WO2024078707A1

WO2024078707A1 - Method for controlling braking of vehicle

Info

Publication number: WO2024078707A1
Application number: PCT/EP2022/078281
Authority: WO
Inventors: Oscar Stjernberg; Martin WILHELMSSON
Original assignee: Volvo Truck Corporation
Priority date: 2022-10-11
Filing date: 2022-10-11
Publication date: 2024-04-18

Abstract

A computer-implemented method of predictive brake regeneration of at least one service brake (10) in a heavy-duty vehicle (1) is provided. The method comprises creating (S1) a brake regeneration model (100) by obtaining (S2) a health model (110), wherein the health model (110) comprises data defining the health status of the at least one service brake (10) in said heavy-duty vehicle (1), and creating (S3) a prediction model (160), wherein the prediction model (160) comprises data relating to a planned route of said heavy-duty vehicle (1) and wherein the prediction model (160) predicts at least one suitable brake event in the planned route for the at least one service brake (10) of the heavy-duty vehicle (1). The Method further comprises analysing (S4) the brake regeneration model (100) in order to generate a brake activation signal to initiate a brake event of the at least one service brake (10).

Description

METHOD FOR CONTROLLING BRAKING OF VEHICLE

TECHNICAL FIELD

[0001] The disclosure relates generally relates to vehicles. In particular aspects, the disclosure relates to a computer-implemented method of controlling braking of a vehicle by predictive brake regeneration. The disclosure can be applied in heavy-duty vehicles, such as trucks, buses, and construction equipment. Although the disclosure may be described with respect to a particular vehicle, the disclosure is not restricted to any particular vehicle.

BACKGROUND

[0002] In addition to the usual service brakes in the form of disc or drum brakes, use is made, in modern, heavy vehicles, of a number of auxiliary brakes or secondary brake units, in the form, for example, of hydraulic or electric retarders and engine brakes. Auxiliary brakes are used, where appropriate, to recover braking energy as useful energy and to prolong the life of service brakes. Vehicles equipped with both service brake and auxiliary brakes or secondary brake unit utilize either manual application of service brake and auxiliary brakes or secondary brake units, or automatically controlled application of service brake and auxiliary brakes or secondary brake unit, where a control unit selects the distribution of brake power between service brake and auxiliary brakes or secondary brake unit as a function of the operating condition of the vehicle.

[0003] The advancement in technology and knowledge of the benefits with eco-driving has led to the service brakes being used very little. Preferably, the service brake is used at a minimum rate, but to such an extent that the risk of rust and dirt formations are reduced. If the service brake is not used sufficiently the occurrence of rusting and dirt deposits increases which means that the friction of the brake disc or brake drum is reduced. This reduces the efficiency of the brake system. At high temperature, rust can be converted into a very wear-resistant phase. If the rust is unevenly distributed, this leads to variations in thickness and variations in friction along the circumference.

[0004] There is thus a need for improved methods for automatically controlling braking of a vehicle. SUMMARY

[0005] According to a first aspect of the disclosure, a computer-implemented method of predictive brake regeneration of at least one service brake in a heavy-duty vehicle is provided. The method comprises creating a brake regeneration model. The brake generation model is created by obtaining a health model, wherein the health model comprises data defining the health status of the at least one service brake in said heavy-duty vehicle, and creating a prediction model, wherein the prediction model comprises data relating to a planned route of said heavy-duty vehicle. The prediction model predicts at least one suitable brake event in the planned route for the at least one service brake of the heavy-duty vehicle. The method further comprises analysing the brake regeneration model in order to generate a brake activation signal to initiate a brake event of the at least one service brake.

[0006] The first aspect of the disclosure may seek to solve the problem of having rust buildup on brakes in the vehicle. A technical benefit may include achieving a method for control of a brake system that is capable of ensuring that the health and efficiency of the brake system is maintained. This is achieved by the first aspect of the disclosure.

[0007] In some example, the method further comprises updating the health model based on the generated brake activation signal, and/or updating the prediction model based on the generated brake activation signal.

[0008] In some example, the prediction model at least comprises data relating to statistical data of the planned route and/or historical data of the planned route.

[0009] In some example, the prediction model at least comprises data relating to vehicle information.

[0010] In some example, vehicle information comprises data relating to the position data of the vehicle, speed of the vehicle, and/or weight of the vehicle.

[0011] In some example, the prediction model at least comprises data relating to the estimated force for a brake event and/or the estimated time for a brake event.

[0012] In some example, the prediction model at least comprises data relating to road information.

[0013] In some example, road information comprises data relating to the inclination of the road, surface of the road and/or the temperature of the road.

[0014] In some example, the brake activation signal is generated once the health status is below a predetermined threshold value and a suitable brake event in the planned route is determined. [0015] In some example, the health model comprises information relating to previous brake usage, ambient conditions, road information, time information, and/or vehicle information.

[0016] According to a second aspect, a computer system comprising a processor device is provided. The processor device is configured to perform the method according to the first aspect.

[0017] According to a third aspect, a computer program product comprising program code is provided. The program code for performing, when executed by the processor device, the method according to the first aspect.

[0018] According to a fourth aspect, a control system comprising one or more control units is provided. The one or more control units are configured to perform the method according to the first aspect.

[0019] According to a fifth aspect, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium comprises instructions, which when executed by the processor device, cause the processor device to perform the method of the first aspect.

[0020] The above aspects, accompanying claims, and/or examples disclosed herein above and later below may be suitably combined with each other as would be apparent to anyone of ordinary skill in the art.

[0021] Additional features and advantages are disclosed in the following description, claims, and drawings, and in part will be readily apparent therefrom to those skilled in the art or recognized by practicing the disclosure as described herein. There are also disclosed herein control units, computer readable media, and computer program products associated with the above discussed technical benefits.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022] With reference to the appended drawings, below follows a more detailed description of aspects of the disclosure cited as examples.

[0023] FIG. 1 is an exemplary view of a heavy-duty vehicle according to one example.

[0024] FIGS. 2A-B illustrates an example of an arrangement in a heavy-duty vehicle according to one example. [0025] FIG. 3 illustrates an example of an arrangement in a heavy-duty vehicle according to one example.

[0026] FIGS. 4A-B illustrates examples of arrangements in a heavy-duty vehicle according to one example.

[0027] FIGS. 5A-B schematically illustrates a method according to one example. [0028] FIG. 6 illustrates parameters of a health model according to one example.

[0029] FIG. 7 illustrates parameters of a prediction model according to one example.

[0030] FIGS. 8A-E schematically illustrates the relationship between FIG. 6 and FIG. 7 according to one example.

[0031] FIG. 9 is a schematic diagram of an exemplary computer system for implementing examples disclosed herein, according to one example.

DETAILED DESCRIPTION

[0032] Aspects set forth below represent the necessary information to enable those skilled in the art to practice the disclosure.

[0033] FIG. 1 illustrates a heavy-duty vehicle 1. This particular example comprises a tractor unit 110 which is arranged to tow a trailer unit 12. The tractor 11 comprises a vehicle control unit (VCU) 130 arranged to control various functions of the vehicle 1. For instance, the VCU may be arranged to perform a vehicle motion management (VMM) function comprising control of wheel slip, vehicle unit stability, and so on. The trailer unit 12 optionally also comprises a VCU 140, which then controls one or more functions on the trailer 12. The VCU or VCUs may be communicatively coupled, e.g., via wireless link, to a remote server 150. This remote server may be arranged to perform various configurations of the ECU, and to provide various forms of data to the ECU 130, such as for example providing data regarding the make and type of tyres mounted on the vehicle 1.

[0034] The vehicle combination 1 may of course also comprise additional vehicle units, such as one or more dolly units and more than one trailer unit. Although the vehicle is illustrated as a tractor unit, it should be realized that other types of vehicles may be equally considered for the purpose of the herein described method such as busses, construction equipment, trucks, etc.

[0035] The vehicle 1 is supported by wheels 22, where each wheel comprises a tyre. The tractor unit 11 has front wheels 22a which are normally steered, and rear wheels 22b of which at least one pair are driven wheels. Generally, the rear wheels of the tractor 11 may be mounted on tag or pusher axles. A tag axle is where the rear-most drive axle is non-powered, also referred to as a free-rolling or dead axle. A pusher axle is where the forward-most drive axle is not powered. The trailer unit 12 is supported on trailer wheels 22c. Trailers with driven wheels, and even a steered axle, are also possible.

[0036] One of the rear axles and/or one of the axles on the trailer 12 may be a liftable axle. A lift axle, also known as a retractable axle, is an axle which can be raised so that its tyres are not touching the road surface. This improves fuel economy and reduces maintenance and tyre wear. It may also reduce or increase dynamic stability features of the vehicle and it can increase or decrease road wear depending on vehicle load, which axles that are lifted and in which driving situation the axle is lifted. One or more of the wheels may also be mounted with an active suspension which may be controlled by the VCU 130, 140, e.g., in order to adjust a normal force of one or more tyres.

[0037] In FIG. 2, further details of the vehicle 1 is shown. An accelerator (not shown), often in the form of a pedal, is used to control propulsion of the vehicle 1. A brake system 20 is provided which controls the braking of the wheels of the vehicle 1. The brake system comprises a service brake 10. The service brake 10 may comprise a friction pair comprising a brake lining and a rotor. The rotor is usually made of a cast iron alloy and the brake lining of an organic material with metal fibres mixed in. In the embodiment shown, the service brake is of the disc or drum brake type and has a friction pair 3 comprising a brake lining and a rotor. In the example shown the service brake consists of a disc brake, which has a brake caliper 4. The brake caliper 4, in a manner known in the art, comprises axially moveable brake linings and pressurizing elements in the form, for example, of a number of hydraulic cylinders, pneumatic cylinders or electronic application devices, which when activated are capable of pressing the linings against the rotor which, where a disc brake is used, is shaped as a disc. If a hydraulic cylinder is used, this is conventionally pressurized, for example by a brake arrangement, in which a conventional disc brake has a brake disc 3 and a brake caliper 4, which supports hydraulic cylinders 5 and brake pads. The hydraulic cylinders 5 are fed from a master cylinder 6, which is pressurized by a servo brake cylinder 7, which is mechanically connected to the master cylinder 6. The servo brake cylinder 8 is in turn fed from a hydraulic reservoir 8 via a controlled valve 9. The invention is not limited to a particular type of brake but can be used on different types of disc or drum brakes. In an alternative example, the service brake therefore consists of a conventional drum brake. [0038] The brake system furthermore comprises at least one auxiliary brake 12 of conventional type in the form, for example, of hydraulic or electric retarders and engine brakes. The auxiliary brake may also be referred to as a secondary brake 12.

[0039] The brake system may further comprises a control system 11 for distributing a requested brake power between the said service brake 10 and auxiliary brake 12. The control system 11 primarily comprises an input signal from a brake pedal (not shown), which generates a requested brake power. The control system 11 thereupon distributes the brake power between auxiliary brake 12 and service brake 10. The brake power may be distributed between the service brake and the auxiliary brake based on different operating schemes. The distribution may for example be designed so that maximum use is made of the auxiliary brake 12, thereby reducing not only wear to the brake linings of the service brake but also the risk of overheating of the brake disc or brake drum. This means that the power available from the auxiliary brake is utilized 100%, and any brake power needed in addition to that is derived from the service brake 10. However, in order to reduce the risk of rust and unhealthy service brakes 10 it is important to make use of the service brake 10 from time to time. Hence, in one operating mode the brake power is distributed so that the service brake 10 is prioritized, thereby ensuring that sufficient energy is fed into the brake disc or brake drum, so that coverings in the form of rust or dirt are worn down and the friction material in the brake is restored to an acceptable condition.

[0040] FIG. 3 schematically illustrates functionality 300 for controlling a wheel 330 by some example MSDs here comprising a friction brake 320 (such as a disc brake or a drum brake, also referred to as 10, 20) and a propulsion device 310 (such as an electric machine or a combustion engine). The friction brake 320 and the propulsion device 310 are examples of wheel torque generating devices, which may also be referred to as actuators and which can be controlled by one or more motion support device control units 340. The control is based on, e.g., measurement data obtained from a wheel rotation speed sensor 350 and from other vehicle state sensors, such as radar sensors, lidar sensors, and also vision based sensors such as camera sensors and infra-red detectors. Other example torque generating motion support devices which may be controlled according to the principles discussed herein comprise engine retarders and power steering devices. An MSD control unit 340 may be arranged to control one or more actuators. For instance, it is not uncommon that an MSD control unit is arranged to control MSDs for both wheels of an axle. By estimating vehicle unit motion using, e.g., global positioning systems, vision-based sensors, wheel rotation speed sensors, radar sensors and/or lidar sensors, and translating this vehicle unit motion into a local coordinate system of a given wheel (in terms of, e.g., longitudinal and lateral velocity components), it becomes possible to accurately estimate wheel slip in real time by comparing the vehicle unit motion in the wheel reference coordinate system to data obtained from a wheel rotation speed sensor 350 arranged in connection to the wheel. A software-based regeneration model 100 will soon be described with reference to FIGS. 5 -9.

[0041] With reference also to FIG. 4A and FIG. 4B, a traffic situation management (TSM) function 370 plans driving operations with a time horizon of, e.g., 1-10 seconds or so. This time frame corresponds to, e.g., the time it takes for the vehicle 1 to negotiate a curve. The vehicle maneuvers, planned and executed by the TSM, can be associated with acceleration profiles and curvature profiles which describe a desired vehicle velocity and turning for a given maneuver. The TSM continuously requests the desired acceleration profiles areq and curvature profiles creq from the VMM function 360 which performs force allocation to meet the requests from the TSM in a safe and robust manner. The TSM function 370 may also base the determination of vehicle maneuver on the model 100, 110, 160, as indicated in FIG. 4B. For instance, the TSM function 370 may compare two or more different maneuvers which accomplish the same objective in terms of, e.g., tyre wear and/or rolling resistance, and then select the one which is most favorable in these respects.

[0042] Desired acceleration profiles and curvature profiles may optionally be determined based on input from a driver via a human machine interface 440 of the heavy-duty vehicle via normal control input devices such as a steering wheel, accelerator pedal and brake pedal, although the techniques disclosed herein are just as applicable with autonomous or semi- autonomous vehicles. The exact methods used for determining the acceleration profiles and curvature profiles is not within scope of the present disclosure and will therefore not be discussed in more detail herein. Notably, the traffic situation management and/or the transport mission and route planning function 420 may configure various properties of the vehicle, such as raising and lowering a liftable axle, adjusting suspensions, and so on.

[0043] Sensors arranged to provide data about the vehicle environment 430 provides input to the overall control stack 400, and a connection to remote processing resources, such as cloud-based processing resources 410 are also optionally comprised in the control stack. The remote server 150 in FIG. 1 may be comprised in this type of cloud layer 410.

[0044] The VMM function 360 operates with a time horizon of about 0, 1-1,5 seconds or so, and continuously transforms the acceleration profiles a_req and curvature profiles Creq into control commands for controlling vehicle motion functions, actuated by the different MSDs of the vehicle 1 which report back capabilities to the VMM, which in turn are used as constraints in the vehicle control. The accuracy of this control is improved by means of the vehicle dynamic models 20, 24 discussed herein.

[0045] With reference mainly to FIG. 4B, the VMM function 360 performs vehicle state or motion estimation 520, i.e., the VMM function 360 continuously determines a vehicle state s (often a vector variable) comprising positions, speeds, accelerations, yaw motions, normal forces and articulation angles of the different units in the vehicle combination by monitoring vehicle state and behavior using various sensors 510 arranged on the vehicle 1, often but not always in connection to the MSDs.

[0046] The result of the motion estimation 520, i.e., the estimated vehicle state s, is input to a global force generation module 530 which determines the required global forces on the vehicle units which need to be generated in order to meet the motion requests from the TSM 370. An MSD coordination function 540 allocates, e.g., wheel forces and coordinates other MSDs such as steering and suspension. The coordinated MSDs then together provide the desired lateral Fy and longitudinal Fx forces on the vehicle units, as well as the required moments Mz, to obtain the desired motion by the vehicle combination 1. As indicated in FIG. 4B, the MSD coordination function 540 may output any of wheel slips Xi, wheel rotation speeds co, torques Ti and/or steering angles 8, to the different MSDs.

[0047] The MSD coordination function 540 is supported by a model function which may continuously update software-based models 100, 110, 160 of the vehicle. The MSD coordination function may decide on a number of different control options and/or different MSD coordination solutions which all meet a current request from the TSM 370, and thereby also improve some secondary objective, like for example reducing a tyre wear rate and/or improving energy efficiency of the transportation mission by reducing a rolling resistance. This selection and/or optimization can be performed by an optimization module 550. In other words, it is appreciated that there are often additional degrees of freedom available when performing the MSD coordination, meaning that a given set of global forces can often be obtained in many different ways.

[0048] As previously been mentioned, containing good brake performance for heavy-duty vehicles is a major safety aspect. Rust build-up on the brake rotors of the service brake 10 occurs with time. Rust that has been build-up during a short amount of time can easily be removed by using the brake 10. If rust is build-up during longer periods of time, without the brakes being used, this can result in the rotors becoming pocked. When brakes with pocked rotors is being used, the contact surface is reduced thus the brake performance can be greatly reduced. With the introduction of electric vehicles, the mechanical brakes are being used less then with traditional drivelines. Electric vehicles can perform regenerative braking which converts the kinetical energy to electrical energy in the batteries or use an onboard resistor to “burn off’ energy in form of heat. This can cause the mechanical brakes to rarely be used or not used at all. Hence, there is a need to ensure that the service brakes 10 are used sufficiently often in order to not degrade.

[0049] The inventors of the present invention has realized that the problem of rust build up in service brakes 10 can be solved by a predictive brake regeneration method as will be described further herein.

[0050] FIG. 5A illustrates the regeneration model 100 comprising a health model 110 and a prediction model 160. As will soon be described more in detail, both the health model 110 and the prediction model 160 can be continuously updated. These models will be described more in detail with reference to FIGS. 5B - 7.

[0051] FIG. 5B illustrates a computer-implemented method of predictive brake regeneration of at least one service brake 10 in a heavy-duty vehicle 1. The method comprises creating SI a brake regeneration model 100. The brake regeneration model 100 is created by obtaining S2 a health model 110 and creating S3 a prediction model 160.

[0052] The health model 110 comprises data defining the health status of the at least one service brake 10 in said heavy-duty vehicle. The prediction model 160 comprises data relating to a planned route of said heavy-duty vehicle 1 and the prediction model 160 predicts at least one suitable brake event in the planned route for the at least one service brake 10 of the heavy-duty vehicle 1.

[0053] The method further comprises analyzing S4 the brake regeneration model 100 in order to generate a brake activation signal to initiate a brake event of the at least one service brake 10. In one example, the brake activation signal is generated once the health status is below a predetermined threshold value and a suitable brake event in the planned route is determined. In one example, a longer potential brake event may be prioritized over an earlier shorter but otherwise good brake event. In one example, the health status might be allowed to get quite low (for example below a first threshold but above a lowest health threshold) in order to await a really good brake event that has been predicted.

[0054] When the brake regeneration model has determined to generate a brake activation signal, the brake activation signal may override the auxiliary brake request and instead active the service brakes. In one example, the brake activation signal partly overrides the auxiliary brake so as to create a combination of a brake event using service brake and auxiliary brakes. [0055] The method may further comprises updating S5 the health model 110 based on the generated brake activation signal and/or updating S6 the prediction model 160 based on the generated brake activation signal.

[0056] By monitoring the health of the brakes with a health-model (this can be onboard or off-board on a server) the need for using the brakes can be determined to regenerate the rotors and get rid of any build-up rust and hinder the rotors from becoming pocked. By using a prediction model that identifies longer brake events that is suitable for regenerating the brakes based on collected data from the vehicle a successful regeneration of the brakes can be performed. This method saves energy, ware on the brakes as well as improves safety.

[0057] Examples of the health model 110 will now be described with reference to FIG. 6. The health model 110 may comprise different kinds of information, or parameters, directly or indirectly relating to the brakes of the vehicle 1.

[0058] The health model 110 may comprise information relating to the road information 120. Road information 120 pertains to the road on which the vehicle is currently travelling on, roads that the vehicle 1 has been travelling on and/or road that the vehicle will travel on in the planned route. Road information 120 may for example relate to the amount of salt 121 on the road, amount of dirt 122 on the road and/or the surface 123 of the road. The surface 123 of the road may for example relate to road surface roughness and/or road surface classification. The road surface may be uneven for many reasons, for example due to eroding roads, potholes, gravel road, dirt road, large water puddles on the road, ice and snow on the road, or oil spill on the road. A higher amount of salt on the road, the faster rusting may occur. A higher amount of dirt on the road, the more dirt is formed in and on the brakes.

[0059] The health model 110 may comprise information relating to the previous brake usage 130. The previous brake usage may relate to the distance since last usage 131, the time since last usage 132, the duration of the last usage 133 or a combination of any of the above. [0060] The health model 110 may comprise information relating to vehicle information 140. Vehicle information 140 may for example relate to the fuel consumption 141 of the vehicle, the speed 142 of the vehicle, the geographic location 143 of the vehicle and/or brake temperature 144. The speed of the vehicle 142 could be the current speed, historical speed and/or predicted speed on the planned route. The geographic location 143 of the vehicle could be the current location, historical locations and/or predicted upcoming locations based on the planned route. The fuel consumption 141 may be a current value, a historical value and/or an estimation of upcoming fuel consumption. Fuel consumption may provide information relating to the road status and driving pattern. The temperature of the brakes 144 may be a current value, a historical value and/or an estimation of upcoming brake temperature. The temperature of the brakes 144 may be achieved by a temperature sensor arranged in connection with the brakes.

[0061] The health model 110 may comprise information relating to the ambient condition 150 of the vehicle. The ambient condition 150 may for example be humidity 151, temperature 152 and/or precipitation 153. The humidity 151 may be a current value, a historical value and/or a prediction of upcoming humidity based on weather data. The humidity 151 may account for fog and mist and may also be referred to as moisture. The temperature 152 may be the temperature of the road and/or the temperature surrounding the vehicle. The temperature may be a current value, a historical value and/or an estimation of upcoming temperature based on weather data. Precipitation 153 may for example be rain, drizzle, sleet, snow, ice pellets, graupel and hail. The precipitation may be a current value, a historical value and/or an estimation of upcoming precipitation based on weather data.

[0062] The health model 110 may comprise information relating to time 115, 116. The time may relate to the time of year 115, such as the month of the year or the season of the year (spring, summer, autumn, winter). The time of year may affect the precipitation, the outdoor temperature, atmospheric humidity and road salting. The time may further relate to the time of the day 116, such as a specific time or time range (morning, midday/noon, afternoon evening, night). The time of day may affect the precipitation, outdoor temperature and atmospheric humidity.

[0063] As should be understood, the health model 110 could comprise a plurality of different parameters as has been mentioned above. In one model, all parameters are present whereas some models only one or two parameters are needed to generate an accurate heath status. Different parameters may be weighted differently.

[0064] The health model 110 defines the health status of the brakes. The health status may be determined in the form of a percentage, for example being 80% healthy. 100% healthy is the baseline where the brakes are in its best condition. This will be discussed more in detail with reference to FIG. 8.

[0065] The prediction model 160 will now be described with reference to FIG. 7. The prediction model 160 may comprise different kinds of information, or parameters, aiming to predict when it is most suitable to initiate a service brake event for the vehicle. The prediction model 160 can determine a suitable brake event on a planned route, and specify the most suitable time, geographical position and/or and duration of such brake event. By using the prediction model that identifies longer brake events that is suitable for regenerating the brakes, a successful regeneration if the brakes can be performed. Thus, the health status of the brakes are increased.

[0066] The prediction model 160 may comprise information relating to the vehicle information 170. The vehicle information 170 may for example relate to the weight of the vehicle 171, speed of the vehicle 172 and/or position data of the vehicle 173. The weight of the vehicle 171 may be a measured value (such as measured in tons or kilos) and/or information relating to the load of the vehicle (fully loaded, half-loaded or no goods). The speed of the vehicle 172 may be the same speed as was described with reference to FIG. 6 and reference numeral 142. The position of the vehicle 173 may be the same geographical position as was described with reference to FIG. 6 and reference numeral 143. Vehicle information 170 may be received by different sensors arranged on the vehicle, such as weight sensors, position sensors, speed sensors, GPS sensors and/or GNSS sensors.

[0067] The prediction model 160 may comprise information relating to upcoming road information 190. The road information 190 is preferably related to the planned routed. The road information 190 may for example relate to road inclination 191, temperature of the road 192 and/or surface of the road 193. The road inclination 191 may comprise information of upcoming changes in inclination of the road, for example if the inclination will be negative or positive, the maximum amount of inclination, a mean value of the inclination and/or the length of the inclination. The road inclination 191 is preferably received from a navigation service.

[0068] The temperature of the road 192 preferably relates to upcoming temperature at a given location. The temperature is preferably received from a weather forecast. The surface of the road may for example relate to predicted road surface roughness and/or road surface classification (gravel road, dirt road, asphalt road, etc.) of the road of the planned route. The road surface information is preferably received from a navigation service.

[0069] The prediction model 160 may comprise information relating to upcoming brake events 180. The upcoming brake events 180 may for example relate to estimated force for brake event 181 and/or estimated time for brake event 182. The upcoming brake events may be based on the upcoming road inclination 191.

[0070] The prediction model may comprise information relating to statistical data 161 and/or historical data 162. The statistical data and historical data may for example relate to previous brake events for a given route, previous brake events for a given weight of the vehicle, previous brake events for a given speed of the vehicle, previous brake events for a given road inclination, and previous brake events for a given road information. [0071] Based on the health status received from the health model 110 and the information gathered from the prediction model, a suitable brake event is determined for the vehicle traveling a planned route.

[0072] The correlation between the models will now be described more in detail with reference to FIGS. 8A-E. In the left part of FIG. 8A, a vehicle 1 is traveling on a road with an altitude Al at time tO. The vehicle 1 continues is travel along the road, to the right, where the inclination 191 of the road is increased at time tl. The vehicle reaches, for the road segment, a maximum altitude A2. A brake actitation event is performed when the inclination 191 turns negative, i.e. when the downhill road begins. The brake action event is terminated just before the decrease of the inclination to an altitude A3.

[0073] FIG. 8B shows an illustration of the prediction model 160 over time. In the time segment tO - 12 and t3-t4, no suitable braking event is predicted. The prediction model 160 has indicated a suitable braking event in time segment t2- 13. The brake prediction indicates the suitable braking event as there is an upcoming downhill in the planned routed.

[0074] FIG. 8C shows an illustration of the health model 110 over time. The health value can range between HMIN and HMAX, where HMAX is the highest health value the brakes can have and HMIN is the lowest health value the brakes can have without causing any permanent damage. In the time segment t0-t2, the health value is Hl, which is not ideal. At time segment t2, a brake event has been executed causing the health value to increase from Hl to H2 and then finally HMAX once the brake event is completed. At time segments t3-t4, the health status of the brakes are good and there is thus no need to perform any more brake actions. Different threshold values may be configured which defines critical levels of the health model. For example, a threshold value could be defined after which it is encouraged to initiate a brake event once a suitable brake event has been determined. Another threshold value could be defined as being a critical health status which should not be exceeded, hence a brake event should be initiated as soon as possible although the brake event is not ideal.

[0075] FIG. 8D shows an illustration of braking events of the vehicle. At time segment t0-t2 and t3-t4, no braking event occurs (as illustrated with “NO”). At time frame t3, a braking event has been initiated thus causing the vehicle to perform a brake event.

[0076] FIG. 8E shows all of the illustrations from FIG. 8A-D in one combined figure to facilitate the understanding of how the different models and brake events correlate.

[0077] FIG. 9 is a schematic diagram of a computer system 700 for implementing examples disclosed herein. The computer system 700 is adapted to execute instructions from a computer-readable medium to perform these and/or any of the functions or processing described herein. The computer system 700 may be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. While only a single device is illustrated, the computer system 700 may include any collection of devices that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Accordingly, any reference in the disclosure and/or claims to a computer system, computing system, computer device, computing device, control system, control unit, electronic control unit (ECU), processor device, etc., includes reference to one or more such devices to individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. For example, control system may include a single control unit or a plurality of control units connected or otherwise communicatively coupled to each other, such that any performed function may be distributed between the control units as desired. Further, such devices may communicate with each other or other devices by various system architectures, such as directly or via a Controller Area Network (CAN) bus, etc.

[0078] The computer system 700 may comprise at least one computing device or electronic device capable of including firmware, hardware, and/or executing software instructions to implement the functionality described herein. The computer system 700 may include a processor device 702 (may also be referred to as a control unit), a memory 704, and a system bus 706. The computer system 700 may include at least one computing device having the processor device 702. The system bus 706 provides an interface for system components including, but not limited to, the memory 704 and the processor device 702. The processor device 702 may include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory 704. The processor device 702 (e.g., control unit) may, for example, include a general-purpose processor, an application specific processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a circuit containing processing components, a group of distributed processing components, a group of distributed computers configured for processing, or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The processor device may further include computer executable code that controls operation of the programmable device.

[0079] The system bus 706 may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and/or a local bus using any of a variety of bus architectures. The memory 704 may be one or more devices for storing data and/or computer code for completing or facilitating methods described herein. The memory 704 may include database components, object code components, script components, or other types of information structure for supporting the various activities herein. Any distributed or local memory device may be utilized with the systems and methods of this description. The memory 704 may be communicably connected to the processor device 702 (e.g., via a circuit or any other wired, wireless, or network connection) and may include computer code for executing one or more processes described herein. The memory 704 may include non-volatile memory 708 (e.g., read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.), and volatile memory 710 (e.g., randomaccess memory (RAM)), or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a computer or other machine with a processor device 702. A basic input/output system (BIOS) 712 may be stored in the non-volatile memory 708 and can include the basic routines that help to transfer information between elements within the computer system 700.

[0080] The computer system 700 may further include or be coupled to a non-transitory computer-readable storage medium such as the storage device 714, which may comprise, for example, an internal or external hard disk drive (HDD) (e.g., enhanced integrated drive electronics (EIDE) or serial advanced technology attachment (SATA)), HDD (e.g., EIDE or SATA) for storage, flash memory, or the like. The storage device 714 and other drives associated with computer-readable media and computer-usable media may provide nonvolatile storage of data, data structures, computer-executable instructions, and the like. [0081] A number of modules can be implemented as software and/or hard-coded in circuitry to implement the functionality described herein in whole or in part. The modules may be stored in the storage device 714 and/or in the volatile memory 710, which may include an operating system 716 and/or one or more program modules 718. All or a portion of the examples disclosed herein may be implemented as a computer program product 720 stored on a transitory or non-transitory computer-usable or computer-readable storage medium (e.g., single medium or multiple media), such as the storage device 714, which includes complex programming instructions (e.g., complex computer-readable program code) to cause the processor device 702 to carry out the steps described herein. Thus, the computer-readable program code can comprise software instructions for implementing the functionality of the examples described herein when executed by the processor device 702. The processor device 702 may serve as a controller or control system for the computer system 700 that is to implement the functionality described herein.

[0082] The computer system 700 also may include an input device interface 722 (e.g., input device interface and/or output device interface). The input device interface 722 may be configured to receive input and selections to be communicated to the computer system 700 when executing instructions, such as from a keyboard, mouse, touch-sensitive surface, etc. Such input devices may be connected to the processor device 702 through the input device interface 722 coupled to the system bus 706 but can be connected through other interfaces such as a parallel port, an Institute of Electrical and Electronic Engineers (IEEE) 1394 serial port, a Universal Serial Bus (USB) port, an IR interface, and the like. The computer system 700 may include an output device interface 724 configured to forward output, such as to a display, a video display unit (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 700 may also include a communications interface 726 suitable for communicating with a network as appropriate or desired.

[0083] The operational steps described in any of the exemplary aspects herein are described to provide examples and discussion. The steps may be performed by hardware components, may be embodied in machine-executable instructions to cause a processor to perform the steps, or may be performed by a combination of hardware and software. Although a specific order of method steps may be shown or described, the order of the steps may differ. In addition, two or more steps may be performed concurrently or with partial concurrence.

[0084] The terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including" when used herein specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

[0085] It will be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the scope of the present disclosure.

[0086] Relative terms such as "below" or "above" or "upper" or "lower" or "horizontal" or "vertical" may be used herein to describe a relationship of one element to another element as illustrated in the Figures. It will be understood that these terms and those discussed above are intended to encompass different orientations of the device in addition to the orientation depicted in the Figures. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present.

[0087] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0088] It is to be understood that the present disclosure is not limited to the aspects described above and illustrated in the drawings; rather, the skilled person will recognize that many changes and modifications may be made within the scope of the present disclosure and appended claims. In the drawings and specification, there have been disclosed aspects for purposes of illustration only and not for purposes of limitation, the scope of the inventive concepts being set forth in the following claims.

Claims

1. A computer-implemented method of predictive brake regeneration of at least one service brake (10) in a heavy-duty vehicle (1), the method comprising: creating (SI) a brake regeneration model (100) by: obtaining (S2) a health model (110), wherein the health model (110) comprises data defining the health status of the at least one service brake (10) in said heavy- duty vehicle (1); creating (S3) a prediction model (160), wherein the prediction model (160) comprises data relating to a planned route of said heavy-duty vehicle (1) and wherein the prediction model (160) predicts at least one suitable brake event in the planned route for the at least one service brake (10) of the heavy-duty vehicle (1); analysing (S4) the brake regeneration model (100) in order to generate a brake activation signal to initiate a brake event of the at least one service brake (10).

2. The method according to claim 1, wherein method further comprises: updating (S5) the health model (110) based on the generated brake activation signal, and/or updating (S6) the prediction model (160) based on the generated brake activation signal.

3. The method according to claim 1 or 2, wherein the prediction model (160) at least comprises data relating to statistical data (161) of the planned route and/or historical data (162) of the planned route.

4. The method according to any proceeding claims, wherein the prediction model (160) at least comprises data relating to vehicle information (170).

5. The method according to claim 4, wherein vehicle information (170) comprises data relating to the position data of the vehicle (173), speed of the vehicle (172), and/or weight of the vehicle (171).

6. The method according to any proceeding claims, the prediction model (160) at least comprises data relating to the estimated force for a brake event (181) and/or the estimated time for a brake event (182).

7. The method according to any proceeding claims, the prediction model (160) at least comprises data relating to road information (190).

8. The method according to claim 7, wherein road information (190) comprises data relating to the inclination of the road (191), surface of the road (193) and/or the temperature of the road (192).

9. The method according to any proceeding claims, wherein the brake activation signal is generated once the health status is below a predetermined threshold value and a suitable brake event in the planned route is determined.

10. The method according to any proceeding claims, wherein the health model (110) comprises information relating to previous brake usage (130), ambient conditions (150), road information (120), time information (115, 116) and/or vehicle information (140).

11. A computer system comprising a processor device configured to perform the method of any of claims 1 - 10.

12. A computer program product comprising program code for performing, when executed by the processor device, the method of any of claims 1- 10.

13. A control system comprising one or more control units configured to perform the method according to any of claims 1 - 10.

14. A non-transitory computer-readable storage medium comprising instructions, which when executed by the processor device, cause the processor device to perform the method of any of claims 1 - 10.