• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The present invention relates to a method and a system for estimating a vagal slope d by utilizing sensor fusion. According to the present invention, it is detected if at least one dynamic course is present. An estimate of said vagal slope d is then performed by means of the sensor fusion, wherein at least one of an input signal and at least one weighting parameter for said sensor fusion is determined based on said detection of whether said at least one dynamic course exists. Fig. 3
    公开号:SE1150291A1
    申请号:SE1150291
    申请日:2011-04-04
    公开日:2012-10-05
    发明作者:Mattias Nilsson；Erik Oehlund
    申请人:Scania Cv Ab；
    IPC主号:
    专利说明:

    10152025the acceleration in the horizontal direction. The accelerometer 101 measures andthus provides a signal corresponding to:as = av + gs fi (u), (eq. 1)where g is the acceleration of gravity.
    This signal can then be used to determine the sloped. For small values of d, sin (d) can be approximated to d, whichthat the road slope d can be determined as:(eq. 2)Thus, here the acceleration of the vehicle is subtracted from that ofthe accelerometer measured the value as for obtaining onlythe gravitational component of the measured acceleration.
    This approach to determine the slope of the road dworks well on roads where the road slope d and the hook forthe road is small. For essentially flat and straight roads, tosuch as highways, this approach provides a relativegood estimation of the road slope d. However, this is still forthe determination of the road slope d is far from optimal for roadsand road sections which are not substantially flat and straight.
    When a vehicle is traveling on a road that is not only smallslopes d and hooks, for example on some country roadsor on smaller roads, the accelerometer 101 will, in additionto measure the acceleration that is important for the determination ofthe road slope d, also to measure other accelerations, whichamong other things due to the curvature of the road. These othersaccelerations, which will then also be included in the signalas provided by the accelerometer 101, will thenadversely affect the reliability of the estimate ofthe road slope d.lOl5202530Brief description of the inventionIt is an object of the present invention thatprovide an efficient and reliable procedure forestimation of a road slope g. This purpose is achieved through the abovesaid method for estimating a road slope d according tocharacterizing part of claim 1. The object is also achieved byabove mentioned computer program according to the characterizing part ofThis object is also achieved by the above-mentioned systemfor estimating a road slope d according to the characteristic partof claim 24.
    According to the present invention, different dynamics are identifiedprocesses which affect the vehicle and its possibilitiescorrect estimation of the road slope g. A sensor fusion is usedaccording to the invention for folding and / or choosing differentmethods for determining the slope of the road d, where these methodsincludes the use of an accelerometer and apower equation. Based on the detection of the presence of dynamiccourse, the sensor fusion is adapted according to the invention so thatthe advantages of the accelerometer method respectivelythe force equation method is used at the same time as the disadvantagesrespective method is avoided.
    If, for example, a dynamic process is detected, it is adaptedthe sensor fusion so that its sensitivity and input signals are optimizedfor the specific detected course. Through the adaptation ofthe sensor fusion, which may be a Kalman filter, mayone when estimating the road slope g then such dynamicprogress is avoided to take into accountacceleration components which depend on the dynamicthe course and not of the road slope itself g. In this way one canefficient and reliable estimation of road slope d alwaysobtained by utilizing the invention.l0l5202530Through the situational adaptation of the sensor fusion canthe sensitivity to the sensor fusion normally increases, that issay when there is no dynamic process, while the sensitivityfor the sensor fusion can be reduced when a dynamic process is in progress.
    This results in a faster estimation of the road slope d andchanges in the curvature of the road, which is very beneficialfor example when off-road driving.
    According to one aspect of the present invention there is utilizedthe estimation of the road slope d when changing gears in a system forautomatic shift selection. It is crucial for an automaticgear selection system to have access to a current value forthe road slope d to be able to select the correct gear at a specifictime. The rapid estimation of the slope of the road d, whichobtained by the present invention, makes a fasterestimation of driving resistance can be done, which is of great importance atoptimal choice of gear.
    The present invention can thus handle dynamic processesby adjusting the sensor fusion based on them so thatthe slope d can also be estimated based on the dynamicthe processes. This has great advantages over if theydynamic processes would not have been taken into account. In socases, these dynamic processes had been filtered out, which hadled to slower updating or possible freezing ofthe estimate. This in turn had led to a delayestimation of the slope of the road d and the change of curvature of the road,and further to the wrong gear being selected by the automaticthe gear selection system.
    Brief list of figuresThe invention will be further elucidated below with reference tothe accompanying drawings, in which like reference numeralsused for equal parts, and in which:10152025Figure 1 schematically shows a vehicle in relation to oneroad slope d,Figure 2 schematically shows a vehicle seen from above,Figure 3 shows a flow chart of a method according tothe invention, andFigure 4 shows a control unit.
    Description of preferred embodimentsAs mentioned above, an accelerometer can be used toprovide a signal, which can be used in estimatingthe road slope d. An estimate of the wave slope d based onMeasurements made by the accelerometer can be made relatively quicklyperform. In addition, provides an accelerometer-based estimatereliable values for roads with a slight slope d andhooking.
    However, the position of the accelerometer in the vehicle causes a problem,because the accelerometer is not normally located invehicle center of oscillation. This is illustrated schematically inFigure 2, where a vehicle 200 with a motor 201, a driveline 202and wheels 203, 204, 205, 206 are illustrated. The vehicle has onecenter of rotation 208, around which the vehicle moves as itturns, the location of which depends, inter alia, on the length of the vehicle,and positioning and distance between the wheels 203, 204, 205,206. However, the accelerometer 207 is usually located inconnection to or near the engine 201. Accelerometer207 is thus usually not positioned so that it coincideswith vibration center 208 for the vehicle 200. This makes thataccelerations arising from oscillations will also beincluded in the signal as provided by the accelerometer 207if the road turns relatively sharply.l0l52025The slope d can also be determined based on apower equation. Such a force equation can look likefollowing:Zf = ma,where f is the force, m is the mass of the vehicle and a is one(eq. 3)acceleration.
    Furthermore, the left link in the force equation can also be expressed as:(Eq. 4)Zf = fd -f, -fa -mgSinwO,where fg is the driving force, fg is the rolling force due torolling resistance, and f¿ is the air force due toair resistance.
    Based on force equations 3 and 4, the road slope ddetermined. To determine the slope of the road d based on thesepower equations provide reliable estimates forthe road slope d for essentially all types of roads, both foressentially flat and straight roads and for roads with largerroad slopes and bends. Models for theseforce equations, from which the road slope d can be triggered, are usedoften when these force equations are used to determinethe slope of the road d. However, these models provide some uncertainty forthe estimated value of the road slope d, which is a disadvantagewith estimation of road slope d based on force equations.
    Thus, there are at least two methods for determiningthe road slope d, one of which is based onacceleration measurements made by an accelerometer 207 and ais based on at least one force equation.
    Accelerometer estimation is fast and reliablel0l5202530relatively straight scales. The power equation estimate is sloweran accelerometer estimate and can not be used thenthe driveline is broken, that is to say the engine torque does nottransmitted to the wheels 203, 204, 205, 206, for example atactivation of a switching function, or if one or morebrakes are on, but years reliable for all types ofvagar.
    The inventors of the present invention have identified theseadvantages and disadvantages of the respective estimation procedure andamnar combine the estimation procedures so that respectivelyprocedure is utilized in a in some sense optimal way.
    The present invention thus combines the advantages ofthe accelerometer estimate with the benefits ofthe power equation estimate while avoiding itthe disadvantages of accelerometer estimation andthe force equation estimate.
    To do this, the present invention utilizes asensor fusion, which can venture together at least twosensor values / inputs provided by one or moresensors / methods. The sensor fusion has at least one input signaland at least one weighting parameter. If this at least oneinput signal and / or at least one weighting parameter is selectedaccording to the invention, which will be described in more detailbelow, the sensor fusion can be used to combinethe benefits of accelerometer estimation andthe force equation estimate.
    According to the present invention, if at least one is detecteddynamic process exists. A dynamic process can haveinclude, for example, at least one sharp turn, a sharp oneacceleration change, or a sharpdeceleration change, and can be detected based on tolO15202530for example a deceleration, a speed, a curve radius, or aselected gear, which is described in more detail below. Dependentthe result of this detection is then performed after the sensor fusion,by at least one of an input signal for the sensor functionand / or at least one weighting parameter ofthe sensor function is determined based on whether a dynamic processpresent or not.
    The at least one input signal can here be selected to be based onexample of the accelerometer if nothing or a certain dynamicprogress is in progress, or is chosen to be based on the force equation ifa certain dynamic process is taking place. In the same way, they can oneor several weighting parameters are selected to different valuesdepending on whether or not a certain dynamic process is taking place.
    Thus, according to the invention, the sensor fusion is adjusted based onwhether a dynamic process exists or not, so that it leadscurrently the best procedure, or the currently bestcombination of procedures, is used in estimatingthe slope of the road d. This makes a reliable value ofthe slope d can always be obtained. In addition, this is obtainedreliable value for road slope d always with minimumpossible delay, which is important in several applications,for example, when gear selection is based on the road slope d.
    According to an embodiment of the invention, the sensor fusion is performedusing a Kalman filter, for which the estimate ofthe slope d is the only condition. Here it is abovesaid at least one input signal, which is determined based on ifthe dynamic course is present or not, at least oneinput signal to the Kalman filter. The above-mentioned at least onethe weighting parameter here constitutes an at least one covariance matrixfor a model noise of this Kalman filter.101520A Kalman filter can be mathematically described as:> ^ c (z + 1 | z) = A, x (r I r)I f) = X0 I f-1) + L (f) (y (f) -C, ff (fIf-1))m) = P (f | f-1) cf "[c, 1 ° (f | f-1) cf +12, I * eq. s)P (r + 1Iz) = A, P (r I z) A, T + Q,P (z | f) = P (f | f-1) -P (f | f-1) c, T [c, P (f | f-1) cf + R, I1c, 1> (f | f -1), dar:- X corresponds to the state vector, which in this case isvaglutningen d;- _y corresponds to the input signal vector for the filter;- A corresponds to the model of the system, which in this case isdefined as A = 1 (described in more detail below);- L corresponds to the gain of the filter;- C corresponds to the input signal model of the filter;- Q corresponds to the covariance matrix for the model noise;- P corresponds to the covariance matrix for the estimation error; and- R corresponds to the covariance matrix for the food noise.
    According to one embodiment of the present invention utilizesThe Kalman filter predicts that the vagal slope d in the nextstate will be as large as the vagal slope d in itcurrent state, that is to say A = l.
    As shown in Equation 5, the gain L depends on the filterof P, C, and R, where P in turn depends on the covariance matrix formodel noise Q. As mentioned above, according to aembodiment covariance matrix for the model noise Q enweighting parameters for the sensor fusion. So this is it101520253010covariance matrix for the model noise Q adjusted in the Kalmanthe filter depending on whether a dynamic process is in progress or not.
    The covariance matrix for the model noise Q is weighted by the filterpredicted the value of the next condition, which is predictedto be the same value as in the current state, withthe input signal, so that the input signal is larger or smallerweight / influence depends on the value of the covariance matrix formodel noise Q. A small value of the covariance matrix forthe model noise Q means that only a small change is acceptedof the filter, so the filter can be made slower if necessary.
    The input signal model C to the filter is selected to be based onthe accelerometer or on the force equation based on about onedynamic course exists or not.
    An important part of the present invention lies in the fact thatidentify different dynamic processes, for which the sensor fusionshould be adjusted to provide a reliable value quicklyfor the slope of the road d.
    According to an embodiment of the present invention, aor several sharp turns, which are performed by the vehicle, onesuch a dynamic course. This at least one turn is considered to besharp if it has a radius which is smaller than a predetermined onevalue. For example, such a sharp turn may have a radiuswhich is less than 25 meters. A turn can also be definedas sharp if it lasts longer than a predetermined time and hasa predetermined radius. For example, a turn can be detectedas sharp if it lasts longer than 2 seconds and has onepredetermined radius.
    According to an embodiment of the present invention, it is selectedat least one input signal to the sensor fusion, which according toabove can be the input signal vector y in the Kalman filter, toto be based on the force equation (equations 3 and 4) ifl0l5202530llat least one sharp turn has been detected. So choosethe method according to this embodiment to base the estimateof the slope of the road d on the force equation and not onthe accelerometer when a sharp turn is in progress. This causes problemsrelated to the accelerometer not being placed inthe center of rotation of the vehicle is avoided. This is not taken into accountirrelevant accelerations measured by the accelerometer whenthe slope d is estimated, which gives a more accurate estimate ofthe road slope d.
    According to an embodiment of the present invention, asharp change of an acceleration a dynamic coursefor which the sensor fusion is to be adapted. Such a powerfulchange in acceleration can occur, for example, in aacceleration from a standstill or at an accelerationduring shifting. Such a sharp acceleration change canaffect an interrelationship between a chassis ofthe vehicle and a horizontal plane. Such a powerfulacceleration change can also affect one anotherrelationship between a suspension, such as a wheel suspension,of the vehicle and a horizontal plane. That is, itThe sharp acceleration change can cause at least oneof the chassis and the suspension increases relativelythe horizontal plane. An acceleration change is considered according to aembodiment as strong if it is at least inthe order of magnitude of 1 m / s3.
    The sensor fusion is adjusted in such a way that it at leastan input signal to the sensor fusion, which can thus be constitutedof the input vector y to the Kalman filter, is based onthe accelerometer 207, and that it has at least onethe weighting parameter is set to a value, resulting inthat the sensitivity of the sensor fusion is lowered relative to that valuethe weighting parameter then has no dynamic process in progress. With101520253012in other words, the value of the weighting parameter is determined here so thatthe sensor fusion becomes slower than if none is strongacceleration change had been present. For the case thatthe sensor fusion consists of a Kalman filter, whereinthe weighting parameter consists of the covariance matrix formodel noise Q of the Kalman filter, the elements in it are setcovariance matrix for the model noise Q to a low value, whichresults in a slower filter with a reduced sensitivity.
    In this way an exact value of the road slope d is quickly obtained,since the accelerometer can be used in the estimation.
    According to another embodiment of the present inventiona sharp deceleration change a dynamic course forwhich sensor fusion should be adjusted. Such a drastic changeof the deceleration can occur, for example, during a decelerationwhich occurs during shifting, during a deceleration whichresults from a partial deceleration, for example while driving,or when fully braking to a standstill.
    According to one embodiment, a deceleration change is considered asstrong if it is at least in the order of 1 m / s3.
    Then a sharp deceleration change is detected as onedynamic course is adapted according to this embodimentthe sensor fusion so that it has at least one more input signalthe sensor fusion, which may correspond to the input signal vector y ofThe Kalman filter, based on the accelerometer 207. The at leasta weighting parameter, which may bethe covariance matrix for the model noise Q of the Kalman filter, is setalso to a value, resulting in sensitivity tothe sensor fusion is lowered relative to the value weighting parameterthen no dynamic process takes place, whereby the sensor fusionbecomes slower than if no strong acceleration / decelerationchange had taken place. So the sensor fusion is adapted herein essentially the same way as for the dynamicl0l5202530l3the course of sharp acceleration change. Sincethe accelerometer can be used in the estimation obtained quicklyan exact care on the vagal slope d.
    In the same way as for the powerfulthe acceleration change can also be the powerful onethe retardation change affect a mutual relationshipbetween the chassis or suspension of the vehicle and ahorizontal plane. The sharp deceleration change may havemake at least one of the chassis and suspension nigerrelative to the horizontal plane.
    According to another embodiment of the present inventiona braking during a turn a dynamic course. According to aanother embodiment also constitutes both a start-up and ashutting down an engine a dynamic process.
    When such a start-up, shutdown or braking duringswing is detected, the sensor fusion is adjusted so that it at leastan input signal to the sensor fusion, which may bethe input signal vector y to the Kalman filter, is determined to correspond to oneprevious estimate of the wave slope d. In other words, frozenThe Kalman filter has, which can also be seen as the Kalman filternot updated. In Equation 5, which describes the Kalmanthe filter, so the elements in the matrix were set forthe input signal model C to the value 0 (zero). Hair is thus determinedthe input signal, by giving the elements of the input signal model matrixthe value 0 (zero), so that the input signal corresponds to the previous onethe estimate. This previous estimate can easily be derived fromthe memory in which it was stored.
    It has been described above how the sensor fusion should be adapted to differentdynamic processes when such have been detected. About the detectionon the other hand, results in no dynamic processadapted according to one embodiment the sensor fusion so that101520253014the input signal is a signal based on the accelerometer 207.
    As mentioned above, estimates of the road slope d are based onan input signal from the accelerometer is relatively fast and isreliable values for roads with a large radius of curvature. Furtherit is set to at least one weighting parameter to a valueresulting in the sensitivity of the sensor fusion being adjustedto the accelerometer noise level. The size of thisweighting parameters are thus determined based on whichaccelerometer used, then different types / makes ofaccelerometers have different noise levels.
    Thus, according to this embodiment, it is mainly usedthe accelerometer 207 to determine the road slope d then nothingdynamic process is in progress, as the accelerometer is the bestsuitable for normal driving situations.
    The embodiments described above, which indicate howthe sensor fusion is to be performed, depends on a detection of if onedynamic course exists or not. Based on about onedynamic process is in progress or not selected which one or moreinputs and / or which one or more weighting parametersto be used in the sensor fusion. This can be seen as being differentmode of sensor fusion is selected based on this detection, whereeach of the modern has one or more predeterminedinput signals and / or one or more predeterminedweighting parameters. Since the sensor fusion consists of a Kalmanfilters therefore have each of modern one specialinput vector y and / or one or more specialcovariance matrices Q for the model noise.
    The detection of whether a dynamic process exists can be basedon various parameters. According to an embodiment of the inventionthe detection can be based on at least one signal related tobraking. As described above, they include101520253015dynamic processes braking. Therefore, one can arbitraryappropriate signal in the system, which indicates a brakingperformed is used in the identification of a dynamic process.
    Brake signals are usually available in vehiclescontrol system, why the use of a brake signal atthe detection of a dynamic process can easilyimplemented.
    According to an embodiment of the invention, the detection is based ona dynamic course at least on a signal related toa speed of the vehicle. This signal can, among other thingsused to determine acceleration change and / ordeceleration change for the vehicle. Speed signals are availableusually available in vehicle control systems, that isadvantageous when the invention is implemented.
    According to an embodiment of the invention, the detection is based ona dynamic course at least on a signal related toa radius of curvature for a turn the vehicle performs. As mentioned aboveincludes the dynamic processes turns, so a suitableany signal for the curve radius is useful in the detection.
    A curve radius can, among other things, be calculated by analyzingmutual differences in wheel speeds for outer and inner wheelswhen the vehicle turns.
    According to an embodiment of the invention, the detection is based ona dynamic course at least on a signal related togear selection. Because several of the above-mentioned dynamic processesincludes switching, the identification of these can be dynamicprocesses are based on information about the selected gear and whenchangeover takes place. When the present invention can, inter alia,as will be described below, utilized in connection withshifting in an automatic shifting system comes signalsrelated to gear selection are typically available tol0l52025l6base the identification of the dynamic processes on. Generallythe automatic gear selection system has very good control overhow and when switching will take place, which can be used bythis embodiment.
    According to an embodiment of the present invention,the detection of a dynamic process is based on an arbitrary oneappropriate combination of the above parameters, that issay on any suitable combination of a brake signal,a speed signal, a curve radius signal and agear selection signal.
    One aspect of the present invention relates to agear selection procedure in a motor vehicle. According to thisprocedure, the vagal slope d is estimated in the manner describedabove, i.e. according to any of those described aboveembodiments of the invention. Then a gear is selectedbased on the estimated vagal slope d. This procedure isvery useful for example in automatic systemsgear selection, as it is central to such a system to takeconsideration of the slope inclination d when determining driving resistance andthus which gear is to be selected at a specificopportunity.
    One skilled in the art will also recognize that the estimation ofthe gradient d can also be used for applications other thanfor automatic gear selection control. For example, canthe gradient d is used in cruise control, in brake systems, and inother driver assistance systems, such as systems which assist the driver todrive more fuel efficiently.
    Figure 3 shows a flow chart of a gear selection procedureaccording to the invention.l0l5202530l7In a first step 301 of the method, a detection of om is performedat least one dynamic course exists, i.e. onedetection of if, for example, a sharp turn, a sharpacceleration change / deceleration change, brakingduring a turn, or engine start-up / shutdown is in progress.
    In a second step 302 of the process, the slope is estimated d,where the estimation is performed by means of a sensor fusion where itat least one input signal and / or at least oneweighting parameters for the sensor fusion are determined based on whethera dynamic process is in progress or not.
    The first step 301 and the second step 302 of the method forgear selection thus together constitute the procedure for estimatingthe road slope d according to the present invention.
    In a third step 303 of the method, a gear is selected based onthe estimation of the road slope d.
    Those skilled in the art will appreciate that the procedure for estimating the slope of the roadd and the gear selection method of the present inventionin addition, can be implemented in a computer program, which when itexecuted in a computer causes the computer to perform the method.
    The computer program is usually a computer program product 403(in Figure 4) stored on a digital storage medium, therethe computer program is included in the computer program productcomputer readable media. Said computer readable medium consists of onesuitable memory, such as: ROM (Read-Only Memory), PROM(Programmable Read-Only Memory), EPROM (Erasable PROM), Flashmemory, EEPROM (Electrically Erasable PROM), a hard disk drive,etc.
    Figure 4 schematically shows a control unit 400. The control unit 400comprises a calculation unit 401, which may be constituted byessentially any suitable type of processor or microcomputer,1015202518for example a digital signal processing circuitProcessor, DSP), or a circuit with a predetermined specificfunction (Application Specific Integrated Circuit, ASIC).
    The calculation unit 401 is connected to one, in the control unit 400arranged, memory unit 402, which providesthe calculation unit 401 e.g. the stored program code and / orthe stored data computing unit 401 needs to be able toperform calculations. The calculation unit 401 is also arranged tostore partial or final results of calculations in the memory device402.
    Furthermore, the control unit 400 is provided with devices 411, 412,413, 414 for receiving and sending input and output, respectivelyoutput signals. These input and output signals can containwaveforms, pulses, or other attributes, which ofthe devices 411, 413 for receiving input signals candetected as information and can be converted into signals such ascan be processed by the computing unit 411. These signalsthen provided by the computing unit 401. The devices 412,414 for transmitting output signals are arranged to convertsignals obtained from the computing unit 401 for creatingoutput signals by e.g. modulate the signals, which cantransferred to other parts of the estimation systemthe road slope d or the gear selection system.
    Each of the connections to the receiving devicesrespective transmission of input and output signals can be constitutedof one or more of a cable; a data bus, such as a CAN bus(Controller Area Network bus), and MOST bus (Media OrientatedSystems Transport bus), or any other bus configuration;or by a wireless connection.101520253019One skilled in the art will appreciate that the above-mentioned computer may be comprised ofthe calculation unit 401 and that the above-mentioned memory canconsists of the memory unit 402.
    One aspect of the present invention relates to asystem for estimating a road slope d by utilizinga sensor fusion. The system here comprises a detection means,which is arranged to detect at least one dynamicprogress is present. The system also includes aestimating means, which is arranged to tax saidroad slope d. When estimating, the estimating body determinesthe adaptation of the sensor fusion according to the procedure describedabove, that is, the tax body determines at least oneof an input signal and / or at least one weighting parameter forsensor fusion based on the detection of if something dynamicprogress is ongoing.
    One aspect of the present invention relates to agear selection system in a motor vehicle. The system includes herea system for estimating a road slope d as above anda gear selection means, for example an automatic gearbox,which is arranged to select gear based on the estimate ofthe road slope d.
    Those skilled in the art will also appreciate that the above system may be modified accordinglythe various embodiments of the method according to the invention.
    In addition, the invention relates to a motor vehicle 100, for examplea car, a truck or a bus, includingat least one system for estimating the slope of the road d ora gear selection system.
    The present invention is not limited to the abovedescribed embodiments of the invention without reference to andincludes all embodiments within the appended independent20the scope of protection of the requirements.
    权利要求:
    Claims (25)
    [1]
    Method for estimating a road slope d by using a sensor fusion, characterized by - detecting whether at least one dynamic course is present; and - estimating said path slope d, wherein at least one input signal and / or at least one weighting parameter for said sensor fusion is determined based on said detection of whether said at least one dynamic course is present.
    [2]
    The method of claim 1, wherein said sensor fusion performs at least one weighing of at least two input signals.
    [3]
    The method of claim 2, wherein said sensor fusion is performed by a Kalman filter.
    [4]
    A method according to claim 3, wherein said at least two input signals constitute input signals to said Kalman filter and said at least one weighting parameter is at least one covariance matrix for a model noise Q of said Kalman filter.
    [5]
    A method according to any one of claims 3-4, wherein said Kalman filter utilizes a prediction which indicates that a road slope in a next state is equal to a road slope in a current state.
    [6]
    A method according to any one of claims 1-5, wherein said at least one dynamic course comprises at least one sharp turn. 10 15 20 25 22
    [7]
    A method according to claim 6, wherein said sharp turn is a turn having a radius which is less than a predetermined value.
    [8]
    A method according to any one of claims 6-7, wherein said at least one input signal is determined to be an input signal based on a force equation if said detection results in said sharp turn being present.
    [9]
    A method according to any one of claims 1-5, wherein said at least one dynamic course comprises a sharp acceleration change.
    [10]
    A method according to claim 9, wherein said dynamic sequence occurs in a situation in the group: - an acceleration from a standstill; and - an acceleration during shifting.
    [11]
    A method according to any one of claims 9-10, wherein, if said detection results in a detection of said sharp acceleration change, said at least one input signal is determined to be an input signal based on an accelerometer (207) and said at least one weighting parameter is determined to be a value so that a sensitivity in the sensor fusion for said input signal is lower than if there is no dynamic course.
    [12]
    A method according to any one of claims 1-5, wherein said dynamic sequence comprises a sharp deceleration change.
    [13]
    A method according to claim 12, wherein said dynamic sequence occurs in a situation in the group: - a deceleration during shifting; - a deceleration; and - a deceleration to a standstill. 10 15 20 25 30 23
    [14]
    A method according to any one of claims 12-13, wherein, if said detection results in a detection of said sharp deceleration change, said at least one input signal is determined to be an input signal based on an accelerometer (207) and said at least one weighting parameter is determined to be a value so that a sensitivity in the sensor fusion for said input signal is lower than if there is no dynamic course.
    [15]
    A method according to any one of claims 9-14, wherein said at least one dynamic course affects a position relative to a horizontal plane of at least one of a chassis and a suspension for a motor vehicle (100).
    [16]
    A method according to any one of claims 1-5, wherein said dynamic course comprises a braking during a turn.
    [17]
    A method according to any one of claims 1-5, wherein said dynamic sequence comprises one of a start-up and a shut-down of an engine.
    [18]
    A method according to any one of claims 16-17, wherein said at least one input signal is determined to correspond to a previous estimate of said road slope if said dynamic course is detected.
    [19]
    A method according to any one of claims 1-8, wherein, if said detection results in no dynamic sequence, said at least one input signal is determined to be an input signal based on an accelerometer (207) and said at least one weighting parameter is determined to a value so that a sensitivity in the sensor fusion of said input signal is adapted to a noise level of said accelerometer (207).
    [20]
    A method according to any one of claims 1-16, wherein said detection is based on at least one of the signals in the group: - a brake signal; - a signal related to a speed; - a signal related to a curve radius; and - a signal related to gear selection.
    [21]
    Method for gear selection in a motor vehicle, characterized by - estimating a road slope d by means of the method according to any one of claims 1-20; and - selection of gear based on said estimate of said road slope d.
    [22]
    A computer program comprising program code, which when said program code is executed in a computer causes said computer to perform the method according to any of claims 1-21.
    [23]
    A computer program product comprising a computer readable medium and a computer program according to claim 22, wherein said computer program is included in said computer readable medium.
    [24]
    24. A system for estimating a road slope d by means of the use of a sensor fusion, characterized by - a detection means, which is arranged to detect if at least one dynamic course is present; and - an estimating means, which is arranged to estimate said path slope d, said estimating means being arranged to determine at least one input signal and / or at least one weighting parameter for said sensor fusion based on the detection of whether said at least one dynamic course is present. 25
    [25]
    System for gear selection in a motor vehicle, characterized by - a system for estimating a road slope d according to claim 24; and - a gear selection means, which is arranged to select gear based on said estimate of said road slope d.
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    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
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