Improved estimators of vehicle slope and mass

Abstract

Recursive estimators rely on modeling the underlying physical system to determine a vehicle's mass from engine torque, longitudinal acceleration measurements, and wheel angular velocity measurements. To improve the estimates, the angular acceleration inherently generated by a polynomial smoother preprocessing the measurements is reused in the recursive estimation of the road slope and the vehicle's longitudinal speed. Furthermore, to obtain observable models without unnecessary simplifications, this recursive slope estimator is coupled with a recursive tire dynamic radius estimator so that their estimated longitudinal speed and estimated dynamic radius are used in the other estimator's estimation.Estimating the dynamic radius allows the use of more complex models; the accuracy of the vehicle mass estimation is improved by the accuracy of the road's actual slope estimation. See Figure 2 for the abbreviation.

Description

Title of the invention: Improved vehicle slope and mass estimators. Technical area of ​​disclosure.

[0001] The present invention relates to the general field of recursive estimators of wheeled vehicle parameters or states. Prior art

[0002] The "states" refer to vehicle data that vary over time, such as road slope or speed, while the "parameters" refer to vehicle data that do not vary over time, such as vehicle surface.

[0003] The term "recursive" indicates that the estimations are performed again as soon as new data are acquired and available. Recursion allows for "real-time" estimation, that is, continuous estimation at each new time of data acquisition.

[0004] Certain states and parameters can be measured directly using sensors. This is the case, for example, for the vehicle's engine torque, the vehicle's longitudinal acceleration, the engine's angular velocity, and the wheels' angular velocity.

[0005] Other states and parameters cannot be measured and are therefore estimated recursively from the measurements obtained. This is the case for the mass of the vehicle, the rolling resistance of the vehicle, the actual longitudinal speed of the vehicle, the effective rolling radius of a tire, the center of gravity of the vehicle, the aerodynamic coefficient of the vehicle, the grip of the tires, the wear of the tires, the load on the tires, the stiffness of the tires (Kx, Dz), etc.

[0006] These estimates are crucial for many applications.

[0007] For example, estimating the vehicle's mass makes it possible to determine the vertical load on each tire, which in turn allows for a precise estimation of tire wear, tire grip, and tire rolling resistance during usage phases, enabling appropriate measures to be taken. Typically, knowledge (estimation) of the vehicle's overall load allows for better prediction of the battery range of electric vehicles (EVs), whose load can vary considerably over time. Improved routing of EVs to charging stations can then be proposed, optimizing battery use and wear.

[0008] To do this, recursive estimators or “virtual sensors” (the data are not directly measured by the sensors, but obtained indirectly, estimated at (starting from existing data without a dedicated sensor) are implemented which rely on a modeling of the underlying physical system.

[0009] To improve the quality of the estimated data, it is important to use accurate and representative physical models of the physical system for the estimation process. However, overly complex models are not observable; that is, they contain more unknown variables or parameters than known (i.e., measured) variables or parameters. Conversely, overly simplified models compromise the quality of the estimated data. "Quality" can refer to data reliability, data accuracy, data adaptability to different types of vehicles, and / or data robustness to different driving scenarios.

[0010] Today in particular, vehicle mass estimators rely on simplifying assumptions of a linear system (model) of the longitudinal dynamics of the vehicle (Newton's second law), to obtain observable models from measurements of the longitudinal acceleration of the vehicle, the angular velocity of the wheels and the engine torque of the vehicle.

[0011] A first simplification consists of neglecting the effects of the headwind, as well as the additional inertial masses due to the rotating components of the vehicle's powertrain.

[0012] A second simplification lies in assimilating the dynamic rolling radius to the nominal radius of the tire, thus neglecting variations in the tire radius over time. Indeed, the dynamic rolling radius is rather difficult to estimate in practice, or at the very least requires a dedicated estimator necessitating additional measurements.

[0013] A third simplification lies in assimilating the road slope as perceived by the accelerometer (measuring longitudinal acceleration) to the actual road slope, thus neglecting any errors (e.g., those arising from accelerator misalignment, the load distribution acting on the vehicle's suspension, and the vehicle's suspension dynamics). The road slope can be estimated using the same estimator as the vehicle mass or a dedicated estimator. However, existing estimators of the actual road slope remain, for the time being, of limited quality.

[0014] These simplifications nevertheless impair the final quality of the estimates, particularly of the vehicle mass.

[0015] There is therefore a need to remedy the aforementioned drawbacks, in particular with a view to improving the recursive estimation of the mass of a vehicle, with the same input measurements. Statement of Disclosure

[0016] Existing recursive estimators of real slope are generally implemented using linear Kalman filters, which receive measurements of the vehicle's longitudinal acceleration and the angular velocity of the wheels from polynomial smoothers. Polynomial smoothing is typically a polynomial regression of a measurement signal to eliminate, locally at each measurement, noise from the sensor used.

[0017] To be effective, the smoothing uses polynomials of multiple order (two or more). Taking advantage of the intrinsic calculations of polynomial smoothers—including that of the (smoothed and accurate) derivative of the input measurements—the inventors considered improving existing real slope estimators by integrating, into the conventional inputs, the angular acceleration of the wheels obtained from the polynomial smoother by differentiating the measured angular velocity. The quality of the estimation is thus improved, without requiring any additional input measurements (and therefore no additional sensor).

[0018] For this purpose, the disclosure relates firstly to a device for monitoring a vehicle operating on a road, the device comprising: a communication interface for obtaining, from one or more sensors, measurements of the vehicle's longitudinal acceleration (ax>1MU) and the angular velocity of the vehicle's wheels (œx), one or more processors implementing: - a polynomial smoother of measurements, generating an angular acceleration of the vehicle's wheels by differentiating the angular velocity of the wheels, and - a recursive slope estimator estimating a road slope from measurements of longitudinal acceleration and angular wheel velocity, and the generated angular wheel acceleration.

[0019] The disclosure also relates to a system comprising a supervisory device as defined above and a vehicle control unit for controlling an on-board function based on estimates from the supervisory device.

[0020] As mentioned previously, the road gradient thus estimated can be used in estimating the vehicle's mass to try to improve vehicle safety and control. For example, it is then possible to accurately estimate the vertical load experienced by each tire, tire wear, tire grip, tire rolling resistance, predict the battery range of electric vehicles (EVs), and thus take appropriate measures (turn on an overload indicator, indicate tire wear status, adjust a vehicle electric range indicator or route the vehicle to charging stations, etc.).

[0021] Optional features of embodiments are defined in the appended claims. Some of these features are explained below with reference to a device, while they can be transposed into process features.

[0022] In one embodiment, the polynomial smoother comprises a polynomial Kalman smoother. Alternatively, a simple Savitzky-Golay smoothing filter may be used.

[0023] In one embodiment, the recursive slope estimator estimates the road slope based on a dynamic tire rolling radius estimated by a recursive radius estimator. The inventors have found that classical models are very sensitive to the rolling radius. Therefore, a dynamic estimation of the rolling radius improves the accuracy of the estimated slope.

[0024] In a particular embodiment, the recursive radius estimator estimates the dynamic rolling radius of the tires from the angular velocity measurement of the wheels and a longitudinal vehicle velocity estimated by the recursive slope estimator. In this configuration, the recursive slope estimator and the recursive radius estimator are coupled to perform a dual estimation and thus reduce the number of unknowns in the model. More complex models (without the second simplification above) can therefore be used.

[0025] Furthermore, this dual estimation improves the quality of the estimates because both estimators now rely on a higher-quality estimate of the longitudinal speed and the dynamic rolling radius. Indeed, the dynamic rolling radius is very sensitive to the longitudinal speed, just as the road gradient is very sensitive to the rolling radius.

[0026] Typically, the estimate obtained by one of the estimators in the previous recursion can be used as input for the other estimator in the next recursion. Alternatively, the two estimators can be run in series, with one using the estimate of the other obtained in the previous recursion as input, while the other (downstream in the series) uses the estimate of the first estimator obtained in the current recursion as input. Alternatively, simultaneous estimation is also possible, where the two estimators perform their calculations in parallel, each estimator improving the accuracy of the other estimator in the same recursion, for example, by exchanging intermediate estimates.

[0027] In a particular embodiment, the underlying model of the recursive radius estimator includes: vx(t)=cox(t).rdyn(t). Compared to a direct calculation of rdyn, the use of the recursive radius estimator allows for a noise-robust (sensor noise, smoothing noise) and therefore accurate estimation.

[0028] In a particular embodiment, the recursive radius estimator includes a recursive least squares filter whose parameter vector is r ir, the * = [rdynJ regression vector is a — [ wx ] and the measures are = L.

[0029] In one embodiment, the recursive slope estimator includes a linear Kalman filter estimating the road slope (a), a longitudinal acceleration of the vehicle (ax), and a longitudinal speed of the vehicle (vx). The filter operates on input data consisting of measurements of the longitudinal acceleration and angular velocity of the wheels, the angular acceleration of the wheels generated by the polynomial smoother, and the rolling radius of the vehicle's tires (optionally estimated by the radius estimator).

[0030] In one embodiment, the underlying model of the recursive slope estimator includes the following state evolution system:

[0031] vx(t)= vx(t-1)+ax(t). At,

[0032] ax(t)= ax(tl)

[0033] g.sin(a(t))=g.sin(a(tl))

[0034] and the following measurement system:

[0035] vx(t)=œx(t).rdyn(t),

[0036] ax(t)=nA(t),rdyn(t),

[0037] ax>1MU(t)=ax(t)+g.sin(a(t)),

[0038] where ax is an estimated longitudinal acceleration of the vehicle, vx an estimated longitudinal velocity of the vehicle, g the gravitational constant, At the time step between two estimation recursions, u'x an angular acceleration of the vehicle's wheels, and rdyn a rolling radius of the tires.

[0039] In one embodiment, the monitoring device further includes a recursive vehicle mass estimator estimating a vehicle mass (mv) from the estimated road slope (a) and, where appropriate, the estimated dynamic rolling radius of the tires (rdyn).

[0040] In a particular embodiment, the recursive mass estimator includes a recursive least squares estimator.

[0041] In another particular embodiment, the recursive mass estimator estimates the mass of the vehicle from further a longitudinal acceleration of the vehicle estimated by the recursive slope estimator, a longitudinal speed of the vehicle estimated by the recursive slope estimator and, where applicable, the dynamic rolling radius of the tires estimated by the recursive radius estimator.

[0042] In a particular embodiment, the underlying model of the recursive mass estimator includes: =mv(t)ax(f) 4- mv(t)#sin(a(t)) * dyn VJ + crl i^ft) + c^i^Cü)4) + |p.,jrXfcdvs(t)2

[0043] where Meng(t) is a motor torque, y(t) a transmission ratio, r|tot(t) a mechanical or motor transmission efficiency, ax an estimated longitudinal acceleration of the vehicle, vx an estimated longitudinal speed of the vehicle, g the gravitational constant, Cjo, cri, cr4 rolling resistance coefficients, pair an ambient air density, Af a frontal area of ​​the vehicle and cd an aerodynamic coefficient of the vehicle.

[0044] Equivalent models may be used in which the term m / tXgXCjo+c ri.vx(t)+cr4.vx(t)4) is replaced by any model of the form f(vx(t), a(t)). By way of example, the expression mv(t).g.cr.cos(a(t)) may also be used.

[0045] The disclosure also relates to a method for supervising a vehicle operating on a road, the method comprising the following steps: to obtain, via a communication interface, measurements of longitudinal acceleration of the vehicle and angular velocity of the vehicle's wheels acquired by one or more sensors, smoothing, using a polynomial smoother, the acquired measurements, the smoothing generating an angular acceleration of the vehicle's wheels by differentiating the angular velocity of the wheels, and to estimate, recursively, a road slope from measurements of longitudinal acceleration and angular velocity of the wheels, and the generated angular acceleration of the wheels.

[0046] At least part of the methods according to the invention can be implemented by computer. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment (comprising firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, all of which can be collectively referred to herein as a "circuit," "module," or "system." Furthermore, the present invention can take the form of a computer program product embedded in any non-transient recording medium comprising computer-readable program code for implementing the above method.

[0047] A tangible or non-transient medium may include a storage medium such as a hard disk drive, a magnetic tape device or a semiconductor memory device and the like. Brief description of the drawings

[0048] Other objects, features and advantages of the invention will become apparent from the following description, given solely by way of non-limiting example, and made with reference to the accompanying drawings in which:

[0049] [Fig-1] illustrates a motor vehicle-type physical system, of which the longitudinal behavior defined by Newton's second law (equilibrium of forces) can be reduced to a more or less complex model;

[0050] [Fig.2] illustrates a vehicle control system according to embodiments;

[0051] [Fig.2A] illustrates the use of the angular acceleration of the wheels (Vx(k) generated by a polynomial smoother in the recursive estimation of the slope of the road â(k) by a slope estimator;

[0052] [Fig.2B] illustrates a parallel coupling of a slope estimator with a radius estimator;

[0053] [Fig.2C] illustrates series couplings of a slope estimator and a radius estimator;

[0054] [Fig.3] illustrates, using a flowchart, the steps of a supervisory process implementing the control system, according to embodiments;

[0055] [Fig. 3A] illustrates, using a flowchart, the steps of a process for estimating the slope of a road; and

[0056] [Fig.4] illustrates a computer hardware architecture of a system or device estimation according to implementation methods.

[0057] Detailed description of at least one embodiment

[0058] Certain states or parameters of a vehicle cannot be measured directly by sensors, but through recursive estimators.

[0059] The term “vehicle” means any type of wheeled motorized vehicle regardless of its energy source (thermal, electric or other), such as a light vehicle or car, a utility vehicle, a truck, a military vehicle, but also an aircraft, an amphibious vehicle, etc.

[0060] Recursive estimators rely on a model of the underlying physical system, typically based on Newton's laws, to determine the mass of a vehicle from the engine torque, longitudinal acceleration measurements of the vehicle and angular velocity measurements of the vehicle's wheels.

[0061] To improve the estimates, the angular acceleration intrinsically generated by a polynomial smoother preprocessing the angular velocity measurements is reused in the recursive estimation of the road slope and the longitudinal speed of the vehicle. Furthermore, to obtain observable models without unnecessary simplifications, this recursive slope estimator is coupled with a recursive tire dynamic rolling radius estimator so that their estimated longitudinal speed and estimated dynamic radius are respectively used in the estimations of the other. estimator. The estimation of the dynamic radius allows the use of more complex models; the quality of the estimation of the mass of the vehicle is increased by that of the estimation of the actual slope of the road.

[0062] Figure 1 represents a physical system of the motor vehicle type, whose longitudinal behavior, defined by Newton's second law (equilibrium of forces), can be reduced to a more or less complex model. The forces involved include:

[0063] the inertial force Finertie(t) whose value is (mv(t)+mpt(t)).ax(t), with mv(t) the mass of the vehicle and mpt(t) the equivalent mass of the rotating parts whose value is mpt(t)=Jtot / rdyn(t)2, with Jtot the moment of inertial of the mass of the components of the powertrain and rdyn(t) the dynamic rolling radius of the tires,

[0064] the traction force Ftract(t) whose value is / \ ' °ù Meng(t) is the motor torque, y(t) is the transmission ratio, and qtot(t) is the efficiency of mechanical or drive transmission (typically fixed between 0.95 and 0.99). The force of Traction can be negative in the case of engine braking. In this case, it is a force of braking,

[0065] the aerodynamic force or resistance Faero(t) whose value is - - 'air v ■ where even is the ambient air density, vx(t) is the velocity longitudinal axis of the vehicle, vwind(t) is the longitudinal wind speed, Af is the surface frontal area of ​​the vehicle and cd is the aerodynamic coefficient of the vehicle,

[0066] the rolling resistance Fron(t) whose value is mv(t).g.(cr 0+cri.vx(t)+cr4.vx(t)4), with g the gravitational constant, cr 0, cri, cr4 the rolling resistance coefficients. Other formulations of the form f(vx(t), a(t)) may be used as alternatives, for example mv(t).g.cr.cos(a(t)), and

[0067] the gravitational force Fgrav(t) whose value is mv(t).g.sin(a(t)), with and a(t) the angle of the slope (between -90° and 90°).

[0068] The equilibrium of forces leads to Finertie(t) = F^^t) + Faero(t) + Fron(t) + Fgrav(t), and thus to the following model (1) which however neglects all random disturbances and unaccounted losses: / f.'-. , / tct X zx , zx ■ ( — “s(0 + sm(a(t)) + crlvx(t) + - ^vmd(O)2

[0069] This complete longitudinal model, although offering an accurate representation of the system in Figure 1, is very difficult to use in practice, particularly because it is not observable (the number of unknown values ​​is greater than those known Meng(t), (A Pair, Af. The random nature of the wind, for example, is very hot\ / difficult to measure, and some vehicle parameters, such as rotational inertias, are vehicle-specific and often unknown to the user.

[0070] A typical first simplification of the model consists of neglecting the effects of the headwind v due to an impact estimated to be negligible on the total inertia of the vehicle, as well as the inertial masses of the rotating components of the powertrain Jtot relative to the total mass of the vehicle. Furthermore, since the longitudinal speed of the vehicle is generally unknown, it is usually calculated from the angular velocity of the vehicle's wheels (t): vx(t) = Wx(t) (O •

[0071] This leads to the following model (2): —*---—---=-mv(0axW + + + CrÆfr)4) +

[0072] This model is not observable at first glance, since rdyn, mv, ax, a, vx and the coefficients c are unknown.

[0073] Embodiments of the present disclosure make this model observable, whereas known techniques consider it preferable to simplify the model further.

[0074] For example, variations in tire radius are neglected: the dynamic rolling radius is considered equal to the nominal (constant) radius of the tire riK>m. Consequently, the longitudinal acceleration of the vehicle can be approximated: "œx(Z)rnom. This results in the following model (3): , za ■ ( --—-------+ mv(t)g sm{a(t})}H.vm 4 LlfXÇ.C[t11.::;*. 4 ) 4“ “" ■

[0075] This model is observable and therefore usable. Only mv, a and the coefficients c are unknown.

[0076] Another simplification involves equating the road slope as perceived by the accelerometer `imu(0)` to the actual road slope `a(t)`, thus neglecting any error. Therefore, the acceleration `x.imu(0)` measured by the accelerometer is related to the vehicle's true acceleration `ax(t)` by the following formula: Æx jMU (O “ «x ( O + g-Sin ( aIMlj (t)), with g.sin ( «IMU ( t ) ) the gravitational acceleration which corrupts the accelerometer measurements and which is a function of the time-varying pitch angle of the accelerometer, then the simplification leads to ûxjmü(O ~ ax(O + g.sin( a(t ) ), hence the following model (4): / z -¾ Z s Z' / • S > XA x A\ —-• --■'■■'■ + îWWÇo + QiûJx(£)rn^ + ^(^Mom) ) A 2 Pair^Ad(¾ (O^nom) ' ■

[0077] This simpler model (only mv and the coefficients c are unknown) is also observable and allows simplified calculations to obtain the mass of the vehicle.

[0078] To solve these different models, recursive estimators are generally used, which evaluate the unknown parameters or states at times (or "recursions") that correspond approximately to the times of acquisition of observable data by sensors. These unknown parameters or states are thus considered to be estimated in real time.

[0079] One aspect of the present disclosure makes model (2) observable by coupling a recursive road slope and vehicle longitudinal speed estimator with a recursive radius estimator estimating the dynamic rolling radius of the vehicle's tires. The coupling consists in the estimators reciprocally providing their estimates for the other estimator's estimates. Thus, the recursive slope estimator estimates the road slope and the vehicle's longitudinal speed from the longitudinal acceleration and wheel angular velocity measurements and the dynamic rolling radius of the tires estimated by the recursive radius estimator, while the recursive radius estimator estimates the dynamic rolling radius of the tires from the wheel angular velocity measurement and the longitudinal speed estimated by the recursive slope estimator.

[0080] Although it is already known in the literature to use recursive estimators of slope and longitudinal velocity, these are not coupled with a dynamic rolling radius estimator to achieve a simultaneous or "dual" estimation.

[0081] This simultaneous estimation from direct sensor measurements (only longitudinal acceleration and wheel angular velocity) allows us to obtain the following states: rdyn(t), ax(t), a(t), vx(t), as will be illustrated later. Therefore, model (2) becomes observable. A mass mv(t) of the vehicle can be estimated from the values ​​of these states.

[0082] Another aspect—whether separate or combined—of the present disclosure improves the estimation of the road slope—and consequently that of the vehicle mass mv(t) from model (2) or (3), for example—by providing that the recursive slope estimator estimates the road slope from measurements of longitudinal acceleration and wheel angular velocity. The wheel angular acceleration is intrinsically evaluated by a polynomial smoother that preprocesses the angular velocity measurements to remove sensor noise. Indeed, the polynomial nature of the smoother (generally a polynomial of degree >1) leads to the calculation of one or more derivatives of the smoothed measurements, in this case, the measured angular velocity.

[0083] Fig. 2 illustrates a vehicle control system 200 which, based on measurements taken on the vehicle, allows for the estimation, for example, of the mass mv of the vehicle and for appropriate measures to be taken accordingly.

[0084] The system 200 includes one or more on-board sensors 210 and a control device 220.

[0085] The sensors 210 are configured to acquire, at time t, measurements or "observations" o(t) of the vehicle. In the example of [Fig. 1], an onboard torque meter measures the vehicle's motor torque Meng(t), an accelerometer measures the vehicle's longitudinal acceleration ax>1MU(t), and gyroscopes measure the angular velocities oex(t) of the wheels (which are considered identical for all wheels). Beyond this example, any type of sensor can be used.

[0086] The control device 220 can be wholly or partially integrated into the vehicle for real-time estimation, or external to the vehicle, in which case the acquired measurements (Meng(t), ax>1MU(t), œx(t)) and / or the estimations performed in the vehicle are transmitted in batches to one or more servers—for example, in the cloud—to perform subsequent estimation. Signals for implementing the appropriate measures can be transmitted back to the vehicle. Transmissions can be carried out via wired (e.g., Ethernet network) or wireless (e.g., Wi-Fi network or Bluetooth link) to / from the server. For example, the control device 220 can be implemented in a remote processing server that manages a fleet of vehicles and transmits commands to them.

[0087] The control device 220 includes a communication interface 230, a monitoring device 240 and a control unit 250.

[0088] The communication interface 230 obtains the measurements Meng(t), ax, iMu(t), and œx(t) from the sensors 210. Typically, for an on-board control device 220, the interface is a serial interface on the vehicle's CAN (Controller Area Network) data bus, from which these measurements are retrieved. For an external control device 220, the interface can be any wired or wireless communication interface (e.g., a mobile phone modem).

[0089] The monitoring device 240 performs estimations of unknown states and parameters of the implemented model, for example, model (2) above. This monitoring device implements particular embodiments of the present disclosure.

[0090] This may be one or more computer processors executing one or more computer programs for the implementation of the treatments and estimators described below.

[0091] In the scenario proposed as an example, the monitoring device 240 allows the estimation of the mass mv(t) of the vehicle.

[0092] The control unit 250 uses the state(s) and parameter(s) estimated by the monitoring device 240 to generate a signal to the vehicle for the implementation of one or more appropriate measures. In the scenario provided as an example, the estimated mass mv(t) of the vehicle makes it possible to evaluate the vertical load experienced by each tire, tire wear, tire grip, tire rolling resistance, or even predict the battery range of electric vehicles (EVs). Appropriate measures can be taken, such as activating an overload indicator, indicating tire wear status, adjusting a vehicle electric range indicator, or adjusting the vehicle's routing to charging stations.

[0093] In detail, the monitoring device 240 of the figure includes a preprocessing unit 241, one or more polynomial smoothers 242, a recursive slope estimator 243, a recursive radius estimator 244 and a mass estimator 245.

[0094] The pre-processing unit 241 is optional. However, it performs processing on the Meng(t), ax>1MU(t), and cox(t) measurements to synchronize them. This is because each sensor has its own acquisition frequency and the sensors are not synchronized with each other.

[0095] The preprocessing may include an interpolation function, so that the measurements are made synchronous: a measurement is interpolated (if necessary) at each interpolation instant. A clock is embedded for this purpose. The interpolation step or frequency (when necessary) defines the recursion step or frequency, since the estimators will perform an estimation at each recursion from the measurements interpolated for that recursion. Hereafter, the index 'k' refers to the recursion 'k' and corresponds to an interpolation instant tk. The recursion frequency can be defined by an operator of the device 220. In the example of the vehicle in [Fig. 1], a recursion period between 10 milliseconds (ms) and 500 ms is compatible with off-the-shelf sensors and real-time operation.

[0096] The preprocessing may also include a function for selecting acquired measurements, which serves to select measurements consistent with assumptions made in the choice of the applied model, and thus eliminate outliers. Typically, model (2) of the proposed scenario models the longitudinal behavior of the vehicle. Measurements acquired on non-longitudinal behaviors can therefore be excluded, since the resulting mass mv estimate would not be reliable.

[0097] By way of example, all or part of the following criteria can be evaluated at time tk to retain the measurements Meng(k), ax>1MU(k), cox(k) corresponding to that time:

[0098] - the yaw rate of the vehicle (which can be calculated directly from the (wheel rotation speed or using a dedicated yaw rate sensor) must remain below a threshold defined by the operator,

[0099] - the acceleration aXjlMU(k) of the vehicle must be positive and greater than a defined threshold by the operator. This condition aims to guarantee full excitation of the input data to the system, an assumption for the proper functioning of RLS (Recursive Least Squares) estimators.

[0100] - the speed cox(k) of the vehicle must be greater than a threshold defined by the operator,

[0101] - the vehicle must not operate in four-wheel drive mode. Also, if the If the vehicle is equipped with all-wheel drive or four-wheel drive, the secondary torque (of the second set of wheels) must remain below a threshold defined by the operator.

[0102] A polynomial smoother 242 allows the sensor noise used to be removed from a measurement signal Meng(k), ax>1MU(k), or cox(k). It therefore also constitutes pre-processing of the measurement signals. In one embodiment, the polynomial smoothing of the measurement signals can be performed before the resynchronization 241. In practice, each measurement signal is smoothed by a dedicated polynomial smoother. Thus, block 242 groups, in the example, three polynomial smoothers, one for each of the signals Meng(k), ax>IMU(k), or cox(k).

[0103] The polynomial smoothing of each measurement signal may include polynomial regression implemented using a polynomial Kalman smoother, or "PKS" (for "Polynomial Kalman Smoother"). The PKS is based on the Savitzky-Golay filter, as described in the publication 'Recursive Generalized Total Least Squares with Noise Covariance Estimation' (S. Rhode et al., 2014). The PKS works by sliding a fixed-size window over the measurements of the processed signal and fitting a polynomial to the points of this window. The value of the polynomial at the center point of the window is then considered the smoothed value. This process is repeated for each measurement, producing a smoothed signal. The degree of the polynomial is greater than 1, which allows, during calculations, to evaluate the derivative of the input measurements, and in particular the angular acceleration of the vehicle's wheels (Vx(k) by differentiating the angular velocity of the wheels during the smoothing of cox(k).

[0104] Algorithm 5 of the publication S. Rhode et al. can be used, where Bt are the measurements at the recursion 't' of the signal to be smoothed, and Wr represents the corrected (smoothed) output measurements. The width of the window 'w' is predefined, for example between 4 and 100, with Wi and wr the left and right half-windows composing the window w. The matrix C is defined by equation (13) in this publication. The forgetting factor X (between 0 and 1) and the dimension of the polynomial (polynomial order) used are prefixed. The noise covariance matrix P and the state transition matrix A are of the same dimension axa, where a is the polynomial order minus one. P is initialized with [3x1] where IeRaxa is the identity matrix of dimension a and [3] is a regularization parameter defined in the range [0; 100]. A is defined in the publication S. Rhode et al. in equation (11).

[0105] Each smoother can be adjusted according to the noise level and frequency dynamics of the signal to be smoothed. For example, the acceleration signal ax>1MU(k) can be considered very noisy and dynamically strong, in which case the PKS smoother can have a polynomial order of four and a window size of 10. The angular velocity signal cox(k), on the other hand, can be considered less noisy and dynamically weaker, in which case the PKS smoother can have a polynomial order of two and a window size of 3.

[0106] The recursive slope estimator 243 receives, as input, the smoothed signals ax>1MU(k), cox(k), and (k). This estimator estimates the slope a(k) of the road (the estimate is denoted â(&)) from these measurements of longitudinal acceleration and angular wheel velocity, and the angular wheel acceleration generated by the smoother 242. In particular, the recursive slope estimator 243 can be based on the following underlying models:

[0107] for the state evolution system:

[0108] vx(t)= vx(tl)+ax(t).At,

[0109] ax(t) = ax(tl)

[0110] g.sin(a(t))=g.sin(a(tl))

[0111] and for the measurement system:

[0112] vx(t)=cox(t).rdyn(t),

[0113] ax(t)= wJO-Mt),

[0114] ax>IMU(t)=ax(t)+g.sin(a(t)).

[0115] In a preferred mode, the recursive slope estimator estimates the slope of the road from further the dynamic rolling radius of the tires (k) estimated by the recursive radius estimator 244 as described below.

[0116] Typically, the recursive slope estimator 243 implements a linear Kalman filter or LKF (for "Linear Kalman Filter") to estimate, in addition to the slope a(k) of the road, a real longitudinal acceleration ax(k) of the vehicle (the estimate is denoted âx( fc)) and a real longitudinal speed vx(k) of the vehicle (the estimate is denoted

[0117] The state vector to be estimated x is composed of the following states vx ( k ), âx ( k ), g.sin(a ( k ) for example:

[0118]

[0119] The corresponding vector of measurements y is defined by:

[0120] UVdyn,

[0121] The state transition matrix F based on the above state evolution system taking into account the vector x, and the model noise covariance matrix Q for filter prediction are fixed as follows:

[0122] '1 Af 0' and 0 0' F - 0 1 0 .0 0 1. Q= 0 y2 0 .° 0 K

[0123] with At the recursion step (the time in seconds between two successive estimates, which must take into account the possible deletion of measures during preprocessing 241) and ^2, and y3 of the regularization parameters defined in the range [0; 10].

[0124] The measurement modeling matrix H based on the above measurement system taking into account the vector y, and the measurement noise covariance matrix R for filter correction are fixed as follows:

[0125] ■10 0' and 'k} 0 0' H = 0 1 0 0 k2 0 .0 1 1. 0 0 ^3.

[0126] with *1, K2, and ^3 regularization parameters defined in the range [0; 10]. We note here that the measurement modeling matrix H includes an additional row compared to the matrices used classically, due to the consideration of the acceleration ubx(k) in the measurement system: ax(t)= cùx(t) .rdyn(t).

[0127] The covariance matrix P (internal parameter of the filter) is initialized to [30xI where IeR3x3 is the identity matrix of dimension 3 and [30 is a regularization parameter defined in the range [0 ; 100]. The estimated vector x is initialized to (0,0,0).

[0128] Figure 2A illustrates the use of the angular acceleration of the wheels wx(k) generated by the polynomial smoother PKS 242 in the recursive estimation of the slope of the road a(k) by the slope estimator 243.

[0129] Although the above example of calculating the road gradient from the angular acceleration generated by the smoother 242 is based on the dynamic rolling radius rdyn(k) within the framework of model (2), the simplified model (3) can be used as an alternative. In this case, the operations of [Fig. 2A] estimate the gradient a(k) from the nominal wheel radius rnom.

[0130] The recursive estimator with radius 244 receives, as input, the smoothed signal cox(k) as well as the estimates of the longitudinal velocity vx(k). This estimator estimates the dynamic rolling radius rdyn(k) of the tires (the estimate is denoted rjv„( fc)) from the measurement of the angular velocity of the wheels and the longitudinal velocity estimated by the slope estimator 243. In particular, the recursive estimator with radius 244 is an estimator based on the following underlying model: vx(t)=cox(t).rdyn(t).

[0131] The use of estimator 244 allows for a noise-robust (sensor, smoothing) and therefore accurate estimation.

[0132] By reciprocally using an estimate of the other estimator, the recursive estimator of slope 243 and recursive estimator of radius 244 are coupled to perform simultaneous, higher quality estimates.

[0133] In one embodiment, the coupling is performed between two successive recursions. In this case, the longitudinal velocity estimate vx(k-1) is used by the recursive estimator with radius 244 during recursion k to estimate the dynamic radius r^,„(k). Conversely, the dynamic radius estimate r^f(k-1) is used by the recursive estimator with slope 243 during recursion k to estimate the slope a(k) and the actual longitudinal acceleration âx(k) and actual longitudinal velocity vx(fc). With this coupling between two successive recursions, the inputs of estimator 244 for recursion k are vx(k-1) and cox(k), while those of estimator 243 are idyn^k-1), ax>1MU(k), cox(k), and ùx(k). This coupling is illustrated, for example, in [Fig. 2B].

[0134] In another embodiment, the estimators 243 and 244 are put in series: the first uses the (k-1) estimate of the second when estimating for the current recursion k, while the second (downstream in the series) uses this k estimate of the first during its recursion k. This serial coupling is illustrated for example in [Fig.2C].

[0135] In the first configuration, the recursive estimator of radius 244 is upstream of the recursive estimator of slope 243. Estimator 244 receives vx( k -1 ) from the previous recursion, calculates rdm(k) which is passed to estimator 243. The latter calculates â(k) and vx(k) as a function of rdyn(k).

[0136] In the second configuration, the recursive estimator with slope 243 is upstream of the recursive estimator with radius 244. Estimator 243 receives ldyn(k-1) from the previous recursion, calculates a(k) and vx(k). vx(k) is passed to estimator 244, which calculates rdyn(k) as a function of vx(k).

[0137] In yet another embodiment, coupling is performed in real time, meaning that the current estimation (at recursion k) is used by the other estimator during the same recursion. Typically, the recursive slope estimator and the estimator Radius recursive estimators perform iterative estimations during an estimation recursion, meaning, for example, that they run their respective filters multiple times (iterations) using the same input measurements, until convergence (e.g., their estimations no longer change) or the expiration of a time interval (based, for example, on the time interval Δt between two successive measurements). Real-time coupling involves the two estimators exchanging their previous iterative estimations (obtained at iteration i-1) to perform the next iterative estimation (iteration i).

[0138] For example, the recursive slope estimator 243 iteratively estimates the slope of the road a and the longitudinal speed vx at recursion k, that is, several estimates ( & ), vxj ( k ) are made at several iterations i during recursion k. The estimation of a;(Æ) and Nxi(k) is based on the measures ax>1MU(k), cox(k) and (àx(k) as well as on the estimation ï^yni-i (&) provided by the recursive radius estimator 244. r^o (k ) =ïdyn ( k -1) to allow the initialization of recursion k.

[0139] Symmetrically, the recursive estimator of radius 244 iteratively estimates the dynamic rolling radius of the tires rdyn at recursion k, i.e., several estimates k) are performed at several iterations i during recursion k. The estimation of Qynj(k) is based on the measure cox(k) as well as on the estimation (k) provided by the recursive estimator with slope 243.

[0140] The recursive estimator of radius 244 can implement a recursive least squares or RLS filter based on the underlying model above.

[0141] For example, the parameter vector to be estimated is defined by r 1T, the vector x = LrJyd regression by a = [ ] and the vector of measures by y ~ estimated by the estimator 243. The forgetting factor is set to a value between 0 and 1; the covariance matrix P (internal parameter of the filter) is initialized to [3iXl where theR1 x 1 is the identity matrix of dimension 1 and [3i is a regularization parameter defined in the range [0; 100]; and the estimated vector x is initialized to 0.

[0142] The recursive mass estimator 245 receives as input the signal Meng(k) (optionally smoothed by a smoother 242), the slope estimates â(k), longitudinal acceleration estimates âx(k), and longitudinal velocity estimates vx(fc) from estimator 243, and the dynamic radius estimates rdyn(k) from estimator 243 when calculated. This estimator estimates a mass mv(k) of the vehicle (the estimate is denoted mr(^)) from the estimated road slope and, where applicable, from an estimated dynamic rolling radius of the tires.

[0143] More precisely, the recursive mass estimator 245 can have as its underlying model the model (2) or “equivalent”, i.e., when for example the term mv(t).g. (cro+cri.vx(t)+cr4.vx(t)4) is substituted by another expression for the rolling resistance Fron(t). In an embodiment based on the use of the nominal wheel radius rnom, model (3) or equivalent can be used. Also, estimator 243 estimates, in addition to the mass mv(Æ) of the vehicle, the rolling resistance coefficients cr0, cr[, cr4, and the aerodynamic coefficient of the vehicle cd.

[0144] The recursive mass estimator 245, for example, implements a recursive least squares RLS filter based on the underlying model (2) or equivalent.

[0145] For example, the parameter vector to be estimated is defined by x=[mv, mv, mv.crf), mv.cri, mv.cr4, ]T, The regression vector is defined by a=[ax, g.sin(a), g, g.vx, 2 ^ajr td g.vx4, vx2], and the output measure of the regression is defined by y=M^g^M. The rdyn factor The forgetting parameter is set to a value between 0 and 1; the covariance matrix P is initialized to P2XI where IeR6 x 6 is the 6-dimensional identity matrix and

[32] is a regularization parameter defined in the range [0; 100]; and the estimated vector x is initialized to the 6-dimensional vector 0.

[0146] Fig. 3 illustrates, using a flowchart, the steps of a supervisory process implementing the control device 220.

[0147] Before executing the algorithm in real time, the different estimators are initialized in step 300.

[0148] At step 305, the device obtains, via interface 230, measurements of longitudinal acceleration of the vehicle ax>IMU(t) and angular velocity of the vehicle wheels cox(t), as well as measurements of the motor torque Meng(t), acquired by sensors 112.

[0149] The following steps are performed at each recursion time.

[0150] In step 310, these measurements are preprocessed by units 241 and 242 to obtain synchronous and smoothed measurements: aXjlMU(k), cox(k) and Meng(k). At this time, measurements of the angular acceleration of the vehicle's wheels Mx(k) are generated by differentiating the angular velocity of the wheels.

[0151] At step 315, the slope of the road a(k), but also the longitudinal speed of the vehicle vx(k) and the longitudinal acceleration ax(k) of the vehicle, are determined, by the slope estimator 243, from aXjlMU(k), cox(k) and ü)x (k).

[0152] In one embodiment, the wheel radius is considered fixed at rnom during these estimations, and the process can continue to step 325 where the recursive mass estimator 245 estimates the vehicle mass nïv(k), as well as the rolling resistance coefficients cr0, cr[, cr4, and the vehicle aerodynamic coefficient cd, from the estimated road slope a(k), the longitudinal acceleration âx(k), the longitudinal speed va(Cl), the engine torque Meng(k), and the nominal radius rnom. This is typically the case when the estimator 245 uses model (3) or equivalent.

[0153] In another embodiment, the dynamic rolling radius fz / w, (k) is also estimated. In this case, at step 315, the slope estimator 243 makes its estimates taking into account the dynamic rolling radius estimated by the radius estimator 244, for example at the previous recursion k-1). Also, simultaneously with step 315, the radius estimator 244 estimates at step 320 the dynamic rolling radius of the tires rjVM(fc) from the angular velocity measurement of the wheels cox(k) and the longitudinal velocity estimated by the slope estimator 243, for example at the previous recursion vx(k-1). Steps 315 and 320 are followed by step 325 in which the recursive mass estimator 245 makes the estimates of mr ( k ), cr0, cri, cr4, and cd, from a ( k ), â*( k ), vx( k ), Meng(k) and also from the estimated dynamic rolling radius ( k ).

[0154] Step 325 can be followed by a step 330 where the estimated mass mv(k) is used by the control unit 250 to generate a control signal to the vehicle. As shown above by way of example, such a signal can consist of activating a tire overload indicator, indicating tire wear, adjusting a vehicle electric range indicator, or routing the vehicle to charging stations.

[0155] The vehicle control system described above offers the advantage of an improved estimation of the actual road slope by taking into account the angular acceleration of the wheels, which is naturally estimated in the conventional smoothers used during measurement preprocessing. It also offers the additional advantage of a joint and high-quality estimation of the vehicle's longitudinal speed and dynamic rolling radius, making complex models observable. This results in a higher-quality estimation of the vehicle's mass using such now-observable complex models.

[0156] Fig. 3A illustrates, using a flowchart, the steps of a process for estimating the slope of a road.

[0157] Before executing the algorithm in real time, the different estimators are initialized in step 350.

[0158] At step 355, the device obtains, via interface 230, measurements of longitudinal acceleration of the vehicle aXjlMU(t) and angular velocity of the vehicle wheels cox(t), acquired by sensors 112.

[0159] The following steps are performed at each recursion time.

[0160] At step 360, these measurements are preprocessed by units 241 and 242 to obtain synchronous and smoothed measurements: ax>1MU(k), cox(k). At this time, measurements of the angular acceleration of the vehicle's wheels are generated by differentiating the angular velocity of the wheels.

[0161] At step 365, the slope of the road a(k) is determined from ax>1MU(k), wx(k) and (k) by the slope estimator 243.

[0162] Fig. 4 illustrates a computer hardware architecture of a control device 220 according to embodiments.

[0163] The device 400 includes a communication bus 401 to which the following are preferably connected: - one or more central processing units 402, such as one or more CPU processors and / or one or more microprocessors; - a 403 storage memory, of type ROM and / or hard disk and / or flash memory, for the storage of computer programs intended to implement all or part of the operations described above; - 404 RAM, or even video RAM (VRAM), for storing the executable code of computer programs as well as registers adapted to store variables and parameters necessary for their execution; - a 405 communication interface connected to a network (e.g. CAN bus or Wifi network) in order to communicate with external equipment, e.g. external 210 sensors or any other unit in the vehicle (especially when part of the estimation is carried out outside the vehicle); - One or more 406 I / O inputs / outputs allowing an operator to interact with computer programs, both during configuration and operation. Typically, the inputs / outputs may include a screen serving as a graphical interface with the operator and displaying the value of unknown parameters, and / or a speaker to output audio content and / or a keyboard or other pointing device allowing the operator to interact.

[0164] Preferably, the communication bus 401 ensures communication and interoperability between the various elements included in or connected to the device 400. The bus representation is not limiting and, in particular, the central processing unit can be used to communicate instructions to any element of the computer device 400 directly or by means of another element of the computer device.

[0165] The executable code stored in memory 403 can be received via the communication network, through interface 405, for storage there prior to execution. Alternatively, the executable code is not stored in non-volatile memory 403 but can be loaded into volatile memory 404 from a remote server via the communication network for direct execution.

[0166] The central processing unit 402 is preferably adapted to control and direct the execution of instructions or parts of software code of the computer program(s). Upon power-up, the program(s) stored in non-volatile memory 403 or on the remote server are transferred / loaded into RAM 404, which then contains the executable code of the program(s), as well as registers for storing the variables and parameters necessary for implementing the invention.

[0167] Of course, the invention is by no means limited to the embodiments described above, as a person skilled in the art is able to freely isolate or combine the aforementioned features, or substitute equivalents for them.

Claims

Demands

1. Supervision device (220) of a vehicle operating on a road, the device comprising: a communication interface (405, 230) for obtaining, from one or more sensors (210), measurements of longitudinal acceleration of the vehicle (ax>1MU) and angular velocity of the vehicle's wheels (œx), one or more processors (402) implementing: - a polynomial smoother (242) of measurements, generating an angular acceleration of the vehicle's wheels (0'*) by derivation of the angular velocity of the wheels, and - a recursive slope estimator (243) estimating a slope of the road (a) from the measurements of longitudinal acceleration and angular velocity of the wheels, and the generated angular acceleration of the wheels.

2. Supervisory device (220) according to claim 1, wherein the polynomial smoother (242) comprises a polynomial Kalman smoother.

3. Supervisory device (220) according to claim 1 or 2, wherein the recursive slope estimator (243) estimates the slope of the road (a) further from a dynamic rolling radius of the tires (rdyn) estimated by a recursive radius estimator (244).

4. Supervisory device (220) according to claim 3, wherein the recursive radius estimator (244) estimates the dynamic rolling radius of the tires (rdyn) from the measurement of angular velocity of the wheels (œx) and a longitudinal velocity of the vehicle (vx) estimated by the recursive slope estimator (243).

5. Supervisory device (220) according to any one of claims 1 to 4, wherein the recursive slope estimator (243) comprises a linear Kalman filter estimating the road slope (a), a longitudinal acceleration of the vehicle (ax) and a longitudinal speed of the vehicle (vx).

6. Supervisory device (220) according to any one of claims 1 to 5, wherein the underlying model of the recursive slope estimator (243) includes the following state evolution system: vx(t) = vx(tl) + ax(t).At, ax(t) = ax(tl), g.sin(a(t)) = g.sin(a(tl)), and the following measurement system: vx(t)=cox(t).rdyn(t), ax(t)= ( t ) ,rdyn(t), ax,iMu(t)=ax(t)+g. sin(a(t)), where ax is an estimated longitudinal acceleration of the vehicle, vx an estimated longitudinal velocity of the vehicle, g the gravitational constant, At the time step between two estimation recursions, an angular acceleration of the vehicle's wheels, and rdyn a rolling radius of the tires.

7. Supervisory device (220) according to any one of claims 1 to 6, further comprising a recursive vehicle mass estimator (245) estimating a vehicle mass (mv) from the estimated road slope and, where appropriate, the estimated dynamic rolling radius of the tires.

8. Supervisory device (220) according to claim 7, wherein the recursive mass estimator (245) comprises a recursive least squares estimator, and estimates the mass of the vehicle (mv) further from a longitudinal acceleration of the vehicle (ax) estimated by the recursive slope estimator (243), a longitudinal speed of the vehicle (vx) estimated by the recursive slope estimator (243) and, where applicable, the dynamic rolling radius of the tires estimated by the recursive radius estimator (244).

9. Supervisory device (220) according to claim 7 or 8, wherein the underlying model of the recursive mass estimator includes: +-mv(t~}g(c10 + <^(0 + + jpair^fCdi^Çt)2. where Meng(t) is a motor torque, y(t) a transmission ratio, qtot(t) a mechanical or motor transmission efficiency, ax an estimated longitudinal acceleration of the vehicle, vx an estimated longitudinal speed of the vehicle, g the gravitational constant, c^, crb cr4 rolling resistance coefficients, pair an ambient air density, Af a frontal area of ​​the vehicle and cd an aerodynamic coefficient of the vehicle.

10. System (200) comprising a vehicle monitoring device (230) according to any one of claims 1 to 9 and a vehicle control unit (250) for controlling an on-board function from the estimates of the monitoring device.

11. Method for supervising a vehicle operating on a road, the method comprising the following steps: obtaining (300), via a communication interface (405, 230), measurements of longitudinal acceleration of the vehicle (ax>1MU) and angular velocity of the vehicle's wheels (œx) acquired by one or more sensors (210), smoothing (305), using a polynomial smoother (242), the acquired measurements, the smoothing generating an angular acceleration of the vehicle's wheels (wx) by derivation of the angular velocity of the wheels, and estimating (310), recursively, a slope of the road (a) from the measurements of longitudinal acceleration and angular velocity of the wheels, and the generated angular acceleration of the wheels.

12. Computer program product embedded in a non-transient recording medium comprising computer-readable program code for implementing the method according to claim 11.

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