SE2351032A1

SE2351032A1 - Handling a maximum torque of a power train

Info

Publication number: SE2351032A1
Application number: SE2351032A
Authority: SE
Inventors: Field Alejandro Correa; Chidambaram Subramanian
Original assignee: Volvo Truck Corp
Priority date: 2023-09-01
Filing date: 2023-09-01
Publication date: 2023-09-01

Abstract

A method for handling a maximum torque of a power train comprised in a vehicle is provided. The method comprises when detecting an activation condition, enabling (201) a limitation of the maximum torque that can be applied by the power train. The method comprises estimating (202) a friction value of the road travelled by the vehicle (1) based on a feedback model. The method comprises adjusting (203) the feedback model by measuring a wheel slip of the vehicle, and adjusting the feedback model based on a difference between the measured wheel slip and an expected slip interval.The method comprises when the limitation of the maximum torque is enabled, determining (204) a maximum torque using a computation model and the estimated friction value of the road travelled by the vehicle. The method comprises limiting (205) the maximum torque of the power train to the determined maximum torque.

Description

HANDLING A |\/|AX||\/|U|\/I TORQUE OF A POWER TRAIN TECHNICAL FIELD Embodiments herein relate to a control unit and a method therein. Furthermore a vehicle, a computer program and a carrier are also provided herein. Particular embodiments herein relate to handling a maximum torque of a power train comprised in a vehicle.

BACKGROUND There has been a growth in heavy duty truck drivers who are not knowledgeable of handling differential locks and truck dynamic systems. As a result, drivers may be at higher risk the vehicle losing traction.

Furthermore, present traction control systems are designed to be very reactive and power limiting after detecting a wheel spin, i.e. a wheel slip above a threshold. This means that the torque may be limited too much, causing lower efficiency in vehicle motion and may also cause a higher instability than if limiting the torque to an optimal traction interval with respect to a wheel slip of the vehicle. Also, when limiting the torque too much, a cab of the vehicle typically vibrates vigorously, causing discomfort for the driver and passengers and general instability to the vehicle.

Hence, there is a strive to improving traction control in vehicles.

SUMMARY An object herein is to provide a mechanism to improve traction control in a vehicle.

According to a first aspect the object is achieved, according to embodiments herein, by providing a method for handling a maximum torque of a power train comprised in a vehicle. The method comprises, when detecting an activation condition, enabling a limitation of the maximum torque that can be applied by the power train. The method further comprises, estimating a friction value of the road travelled by the vehicle based on a feedback model. The method further comprises adjusting the feedback model by measuring a wheel slip of the vehicle and adjusting the feedback model based on a difference between the measured wheel slip and an expected slip interval. The expected slip interval may be predetermined or based on an estimation of a wheel slip of the vehicle. The method further comprises, when the limitation of the maximum torque is enabled, determining a maximum torque using a computation model and the estimated friction value of the road travelled by the vehicle. The method further comprises limiting the maximum torque of the power train to the determined maximum torque.

According to a second aspect, the object is achieved by a control unit configured to perform the method according to the first aspect.

According to a second aspect, the object is achieved by vehicle comprising the control unit according to the second aspect.

According to a third aspect, the object is achieved by a computer program comprising instructions, which when executed by a processor causes the processor to perform actions according to the first aspect.

According to a fourth aspect, the object is achieved by a carrier comprising the computer program according to the third aspect, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

Since the friction value is used as a basis for the maximum torque limitation, the wheel slip will be based on the friction value. This is since the wheel slip is dependent on the maximum torque limitation, at least when the activation condition is fulfilled. As the friction value is estimated based on the feedback model which model is adjusted based on the difference between the measured wheel slip and the expected slip interval, the method ensures that the friction value is adjusted until the friction value causes the measured wheel slip to be within the expected slip interval. This allows the vehicle to have an improved grip and to operate as per expectations.

BRIEF DESCRIPTION OF THE DRAWINGS Embodiments will now be described in more detail in relation to the enclosed drawings, in which: Fig. 1 illustrates a vehicle according to embodiments herein; Fig. 2 illustrates a flow chart depicting a method according to embodiments herein; Fig. 3 illustrates a flow chart depicting a method according to embodiments herein; Fig. 4 illustrates a flow chart depicting a method according to embodiments herein; Fig. 5 illustrates a flow chart depicting a method according to embodiments herein; Fig. 6 illustrates a flow chart depicting a method according to embodiments herein; Fig. 7 illustrates a block diagram depicting a control unit according to embodiments herein.

DETAILED DESCRIPTION Fig. 1 illustrates a vehicle 1 according to some embodiments herein. The vehicle 1 may be any suitable vehicle, e.g., a heavy-duty vehicle, a truck, a car, a bus, a construction equipment, etc.

The vehicle 1 comprises a power train 10. The power train 10 may comprise an engine and/or motor (not shown). The power train 10 may comprise a drive line (not shown). The power train may control a torque applied in the vehicle 1, e.g., to one or more wheels of the vehicle 1. The torque may be provided to the one or more wheels by a motor or engine of the vehicle 1 through a drive line of the power train 10.

The vehicle 1 may comprise or be associates with a set of sensors 20. The set of sensors may comprise one or more sensors for measuring any suitable parameters, configurations, and/or values of embodiments herein. As a non-limiting example, the set of sensors 20 may comprise any one or more out of a camera, a smart tire, a tire pressure sensor, a tire temperature sensor, a temperature sensor, a wheel speed sensor, a vehicle speed sensor, a throttle sensor, a pedal sensor, an engine torque sensor, a position sensor, a rain wiper sensor for detecting rain wiper activity of the vehicle 1.

Methods herein may in one aspect be performed by a control unit 70. The control unit 70 may be an Electronic Control Unit (ECU) or a Central Processing Unit (CPU), e.g., a processor comprised in an ECU. The control unit 70 may be comprised in the vehicle 1, or located in a remote location, e.g., in a server such as in a cloud network node.

Embodiments herein may relate to a pro-active traction torque limit control using artificial intelligence (Al) and/or Machine Learning (ML). Embodiments herein maybe be used as a driver assistance feature for heavy duty trucks such as the vehicle 1 for optimizing traction on driven wheels during propulsion, without excessive spinning of the wheels. Embodiments herein may comprise Reinforcement Learning (RL) to assist in learning the surface quicker and optimizing a friction value of the surface. Embodiments herein may use signals from a camera and/or a rain wiper of the vehicle 1 to detect low friction in advance. As used herein, friction may be referred to as mu and/or measured in units of mu. Mu may be a friction value representing a friction or friction coefficient relating to wheels of the vehicle 1 travelling over a road surface. Mu may also be represented as the symbol u.

Fig. 2. lllustrates an example method for handling a maximum torque of the power train 10 comprised in the vehicle 1, according to embodiments herein.

The method may comprise any one or more of the following actions. The actions do not have to be taken in the order stated below but may be taken in any suitable order. The actions may be periodically and/or continuously triggered in any suitable order and/or based on any suitable trigger condition. ln some embodiments, the method may be iterative, e.g., at least parts of the actions below may be iterated.

Action 201 The method comprises, when detecting an activation condition, enabling a limitation of the maximum torque that can be applied by the power train 10.

Enabling the limitation of the maximum torque may comprise transmitting a enabling torque limit control signal to the power train 10. ln some embodiments, the activation condition comprises detecting that a throttle of the vehicle 1 fulfills a condition, e.g., is below a threshold.

Enabling the limitation of the maximum torque, may enable the maximum torque to be limited as in Action 205 further described below.

Additionally or alternatively, in some embodiments, the activation condition comprises detecting that a longitudinal speed of the vehicle 1 is within an interval, e.g., a predefined interval or a dynamically determined interval.

Alternatively, the limitation of the maximum torque may be enabled by any other means, e.g., by default or by any suitable trigger condition.

Action 202 The method comprises, estimating a friction value of the road travelled by the vehicle 1 based on a feedback model.

The feedback model may be an ML model, e.g., comprising a neural network. ln particular, the feedback model may be an RL model. ln some embodiments, estimating the friction value further comprises the feedback model using one or more vehicle condition parameters as input for estimating the friction value.

The one or more vehicle condition parameters comprises any one or more of: - a position of the vehicle 1, - a status and/or activity of a rain wiper of the vehicle 1, - a load on one or more respective drive axles of the vehicle 1, - a tire pressure of one or more respective wheels of the vehicle 1, - a tire temperature of one or more respective wheels of the vehicle 1, - an ambient temperature of the vehicle 1, - a steering wheel angle of the vehicle 1, - an engine torque of the vehicle 1, - weather data, e.g., in real time, - lateral and/or longitudinal speed and/or acceleration of the vehicle 1, - lateral and/or longitudinal speed and/or acceleration of one or more respective wheels of the vehicle 1, and - road information optionally obtained from camera input and/or smart wheel sensors of the vehicle 1. ln other words, the one or more vehicle condition parameters may indicate any suitable motion, position, and/or activity of the vehicle which affect traction to the road travelled by the vehicle 1.

Action 203 The method comprises, adjusting the feedback model by measuring a wheel slip of the vehicle 1 and adjusting the feedback model based on a difference between the measured wheel slip and an expected slip interval.

The expected slip interval may be an estimated slip interval.

The expected slip interval may be a predefined interval, or bound by a predefined interval.

The expected slip interval may be based on an estimated wheels slip of the vehicle.

The expected slip interval may be obtained based on an estimated wheel slip and based on a predefined accuracy and/or precision.

The expected slip interval may be estimated based on a predefined model.

The expected slip interval may be estimated based on a normal load on a tire, wheel, and/or axle of the vehicle 1.

Additionally or alternatively, the expected slip interval may be estimated based on friction coefficient, e.g., of a tire, Wheel and/or axle of the vehicle 1.

Additionally or alternatively, the expected slip interval may be estimated based on an applied torque.

Additionally or alternatively, the expected slip interval may be estimated based a rotational moment of inertia of the wheel and/or axle of the vehicle 1.

Additionally or alternatively, the expected slip interval may be estimated based on a vehicle speed of the vehicle 1.

The measured wheel slip may be a wheel slip of one or more wheels of the vehicle 1, i.e. individually or aggregated. The measured wheel slip may be measured in relative units, e.g., percentages of slip.

The expected slip interval may be dependent on driving surface.

The feedback model may use the one or more vehicle condition parameters, e.g., as in action 202, e.g., as input, for adjusting the feedback model. The one or more vehicle condition parameters of action 202 and 203 may be the same, or obtained during different time steps. ln other words, the adjustment of the feedback model may be performed before or after the estimation of the friction value of action 202.

The feedback model may be initialized for a certain friction value e.g., based on positioning data and/or environmental data, e.g., real-time weather data. The initial friction value may be based on some other estimation, e.g., using road data, and/or by estimating the friction value using image detection or rain wiper input. Any suitable manner of initializing the friction estimation may be valid for embodiments herein. The feedback model may generate a reward associated with the friction value based on the difference between the measured wheel slip and the expected slip interval. The reward may be increasingly negative with a larger difference, and increasingly positive with a smaller difference.

The feedback model may thereby be trained to adapt such that the friction value is adapted to minimize the difference between the measured wheel slip and the expected slip interval. ln other words, if the reward is negative, the friction estimation may be adapted to change, e.g., by a predefined, learnt, or randomized coefficient or multiplier. The adjustment in friction estimation may be based on a magnitude of the reward.

As an example, the feedback model may in a predefined controlled manner test which manner of estimating the friction value results in a positive reward, e.g., by varying inputs to the friction estimation based on the reward.

The feedback model may be trained in advance and/or while performing the method herein, e.g., as part of action 203.

As further explained in Actions 204-205 there is a correlation between the estimated friction value and the measured wheel slip. This is since when the limitation of the maximum torque is enabled, the maximum torque of the power train 10 will at least partly define the wheel slip of the wheels of the vehicle 1, which maximum torque is based on the estimated friction value.

Action 204 The method comprises, when the limitation of the maximum torque is enabled, determining a maximum torque by using a computation model and the estimated friction value of the road travelled by the vehicle 1. The maximum torque may be determined to be a reduction in torque compared to a current torque. The maximum torque may be determined to improve traction of one or more wheels of the vehicle 1. The maximum torque may be a maximum allowed torque to be supplied to the drive train and/or the wheels of the vehicle 1 by the power train 10.

The computation model may be any suitable model used for determining the maximum torque using the friction value. The computational model may be predefined or dynamic. ln some embodiments, the computation model comprises a first computation model used for determining the limitation of the maximum torque based on: - a load of drive axles of the vehicle 1, and - the estimated friction value, and - a wheel circumference of driving wheels of the vehicle 1.

Optionally, the first computation model used for determining the limitation of the maximum torque may further be based on a gear and/or torque delivery configuration of the vehicle 1.

The first computational model may be predefined or dynamic. ln some embodiments, the computation model comprises a second computation model used for determining the limitation of the maximum torque based estimating a wheel slip of at least one wheel of the vehicle 1 using an inverse tire model and the estimated friction value. ln some embodiments, different models than inverse tire models may also be used.

The maximum torque may further be determined based on a correction slip needed to meet an ideal slip value, e.g., the expected slip interval, e.g., how much torque is needed to be limited to meet an ideal slip value.

Action 205 The method comprises, limiting the maximum torque of the power train 10 to the determined maximum torque. ln some embodiments, limiting the maximum torque of the power train 10 to the determined maximum torque comprises limiting the maximum torque that can be applied by the power train 10, as a percentage of a predefined engine torque.

Embodiments or examples herein such as the embodiments or examples mentioned above will now be further described and exemplified. The text below is applicable to, and may be combined with any suitable embodiments or examples described above.

Fig. 3 illustrates a flow chart of part of some embodiments herein, in particular relating to detecting an activation condition e.g., as in action 201. A throttle 301, and a throttle limit 302 may be compared 311. lfthe throttle is below the limit, and wherein a vehicle speed comparison 312 indicates that the vehicle speed is above a minimum speed, and below a maximum speed, the activation condition may be detected, i.e. fulfilled. A signal may in response to the detection transmit a torque limit control signal to the power train 10. ln other words, as shown in Fig _ 4, when the throttle 301 is less than X%, e.g., an End of Line (EoL) configured parameter, and the vehicle speed is within V_Min and V_Max, e.g., EoL configured parameters, then the controller, e.g., the control unit 70, would constantly send a dynamic positive torque limit e.g., to the powertrain 10 and/or to the engine of the power train 10. The amount of dynamic torque limit control may further be defined in examples below or above.

Fig. 4 illustrates a flow chart of part of some embodiments herein, in particular relating to determining and limiting the maximum torque, e.g., as in actions 204-205.

Embodiments herein may comprise estimating 411 a max allowed torque at wheels, e.g., as part of determining the maximum torque in action 204. l.e., the max allowed torque may be the maximum torque. Estimation as used herein may mean the same as determination as in action 204. The max allowed torque may be estimated based on a load on: - a drive axle 401 of the vehicle 1, - a mu value 402, e.g., of the road travelled by the vehicle 1, - and a wheel circumference of the one or more wheels of the vehicle 1.

Using a differential ratio and/or gear ratio 404, embodiments herein may comprise estimating an equivalent torque and/or speed at a power source, e.g., engine or motor of the power train 10.

Embodiments herein may further convert the torque 421 or 411, to a percentage reference torque value relative to a reference engine torque. The percentage reference torque value may further be used to limit the maximum torque as in action 205, e.g., by sending a dynamic torque limit control signal indicative of the percentage reference torque value, to the power train 10.

Two particular embodiments A and B may be considered, as discussed below.

A. Basic Version A torque at a wheel may be estimated using a load on a drive axle of the vehicle 1. A coefficient friction value may be estimated e.g., as in actions 202-203, or as further explained below. A wheel circumference which may be a learnt or othenNise obtained precise value.

A torque at the power source Engine/ Fuel cell / Motor, i.e., the power train 10, is calculated based on the above parameters along with a power source speed, e.g., torque or speed of the power train 10, using a differential gear and other trans gear ratio known values.

A J1939 and general protocol for torque limit control to a power source controller may be standardized to be sent as a %, the reference engine torque value is used to convert 431 it to a % and then broadcasted, e.g., sent as a dynamic torque limit control signal 441. ln some embodiments, percentages are not needed and absolute torque and slip may be used instead.

B. Complex Version The complex version of embodiments herein may include an inverse tire model such as a Pacejka with a coefficient or brush model for traction, e.g., with parameters or any other complex tire model with necessary parameters provided as inputs as real-time or predefined parameters, e.g., any suitable parameter as defined herein. ln both A and B, basic and complex versions above, the required torque needed may be calculated to an EoL programmable parameter, a correction slip, which indicates a percentage of slip above or below the ideal slip value, e.g.,. above or below the expected slip interval.

Estimation of Friction Value and Reinforcement learninq Embodiments herein may comprise an EoL parameter as the friction value. The EoL parameter may be programmable and/or updated/initialized. EoL. EoL, as defined above, may in embodiments herein be an end of line parameter in a memory of an ECU, e.g., the control unit 70.

The friction value may in some embodiments be a dynamically updated EoL parameter based on rain wiper lnput of the vehicle 1 and/or based on a position of the vehicle 1, e.g., a Global Positioning System (GPS) position. ln other words, the rain wiper input and/or the position of the vehicle 1 may determine or map to an initial friction value for embodiments herein. This may be used as an initial estimation of action 202.

Simple reinforcement learning - Single point value ln some embodiments, the friction value may be associated with a positive reward or negative reward based on the outcome of its prediction to a true value. For example, if the friction value is set to 0.7 and a wheel of the vehicle 1 slips 10% more than the estimated slip interval, a corresponding reward would be negative. Further estimations of the friction values may therefore be adjusted, e.g., by adjusting the feedback model as in action 203. lfthe wheel does not slip by much, e.g., 5%, e.g., within the expected slip interval, then the friction value may be associated with a positive reward, thereby establishing that the friction value does not need to change and/or is a good estimation based on the parameters used for estimating the friction. ln other words, in this way the friction value for the simple reinforcement learning would constantly be optimized when not being within the expected slip interval.

When given a negative reward, the feedback model may be adjusted such that a correctional value is added when estimating the expected slip interval and/or when estimating the friction value, e.g., as in actions 202-203. The correctional value may be based on a difference bet\Neen the measured slip and the expected slip interval.

Multi-input ML model with reinforcement learning Similar to the method above, however, the input may further comprise the vehicle condition parameters to estimate the friction value, e.g., any one or more out of GPS, 11 Wiper, Load, Tire pressure, Steering angle, Vehicle Speed, Wheel speed, ambient temp, tire temp, engine torque, and estimated applied wheel torque under the tire of a wheel of the vehicle 1.

The multi-ML model may use the same logic as above but using the rewards, the feedback model may be trained to recognize how to best estimate the friction value based on the vehicle condition parameters.

The multi-input model may also include categorical data about a road surface from a camera source ML and/or intelligent tires. The categorical data may comprise any one or more out of asphalt, dry, concrete, wet, snow and ice.

When given a negative reward, the feedback model may be adjusted such that a correctional value is added when estimating the expected slip interval and/or when estimating the friction value, e.g., as in actions 202-203. The correctional value may be based on a difference bet\Neen the measured slip and the expected slip interval. ln some embodiments, the multi-input ML model may utilize and/or be merged with one or more inverse tire models to ensure that the ML is not providing any out of bounds friction values and we utilize physics based estimations to saturate the output of the ML. ln other words an inverse tire model and/or any other physical or boundary model associated with the friction value may limit the output of the multi-ML model.

Fig. 5 illustrates a flowchart of some predictions and estimations herein.

Camera data 501 and/or smart tire information 502, e.g., tire pressure, temperature, revolutions, etc., may be used in a surface prediction model 511 to estimate part of the surface, e.g., to classify the surface.

The friction value may be estimated 521, e.g., as an initial value and/or as part of any friction value estimations herein, based on any one or more out of: the surface prediction model 511, - an ambient temperature 503, - a tire temperature and/or pressure 504, - a steering angle 505, - a wheel speed 506 of one or more wheels of the vehicle 1, - a wiper sensor input (not shown), - a vehicle speed 507 of the vehicle 1, and - an estimated wheel torque 512, estimated by an engine torque of the power train 10. 12 The estimated 521 friction value may further be estimated based on the rewards as described above and/or based on adjustments to the feedback model, e.g., as in action 203. ln some embodiments, using all standardized J1939 inputs, curve factors, slip factors, and friction factors may further be used to determine the maximum torque. The maximum torque may be determined to propel the vehicle 1, but anything above, at least by a margin, may be considered as a resulting in slipping of the tires/wheels of the vehicle 1. An EoL parameter to increase or decrease from this ideal value may therefore be part of configurations herein. ln some embodiments, in real time, with and/or without tire models, the dynamic torque limit control may be sent with a set priority. The priority may define or indicate a priority of that control over other one or more controllers, e.g., control units and/or ECUs, which are also requesting the power source to control a torque of the power train 10.

For example, when a medium priority is set, and when the vehicle speed of the vehicle 1 is below a threshold, i.e., V_test being an EoL parameter, the feedback model will monitor the slip of one or more wheels of the vehicle 1 and check if the slip is above 10 percent. lf so, then embodiments herein may generate a negative reward to the feedback model. OthenNise, if the slip is acceptable, e.g., within the expected slip interval, e.g., 5 to 10 percentage of expected limits, embodiments herein may generate a positive reward, optimizing the Mu value. lf below 5 percentages, then no action may be configu red.

Fig. 6 illustrates a flowchart of a method herein. The method may be combinable with the above embodiments, method, and actions.

The method may comprise estimating 601 the friction value, e.g., as in action 202. Based on the friction value, the method may comprise estimating 611 the max allowed torque at one or more wheels of the vehicle 1, e.g., as in action 204.

The method may comprise estimating 611 an equivalent torque and/or speed of the power train 10, based on the estimated max allowed torque, e.g., as in action 421 above.

The method may further convert 631 equivalent torque and/or speed of the power train 10, e.g., as in action 431, to a percentage reference torque value relative to a reference engine torque. 13 The maximum torque may be determined, e.g., as in action 204, based on a curve and slip factor 641, based on the converted reference torque value, and based on a curve estimation 632 and a slip estimation 633, e.g., as in action 202. The slip estimation 633 may be compared, e.g., as in the feedback model adjustment in action 203, to see if outside the expected slip interval. When a slip is detected by the comparison 605, a reward may be generated to adjust the feedback model to adjust the friction value estimation of 601.

The determined maximum torque may be used , e.g., when enabled according to action 201, to send a dynamic torque limit control to the power train 10 to limit the maximum torque, e.g., as in action 205.

Since the slip comparison 605 feeds back if the current estimated/expected slip to the friction value estimation, the friction value estimation may be reinforced and/or dynamically updated until estimating a friction value causing low slip by how it is used to determine and limit the maximum torque of the power train.

Fig. 7 is a block diagram depicting an example arrangement of the control unit 70 for performing methods of embodiments herein, e.g., actions 201-205 above. The control unit 70 may be comprised in the vehicle 1. The control unit 70 may be an ECU of the vehicle 1, e.g., any one of the ECUs described in any example and/or embodiment above.

The control unit 70 may comprise an input and output interface configured to communicate with other devices or entities internal and/or external to the vehicle 1, e.g., the power train 10 and/or associated controllers and/or entities. The input and output interface may comprise a wireless or wired receiver (not shown) and a wireless or wired transmitter (not shown).

The control unit 70 may comprise an enabling unit, a estimating unit, a adjusting unit, determining , and a limiting unit to perform the method actions as described herein.

The embodiments herein may be implemented through a respective processor or one or more processors, such as the processor of a processing circuitry in the control unit 70 depicted in Fig. 7, together with computer program code for performing the functions and actions of the embodiments herein. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the control unit 70. One such carrier may be in the form of a CD ROM disc. lt is however feasible with other data carriers such as a memory stick. The computer 14 program code may furthermore be provided as pure program code on a server and downloaded to the control unit 70.

The control unit 70 may further comprise respective a memory comprising one or more memory units. The memory comprises instructions executable by the processor in the control unit 70.

The memory is arranged to be used to store instructions, data, parameters, values, configurations, and applications to perform the methods herein when being executed in the control unit 70. ln some embodiments, a computer program comprises instructions, which when executed by the at least one processor, cause the at least one processor of the control unit 70 to perform the actions above. ln some embodiments, a respective carrier comprises the respective computer program, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

Those skilled in the art will also appreciate that the functional modules in the control unit 70, described below may refer to a combination of analog and digital circuits, and/or one or more processors configured with software and/or firmware, e.g. stored in the control unit 70, that when executed by the respective one or more processors such as the processors described above cause the respective at least one processor to perform actions according to any of the actions above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuitry (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a system-on-a-chip (SoC).

Claims

1.A method for handling a maximum torque of a power train (10) comprised in a vehicle (1), the method comprising: when detecting an activation condition, enabling (201) a limitation of the maximum torque that can be applied by the power train (10), estimating (202) a friction value of the road travelled by the vehicle (1) based on a feedback model, adjusting (203) the feedback model by measuring a wheel slip of the vehicle (1), and adjusting the feedback model based on a difference between the measured wheel slip and an expected slip interval, when the limitation of the maximum torque is enabled, determining (204) a maximum torque using a computation model and the estimated friction value of the road travelled by the vehicle (1), and limiting (205) the maximum torque of the power train (10) to the determined maximum torque.

2.The method according to claim 1, wherein the activation condition comprises detecting that a throttle of the vehicle (1) is below a threshold and/or that a longitudinal speed of the vehicle (1) is within an interval.

3.The method according to claim 1 or 2, wherein estimating (202) the friction value further comprises the feedback model using one or more vehicle condition parameters as input for estimating the friction value, and for adjusting the feedback model, wherein the one or more vehicle condition parameters comprises any one or more of: a position of the vehicle (1 ), a status and/or activity of a rain wiper of the vehicle (1 ), a load on one or more respective drive axles of the vehicle (1 ), a tire pressure of one or more respective wheels of the vehicle (1 ), a tire temperature of one or more respective wheels of the vehicle (1 ), an ambient temperature of the vehicle (1 ), a steering wheel angle of the vehicle (1 ), an engine torque of the vehicle (1 ), lateral and/or longitudinal speed and/or acceleration of the vehicle (1 ),- lateral and/or longitudinal speed and/or acceleration of one or more respective wheels of the vehicle (1), and - road information optionally obtained from camera input and/or smart wheel sensors of the vehicle (1 ).

4. The method according to any of claims 1 - 3, Wherein the computation model comprises a first computation model used for determining the limitation of the maximum torque based on: a load of drive axles of the vehicle (1), and - the estimated friction value, and a Wheel circumference of driving wheels of the vehicle (1), and optionally - a gear and/or torque delivery configuration of the vehicle (1 ).

5. The method according to any of claims 1 - 4, wherein the computation model comprises a second computation model used for determining the limitation of the maximum torque based estimating a wheel slip of at least one wheel of the vehicle (1) using an inverse tire model and the estimated friction value.

6. The method according to any of claims 1-5, wherein and wherein limiting the maximum torque of the power train (10) to the determined maximum torque comprises limiting the maximum torque that can be applied by the power train (10), as a percentage of a predefined engine torque.

7. A control unit (70) configured to perform the method according to claims 1-

8. A vehicle (1) comprising the control unit (70) according to claim

9. A computer program (720) comprising instructions, which When executed by a processor (710), causes the processor to perform actions according to any of the claims 1-

10. A carrier (740) comprising the computer program (720) of claim 9, wherein the carrier is one of an electronic signal, an optical signal, an electromagnetic signal, amagnetic signal, an electric signal, a radio signal, a microwave signal, or a computer- readable storage medium.