, Man-Feng
• Granted Patent: US 7,360,615 B2
• Time in Patent Office: Days
• US Class Current: 180/65.280
• CPC Class Codes: B60K 6/52 Driving a plurality of drive axles, e.g. four-wheel drive
• CPC Class Codes: B60L 15/2045 for optimising the use of energy
• CPC Class Codes: B60L 2240/62 Vehicle position
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 3/0046 relating to electric energy storage systems, e.g. batteries or capacitors
• CPC Class Codes: B60L 50/16 with provision for separate direct mechanical propulsion
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/08 including control of electric propulsion units, e.g. motors or generators
• CPC Class Codes: B60W 10/26 for electrical energy, e.g. batteries or capacitors
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• CPC Class Codes: B60W 20/12 using control strategies taking into account route information estimation or calculation of non-directly measurable driving parameters B60W40/00
• CPC Class Codes: B60W 20/13 in order to stay within battery power input or output limits; in order to prevent overcharging or battery depletion
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: Y02T 10/62 Hybrid vehicles
• CPC Class Codes: Y02T 10/64 Electric machine technologies in electromobility
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 : Electric energy management in electromobility
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles

Abstract

A predictive energy management system for a hybrid vehicle that uses certain vehicle information, such as present location, time, 3-D maps and driving history, to determine engine and motor power commands. The system forecasts a driving cycle profile and calculates a driver power demand for a series of N samples based on a predetermined length of time, adaptive learning, etc. The system generates the optimal engine and motor power commands for each N sample based on the minimization of a cost function under constraint equations. The constraint equations may include a battery charge power limit, a battery discharge power limit, whether the battery state of charge is less than a predetermined maximum value, whether the battery state of charge is greater than a predetermined minimum value, motor power output and engine performance. The system defines the cost function as the sum of the total weighted predicted fuel consumed for each sample. The system then selects the motor and engine power commands for the current sample.

Claims

1. A method for providing power commands for a hybrid electric vehicle, said hybrid electric vehicle including an engine, at least one electric motor and a battery, said method comprising:

providing a plurality of vehicle inputs of vehicle operation and vehicle environment;

determining a sequence of driver power demands for each sample of a series of N samples based on the plurality of vehicle inputs;

determining an optimum sequence of power commands for the at least one electric motor and the engine for the series of samples; and

selecting a current one of the power commands to operate the at least one electric motor and the engine.

2. The method according to claim 1 wherein determining a sequence of driver power demands includes determining a sequence of driver power demands based on vehicle parameters, current and predicted driving cycles and terrain.

3. The method according to claim 1 wherein determining an optimum sequence of power commands includes determining an optimum sequence of power commands by defining a cost function based on minimizing a total predicted weighted fuel economy to be consumed for the N samples.

4. The method according to claim 3 wherein the cost function uses weights selected from the group consisting of credibility of forecast, type of journey and size of battery.

5. The method according to claim 1 wherein determining an optimum sequence of power commands includes calculating a positive and negative part of power demanded at an axle of the vehicle.

6. The method according to claim 1 wherein determining an optimum sequence of power commands includes using a plurality of constraint equations to minimize a total predicted fuel economy.

7. The method according to claim 6 wherein using a plurality of constraint equations includes employing constraint equations selected from the group consisting of the difference between an initial and end of horizon state of battery charge, a battery charge power limit, a battery discharge power limit, a battery state of charge less than a maximum limit, a battery state of charge greater than a minimum limit, motor output power, engine performance and total emissions.

8. The method according to claim 1 wherein providing a plurality of vehicle inputs includes providing a plurality of vehicle inputs selected from the group consisting of vehicle location, time, day, 3-D map inputs, vehicle speed and accelerator pedal position.

9. The method according to claim 8 further comprising forecasting a driving cycle profile for the N samples based on the vehicle inputs.

10. A method for providing power commands for a hybrid electric vehicle, said hybrid electric vehicle including an engine, at least one electric motor and a battery, said method comprising:

providing a plurality of vehicle inputs of vehicle operation and vehicle environment, wherein providing a plurality of vehicle inputs includes providing a plurality of vehicle inputs selected from the group consisting of vehicle location, time, day, 3-D map inputs, vehicle speed and accelerator pedal position;

forecasting a driving cycle profile for each sample of a series of N samples based on the vehicle inputs;

determining a sequence of driver power demands for each sample of the series of N samples based on the plurality of vehicle inputs;

determining an optimum sequence of power commands for the at least one electric motor and the engine for the series of samples, wherein determining an optimum sequence of power commands includes determining an optimum sequence of power commands by defining a cost function based on minimizing a total predicted weighted fuel economy to be consumed for the N samples and using a plurality of constraint equations to minimize the total predicted fuel economy; and

selecting a current one of the power commands to operate the at least one electric motor and the engine.

11. The method according to claim 10 wherein determining a sequence of driver power demands includes determining a sequence of driver power demands based on vehicle parameters, current and predicted driving cycles and terrain.

12. The method according to claim 10 wherein the cost function uses weights selected from the group consisting of credibility of forecast, type of journey and size of battery.

13. The method according to claim 10 wherein determining an optimum sequence of power commands includes calculating a positive and negative part of power demanded at an axle of the vehicle.

14. The method according to claim 10 wherein using a plurality of constraint equations includes employing constraint equations selected from the group consisting of the difference between an initial and end of horizon state of battery charge, a battery charge power limit, a battery discharge power limit, a battery state of charge less than a maximum limit, a battery state of charge greater than a minimum limit, motor output power, engine performance and total emissions.

15. A predictive energy management system for providing power commands in a hybrid electric vehicle, said hybrid electric vehicle including an engine, at least one electric motor and a battery, said controller comprising:

a system for providing a plurality of vehicle inputs of vehicle operation and vehicle environment;

a system for determining a sequence of driver power demands for each sample of a series of N samples based on the plurality of vehicle inputs;

a system for determining an optimum sequence of power commands for the at least one electric motor and the engine for the series of samples; and

a system for selecting a current one of the power commands to operate the at least one electric motor and the engine.

16. The predictive energy management system according to claim 15 wherein the system for determining a sequence of driver power demands determines the sequence of driver power demands based on vehicle parameters, current and predicted driving cycles and terrain.

17. The predictive energy management system according to claim 15 wherein the system for determining an optimum sequence of power commands determines the optimum sequence of power commands by defining a cost function based on minimizing a total predicted weighted fuel economy to be consumed for the N samples.

18. The predictive energy management system according to claim 17 wherein the cost function uses weights selected from the group consisting of credibility of forecast, type of journey and size of battery.

19. The predictive energy management system according to claim 15 wherein the system for determining an optimum sequence of power commands calculates a positive and negative part of power demanded at an axle of the vehicle.

20. The predictive energy management system according to claim 15 wherein the system for determining an optimum sequence of power commands uses a plurality of constraint equations to minimize a total predicted fuel economy.

21. The predictive energy management system according to claim 20 wherein the plurality of constraint equations includes constraint equations selected from the group consisting of the difference between an initial and end of horizon state of battery charge, a battery charge power limit, a battery discharge power limit, a battery state of charge less than a maximum limit, a battery state of charge greater than a minimum limit, motor output power, engine performance and total emissions.

22. The predictive energy management system according to claim 15 wherein the system for providing a plurality of vehicle inputs provides a plurality of vehicle inputs selected from the group consisting of vehicle location, time, day, 3-D map inputs, vehicle speed and accelerator pedal position.

23. The predictive energy management system according to claim 22 further comprising a system for forecasting a driving cycle profile for the N samples based on the vehicle inputs.