Method for controlling a hybrid vehicle

• US 6,381,522 B1
• Filed: 02/09/2000
• Issued: 04/30/2002
• Est. Priority Date: 02/09/1999
• Status: Expired due to Fees

• Current Assignee: Hitachi, Ltd.
• Original Assignee: Hitachi, Ltd.
• Inventors: Watanabe, Tohru, Takahashi, Shinsuke, Sekozawa, Teruji
• Primary Examiner(s): Nguyen, Tan
• Assistant Examiner(s): Tran, Dalena
• Application Number:
  US09/501,057
• Time in Patent Office: 811 Days
• Field of Search: 701/22, 701/213, 701/1, 701/123, 701/99, 701/209, 180/65.4, 180/65.1, 180/65.3, 180/65.2, 180/65.8, 180/243, 318/587, 318/139, 702/63
• US Class Current: 701/22
• CPC Class Codes: B60K 6/48 Parallel type
• CPC Class Codes: B60L 15/2045 for optimising the use of energy
• CPC Class Codes: B60L 2240/62 Vehicle position
• CPC Class Codes: B60L 2260/52 drive range estimation, e.g. of estimation of available travel distance
• CPC Class Codes: B60L 50/15 with additional electric power supply with capacitors charged by engine-driven generators B60L50/40; with batteries charged by engine-driven generators B60L50/61
• 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 10/18 including control of braking systems
• 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 2510/244 Charge state
• CPC Class Codes: B60W 2540/10 Accelerator pedal position
• CPC Class Codes: B60W 2555/40 Altitude
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: Y02T 10/62 Hybrid vehicles
• CPC Class Codes: Y02T 10/64 Electric machine technologies in electromobility
• 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 10/92 Energy efficient charging or discharging systems for batteries, ultracapacitors, supercapacitors or double-layer capacitors specially adapted for vehicles
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles
• CPC Class Codes: Y02T 90/167 : Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: Y04S 30/12 : Remote or cooperative charging
• CPC Class Codes: Y10S 903/903 : having energy storing means, e.g. battery, capacitor
• CPC Class Codes: Y10S 903/947 : Characterized by control of braking, e.g. blending of regeneration, friction braking

Abstract

In a vehicle equipped with an engine and a regenerative system, the quantity of energy storage by the regenerative system is scheduled, and fuel efficiency and driving ease are controlled. If the destination of a vehicle and a route to reach it are not indicated, automatic scheduling is performed. Vehicle scheduling is performed by predicting the distribution of height positions the vehicle is predicted to reach at future points of time and using the representative value of the height at each point of time.

Claims

1. A method for controlling a hybrid vehicle which includes a regenerative system for converting kinetic energy and an engine, comprising the steps of:

predicting a plurality of points that the hybrid vehicle can reach at a predetermined time, using stored map information;

calculating a probability that the hybrid vehicle reach each one of the points;

calculating a distribution of elevations that the hybrid vehicle can reach at the predetermined time; and

controlling the regenerative system and the engine based on the distribution of elevations.

2. The method for controlling a hybrid vehicle according to claim 1 wherein said step of controlling calculates representative value of elevations using the distribution of elevations and controls the regenerative system and the engine using the representative value of elevations.

3. The method for controlling a hybrid vehicle according to claim 1 wherein said step of controlling calculates a distribution of energy states of the hybrid vehicle at the predetermined time using the distribution of elevations and controls the regenerative system and the engine using the distribution of energy states.

4. The method for controlling a hybrid vehicle according to claim 3 wherein said step of controlling determines a setting range of target energy storage quantity according to the distribution of energy state at the predetermined time and controls the regenerative system and the engine to satisfy the setting range.

5. The method for controlling a hybrid vehicle according to claim 1 wherein said step of calculating the probability calculates the probability by calculating a probability of route choice at a junction including the map information, and the probability of route choice uses the route chosen from routes connected to the junction.

6. The method for controlling a hybrid vehicle according to claim 5 wherein said step of calculating the probability changes the value of the probability of route choice according to width of the routes.

7. The method for controlling a hybrid vehicle according to claim 5, further comprising the step of receiving traffic congestion information that shows a degree of traffic congestion of the routes and the step of calculating the probability changes the value of the probability of route choice according to the traffic congestion information.

8. A controller of a hybrid vehicle for controlling a regenerative system for converting kinetic energy and an engine included in the hybrid vehicle comprising:

means for predicting a plurality of points that the hybrid vehicle can reach at predetermined time, using stored map information;

means for calculating a probability that the hybrid vehicle reaches each point of the points;

means for calculating a distribution of elevations that the hybrid vehicle can reach at the predetermined time; and

means for outputting a signal for controlling the regenerative system and the engine based on the distribution of elevations.

9. The controller of a hybrid vehicle according to claim 8 wherein said means for outputting calculates representative value of elevations using the distribution of elevations, and generates the signal for controlling the regenerative system and the engine using the representative values of elevations.

10. The controller of a hybrid vehicle according to claim 8 wherein said means for outputting calculates distribution of energy state of the hybrid vehicle at the predetermined time using the distribution of elevations, and generates the signal for controlling the regenerative system and the engine using the distribution of energy state.

11. The controller of a hybrid vehicle according to claim 10 wherein said means for outputting determines the setting range of target energy storage quantity according to the distribution of energy state at the predetermined time and controls the regenerative system and the engine satisfying the setting range.

12. The controller of a hybrid vehicle according to claim 8 wherein said means for calculating the probability calculates the probability by calculating probability of route choice at a junction using the map information, and the probability of route choice shows degree of which route chosen from routes connected to the junction.

13. The controller of a hybrid vehicle according to claim 12 wherein said means for calculating the probability changes a value of the probability of route choice according to width of the routes.

14. The controller of a hybrid vehicle according to claim 12, further comprising means for connecting a receiver within the hybrid vehicle to receive traffic congestion information that shows a degree of traffic congestion of the routes and wherein said means for calculating the probability changes the value of the probability of route choice according to the traffic congestion information.

15. A hybrid vehicle including a regenerative system for converting kinetic energy and an engine comprising a vehicle controller responsive to stored map information, and a processor for predicting a plurality of points that the hybrid vehicle can reach at a predetermined time, based on stored map information which calculates a probability that the hybrid vehicle reach designated locations, the processor calculating a distribution of elevations that the hybrid vehicle can reach at the predetermined time using the probability, and outputting a signal for controlling the regenerative system and the engine based on the distribution of elevations;

an engine controller connected to said vehicle controller and said engine, and controlling said engine based on the signal; and

a regenerative system controller connected to said vehicle controller and said regenerative system, and controlling said regenerative system based on the signal.

16. The hybrid vehicle according to claim 15, further comprising a map database.

17. The hybrid vehicle according to claim 15 wherein said processor calculates representative values of elevations using the distribution of elevations, and generates the signal for controlling the regenerative system and the engine using the representative value of elevations.

18. The hybrid vehicle according to claim 15 wherein said processor calculates distribution of energy state of the hybrid vehicle at the predetermined time using the distribution of elevations, and generates the signal for controlling the regenerative system and the engine using the distribution of energy state.

19. The hybrid vehicle according to claim 18 wherein said processor determines range of target energy storage quantity according to the distribution of energy state at the predetermined time and controls the regenerative system and the engine to satisfy the range.

20. The hybrid vehicle according to claim 15 wherein said processor calculates the probability by calculating probability of route choice at a junction included the map information, and the probability of route choice shows a degree of which route is chosen from routes connected to the junction.

21. The hybrid vehicle according to claim 20 wherein said processor changes a value of the probability of route choice according to width of the routes.

22. The hybrid vehicle according to claim 20, further comprising a receiver connected to said vehicle controller which receives traffic congestion information that shows degree of traffic congestion of the routes, wherein said processor changes value of probability of route choice according to the traffic congestion information.

23. A controller for a hybrid vehicle for controlling a regenerative system for converting kinetic energy and an engine included the hybrid vehicle comprising:

means for predicting a plurality of points that the hybrid vehicle can reach at predetermined time, using stored map information;

means for calculating the hybrid vehicle'"'"'s velocity variable range at the predetermined time;

means for calculating distribution of the velocity variable range at the predetermined time, using the probability and the velocity variable range; and

means for outputting a signal for controlling the regenerative system and the engine based on the distribution of the velocity variable range.