Method for determining the coefficient of friction in a vehicle

Abstract

Method for determining a coefficient of friction between at least one tire of a vehicle and a road surface, wherein the coefficient of friction is formed as a function of at least one respective driving state of the vehicle, which is to be classified in advance, as well as respective ambient conditions, which are to be classified in advance, wherein the coefficient of friction is estimated by at least one estimation method, wherein the at least one estimation method is assigned at least one characteristic value of a confidence factor and the respective ambient conditions of the vehicle are determined using at least one sensor from the following list of sensors: rain sensor, air humidity sensor, outside temperature sensor, tire temperature sensor, tire vibration sensor, wheel carrier vibration sensor, sound or structure-borne sound sensors, vehicle environment sensors, front-rear camera, driving dynamics sensors, characterized in that using the at least one sensor, an energy,which is converted into heat in a respective tire of the vehicle, is calculated and the calculated energy is used to estimate the height of any water film present on the road surface as a function of the driving speed.

Description

[0001] The present invention relates to a method for calculating a coefficient of friction between at least one tire of a vehicle and a road surface. The calculation is based on various estimation methods, each of which is assigned a confidence factor.

[0002] The handling of a vehicle is heavily dependent on the respective coefficient of friction between tires and road surface. If the coefficient of friction is high, the vehicle can accelerate or decelerate accordingly, or corner correspondingly quickly. If the coefficient of friction is low, the vehicle can easily become unstable due to a loss of grip on the road surface, resulting in dangerous driving situations. It is of great importance for a driver to know how much traction or power transmission capacity is available to the road surface, i.e. what the maximum available adhesion is between tires and road surface. This information can also improve the control quality of all vehicle dynamics control systems, such as anti-lock braking systems (ABS), if processed accordingly.

[0003] Individual methods for calculating the coefficient of friction between tires and the road surface have already been described. For example, the German publication DE 10 2009 002 245 A1 discloses a method for determining the coefficient of friction between tires and the road surface in a vehicle. This method involves comparing an actual tire aligning torque with a reference tire aligning torque, with the current coefficient of friction being determined from the ratio of the two torques.

[0004] EP 0 412 791 A2 discloses the subject matter of the preamble of claim 1.

[0005] In contrast, the German publication DE 10 2010 036 638 A1 presents a method for determining the coefficient of friction in a vehicle, in which a lateral guidance force is determined at at least one steered wheel. A rack force is recorded, and a restoring torque at the steered wheel is determined as a function of the caster, depending on the rack force and the lateral guidance force. The coefficient of friction is determined based on the restoring torque.

[0006] Also based on a rack force, the document DE 10 2006 036 751 A1 determines a state variable of a vehicle according to a predetermined kinematic relationship as a function of the respective rack force, which in turn is calculated as a function of the steering torques acting in the steering system.

[0007] In contrast, German patent document DE 195 23 354 A1 discloses a device for estimating current road friction coefficients based on data from a steering angle sensor, a vehicle speed sensor, and a yaw angle sensor. It further describes incorporating these data into the calculation of the torque distribution to the front and rear wheels. Furthermore, a yaw moment calculation unit is provided to prevent the phenomenon of oversteering / understeering of the vehicle by controlling the left and right wheels accordingly.

[0008] Against this background, a method for determining a coefficient of friction between at least one tire of a vehicle and a road surface is presented with the features of patent claim 1. Further embodiments can be found in the corresponding subclaims.

[0009] The method according to the invention provides that the coefficient of friction is determined as a function of at least one respective driving state of the vehicle, which is to be classified in advance, as well as respective environmental conditions, which are to be classified in advance. The coefficient of friction is estimated using at least one estimation method, wherein the at least one estimation method is assigned at least one characteristic value of a confidence factor. In the context of the invention presented, the term "assign" is understood to mean a mathematical assignment, preferably a weighting, for example, by multiplication by a factor.

[0010] In the context of the present invention, a coefficient of friction or adhesion refers to a coefficient of friction or adhesion coefficient or a coefficient of friction. The coefficient of friction is a measure of the frictional force in relation to the contact force between two bodies, in particular between the tire and the road surface.

[0011] In a broader sense, the term coefficient of friction also includes the more phenomenological term of the tire's ability to transmit force to the road surface, although this can be very low in the case of aquaplaning or slippery road surfaces.

[0012] In addition to the quantitative calculation of the aforementioned physical parameters, such as the coefficient of friction, the method also includes the highly qualitative determination or estimation of whether, for example, aquaplaning or slippery road surfaces are present. This can be done using methods such as fuzzy logic or associative memory, or directly, for example, using simple phenomenological considerations.

[0013] The method according to the invention is based on several successive method steps. In a first step, a respective driving state is classified. The respective driving state of the vehicle is advantageously classified using the vehicle's longitudinal, lateral, and vertical dynamic state.

[0014] In the context of the present invention, a driving state is understood to mean the movement of a vehicle relative to the roadway. The current driving state can be described by a time-varying, vectorial direction of travel, consisting of longitudinal, transverse, and vertical dynamic components.

[0015] The lateral dynamics are divided into potential driving states: "straight ahead," "steady-state cornering," and "unsteady cornering." Furthermore, a distinction can be made between left and right turns. Unsteady cornering can be further divided into entering or exiting a curve.

[0016] In longitudinal dynamics, a distinction is made between "constant speed," "accelerated," and "braked." In the "braked" driving state, a further distinction can be made between "braked with service brake" and "braked with engine brake." This distinction is necessary because the respective braking types can result in a noticeable difference in the utilization of traction between individual wheels, meaning that individual wheels may lock more easily, for example, when braking with the service brake.

[0017] Longitudinal dynamics can therefore be divided into driving states such as "accelerated," "constant," "engine-braked," or "braked." Further classifications would include "accelerated," "constant," "decelerated," and "driven," as well as "engine braking" and "service braking." The latter example offers the advantage that downhill driving states with engine braking or service braking at a constant or even increasing speed can also be clearly described.

[0018] The vertical dynamic motion state of the vehicle is also taken into account here, whereby this term includes both the overall movement of the vehicle in the topography of a landscape (uphill or downhill driving or driving on the level) or the motion states of individual wheels with respect to the vehicle body above the road surface, such as compressed and rebounded, and thus also the connection to the effective contact forces or normal forces in the tire contact areas.

[0019] The lateral dynamics, on the other hand, are divided into, for example, “curve (left)”, “transition (left)”, “straight ahead”, “transition (right)”, “curve (right)”.

[0020] In order to stabilize or optimize a vehicle's handling, precise knowledge of the vehicle's current driving state is first required. Therefore, in the method according to the invention, the lateral dynamics are first classified using a yaw rate or yaw acceleration, and the longitudinal dynamics are then classified using an actual longitudinal acceleration and / or brake pressure, i.e., assigned to one of the aforementioned driving states. The sensors required for both the classification of the longitudinal acceleration and the classification of the lateral acceleration are generally already present in vehicles with driving dynamics control.

[0021] Furthermore, in a second step, the respective environmental conditions of the vehicle are classified, since these also influence the coefficient of friction and thus the driving behavior of the vehicle.

[0022] The environmental conditions are classified into at least three categories: "dry," "slippery road surface," and "wet." A further breakdown of slippery road surfaces is conceivable using suitable sensors. Differentiation between the various categories is achieved using at least one sensor from the following list of sensors: rain sensor, humidity sensor, outside temperature sensor, tire temperature sensor, tire vibration sensor, wheel carrier vibration sensor, sound or structure-borne sound sensors, vehicle environment sensors, front-rear camera, and vehicle dynamics sensors. If a humidity sensor is also available, the outside temperature can be used to estimate whether dew is forming on the road. If the outside temperature is below approximately 3°C, ice is expected to form.

[0023] According to the invention, it is provided that, using at least one sensor, an energy which is converted into heat in a respective tire of the vehicle is calculated and the calculated energy is used to estimate a height of a possibly present water film on the roadway as a function of the driving speed.

[0024] If a vehicle is additionally equipped with internal tire temperature sensors, the respective forces acting between the tire and the road surface are estimated using the parameters required to classify longitudinal and lateral dynamics. From the estimate of these forces acting between the tire and the road surface, the energy converted into heat in the tire is calculated.

[0025] Using measurements provided by the tire internal temperature sensors, the respective energy released by each tire into the environment is also estimated. If more energy is released into the environment than is possible through air and road contact at the measured ambient temperature, it can be assumed that cooling is currently taking place via a water film that absorbs the additional energy released by the respective tire. The additional energy released can be calculated as a function of the driving speed and the height of the water film on the road surface. This allows the height of the water film to be determined given the amount of energy released and the driving speed.

[0026] In a possible embodiment of the method according to the invention, it is provided that the at least one estimation method is selected as a function of a respective driving state to be previously classified.

[0027] Since the current coefficient of friction significantly influences a respective driving condition, it is provided that the respective at least one estimation method by which the current coefficient of friction is determined is selected depending on previously determined classification results of the driving condition, wherein the selection of the at least one estimation method is preferably carried out automatically.

[0028] Using the previously classified driving conditions, it is possible to select suitable methods for estimating the friction coefficient at a given time. Furthermore, the classification results allow for weighting the results of the methods involved in estimating the friction coefficient if more than one method is used.

[0029] In a further possible embodiment of the method according to the invention, it is provided that the at least one estimation method is selected as a function of respective classified environmental conditions.

[0030] Since the respective environmental conditions of the vehicle, in addition to the driving state, also have or can have a considerable influence on the current coefficient of friction between the vehicle or tires and the road, it is provided that the respective at least one estimation method for estimating the current coefficient of friction is selected depending on the previously determined classification results of the environmental conditions and / or the driving state, wherein the selection is preferably carried out automatically.

[0031] In principle, all suitable methods for the possible description of factors influencing the coefficient of friction between tires and road surface can be used, whereby preferably methods for estimating respective driving conditions via longitudinal and lateral acceleration sensors as well as methods for determining respective slip fluctuations between different wheels, a tire caster, a surge resistance, a quantity of precipitation or a water film on the road surface are used to determine the coefficient of friction.

[0032] The different estimation methods can be included in the calculation, i.e. the estimation of the friction coefficient, equally or with a different weighting adapted to the respective classification results.

[0033] In order to adapt the respective estimation methods to respective environmental conditions and / or driving conditions, the invention provides for individually weighting the respective estimation methods, ie calculating them with a confidence factor or a respective characteristic value of the confidence factor assigned to the respective estimation method for a respective environmental condition and / or driving condition.

[0034] In a further possible embodiment of the method according to the invention, it is provided that at least one possible disturbance variable is identified and weighted with a confidence factor or a corresponding characteristic value of a confidence factor.

[0035] In such complex calculations as estimating or determining the coefficient of friction, significant consideration is given to disturbances such as crosswind, transverse inclination, longitudinal inclination, rutting, differing adhesion in the left and right lanes, and ABS or FDR control intervention. Therefore, it is planned to determine and identify such disturbances, for example, using measurements of steering torque and / or suspension travel or vertical acceleration of the vehicle, and, if necessary, to include them in the calculation of the coefficient of friction.

[0036] In a further possible embodiment of the method according to the invention, it is provided that respective disturbance variables are also weighted by at least one confidence factor, ie that each disturbance variable is assigned or weighted with a respective corresponding characteristic value of the at least one confidence factor.

[0037] By using a confidence factor for the estimation procedures and, analogously, another confidence factor for the disturbance variables, corresponding influences can be weighted and thus assessed for their reliability.

[0038] In the context of the invention presented, a confidence factor is understood to be a factor used to weight a particular function or method, e.g., an estimation method, against other methods and thereby determine its specific relative contribution to an overall result obtained based on multiple methods. A confidence factor consists of characteristic values ​​assigned to specific driving states and / or environmental conditions or estimation methods, which are offset against these to weight the respective estimation methods.

[0039] Since the method according to the invention provides at least one estimation method, and in particular a combination of a plurality of estimation methods, for determining or calculating the coefficient of friction between the tire and the road surface, it may be appropriate to weight individual estimation methods from the plurality of estimation methods. This weighting is performed, as explained above, for example, based on environmental conditions and / or driving conditions.

[0040] In order to enable the estimation methods to be weighted against each other when calculating the coefficient of friction, each result for the coefficient of friction obtained using an estimation method is multiplied by a respective characteristic value of the respective confidence factor assigned to the estimation method, i.e. it is weighted, and all the weighted results of the individual estimation methods are added together. Weighting also includes other mathematical methods which can, for example, take into account the mutual influence of several confidence factors. For reasons of standardization, when calculating using weighted methods it is necessary to include all of the respective weightings in the calculation. Such inclusion can, for example, take the form of dividing the overall result obtained from the summation by a sum of the respective characteristic values ​​of the confidence factor.

[0041] The influence of disturbances is taken into account by calculating the friction coefficient based on disturbances weighted by a confidence factor associated with the respective environmental conditions and / or driving state. For this purpose, the respective weighted disturbances are offset against each other and compared with a specified minimum value. If the result of the offset of the weighted disturbances is below the specified minimum value, a reliable estimate of the friction coefficient is currently not possible.

[0042] The use of confidence factors enables a reliable determination or estimation of the coefficient of friction and thus offers the driver and the respective vehicle control system a trustworthy way to assess respective driving maneuvers.

[0043] If fixed confidence factors or characteristic values ​​are assigned to the respective estimation methods for determining the coefficient of friction, the corresponding characteristic values, i.e. values ​​of the confidence factor for a specific driving state and / or specific external conditions, must be provided by a technician, for example, before the vehicle is put into operation. These characteristic values ​​require a fixed hierarchy of the respective estimation methods among each other, in which, for example, a method based on tire temperature as well as a method based on wheel speed is included in the calculation of the coefficient of friction. Due to the respective confidence factor or characteristic value, a contribution from an estimation method based on tire temperature, for example, is only taken into account with a quarter of the contribution from an estimation method based on wheel speed when calculating or estimating the coefficient of friction.

[0044] The described relative influence of the respective estimation methods is described by the respective confidence factors or their characteristic values, which are stored, for example, in a vehicle control unit. The characteristic values ​​of the confidence factors can assume any value and, if necessary, can also be dynamically adapted to previously classified driving situations or environmental conditions, whereby the relative influence of the respective estimation methods on the friction coefficient changes accordingly. Through weighting and subsequent normalization, the respective methods can contribute more or less strongly to the respective overall result.

[0045] In a further possible embodiment of the method according to the invention, it is provided that the at least one estimation method is weighted with a respective characteristic value of a corresponding confidence factor and a value that results in an estimated maximum adhesion is calculated from all weighted estimated values ​​of respective methods or estimation methods.

[0046] By using multiple weighted estimation methods, i.e., methods calculated with the respective parameters of the respective confidence factors, a reliable estimate of the currently available maximum adhesion or friction coefficient between the tire and the road surface can be achieved. The currently available maximum adhesion can be converted into the current friction coefficient, and a conversion from the current friction coefficient to the currently available maximum adhesion is also possible.

[0047] In the context of the invention, maximum available traction is understood as a value that indicates how much traction is currently available to a particular tire or the entire vehicle. Therefore, the respective tire can be loaded up to this value before contact with the road surface is lost, causing, for example, the respective tire to lock or spin, generating excessive slip.

[0048] In a further possible embodiment of the method according to the invention, it is provided that respective characteristic values ​​of respective confidence factors of respective estimation methods or disturbance variables are offset and a value calculated thereby is compared with a previously provided minimum value and if the calculated value falls below the minimum value, a message is generated stating that a reliable estimate of the maximum available traction is currently not possible.

[0049] Since the respective confidence factors or the corresponding characteristic values ​​can change depending on the classified driving state and / or environmental conditions, it is planned to take into account a quality criterion in the form of a minimum value. For this purpose, all relevant characteristic values ​​are offset against one another, i.e., for example, summed up and compared with the previously provided minimum value. If the resulting value, e.g., an average value of the confidence factor, is below the minimum value, a reliable estimate of the maximum available adhesion or the coefficient of friction is currently not possible. In such a case, where the result of the offsetting of the characteristic values ​​of the respective confidence factor is less than the minimum value, it can be provided to provide the driver of the vehicle with a warning or to inform the control units that a reliable estimate of the maximum adhesion or the coefficient of friction is currently not possible.

[0050] In a further possible embodiment of the method according to the invention, it is provided that the respective disturbance variables are weighted and a value, for example a mean value, is calculated from the respective weighted disturbance variables.

[0051] Just as already explained for the confidence factors of the at least one estimation method for estimating the friction coefficient or maximum adhesion, the determined disturbance variables are also calculated with the respective characteristic values ​​of the confidence factors, which can be fixed or dynamically assigned, and compared with a previously provided minimum value. If the calculated value is below the minimum value, a warning can be provided to the vehicle driver or information can be provided to the control units indicating that a reliable estimate of the maximum adhesion or the friction coefficient is currently not possible.

[0052] In a further possible embodiment of the method according to the invention, it is provided that the respective characteristic value of the further confidence factor is compared with the minimum value to be provided in advance and, if the further confidence factor falls below the value to be provided, a last estimated friction value is assumed to continue to be valid.

[0053] In a further possible embodiment of the method according to the invention, it is provided that the at least one estimation method is selected from the following list of estimation methods: a) Calculation of a global adhesion between at least one tyre and the road surface by means of a measured longitudinal and lateral acceleration, b) Calculation of respective wheel loads based on the currently occurring longitudinal and lateral acceleration as well as knowledge of the loading condition, chassis and suspension system, c) Estimation of respective longitudinal and lateral forces by knowledge of the use of a calculation according to a) and b) as well as knowledge of a drive and control system of the respective vehicle, d) Determining a tyre longitudinal stiffness or an equivalent value by forming a quotient of tyre longitudinal force and tyre longitudinal slip, e) Determining a tyre lateral stiffness or an equivalent value by forming a quotient of tyre lateral force and tyre lateral slip, f) Determining an overall tire stiffness or an equivalent value by forming a quotient of tire force and tire slip by combining claims d1 and d2 or by means of an overall vectorial approach, g) Determining the maximum available adhesion between at least one tyre and the road surface by a ratio between the current longitudinal, transverse or overall tyre stiffness and the specified longitudinal, transverse or overall tyre stiffness on a grippy road surface, h) Calculating a restoring moment of a front axle of the respective vehicle by converting an EPS tie rod force using knowledge of axle and steering kinematics, i) Calculating a lateral force on the front axle from a respective vehicle movement, j) Calculating a caster distance from a quotient of restoring moment and lateral force with a freely rolling wheel, and k) Calculating the maximum available adhesion between the respective tyre and the road surface by comparing the current aligning torque with a specified aligning torque on a dry, non-slip road surface, l) Determining a water film thickness on the road surface by determining different patterns of a longitudinal acceleration signal and a steering wheel angle in relation to a steering torque and a wheel speed, m) Determining the coefficient of friction by using route-related data stored in a vehicle-specific database and including descriptive parameters of the road surface, and o) Determining the friction coefficient by using data from communication with external vehicle sources or external data servers.

[0054] The route-related data can be stored, for example, in a navigation device as an on-board database. Using vehicle positioning, the data can be used predictively in the vehicle. This includes, for example, the geographical position of the roadway, geometric contour and road elevation above sea level, longitudinal and transverse gradients, surface type, micro- and macrotexture, other characteristics that permanently influence the road friction coefficient, such as crests and dips, ruts, exposure on bridges, and shadows.

[0055] Route-related, time-variable data can be made available in the vehicle from sources external to the vehicle, such as other vehicles or external data servers, in a timely manner and related to the vehicle's location and route, locally and / or in advance. This covers the entire field of information relevant to the road friction coefficient. This includes forecast and / or current and / or past information that is measurable or observable. This can have been collected by other road users or stationary observation devices or weather stations. This includes, for example, the current road condition including the degree of coverage with media such as water, snow, ice, frost, oil, leaves; as well as the location, time, forecast and course of the comprehensively characterized weather situation includingTemperature and, in particular, the location and timing of the type and amount of precipitation and the prevailing visibility conditions, which, in conjunction with the coefficient of friction, influence the driver's driving style and the activation of vehicle systems.

[0056] Important information about the driving state, environmental conditions or potential risks due to approaching limit states can also be derived from other sensors in the vehicle and used with appropriate weighting in an overall system approach.

[0057] These include, for example, the following sensors: The rain sensor for controlling the windshield wiper provides information about the current amount of precipitation on the windshield and thus, by correcting for various factors such as driving speed and road gradient, approximately determines the water level on the road. Acceleration or structure-borne sound sensors in the vehicle are capable of contributing to the overall vehicle system for determining water depth, road surface micro- and macrotexture, snow and ice, and aquaplaning conditions. Water depth determination works, for example, by measuring sound or structure-borne sound intensities in frequency bands characteristic of road spray. Road surface micro- and macrotexture, snow, and ice can be obtained using similar methods from analyzing the vibration behavior in the tire, wheel suspension, or the entire vehicle. Other insightful sources of information on friction coefficient detection are sensors that monitor the vehicle's surroundings, such as ACC radar and cameras. Certain weather and operating conditions are detected using self-diagnosis (e.g., ACC radar blindness due to icing) to identify and prevent functional limitations. This can be used in an integrated system. Cameras can provide information about road conditions (dry, wet, icy, snowy), or about spray behind the vehicle caused by water on the road.

[0058] By using respective estimation methods to determine the coefficient of friction or the maximum available adhesion of the respective tire of the vehicle with the road, it is possible to reliably determine the values ​​of the coefficient of friction or the maximum available adhesion and to continuously adapt them to the respective circumstances, i.e. to the respective environmental conditions and / or driving situations or driving conditions. In this case, the invention provides for respective estimation methods to be weighted with a confidence factor and to be used as a function of respective disturbance variables, which can also be weighted, for example, using a further confidence factor. Since different vehicles have different sensor configurations, the method according to the invention is based on a large number of potentially usable estimation methods, wherein, with a corresponding availability of the necessary sensors in each case, the scope of the sensors required to determine the coefficient of friction orof the maximum available adhesion can be increased.

[0059] In a further possible embodiment of the method according to the invention, it is provided that the determined coefficient of friction or the determined maximum available adhesion can be combined with data from a navigation system and used to calculate respective braking distances, braking intervention times and / or cornering speeds.

[0060] By knowing the current friction coefficient in advance, it is possible to regulate the brake pressure in such a way that locking of the respective wheel or tire can be prevented even before the wheel comes to a complete stop. Such pre-control or pre-regulation reduces the required braking distance compared to a conventional anti-lock braking system, which only intervenes when a particular tire or wheel has already entered a locked state. Such late intervention has a negative impact on the required braking distance, particularly on slippery road surfaces.

[0061] By estimating the current coefficient of friction, vehicle control systems such as an anti-lock braking system (ABS), a stability control system, a traction control system of the powertrain, the spring and damper system, or a steering system can be efficiently regulated. This means that the respective control or regulation operations of the respective vehicle control system can be adapted to the current coefficient of friction, thereby, for example, reducing the brake pressure to be applied and preventing tire locking even before the lockup is detected, as is the case with ABS, for example.

[0062] It is understood that the above-mentioned features can be used not only in the combination specified in each case, but also in other combinations or on their own, without departing from the scope of the present invention.

[0063] The invention is illustrated schematically in the drawing using exemplary embodiments and is described in detail with reference to the drawing. Fig. 1 shows a schematic progression of a classification of a respective driving state in a possible embodiment of the method according to the invention. Fig. 2 shows a schematic progression of a classification of respective environmental conditions of a vehicle in a possible embodiment of the method according to the invention. Fig. 3 shows a schematic course of a determination of the respective disturbance variable in a calculation of a friction coefficient between tire and road surface in a possible embodiment of the method according to the invention. Fig. 4 shows a schematic overview of methods used to calculate the coefficient of friction in a possible embodiment of the method according to the invention. Fig. Figure 5 shows a possible embodiment of the invention of a table with characteristics of confidence factors used to weight methods used to calculate the coefficient of friction. Fig. 6 shows a diagram of a possible estimation according to the invention of a maximum adhesion between respective tires and a road surface. Fig. 7 shows a further possible embodiment according to the invention of a table with confidence factors of respective disturbance variables used to calculate the coefficient of friction.

[0064] The Fig. The classification process of a respective driving state 1 of a vehicle illustrated in Figure 1 serves to assign or classify the respective driving situation 1 into one of nine current driving states 2, 4, 6, 8, 10, 12, 14, 16, 18 by measuring a current direction of travel (lateral dynamics) 20, a current speed profile (longitudinal dynamics) 22, and an applied brake pressure 27. The speed profile is determined by measured values ​​from a speedometer, the direction of travel by measured values ​​from a yaw rate sensor, and the brake pressure by a brake pressure sensor. The respective values ​​from the speedometer and the yaw rate sensor are calculated, interpreted, and finally assigned to a specific, predefined driving state.

[0065] Based on the measured values ​​of the yaw rate sensor, the lateral dynamics 20 are pre-classified using a yaw rate ψ̇ 21, i.e., if the yaw rate ψ̇ 21 is approximately zero, the driving situation 1 is assigned to a current driving state "straight ahead" 6. If the yaw rate 21 is greater or less than zero, a further calculation of a respective yaw acceleration ψ̈ 23, 25 is performed. If the yaw rate 21 has assumed a value less than zero and the corresponding yaw acceleration 25 has assumed approximately zero, the driving situation 1 is assigned to the driving state "curve right" 10. If the yaw acceleration 25 is not equal to zero, the vehicle is in a "transitional state" 8.

[0066] If the yaw rate 21 has assumed a value greater than zero and the corresponding yaw acceleration 23 is approximately zero, the current driving state is assigned to a “left turn” 2, whereas if the value of the yaw acceleration 23 is not equal to zero, the vehicle is in a “transition state” 4.

[0067] To classify the longitudinal dynamics 22, an interpretation of a current actual longitudinal acceleration v̇ x26, which is calculated from the current driving speed. If the current longitudinal acceleration 26 is approximately zero, the longitudinal acceleration is assigned to the driving state "constant" 14, if the longitudinal acceleration 26 is greater than zero, the current driving state is classified as "accelerated" 12 and if the longitudinal acceleration value is less than zero, the driving state is classified as "braked" 18. Since a vehicle can be braked both by engine braking and by a service brake, if the longitudinal acceleration value is less than zero, a further differentiation is made via an applied brake pressure 27. If the brake pressure 27 is greater than zero, the current driving state is "braked" 18, whereas if the brake pressure 27 is equal to zero, the driving state is classified as "engine-braked" 16.

[0068] By including the brake pressure 27 in the classification, it is possible to clearly describe driving situations such as downhill driving with engine or service brakes at a constant or even increasing speed.

[0069] If vertical acceleration is also measured, a further classification based on vertical dynamics is conceivable. The impact on the estimation of the coefficient of friction is particularly important for longer-lasting vertical accelerations, for example, in dips and on hilltops.

[0070] By merging the classification results of longitudinal and lateral acceleration, respective driving states can be assigned to respective environmental conditions, such as “braked wet” or “accelerated dry”.

[0071] Since during the respective driving situation 1 also the respective environmental conditions are relevant for the driving behavior or the friction coefficient between tires and road, these are taken into account in a further, in Fig. 2. The classification method for interpreting the current ambient conditions is based on measured values ​​from sensors 201, 203, 205 and 207, whereby the measured values ​​determined from the various sensors 201, 203, 205 and 207 are compared with one another in order to ensure a reliable classification result. First, the measured values ​​of a rain sensor 201 are interpreted in an intermediate step 211. If precipitation is currently to be measured, a comparison step 213 follows, as indicated by the arrow 214, or a comparison step 215, as indicated by the arrow 212. If an outside temperature sensor 203 or a tire temperature sensor 205 produces suitable measurements in the respective comparison steps 215 or 213, i.e. measurements leading to the same classification result, the environment is classified as “wet” 233.However, if the outside temperature sensor 203 measures a value below 3°C, the environment is classified as "potentially slippery" 232. If the rain sensor 201 currently measures no precipitation and a comparison step 216 shows that, even using data from the tire temperature sensor 205, there is currently no cooling water film 220, the current ambient conditions are classified as "dry" 231.

[0072] An estimation of the presence of the water film 220 on the tires of the vehicle is carried out using both the outside temperature sensor 203, the tire temperature sensor 205 and driving dynamics sensors 207, wherein the driving dynamics sensors 207 are used in particular for calculating a respective energy input into the tires 218, which is compared with the measurements of the outside temperature sensor 203 and the tire temperature sensor 205 in a comparison step 219.

[0073] In a comparison step 219, the presence of a water film 220 is inferred from the measured values ​​provided by the outside temperature sensor 203, the tire temperature sensor 205, and the driving dynamics sensors 207, as well as an energy 218 that is converted into heat in a respective tire, according to the arrow directions. By measuring the respective tire internal temperatures, the energies that the respective tires release to the environment are estimated. If more energy is released to the environment than is possible at the ambient temperature measured by the outside temperature sensor 203 due to the air and road surface contact of the respective tire, it can be assumed that the respective tire is being cooled by a water film 220, which also classifies the environment as "wet" 233. The amount of heat released in this process can be calculated, for example, as a function of the current driving speed and the height of the water film 220.If respective adjustment steps 216, 215, 213 or 219 have to compare contradictory measurements or partial results, a new measurement is carried out and a classification of the contradictory values ​​is omitted.

[0074] The Fig. 1 and Fig. The classification methods presented in Figure 2 allow a selection of methods for estimating the coefficient of friction that are suitable at a given point in time and a weighting of the results of the estimation of the coefficient of friction if more than one estimation method is used.

[0075] The Fig. The method illustrated in Figure 3 is based on estimating a steering torque. Starting from a starting state 301, a current state of a vehicle control system 303 is detected. If the vehicle control system is active, a disturbance variable exists in an active control state 305. If the vehicle control system 303 is not active, a comparison of a determined longitudinal acceleration in the vehicle-fixed coordinate system with a current actual longitudinal acceleration 26 is used to assess whether the vehicle is currently negotiating an incline 309, which is the case if the longitudinal acceleration in the vehicle-fixed coordinate system is not equal to the current actual longitudinal acceleration.If the longitudinal acceleration in the vehicle-fixed coordinate system approximately corresponds to the current actual longitudinal acceleration 26, a further comparison step 313 of a lateral acceleration in the vehicle-fixed coordinate system with a product of the actual yaw rate and the current driving speed classifies a current transverse inclination 315 of the roadway. The vehicle is on a transverse inclination 315 if the lateral acceleration in the vehicle-fixed coordinate system does not correspond to a product of the actual yaw rate and the current driving speed. If the vehicle is equipped with a 3-axial acceleration and yaw rate sensor, the vehicle movement in space can be determined much more accurately. From the vehicle movement and the spring travel, the topography of the roadway with longitudinal and transverse inclination can also be approximately calculated. AExisting influence of crosswind 317 is classified via calculations 320, 322, and 324 and the adjustment steps 321, 323 and 325.

[0076] Furthermore, depending on the result of the comparison step 325 and a subsequent further comparison step 327, a conclusion is drawn about the current occurrence of ruts 318 or different adhesion in the left and right lanes 319. If the respective comparison steps 321 and 323, together with the calculations 320 and 322, lead to a clear result, the process ends in a final state 330. If the respective disturbance variables 305, 309, 315, 317, 318, or 319 are currently present, they are made available for a further calculation of the friction coefficient and, if necessary, offset against a confidence factor. Now, or after a fixed time interval, the process is run again from starting point 301.

[0077] The Fig. The method shown in Figure 4 for estimating a currently used adhesion 440 and a maximum available adhesion 450 between tires and road surface is based on various sub-methods 401, 402, 403, 404, 405, 406, and 407. In sub-method 401, a global adhesion coefficient is determined from a currently measured longitudinal acceleration 411 and a currently measured lateral acceleration 412. To increase the accuracy of this determination, the measured values ​​of the longitudinal acceleration 411 and the lateral acceleration 412 can be converted into road-parallel values ​​using roll and pitch angles. If a 3-axial acceleration sensor is used, taking into account the currently acting vertical acceleration can increase the accuracy of the calculation of the lateral and longitudinal acceleration acting parallel to the road surface.The necessary knowledge of the roll and pitch angles can either be estimated using knowledge of the vehicle from the respective accelerations or can be approximately calculated using spring travel sensors that may be provided.

[0078] If a 3-axis yaw rate sensor is also available, the vehicle's spatial motion can be determined even more precisely. This allows for the conversion of accelerations into lateral and longitudinal acceleration parallel to the roadway with even greater accuracy.

[0079] A fusion of longitudinal 411 a x and lateral acceleration a y 412 is calculated using the formula μglobal≈ax2+ay2g calculated.

[0080] In sub-process 402, in order to consider wheel-specific adhesion utilization, the respective wheel loads 413 are inferred from the currently occurring longitudinal and lateral accelerations 411, 412 as well as knowledge of the chassis and suspension system of the respective vehicle. Additional knowledge of the drive and braking system of the respective vehicle allows the longitudinal and lateral forces transmitted to the respective contact surfaces and thus also the adhesion 414 currently utilized at each wheel to be estimated.

[0081] An evaluation of the respective wheel speeds 415 provided in sub-process 403 offers a wide range of potential for calculating adhesion-relevant parameters. In vehicles with single-axle drive, the respective slip at the driven wheels can be determined by comparing driven and non-driven wheels. If the drive force and axle load on a rear axle of the vehicle are known, a longitudinal force stiffness 435 can be determined by forming a quotient of drive force to slip. If the longitudinal force stiffness 435 is lower than on a non-slip, dry road surface and the same wheel load, then the tire in question is already partially slipping. A maximum available adhesion 450 can be deduced from a currently utilized adhesion 440 and a ratio of the current longitudinal force stiffness 435 to a longitudinal force stiffness on a non-slip road surface.Since respective lateral forces also influence the longitudinal force stiffness 435, this calculation only provides reliable values ​​if the longitudinal acceleration 411 is significantly greater than the lateral acceleration 412. Strong water film thicknesses can also lead to wheel speed fluctuations, which is why these are taken into account in corresponding process steps, ie, estimation methods.

[0082] An EPS tie rod force 416 can be converted into a restoring torque of the vehicle's front axle in sub-process 404, given knowledge of the axle and steering kinematics. The restoring torque initially increases with increasing lateral force. However, at a traction utilization of approximately 60%, the restoring torque already reaches its maximum.

[0083] A lateral force on the front axle can be calculated from the current vehicle movement. From the quotient of the aligning torque and the lateral force, a tire caster distance 417 can be calculated with a freely rolling wheel. This distance is composed of the geometric caster of the respective axle kinetics and the tire aligning torque. Analogous to the slip analysis already described, the current aligning torque is compared with a aligning torque that can be achieved on a dry, grippy road surface with the same lateral force. If the current aligning torque is lower than the aligning torque that can be achieved on a dry, grippy road surface with the same lateral force, the maximum available adhesion 450 can be determined from the currently used adhesion and a ratio of the current aligning torque to the aligning torque on a grippy road surface.These calculations are reliable if there are no significant longitudinal forces on the steered front wheels, since deformations of the tire during cornering generally also result in a decentralized application of resulting longitudinal forces, whereby an additional part of the restoring moment is caused by the longitudinal forces.

[0084] Using a 3-axial acceleration and yaw rate sensor, the slip angle can be determined, and if the axle kinematics and elastokinematics are known, the slip angle can also be determined. Similar to the slip analysis described above, the slip angle can also be evaluated.

[0085] Sub-method 405 relates to driving through individual road sections with a thick water film, whereby a significant increase in the current driving resistance is caused by a surge resistance 420. Due to the increase in the current driving resistance, the driving speed decreases slightly and a current longitudinal acceleration signal 411 changes abruptly. If the steering wheel angle and, "inappropriately" the steering torque change simultaneously, this results in a one-sided increase in the water film thickness. In this context, "inappropriate" means that the steering torque is not caused by the respective driver of the vehicle but by the road surface, which can be assessed by evaluating the yaw rate 419 and other driving dynamics variables. If, in addition, the wheel speed at individual wheels suddenly decreases or the wheel speed becomes unstable, the respective wheel is already floating or briefly floating.By detecting specific patterns, a critical driving speed can be determined for the current water film height. A challenge here is identifying other disturbing factors, such as potholes or gusts of wind, which can also lead to wheel speed fluctuations or sudden vehicle deceleration.

[0086] Sub-proceedings 406 and 407 have already been Fig. 2 and relate to a rain sensor, which, taking into account a driving speed 421, is used to infer a current amount of precipitation 422. Whereas, in sub-method 407, the tire temperature sensor is used to infer the energy input into the tires 218 and thus the presence of a water film 220 in method step 431.

[0087] By combining the respective sub-processes 401 to 407, it is possible to determine additional knowledge about, for example, existing environmental conditions. Thus, if the presence of the water film 220 is known through process step 431 and an inappropriate change in the steering wheel angle occurs, the presence of aquaplaning 430 can be inferred.

[0088] If an activity state of a vehicle control system is present, a traction determined from sub-processes 401 and 402 is used in a comparison step 423 to determine the maximum available traction 450. If, however, no vehicle control system is active, the results from sub-processes 401 and 402 are used to determine the currently used traction 440.

[0089] Furthermore, a respective tread depth 433 of the vehicle's tires has a considerable influence on the corresponding measurements required in the respective sub-procedures 402, 403 and 404 and is therefore included in these calculations. The same applies to a transverse and longitudinal inclination 434, which is taken into account in sub-methods 404 and 406. Since the various sub-procedures 401 to 407 can only provide reliable results in certain driving situations, a confidence factor or characteristic values ​​of the confidence factor are determined for each estimation method depending on the driving condition, as in Fig. 5 shown.

[0090] The respective sub-procedures 401 to 407 are assigned the Fig. 1 described classified driving situations "straight ahead" 6, "curve" 2, 10 or "transition" 4,8 are assigned corresponding characteristic values ​​555, as shown by way of example in tables 520, 530 and 540. For this purpose, the tables pre-classified by the described components of the lateral dynamics 20 are further classified according to the also in Fig. 1 reported driving conditions of the longitudinal dynamics 22 were divided into “accelerated” 12, “constant” 14, “engine braked” 16 and “service braked” 18.

[0091] The sub-processes “Active Control” 501, which consists of a fusion of sub-processes 401 and 402, “Wheel Speeds” 403, “Tie Rod Force” 404, “Acoustic Resistance” 405, “Rain Sensor” 406 and “Tire Temperature” 407 are assigned a characteristic value 555 according to the respective driving conditions and made available for further calculations.

[0092] Accordingly, for example, the confidence factor or characteristic value 555 of the acoustic resistance 405 changes depending on the driving conditions "straight ahead" 6, "curve" 2, 10, or "transition" 4.8, since during cornering, the individual wheels usually rotate at different speeds due to the different distances to be covered. As a result, detecting aquaplaning 430 during cornering is more difficult than during straight-ahead driving. Accordingly, lower characteristic values ​​555 are assigned to the driving conditions "curve" 2, 10 and "transition" 4, 8. The characteristic values ​​555 of the respective driving conditions can be changed dynamically, e.g., depending on the respective classification results and / or sensor measurements, or can be predefined by a technician in a programming step, e.g., of a control unit.

[0093] The respective confidence factors can, for example, lead to a very precise and reliable estimate of a maximum available traction µ max 660 with confidence factor according to formula 630 as in Fig. 6 shown.

[0094] The respective characteristic values ​​555 are calculated, ie added together, in order to obtain a current total value of the key figures of the confidence factor V that is specific to the driving condition. µges according to formula 630: V µges = A · V µA + Δn · V µges + ΔF · V µges + RS · V µRS + RT · V µges to be calculated. By dividing the result of a sum of multiplications of the sub-methods 501, 403, 404, 405, 406, 407 with the characteristic value 555 corresponding to the respective driving state 2, 4, 6, 8, 10, 12, 14, 16 or 18, by the current total value of the confidence factor V µges the maximum available traction can be 660 µ max according to formula 620: μmax≈A⋅μA⋅VμA+Δn⋅μΔn⋅VμΔn+ΔF⋅μΔF⋅VμΔF+RS⋅μRS⋅VμRS+RT⋅μRT⋅VμRTVμges be estimated. If the current total confidence factor V µges below a critical value V µmin , there is currently no reliable estimate of the maximum available traction µ max 660 is possible and a warning 610 can be sent to the driver or corresponding information can be transmitted to control units. The last estimated maximum available traction is still assumed to be valid. If the current total value of the confidence factor V µges 630 above the critical value V µmin or is identical to it, the maximum available adhesion µ max 660 can be validly calculated.

[0095] To further improve the estimation of the maximum available traction µ max 660 is intended, if necessary, disturbance variables according to Fig. 3, included in the estimation of the maximum available traction µmax 660. For this purpose, the disturbance variables 309, 315, 317, 318 and 319 are also to be assigned driving condition-specific characteristic values ​​555 of a further confidence factor, according to the Fig. 5 described system.

[0096] By offsetting the respective Fig. By comparing the characteristic values ​​555 of the disturbance variables 309, 315, 317, 318, and 319 shown in Figure 7 with the determined values ​​of the disturbance variables 309, 315, 317, 318, and 319, a further total value of a further confidence factor V Sges If the total value of the additional confidence factor V Sges below a previously provided critical value V Smin, a reliable estimate of the friction coefficient or adhesion is not possible at this time or for this driving condition due to a lack of information on disturbance variables, and the last estimated friction coefficient or maximum available adhesion is still assumed to be valid. In this case, too, corresponding information can be forwarded to the driver or control units, indicating that a reliable estimate of the current maximum available adhesion is currently not possible.

Claims

[1] Method for determining a coefficient of friction between at least one tire of a vehicle and a road surface, wherein the coefficient of friction is formed as a function of at least one respective driving state of the vehicle to be classified in advance and respective ambient conditions to be classified in advance, wherein the coefficient of friction is estimated by at least one estimation method, wherein the at least one estimation method is assigned at least one characteristic value of a confidence factor and the respective ambient conditions of the vehicle are determined using at least one sensor from the following list of sensors: rain sensor, air humidity sensor, outside temperature sensor, tire temperature sensor, tire vibration sensor, wheel carrier vibration sensor, sound or structure-borne sound sensors, vehicle environment sensors, front-rear camera, driving dynamics sensors, characterized bythat, using the at least one sensor, an energy which is converted into heat in a respective tire of the vehicle is calculated and the calculated energy is used to estimate a height of a possibly present water film on the roadway as a function of the driving speed. [2] Method according to claim 1, wherein the respective driving state of the vehicle is classified by means of a longitudinal, transverse and vertical dynamic state of the vehicle. [3] Method according to one of the preceding claims, in which the at least one estimation method is selected as a function of a respective classified driving condition. [4] Method according to one of the preceding claims, in which the at least one estimation method is selected as a function of respective classified environmental conditions. [5] Method according to one of the preceding claims, in which the coefficient of friction is calculated as a function of a respective characteristic value of a confidence factor of weighted disturbance variables. [6] Method according to claim 5, in which the disturbance variables are selected from the following list of disturbance variables: longitudinal inclination, transverse inclination, crosswind, ruts, different adhesion in the left and right lanes, control intervention of an anti-lock braking system, control intervention of a vehicle dynamics control system. [7] Method according to one of the preceding claims, in which the at least one estimation method is weighted with a respective characteristic value of a confidence factor and a value which results in an estimated maximum adhesion between the tyre and the road surface is calculated from all weighted estimation values ​​of respective estimation methods. [8] Method according to claim 7, in which respective confidence factors of respective estimation methods are offset and the value calculated thereby is compared with a minimum value to be provided beforehand and if the calculated value falls below the minimum value, a message is generated stating that a reliable estimate of the maximum available traction is currently not possible. [9] Method according to claim 8, in which the further confidence factor is compared with the value to be provided in advance and if the confidence factor falls below the value to be provided, the last estimated maximum available traction is assumed to continue to be valid. [10] Method according to one of the preceding claims, in which respective characteristic values ​​of the at least one confidence factor are defined by a fixed value. [11] Method according to one of the preceding claims, in which respective characteristic values ​​of the at least one confidence factor are formed as a function of a respective driving condition. [12] Method according to one of the preceding claims, wherein the at least one estimation method is selected from the following list of estimation methods: a) Calculation of a global adhesion between at least one tyre and the road surface by means of a measured longitudinal and lateral acceleration, b) Calculation of respective wheel loads based on the currently occurring longitudinal and lateral acceleration as well as knowledge of the loading condition, chassis and suspension system, c) Estimation of respective longitudinal and lateral forces by knowledge of the use of a calculation according to a) and b) as well as knowledge of a drive and control system of the respective vehicle, d) Determining a tyre longitudinal stiffness or an equivalent value by forming a quotient of tyre longitudinal force and tyre longitudinal slip, e) Determining a tyre lateral stiffness or an equivalent value by forming a quotient of tyre lateral force and tyre lateral slip, f) Determining an overall tire stiffness or an equivalent value by forming a quotient of tire force and tire slip by combining claims d1 and d2 or by means of an overall vectorial approach, g) Determining the maximum available adhesion between at least one tyre and the road surface by a ratio between the current longitudinal, transverse or overall tyre stiffness and the specified longitudinal, transverse or overall tyre stiffness on a grippy road surface, h) Calculating a restoring moment of a front axle of the respective vehicle by converting an EPS tie rod force using knowledge of axle and steering kinematics, i) Calculating a lateral force on the front axle from a respective vehicle movement, j) Calculating a caster distance from a quotient of restoring moment and lateral force with a freely rolling wheel, and k) Calculating the maximum available adhesion between the respective tyre and the road surface by comparing the current aligning torque with a specified aligning torque on a dry, non-slip road surface, I) Determining a water film thickness on the road surface by determining different patterns of a longitudinal acceleration signal and a steering wheel angle in relation to a steering torque and a wheel speed, m) Determining the coefficient of friction by using route-related data stored in a vehicle-specific database and including descriptive parameters of the road surface, and o) Determination of the friction coefficient by using data from communication with external vehicle sources or external data servers.

Images