SELF-DRIVING VEHICLE WITH INTEGRATED ACTIVE SUSPENSION

• US 20140297116A1
• Filed: 04/01/2014
• Published: 10/02/2014
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Levant Power Corporation
• Original Assignee: Levant Power Corporation
• Inventors: Anderson, Zackary Martin, Giovanardi, Marco
• Application Number:
  US14/242,691
• Publication Number:
  US 20140297116A1
• Time in Patent Office: Days
• US Class Current: 701/37
• CPC Class Codes: B60G 11/265 hydraulic springs
• CPC Class Codes: B60G 13/14 having dampers accumulating utilisable energy, e.g. compressing air fluid springs with an accumulator B60G11/30
• CPC Class Codes: B60G 17/00 Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load levelling or stabilising systems for tippers B60P1/045
• CPC Class Codes: B60G 17/015 the regulating means comprising electric or electronic elements B60G17/002, B60G17/005 take precedence
• CPC Class Codes: B60G 17/0152 characterised by the action on a particular type of suspension unit B60G17/01941 takes precedence
• CPC Class Codes: B60G 17/018 characterised by the use of a specific signal treatment or control method
• CPC Class Codes: B60G 17/019 characterised by the type of sensor or the arrangement thereof B60G17/01941 takes precedence
• CPC Class Codes: B60G 17/0195 characterised by the regulation being combined with other vehicle control systems conjoint control of vehicle sub-units including control of suspension systems B60W10/22
• CPC Class Codes: B60G 17/08 Characteristics of fluid dampers adjusting fluid dampers in general F16F9/44 - F16F9/53
• CPC Class Codes: B60G 2400/90 Other conditions or factors
• CPC Class Codes: F16F 9/06 using both gas and liquid F16F9/486 take precedence; self-pumping fluid springs B60G17/044
• CPC Class Codes: F16F 9/19 with a single cylinder and of single-tube type
• CPC Class Codes: F16F 9/512 Means responsive to load action, i.e. static load on the damper or dynamic fluid pressure changes in the damper, e.g. due to changes in velocity F16F9/504, F16F9/516 take precedence; non-automatic damper adjustment from a distance using servo control, the servo pressure being created by the flow of damping fluid F16F9/465; self-pumping fluid springs in vehicle suspensions B60G17/044
• CPC Class Codes: H02K 11/215 Magnetic effect devices, e.g. Hall-effect or magneto-resistive elements
• CPC Class Codes: H02K 11/22 Optical devices
• CPC Class Codes: H02K 11/33 Drive circuits, e.g. power electronics H02K11/38 takes precedence
• CPC Class Codes: H02K 29/08 using magnetic effect devices, e.g. Hall-plates, magneto-resistors H02K29/12 takes precedence
• CPC Class Codes: H02K 29/10 using light effect devices
• CPC Class Codes: H02K 5/12 specially adapted for operating in liquid or gas combined with cooling arrangements H02K9/00
• CPC Class Codes: H02K 7/14 Structural association with mechanical loads, e.g. with hand-held machine tools or fans with fan or impeller for cooling the machine H02K9/06
• CPC Class Codes: H02K 7/1823 : structurally associated with turbines or similar engines
• CPC Class Codes: H02P 6/16 : Circuit arrangements for detecting position

Abstract

A self-driving vehicle with an integrated fully-active suspension system. The fully-active suspension utilizes data from one or more sensors used for autonomous driving (e.g. vision, LIDAR, GPS) in order to anticipate road conditions in advance. The system builds a topographical map of the road surface. Suspension and road data is delivered back to the vehicle in order to change autonomous driving behavior including route planning. Energy storage is regulated based on a planned route. Forward and lateral acceleration feel is mitigated through active pitch and tilt compensation. The fully-active suspension pushes and pulls the suspension in three or more operational quadrants in order to deliver superior ride comfort, handling, and/safety of the vehicle.

Claims

1. An active suspension system, comprising:

a plurality of active suspension actuators capable of operation in at least three operational quadrants;

at least one forward-looking sensor capable of detecting a future road condition;

a location sensor for the vehicle;

a least one relative sensor indicating at least one of relative position and relative movement between the vehicle and the ground;

a sensor fusion system that determines an absolute position of the vehicle by using the information from the location sensor and the at least one relative sensor;

a memory system comprising a topographical map comprising of three-dimensional terrain information; and

an active suspension controller that receives information from the sensor fusion system and memory system and controls the active suspension system as a function of the topographical map.

2. The system of claim 1, wherein the active suspension controller updates the topographical map based on a parameter sensed by at least one of an active suspension actuator and a forward-looking sensor.

3. The system of claim 1, wherein the at least one forward-looking sensor comprises at least one of a vision, LIDAR, radar, sonar, and IR sensor.

4. The system of claim 1, wherein the at least one forward-looking sensor comprises a sensor disposed on the front actuators of an active suspension system.

5. The system of claim 1, wherein the location sensor for the vehicle is a GPS receiver.

6. The system of claim 1, wherein the relative sensor is at least one of an IMU, an accelerometer, a speed sensor, a suspension velocity sensor, and a steering angle sensor.

7. The system of claim 1, wherein the sensor fusion system comprises a Kalman Filter.

8. The system of claim 1, wherein the sensor fusion system is further enhanced with at least one of DGPS and WiFi localization.

9. The system of claim 1, wherein the topographical map is relative to the vehicle, comprising a coordinate system about the moving vehicle and is dynamically updated.

10. The system of claim 1, wherein the topographical map is absolute, comprising a coordinate system related to latitude/longitude coordinates and containing road surface height information.

11. The system of claim 1, wherein only a portion of the topographical map is buffered on the vehicle memory system.

12. The system of claim 1, wherein the topographical map is downloaded to the vehicle via a network connection.

13. The system of claim 1, wherein the topographical map is stored on a server on the Internet and downloaded over a cellular network.

14. The system of claim 1, wherein topographical map information is transmitted from another vehicle.

15. The system of claim 1, wherein the topographical map is retrieved from a past driving event.

16. The system of claim 1, wherein the sensed parameter that updates the topographical map comprises data representing road surface information from the at least one forward-looking sensor.

17. The system of claim 1, wherein the sensed parameter that updates the topographical map comprises data representing road surface information from the plurality of active suspension actuators.

18. The system of claim 1, wherein the sensed parameter that updates the topographical map comprises data representing vertical disturbance of the vehicle.

19. The system of claim 1, wherein the vehicle is one of a human-driven vehicle and a self-driving vehicle.

20. The system of claim 1, wherein controlling the active suspension system as a function of the topographical map comprises:

determining a wheel trajectory across the topographical map; and

setting an actuator force/position in advance of each wheel encountering a road surface event so as to mitigate impact to the vehicle.

21. A method of controlling a self-driving vehicle, comprising:

receiving a driving plan that comprises an anticipated route for the vehicle;

gathering road condition data for a variety of points along the route;

determining a road roughness impact on the vehicle for at least a portion of the gathered points of road condition data; and

adjusting the driving plan to reduce the road roughness impact on the vehicle.

22. The method of claim 21, wherein determining a road roughness impact on the vehicle comprises a measure of vertical acceleration on the chassis of the vehicle.

23. The method of claim 22, wherein road roughness impact comprises a bandpass filtered RMS value of acceleration.

24. The method of claim 22, wherein road roughness impact comprises a comfort heuristic that is a frequency-weighted function of chassis acceleration.

25. The method of claim 21, wherein road condition data is gathered by at least one of measured vertical acceleration, forward-looking vision system, a topographical map, GPS-indexed data, data from other vehicles, and a measure of at least one state variable from an electronic suspension system.

26. The method of claim 21, wherein road condition data is stored for future use.

27. The method of claim 21, wherein adjusting the driving plan causes the vehicle to choose the anticipated route course in order to avoid roads based on their associated road condition data.

28. The method of claim 21, wherein adjusting the driving plan comprises choosing vehicle target speeds for each section of travel based on the roads'"'"' associated road condition data.

29. The method of claim 21, wherein adjusting the driving plan occurs in real-time in response to road condition data that is gathered in real-time.

30. The method of claim 21, wherein road condition data is gathered from a vehicle suspension system that controls vehicle suspension operation and further comprises at least one sensor.

31. The method of claim 30, wherein the vehicle suspension is a distributed system that comprises a separate, networked controller for each wheel in the vehicle.

32. The method of claim 21, wherein adjusting the driving plan causes the vehicle to change course temporarily in real-time in response to road condition data that is gathered in real-time from a vehicle suspension system that controls vehicle suspension operation.

33. The method of claim 21, wherein adjusting the driving plan causes the vehicle to change speed in real-time in response to road condition data that is gathered in real-time.

34. An intelligent energy storage system for a self-driving vehicle, comprising:

an electrical bus capable of delivering power to a plurality of connected loads;

an energy storage apparatus coupled to the electrical bus, wherein the energy storage is characterized by a state of charge, and wherein the energy storage can deliver energy to the plurality of connected loads;

a power converter configured to provide power to the energy storage apparatus to set a state of charge;

an electronic controller for a self-driving vehicle that calculates a driving plan comprising an anticipated route for the vehicle; and

an algorithm that calculates energy usage for a variety of points along the route;

wherein the state of charge of the energy storage apparatus is dynamically and predictively set during execution of the driving plan as a function of calculated energy usage for points along the route.

35. The system of claim 34, wherein the energy storage apparatus is one of a battery and a capacitor.

36. The system of claim 34, wherein a state of charge is calculated using at least one of measured voltage and coulomb counting.

37. The system of claim 34, wherein at least one of the plurality of connected loads can regenerate energy into the energy storage apparatus, and calculating energy usage further comprises both consumption and regeneration.

38. The system of claim 34, wherein the self-driving vehicle propulsion system is at least one of hybrid and electric, and at least one connected load is a drive motor for the vehicle.

39. The system of claim 34, wherein an algorithm that calculates energy usage comprises a system to estimate energy usage in the main propulsion system based on road conditions along the anticipated route of the vehicle.

40. The system of claim 39, wherein road conditions comprises a combination of anticipated vehicle speed, acceleration, road incline, traffic lights on route, and previous driving information.

41. The system of claim 34, wherein at least one of the plurality of connected loads comprises an active suspension system.

42. The system of claim 34, wherein an algorithm that calculates energy usage comprises a system to estimate energy usage in the active suspension system based on road conditions along the anticipated route of the vehicle.

43. The system of claim 42, wherein road conditions comprises a combination of road surface condition and anticipated steering control.

44. The system of claim 34, wherein the algorithm that calculates energy usage comprises a combination of a plurality of sub-algorithms, each associated with one of the plurality of loads.

45. The system of claim 34, wherein at least one of the plurality of loads is further controlled to ensure the state of charge of the energy storage apparatus does not fall below a threshold.

46. The system of claim 45, wherein controlling the load comprises reducing energy consumption in the load.

47. An active suspension system for a self-driving vehicle, comprising:

a plurality of active suspension actuators, wherein an actuator is disposed at each wheel of the vehicle, and an actuator command creates a force between the vehicle chassis and the wheel;

a self-driving controller that commands steering, acceleration, and deceleration of the vehicle;

an active suspension controller in communication with the self-driving controller such that the active suspension controller receives feed-forward steering, acceleration, and deceleration signals from the self-driving controller; and

an algorithm to mitigate acceleration-related passenger disturbance, wherein a compensation attitude is set using the active suspension actuators in response to the feed-forward steering, acceleration, and deceleration signals;

the algorithm comprising commanding a pitch-up attitude during deceleration, a pitch-down attitude during acceleration, and a roll-in attitude during steering such that the side of the vehicle on the inside radius of the turn is lower than the outer side.

48. The system of claim 47, wherein setting of the compensation attitude is further a function of a driver-selected operational mode.

49. The system of claim 47, wherein a pitch up attitude comprises the front of the vehicle above the vehicle centerline, a pitch-down attitude comprises the front of the vehicle below the vehicle centerline, and a roll-in attitude comprises the side of the vehicle on the inside radius of the turn below the roll centerline.

50. The system of claim 47, wherein in a force-limited mode, the active suspension actuators limit force output such that during high acceleration, deceleration, and roll events the compensation attitude is not fully reached.

51. The system of claim 50, wherein entry into the compensation attitude occurs over a period of time that is a function of the feed-forward signal from the self-driving controller.

52. The system of claim 47, wherein the compensation attitude is proportional to the feed-forward signal from the self-driving controller