Travel energy learning device, method, and program

• US 8,229,613 B2
• Filed: 10/22/2009
• Issued: 07/24/2012
• Est. Priority Date: 11/07/2008
• Status: Expired due to Fees

• Current Assignee: Aisin AW Company Limited (Aisin Corp.)
• Original Assignee: Aisin AW Company Limited (Aisin Corp.)
• Inventors: Kato, Kazuya, Hidaka, Tatsuya
• Primary Examiner(s): EL CHANTI, HUSSEIN A
• Application Number:
  US12/603,609
• Publication Number:
  US 20100121514A1
• Time in Patent Office: 1,006 Days
• Field of Search: 701/22, 701/32.5, 701/34.2, 701/400
• US Class Current: 701/22
• CPC Class Codes: B60K 6/48 Parallel type
• CPC Class Codes: B60L 2240/486 Operating parameters
• CPC Class Codes: B60L 3/00 Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption methods or circuit arrangements for monitoring or controlling batteries or fuel cells B60L58/00
• 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 20/11 using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance utilising navigation and traffic information in the control strategy B60W20/12
• CPC Class Codes: B60W 2050/0031 Mathematical model of the vehicle
• CPC Class Codes: B60W 2050/0088 Adaptive recalibration
• CPC Class Codes: B60W 2510/104 Output speed
• CPC Class Codes: B60W 2510/105 Output torque
• CPC Class Codes: B60W 2510/244 Charge state
• CPC Class Codes: B60W 2520/06 Direction of travel
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/14 Yaw
• CPC Class Codes: B60W 2530/10 Weight
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/50 : of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 40/10 : related to vehicle motion
• CPC Class Codes: Y02T 10/40 : Engine management systems
• CPC Class Codes: Y02T 10/62 : Hybrid vehicles
• CPC Class Codes: Y02T 10/70 : Energy storage systems for electromobility, e.g. batteries

Abstract

A travel energy learning device includes: a road section identifying unit that identifies a road section where a host vehicle is currently located; a travel condition information obtaining unit that obtains travel condition information that indicates a travel condition when the host vehicle traveled the road section; a work rate deriving unit that derives information indicating work rate of a transmission shaft based on rate of rotation of the transmission shaft and torque transmitted by the transmission shaft; and an energy information accumulating unit that derives required energy information indicating the energy required for traveling the road section based on the derived information that indicates the work rate during travel of the road section, links the derived required energy information to the road section and the travel condition information, and accumulates the required energy information.

Claims

1. A travel energy learning device for a host vehicle having a transmission shaft, a motor for driving the transmission shaft to transmit a driving force from the motor to a wheel and a rechargeable battery for powering the motor, a revolution sensor which detects rotary speed of the transmission shaft and a torque sensor which detects torque output by the transmission shaft, the energy learning device comprising:

a road section identifying unit that identifies a road section on which the host vehicle is currently located;

a travel condition information obtaining unit that obtains travel condition information that indicates a travel condition when the host vehicle has traveled the road section;

a work rate deriving unit that derives information that indicates a work rate of the transmission shaft based on a signal from the revolution sensor and on a signal from the torque sensor; and

an energy information accumulating unit that;

derives energy information that indicates the energy required for traveling the road section based on the derived information that indicates the work rate of the transmission shaft during traveling of the host vehicle through the road section, wherein the derived energy information includes energy output information indicating the energy output by the transmission shaft and energy efficiency information indicating a ratio of the energy output by the transmission shaft to energy output from the rechargeable battery;

links the derived energy information that indicates the energy required for traveling the road section to the road section and to the travel condition information; and

accumulates the information that indicates the energy required for traveling the road section.

2. The travel energy learning device according to claim 1, wherein

the travel condition information obtaining unit determines whether the travel condition belongs to any one of a plurality of categories set in advance, and obtains information that indicates the category to which the travel condition belongs as the travel condition information.

3. The travel energy learning device according to claim 2, wherein

the category is predetermined in accordance with a magnitude of electric power consumption of an electrical component in the host vehicle, and

the travel condition information obtaining unit obtains a usage state of the electrical component in the host vehicle when traveling the road section, and obtains information that indicates the category to which the electric power consumption in the obtained usage state belongs as the travel condition information.

4. The travel energy learning device according to claim 2, wherein

the category is predetermined in accordance with the size of a weight of the host vehicle, and

the travel condition information obtaining unit obtains the weight of the host vehicle when traveling the road section, and obtains information that indicates the category to which the obtained weight belongs as the travel condition information.

5. The travel energy learning device according to claim 3, wherein

the category is predetermined in accordance with the size of a weight of the host vehicle, and

the travel condition information obtaining unit obtains the weight of the host vehicle when traveling the road section, and obtains information that indicates the category to which the obtained weight belongs as the travel condition information.

6. The travel energy learning device according to claim 2, wherein

the category is predetermined in accordance with a weather condition, and

the travel condition information obtaining unit obtains the weather condition when the host vehicle travels the road section, and obtains information that indicates the category to which the obtained weather condition belongs as the travel condition information.

7. The travel energy learning device according to claim 3, wherein

the category is predetermined in accordance with a weather condition, and

the travel condition information obtaining unit obtains the weather condition when the host vehicle travels the road section, and obtains information that indicates the category to which the obtained weather condition belongs as the travel condition information.

8. The travel energy learning device according to claim 2, wherein

the travel condition information obtaining unit combines two categories into one category if a difference in the energy required for traveling the road section for travel conditions belonging to different categories is equal to or less than a predetermined standard.

9. The travel energy learning device according to claim 3, wherein

the travel condition information obtaining unit combines two categories into one category if a difference in the energy required for traveling the road section for travel conditions belonging to different categories is equal to or less than a predetermined standard.

10. A travel energy learning device according to claim 1, wherein the host vehicle is a hybrid vehicle further having an engine and wherein the travel energy learning device further comprises:

a drive control unit which controls operation of one or both of the motor and the engine in accordance with the accumulated energy information that indicates the energy required for traveling the road section and state of charge of the rechargeable battery.

11. A travel energy learning device according to claim 1, wherein the travel condition information comprises information indicative of at least one of weight of the host vehicle when traveling the road section, a usage status of an electrical component when traveling the road section, and a weather condition when traveling the road section.

12. A travel energy learning method for a host vehicle having a transmission shaft, a motor for driving the transmission shaft to transmit a driving force from the motor to a wheel and a rechargeable battery for powering the motor, a revolution sensor which detects rotary speed of the transmission shaft and a torque sensor which detects torque output by the transmission shaft, the travel energy learning method comprising:

identifying a road section where on which the host vehicle is currently located;

obtaining travel condition information that indicates a travel condition when the host vehicle has traveled the road section;

deriving information that indicates a work rate of the transmission shaft based on a signal from the revolution sensor and on a signal from the torque sensor; and

deriving energy information that indicates the energy required for traveling the road section based on the derived information that indicates the work rate of the transmission shaft during traveling of the host vehicle through the road section, linking the calculated information that indicates the energy required for traveling the road section to the road section and to the travel condition information, and accumulating the energy information that indicates the energy required for traveling the road section;

wherein the derived energy information includes energy output information indicating the energy output by the transmission shaft and energy efficiency information indicating a ratio of the energy output by the transmission shaft to energy output from the rechargeable battery.

13. A travel energy learning method according to claim 12, wherein the host vehicle is a hybrid vehicle further having an engine and wherein the travel energy learning method further comprises:

controlling operation of one or both of the motor and the engine in accordance with the accumulated energy information that indicates the energy required for traveling the road section and state of charge of the rechargeable battery.

14. A travel energy learning method according to claim 12, wherein the travel condition information comprises information indicative of at least one of weight of the host vehicle when traveling the road section, a usage status of an electrical component when traveling the road section, and a weather condition when traveling the road section.

15. A non-transitory computer-readable storage medium having, encoded thereon, a travel energy learning program which, when executed by a computer, causes the computer to execute the following steps:

identifying a road section on which a host vehicle is currently located, wherein the host vehicle has a transmission shaft, a motor for driving the transmission shaft to transmit a driving force from the motor to a wheel and a rechargeable battery for powering the motor, a revolution sensor which detects rotary speed of the transmission shaft and a torque sensor which detects torque output by the transmission shaft;

obtaining travel condition information that indicates a travel condition when the host vehicle has traveled the road section;

deriving information that indicates a work rate of the transmission shaft based on a signal from the revolution sensor and on a signal from the torque sensor; and

deriving energy information that indicates the energy required for traveling the road section based on the derived information that indicates the work rate of the transmission shaft during traveling of the host vehicle through the road section, wherein the derived energy information includes energy output information indicating the energy output by the transmission shaft and energy efficiency information indicating a ratio of the energy output by the transmission shaft to energy output from the rechargeable battery;

linking the calculated information that indicates the energy required for traveling the road section to the road section and to the travel condition information; and

accumulating the energy information that indicates the energy required for traveling the road section.

16. A non-transitory computer-readable storage medium according to claim 15, wherein the host vehicle is a hybrid vehicle further having an engine and wherein the travel energy learning program, when executed by the computer, causes the computer to execute the following additional step:

controlling operation of one or both of the motor and the engine in accordance with the accumulated energy information that indicates the energy required for traveling the road section and state of charge of the rechargeable battery.

17. A non-transitory computer-readable storage medium according to claim 15, wherein the travel condition information comprises information indicative of at least one of weight of the host vehicle when traveling the road section, a usage status of an electrical component when traveling the road section, and a weather condition when traveling the road section.