Patent Description:
Vehicles are known, in particular wheeled vehicles such as cars and trucks, which comprise electronic devices for determining the mass of the vehicles.

In detail, vehicles currently use a direct method of mass estimation, i.e., through dedicated weight sensors.

However, these sensors do not have sufficient features to meet the requirements of the new regulations in the sector. In fact, such regulations require high accuracy and a higher refresh rate compared to the systems currently in use.

In addition, ADAS (Advanced Driver Assistance Systems) systems are increasingly widespread, for example automatic driving systems, which also require high accuracy in estimating the mass of vehicles, in order to obtain a correct operation of the ADAS systems. Clearly, the value of the mass is necessary to allow the calculation of the braking distance/time or the drive torque such as to reach a predetermined speed or to maintain the acceleration of the vehicle in a predetermined range of comfort for the passengers.

Accordingly, vehicles are also known which incorporate On-Board Weighing Systems (OBW), capable of obtaining a direct and sufficiently accurate detection of the mass. However, these systems are additional devices to be housed on the vehicle, with consequent increase in costs, reduction in available space, and increase in vehicle production time.

Therefore, there is a need to provide a vehicle which comprises a system for estimating its mass accurately and at low cost.

The object of the present invention is to meet the above requirements in a cost-effective and optimized manner.

Document <CIT> discloses a vehicle provided with a controller configured to generate an output indicative of a kinematic road gradient estimate using an extended Kalman filter.

The above object is achieved by a system and a method for estimating the mass as claimed in the appended claims.

For a better understanding of the present invention, a preferred embodiment is described below by way of nonlimiting example and with reference to the accompanying drawings, wherein:.

<FIG> schematically shows a vehicle <NUM> comprising a plurality of sensors <NUM>, each configured to acquire one or more respective quantities as a function of an operating condition of the vehicle <NUM> and configured to provide a plurality of electrical signals I, each as a function of such one or more quantities, to a processing unit <NUM>.

In detail, the plurality of sensors <NUM> includes:.

The plurality of electrical signals I, for example, can be supplied by the plurality of sensors <NUM> to the processing unit <NUM> via a CAN-type ("Controller Area Network") bus.

According to the present invention, the processing unit <NUM> is configured to determine, starting from the plurality of electrical signals I, an estimate of the mass m of the vehicle <NUM>, according to the method represented in the flow diagram of <FIG>, and generate at output a mass signal IM as a function of the estimation of the mass m.

In detail, the present method is based on the equation Eq of longitudinal dynamic equilibrium of the vehicle <NUM>. For example, the equation Eq can take the following form: <MAT> wherein:.

The equation Eq can be rewritten in terms of a cost function C(m), obtained as the difference between a first function F(m), comprising a first group of terms of the equation Eq that depend on the unknown mass m of the vehicle <NUM>, and a second function G, comprising a second group of terms of the equation Eq that do not depend on the mass m of the vehicle <NUM>, according to the relation C(m) = F(m) - G.

In detail, the first function F(m) comprises the sum of a first term m·g·sen(θ), a second term m·F<NUM>·cos(θ) and a third term m·a, i.e.: <MAT>.

The first term m·g·sen(θ) and the second term m·F<NUM>·cos(θ) depend on the mass m of the vehicle <NUM>, as well as on the angle of inclination θ. Likewise, the third term ma depends on the mass m of the vehicle <NUM>, as well as on its longitudinal acceleration a.

In this embodiment, the longitudinal acceleration a is obtained starting from the electrical speed signal Iv.

According to one aspect of the present invention, the longitudinal acceleration a can be detected directly through a dedicated sensor.

The second function G comprises the difference between a fourth term A·Tem and a fifth term F<NUM>·v<NUM> of the equation Eq, i.e., : <MAT> which are a function of the drive torque Tem and the longitudinal speed v of the vehicle <NUM>, respectively, therefore do not depend on the mass m.

It follows that a minimization algorithm, applied to the cost function C(m), is able to determine, at output, the unknown coefficients that minimize the cost function C(m); that is, in this case, the mass m of the vehicle <NUM>.

In detail, in this embodiment, the processing unit <NUM> uses, as minimization algorithm, a Recursive Least Squares (RLS) algorithm, known to the person skilled in the art and therefore not further described.

The RLS algorithm is recursive and requires an initial mass value m<NUM> to calculate the estimated mass value mn in a given iteration n (current iteration n). The initial mass value m<NUM> can be a fixed initial mass value, for example in the case of initial configuration of the vehicle <NUM>, or a mass value mn-<NUM> estimated in a previous iteration n-<NUM>, stored in a memory internal or external to the processing unit <NUM>.

Here, the processing unit <NUM> uses the estimated mass value mn-<NUM>, calculated in the previous iteration n-<NUM>, as the initial mass value m<NUM> for the current iteration n.

As regards the RLS algorithm, in the current iteration n, the first function F(m) of the cost function C(m) can be expressed as F(m) = Φ·mn-<NUM>, wherein Φ = g·sen(θ) + F<NUM>·cos(θ) + a represents the known portion of the first function F(m), i.e., the sum of the elements of the first, the second and the third terms m·g·sen(θ), m·F<NUM>·cos(θ) and m·a, which are multiplied by the mass mn-<NUM> estimated in the previous iteration n-<NUM>. As shown schematically in <FIG>, the present method for estimating the mass m of the vehicle <NUM> comprises an acquisition step <NUM> in which the plurality of electrical signals I is supplied to the processing unit <NUM>.

The plurality of electrical signals I are continuously supplied to the processing unit <NUM>.

Subsequently, in the processing step <NUM>, the processing unit <NUM> processes the values of each signal of the plurality of electrical signals I received in a time window of duration ts. For example, the processing unit <NUM>, in the time window of duration ts, can perform a moving average of the values of each signal of the plurality of electrical signals I. Moreover, in the processing step <NUM>, the processing unit <NUM> determines the value of the known portion Φ of the first function F(m) and the value of the second function G, by using the gravitational acceleration constant g and the coefficients A, F<NUM> and F<NUM>, which can also be stored in a memory internal or external to the processing unit <NUM>.

A verification step <NUM> follows, in which the processing unit <NUM> verifies that a group of algorithm trigger conditions are satisfied.

In detail, checking the group of algorithm trigger conditions includes verifying that:.

If each condition of the group of algorithm trigger conditions is satisfied, a loading/unloading verification step <NUM>, described in detail below with reference to <FIG>, and an estimation step <NUM> follow, in which the processing unit <NUM> calculates the cost function C(m) and executes the RLS algorithm.

The RLS algorithm then generates at output an estimate of the mass m of the vehicle <NUM> for the current iteration n, as described above.

If in the verification step <NUM> at least one of the algorithm triggering conditions is not verified, the vehicle <NUM> is in an operating condition which would not allow an accurate estimate of the mass m. For example, in fact, if the longitudinal speed v is too low, i.e., lower than the threshold speed, the vehicle <NUM> is starting, or if the vehicle <NUM> is braking, the longitudinal acceleration a varies very quickly and the estimate is not sufficiently accurate.

Consequently, in the event that the verification step <NUM> has given a negative result, the processing unit <NUM> does not execute the RLS algorithm (estimation step <NUM>), but waits (waiting step <NUM>) for a waiting interval tw greater than or equal to, in this case equal to, the duration ts of the time window.

During the waiting interval tw, the processing unit <NUM> stores the estimated mass value calculated in the previous iteration n-<NUM>. Consequently, the mass signal IM supplied by the processing unit <NUM> also corresponds to the estimated mass value calculated in the previous iteration n-<NUM>.

Once the waiting interval tw ends, the verification step <NUM> is performed again.

In addition, the RLS algorithm takes into account conditions that could substantially modify the mass of the vehicle through a forgetting factor λ, comprised between <NUM> and <NUM>, which is used as weight for the initial mass value m<NUM> in each iteration of the estimation method.

In detail, a forgetting factor λ value close to <NUM> assigns a low weight to the initial mass value m<NUM>, i.e., to the mass value estimated in the previous iteration n-<NUM>. This is particularly useful after a variation in the mass m of the vehicle <NUM>, for example after loading/unloading steps.

In fact, if the initial mass value m<NUM> used by the RLS algorithm is very different from the actual value of the mass m, the RLS algorithm may have convergence problems, for example, the time it takes the algorithm to correctly estimate the mass m could be too long.

Conversely, a forgetting factor λ value close to <NUM> assigns a high weight to the initial mass value m<NUM>, i.e., to the mass value estimated in the previous iteration n-<NUM>. This is particularly useful if the vehicle <NUM> is in a stationary state, in which the mass m does not undergo variations, for example, while driving.

In fact, if the initial mass value m<NUM> of the RLS algorithm is very close to the actual mass value m, the present method allows an estimate of the mass m with high accuracy, for example, with an error smaller than <NUM>%.

Accordingly, as stated above and shown in <FIG>, after the verification step <NUM> and before the estimation step <NUM>, the processing unit <NUM> performs the loading/unloading verification step <NUM>, which comprises verifying a set of loading/unloading conditions (loading/unloading conditions verification step <NUM>), designed to predict whether the vehicle <NUM> has been subjected to a loading/unloading step.

For example, the set of loading/unloading conditions comprises examining whether the vehicle <NUM> has just been switched on, whether it has just started moving, or whether the door of the vehicle <NUM> used to access the load space has been opened/closed.

In detail, in order to check whether the vehicle has just been switched on, the processing unit <NUM> checks whether in the time window of duration ts, the value of the state signal Is has switched between a value corresponding to the vehicle <NUM> in the off state and a value corresponding to the vehicle <NUM> in the on state.

In addition, in order to check whether the vehicle <NUM> has just started moving, the processing unit <NUM> checks whether in the time window of duration ts, the speed signal Iv has taken a value corresponding to a null longitudinal speed v.

In addition, in order to check whether the door of the vehicle <NUM> has been opened/closed, the processing unit <NUM> checks whether in the time window of duration ts, the door opening signal Ic has taken a value indicating the opening of the door. In an exemplary variant, this condition is verified only if a minimum time tc (preferably a multiple of ts) has elapsed between the value indicating the opening of the door and a corresponding value of the door opening signal Ic indicating the closing of the door, so that a loading/unloading operation could have been performed.

If at least one of the sets of loading/unloading conditions is verified, i.e., output "YES" from the loading/unloading conditions verification step <NUM>, then it is possible/probable that the vehicle <NUM> has been subjected to loading/unloading and therefore to a mass variation. In this case, i.e. possible loading/unloading step <NUM>, the forgetting factor λ of the current iteration n is set to a minimum value, much less than <NUM>, for example equal to approximately <NUM>.

The estimation step <NUM> is then performed.

If the loading/unloading conditions are not verified (output "NO" from the loading/unloading conditions verification step <NUM>), the vehicle is in an operating condition in which the load is stationary as well as the mass m.

In this condition, the processing unit <NUM> checks whether the forgetting factor A(n-<NUM>) of the previous iteration n-<NUM> is equal to a maximum value, for example close to <NUM>, in particular <NUM>, which can be determined through of a suitable calibration (forgetting factor verification step <NUM>).

In affirmative case (step <NUM>), the value of the forgetting factor λ(n) of the current iteration n is kept equal to the value of the forgetting factor λ(n-<NUM>) of the previous iteration n-<NUM>.

If not (step <NUM>), the value of the forgetting factor λ(n) of the current iteration n is increased by an amount X with respect to the value of the forgetting factor λ(n-<NUM>) of the previous iteration n-<NUM>.

For example, the amount X can be kept constant for each iteration, so that the forgetting factor λ increases from the minimum value to the maximum value, following a ramp.

The amount X, and therefore, for example, the speed of the ramp between the minimum value and the maximum value of the forgetting factor λ, can be determined, during calibration, in order to ensure an appropriate compromise between the speed of convergence of the estimated mass value and the actual mass value and the mass estimation accuracy under stationary load conditions.

With reference again to <FIG>, the vehicle <NUM> further comprises an ADAS system <NUM> configured to receive, from the processing unit <NUM>, the mass signal IM as a function of the mass m of the vehicle <NUM>, estimated by the processing unit <NUM>.

The ADAS system <NUM> therefore receives an accurate estimate of the mass m of the vehicle <NUM> with a sufficient refresh rate.

Consequently, the ADAS system <NUM> can be configured, for example, to allow the braking of the vehicle <NUM> in the required space/time or to provide a drive torque such as to allow a predetermined speed to be reached, or also to maintain the acceleration of the vehicle <NUM> in a predetermined range of comfort.

Lastly, it is clear that modifications and variations may be made to the vehicle <NUM> and the method according to the present invention, without however departing from the scope of protection defined by the claims.

For example, the plurality of sensors <NUM> can provide additional quantities associated with the dynamic equation of longitudinal equilibrium of the vehicle <NUM>, which are useful for estimating the mass m of the vehicle.

For example, the plurality of sensors <NUM> can provide quantities associated with the dynamic equation of longitudinal equilibrium of the vehicle which differ according to the type of vehicle.

For example, the minimization algorithm can provide further output results in addition to the mass m of the vehicle <NUM>, for example an estimate of the mass estimation error.

Claim 1:
A method for estimating the mass of a vehicle (<NUM>),
the vehicle comprising a plurality of sensors (<NUM>) each configured to detect at least one quantity depending on an operating condition of the vehicle and a processing unit (<NUM>) configured to receive electrical signals (I) from the plurality of sensors,
the method including:
• detecting (<NUM>), by the plurality of sensors (<NUM>), a plurality of quantities comprising quantities which are a function of the angle of inclination (θ), the longitudinal speed (v), the longitudinal acceleration (a) and the drive torque (Tem) of the vehicle;
• providing, from the plurality of sensors (<NUM>), electrical signals (I) depending on the plurality of quantities, to the processing unit (<NUM>);
• processing (<NUM>), by the processing unit (<NUM>), the electrical signals as a function of the plurality of quantities, to determine a cost function (C(m)) associated with a dynamic equation of longitudinal equilibrium of the vehicle, the cost function being a function of the mass of the vehicle;
• executing (<NUM>), by the processing unit (<NUM>), a minimization algorithm of the cost function; and
• determining the mass of the vehicle resulting from the minimization algorithm,
wherein the cost function is given by the difference between a first and a second function (F(m), G), the first function (F(m)) comprising a known portion (Φ) and being given by the sum of a first group of terms and the second function (G) being given by the sum of a second group of terms, the first group of terms being a function of the mass of the vehicle and wherein the cost function minimization algorithm is performed iteratively,
wherein the electrical signals also comprise a braking signal and wherein, before executing the cost function minimization algorithm, an algorithm trigger condition including checking if the vehicle is braking is verified (<NUM>), the algorithm trigger condition being satisfied if the braking signal indicates that the vehicle is not braking.