Predictive energy management system for hybrid electric vehicles

• US 7,360,615 B2
• Filed: 06/09/2004
• Issued: 04/22/2008
• Est. Priority Date: 06/09/2004
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: General Motors Corporation (Motors Liquidation Co. GUC Trust)
• Inventors: Salman, Mutasim A., Chen, Jyh-Shin, Chang, Man-Feng
• Primary Examiner(s): VANAMAN, FRANK BENNETT
• Application Number:
  US10/864,670
• Publication Number:
  US 20050274553A1
• Time in Patent Office: 1,413 Days
• Field of Search: 180/65.2, 180/65.3, 180/65.4, 701/22, 701/48, 701/70, 903/922, 903/940
• US Class Current: 180/65.265
• 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, said plurality of vehicle inputs including time and day information;

forecasting a driving cycle profile for each sample of a series of N samples based on the vehicle inputs, wherein forecasting a driving cycle profile includes using driving history including the time and day information and a travel destination;

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

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 using a plurality of constraint equations to minimize a total predicted fuel economy, wherein using a plurality of constraint equations includes employing a separate constraint equation for all of a 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; 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 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.

7. The method according to claim 6 further comprising forecasting a driving cycle profile for the N samples based on the vehicle inputs.

8. 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, wherein forecasting a driving cycle profile includes using driving history for the time and day input and a final driving time to forecast the driving cycle profile;

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

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, wherein using a plurality of constraint equations includes employing a separate constraint equation for all of a 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; and

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

9. The method according to claim 8 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.

10. The method according to claim 8 wherein the cost function uses weights selected from the group consisting of credibility of forecast, type of journey and size of battery.

11. The method according to claim 8 wherein determining an optimum sequence of power commands includes calculating a positive and negative part of power demanded at an axle of the vehicle.

12. 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 forecasting a driving cycle profile for each sample of a series of N samples based on the vehicle inputs, wherein the system for forecasting a driving cycle profile includes forecasting a driving cycle profile using driving history including the day and time information and a travel destination;

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

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, wherein the system for determining an optimum sequence of power commands uses a plurality of constraint equations to minimize a total predicted fuel economy, wherein the plurality of constraint equations includes a separate constraint equation for all of a 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; and

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

13. The predictive energy management system according to claim 12 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.

14. The predictive energy management system according to claim 12 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.

15. The predictive energy management system according to claim 14 wherein the cost function uses weights selected from the group consisting of credibility of forecast, type of journey and size of battery.

16. The predictive energy management system according to claim 12 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.

17. The predictive energy management system according to claim 12 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, 3D map inputs, vehicle speed and accelerator pedal position.

18. The predictive energy management system according to claim 17 further comprising a system for forecasting a driving cycle profile for the N samples based on the vehicle inputs.