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EP-4554832-B1 - IMPROVED ESTIMATION OF EFFECTIVE WHEEL ROLLING RADIUS

EP4554832B1EP 4554832 B1EP4554832 B1EP 4554832B1EP-4554832-B1

Inventors

  • VU, MICHAEL
  • RYDSTRÖM, Mats
  • JONASSON, MATS

Dates

Publication Date
20260506
Application Date
20220711

Claims (15)

  1. A method for determining an effective rolling radius (R E ) of a wheel (102) mounted on a heavy-duty vehicle (100), the method comprising obtaining (S1) a mapping (410) between a vehicle operating state of the heavy-duty vehicle (100) and an effective rolling radius (R E ) of the wheel (102), wherein the mapping (410) is associated (S11) with a type of the heavy-duty vehicle (100), determining (S2) a current vehicle operating state of the heavy-duty vehicle (100), initializing (S3) an algorithm (420) for estimating effective rolling radius (R E ) of the wheel (102) based on the current vehicle operating state and on the mapping (410), and, upon the algorithm (420) reaching a convergence criterion, updating (S4) the mapping (410) based on the estimated effective rolling radius (R E ).
  2. The method according to claim 1, wherein the mapping (410) is a vehicle specific mapping (S12).
  3. The method according to any previous claim, wherein vehicle operating state comprises a current vehicle weight (S21).
  4. The method according to any previous claim, wherein vehicle operating state comprises one or more tyre properties of the wheel (S22).
  5. The method according to any previous claim, where the algorithm (420) is at least partly based on an acceleration of the vehicle obtained from an inertial measurement unit, IMU, and on an angular acceleration of the wheel obtained from a wheel speed sensor of the heavy-duty vehicle (S31).
  6. The method according to any previous claim, where the algorithm (420) is at least partly based on a travelled distance obtained from a global positioning system, GPS, and/or from geographic data, in relation to a number of wheel rotations obtained from a wheel speed sensor of the heavy-duty vehicle (S32).
  7. The method according to any previous claim, comprising initializing (S33) the algorithm using interpolation between two or more entries in the mapping.
  8. The method according to any previous claim, where the algorithm (420) convergence criterion comprises a condition on rate of change in the estimated effective rolling radius (S41).
  9. The method according to any previous claim, where the algorithm (420) convergence criterion comprises a condition on execution time of the algorithm (S42).
  10. The method according to any previous claim, where the updating (S43) of the mapping (410) comprises replacing an entry in the mapping (410) associated with the current vehicle operating state by a weighted combination of the entry and the estimated effective rolling radius (R E ).
  11. The method according to any previous claim, where the updating (S44) of the mapping (410) comprises replacing an entry in the mapping (410) associated with the current vehicle operating state by the estimated effective rolling radius (R E ).
  12. The method according to any previous claim, further comprising monitoring vehicle operating state, and re-initializing (S5) the algorithm (420) based on new current vehicle operating state and on the mapping (410) in response to detecting a change in vehicle operating state.
  13. A control unit (130, 140, 500) comprising processing circuitry (510) configured to perform the method of any of claims 1-14.
  14. A heavy-duty vehicle (100) comprising the control unit (130, 140, 500) according to claim 13.
  15. A computer program (620) comprising program code means which perform the method of any of claims 1-12 when said program is run on a computer or on processing circuitry (510) of a control unit (130, 140, 500).

Description

TECHNICAL FIELD The present disclosure relates to methods and control units for vehicle motion management (VMM) involving heavy-duty vehicles. The methods are particularly suitable for use with cargo transporting vehicles, such as trucks and semi-trailers. The invention can however also be applied in other types of heavy-duty vehicles, e.g., in construction equipment and in mining vehicles, as well as in cars. BACKGROUND Heavy-duty vehicles have traditionally been controlled using torque request signals generated based on the position of an accelerator pedal or brake pedal and sent to motion support devices (MSDs) such as service brakes and propulsion devices over a controller area network (CAN) bus. However, advantages may be obtained by instead controlling the actuators using wheel slip or wheel speed requests sent from a central vehicle controller to the different actuators. This moves the actuator control closer to the wheel end, and therefore allows for a reduced latency and a faster more accurate control of the MSDs. Wheel-slip based MSD control approaches are particularly suitable for use with wheel-end electrical machines in a battery or fuel cell powered heavy-duty vehicle, where motor axle speeds can be accurately controlled at high bandwidth and with low latency. Wheel-slip based vehicle motion management (VMM) and its associated advantages are discussed, e.g., in WO 2017/215751 A1 and also in WO 2021/144010 A1. Wheel slip based control of heavy-duty vehicles rely on accurate knowledge of the vehicle speed over ground, on the rotation speed of the wheel, and on the effective rolling radius of the wheel. DE 102017116248 A1 discloses a method for determining the circumference of a wheel by means of a database. There is a need for robust methods of determining effective wheel rolling radius. SUMMARY It is an objective of the present disclosure to provide improved methods for determining effective wheel rolling radius of the wheels on a heavy-duty vehicle. The objective may at least in part be obtained by a method for determining an effective rolling radius of a wheel mounted on a heavy-duty vehicle. The method comprises obtaining a mapping between a vehicle operating state of the heavy-duty vehicle and an effective rolling radius of the wheel. The method also comprises determining a current vehicle operating state of the heavy-duty vehicle and initializing an algorithm for estimating effective rolling radius of the wheel based on the current vehicle operating state and on the mapping. The, once the algorithm reaches a convergence criterion, the method updates the mapping based on the estimated effective rolling radius. This way the convergence time of the algorithm is significantly decreased since the initialization of the algorithm will be better compared to a more arbitrary initialization. An advantage of this is that the effective rolling radius used by the vehicle in, e.g., VMM functions, will be of higher accuracy. The mapping may be associated with a type of the heavy-duty vehicle, such as the model of the vehicle, its number of axles, etc. However, an even more accurate estimation of effective wheel rolling radius can be obtained if the mapping is a vehicle specific mapping which accounts for properties unique to a given vehicle. The vehicle operating state preferably comprises at least a current vehicle weight, since this is an important parameter which influences the effective wheel rolling radius. However, the vehicle operating state may also comprise more parameters, such as one or more tyre properties of the wheel. The algorithm used to determine effective rolling radius may at least partly be based on an acceleration of the vehicle obtained from an inertial measurement unit (IMU) and on an angular acceleration of the wheel obtained from a wheel speed sensor of the heavy-duty vehicle. This allows the effective rolling radius to be determined in most driving scenarios, including in tunnels and in other passageways where travelled distance from a global positioning system, GPS, is not readily available. However, in case accurate information on travelled distance is available, e.g., from GPS, and/or from geographic data. Then the effective wheel rolling radius can also be determined in a reliable manner based on a number of wheel rotations obtained from a wheel speed sensor of the heavy-duty vehicle for the travelled distance. It is appreciated that these two algorithms, and also similar methods for determining effective wheel rolling radius, are relatively slow in convergence, requiring either an extended travelled distance or significant averaging in order to suppress noise and distortion in the measurement date. The present applications reduce the convergence time of the algorithms, since a better initialization is provided based on the mapping. The mapping may not always be complete in that every possible vehicle state is associated with a respective initialization value for the algorit