DE4221030A1 - Dynamic detection state of vehicle ABS system - using front wheel brake pressure difference and transverse acceleration to determine curve, split or straight braking. - Google Patents

Abstract

The transverse acceleration (by) and the difference between the braking pressures in the front two wheels are measured. A parameter (a) derived from the transverse acceleration is 0.5 for small accelerations and rises to 1 with increasing transverse acceleration, then remains at 1. A parameter r = abs (1/a - 1) is used in several formulae contg. defined constants to derive cornering braking signals, split braking signals and linear braking signals. The maximum values of the braking signals are determined to characterise the vehicle situation. USE/ADVANTAGE - Small number of vehicle measurement signals are evaluated in real time to rapidly evaluate vehicle situation using limited computing capacity and with very low error rates.

Description

State of the art

In recent years, increasingly vehicles with a active rear-axle steering offered in the market. From the beginning pure control concepts in the active influence of the Hinter Axis kinematics may be a trend in the direction of regulated concepts in chassis development. Both simulations and experimental results have demonstrated that there are more intelligent strategies to fulfill strict safety, stability and safety requirements the ride comfort of a complex motor vehicle under various driving maneuvers, especially in borderline cases used Need to become. So it was found that for cornering and Lane change completely opposite control measures of the hind axle steering must be used. Therefore, it is desirable too know which current situation exists. The compensation of the Bremsgiermoments by rear axle steering (GMK) is a very sensible full measure in μ-split braking, but brings undesirable Effects when cornering and when changing lanes. Therefore, was based on Transverse acceleration signals the GMK function at the latter Situations mitigated (indirect  Situation adjustment). This shows how important the detection of Driving situations in the completion of a vehicle dynamics control be can.

Task and solution

The object of the invention is therefore a technically feasible To develop a method that makes it possible by evaluating a small number of measurement signals available in the vehicle to recognize on-line the present situation. There are certain Restrictions / requirements due to the automotive engineering Conditions inevitably predetermined; namely

• - situation identification must be rapid,
• - the computer capacity is limited to on-board computer,
• - The error rate of the classification must be kept very small.

In the latter point, it is particularly critical when the Available data / characteristics imprecise (inaccurate by measurement, not unique in the evaluation, etc.). It is therefore the Intent to exploit this recognition problem by using "fuzzy logic" support. By using fuzzy logic, there are the pros Share that

• - ambiguous situations (decisions) easier to handle are,
• - The effort for the evaluation is limited.

With regard to the project yaw moment compensation (GMK) as Practical application is to describe a method the driving situation braked cornering of the driving situation Can distinguish braking on μ-split. The result is in Form of a signal that values between 0. , , 1, to Available. The output signal is continuously updated so that also changes in the driving situation during a braking process be taken into account. The result is not later than 100 msec Start of braking or after the occurrence of a change in the driving situation to disposal.

The following signals are available, which are also required for GMK and ABS will be available:

• Front and rear steering angles (δ _v , δ _h ),
• - Brake pressures front left and front right (P _vl , P _vr ),
• - Vehicle speed (v _x ).

figure description

Reference to the drawings, embodiments of the invention be explained. It shows

Fig. 1 a control loop using different controls,

Fig. 2 is a block diagram of a classifier,

FIGS. 3 to 6 are diagrams

Fig. 7 shows a detailed embodiment of a classifier.

In Fig. 1, 1 is a vehicle on which certain quantities are measured. In a classifier 2 , the situation μ-split braking or cornering braking or straight-braking is detected and depending on the situation is switched to one of three controllers 3 with different Regelgesetzt, z. B. adjusts the Hinterachslenkwinkel δ _{h of} a vehicle in the sense of compensation of the yawing moment optimally.

Fig. 2 shows a block diagram of the classifier 2 consisting of the blocks feature generator 2.1 , evaluation 2.2 and Situationszu folder 2.3 .

The feature generator, the signals of the sensors available in the vehicle (δ _v , δ _h , P _vl , P _vr , V _x ) are supplied to a terminal 4 . From this, a suitable feature vector x _M (sj) is formed, which characterizes the corresponding situations on the basis of motor vehicle technical / control technical features. This feature vector becomes

• - easy to win,
• - sufficient for further processing in the classifier Provide information, and
• - contain as few ambiguities as possible for the decision hold.

The feature vector serves as the basis for the situation detection. With the help of the evaluator 2.2 , which works on the basis of fuzzy logic, the feature vector is evaluated. The result of the assessment is further used by a situation mapper to make a decision as to which situation exists.

One approach to the feature builder is the combination of the following sizes:

• - lateral acceleration b _y , the z. B. from δ _v , δ _h and v _x can be derived
• - Brake pressure difference .DELTA.P = P _vl - P _vr and
• - Brake pressure difference ΔP _mod in the event of a braking curve.

From the steering angle front and rear as well as from the vehicle speed can be the stationary lateral acceleration with the calculate the following relationship

The characteristic speed V _ch is a parameter dependent on vehicle data. This may even have a characteristic speed dependent on the estimated lateral acceleration. l₀ is the center distance.

Since the vehicle can not follow the steering angle directly, becomes Replica of the driving dynamics a dynamic link (PT1) turned on on. The driving dynamics change with the cross acceleration. At low values, the vehicle reacts faster on steering angle changes than on big ones.

To distinguish the different driving situations, the history is sometimes also important. For this reason, the lateral acceleration is estimated according to the above equation from the highly filtered front steering angle. As a steering angle filter, a moving averaging over a fairly long period is used. In this case, short-term fast dynamic steering interventions do not estimate large lateral accelerations, whereas for longer-lasting cornering, the lateral accelerations estimated with filtered and unfiltered steering angles are almost equal. This estimate is in the detailed embodiment of Fig. 7 in block 5 Runaway leads.

The pressure difference between the left and right wheels is on Measure for the friction coefficient difference and thus for the torque around the Vehicle vertical axis:

ΔP = P _vl - P _vr .

This difference is formed in a block 6 .

We are looking for the transfer function between the steering angle and brake pressure difference in the event of a curve braking. This modeled brake pressure difference ΔP _mod is to be compared with the measured brake pressure difference ΔP. In the case of a curve braking, a good agreement of both signals is to be expected, while with a μ-split braking, larger deviations occur.

You now define a learning factor

which characterizes the different driving situations as shown in FIG. 3:
α <1 μ split braking
α = 1 curve braking
α <1 straight braking.

One can thus recognize with the help of this factor, whether a curves braking or non-curve braking is present.

This factor is "learned" from existing measuring signals. A potential size is the lateral acceleration. Therefore, the learning becomes factor α as a function of the estimated lateral acceleration, the top of steering angle front, steering angle rear and vehicle speed is reproduced.

α = α (b _y )

In Fig. 4, the given for the vehicle is Darge presents. The situations of curve braking and μ-split braking are qualitatively described here in the form of the factor α (block 7 ).

The case of straight braking is introduced by the following Evaluation parameter r takes into account:

From Fig. 5 it can be seen that the very pronounced minimum of the evaluation variable r (= 0) characterizes the curve braking (block 8 ). However, the distinction between the driving situations μ-split braking and straight braking is not clear. One still needs the size Δ P, which is formed in block 6 .

It was thus found without modeling a rating that qualitatively describes the ratio Δ P _mod / Δ P.

From the variables b _y , Δ P and r, the corresponding membership functions can now be formed for the evaluation:

where the parameters w can be selected as follows
w _by = 10
w _{Δ P} = 0.04
w _r = 0.5

This process is taken in the evaluator 2.2 in blocks 9 , 10 and 11 before. The associated courses are shown in FIG. 6.

For situation recognition, the following rules are used in the situation mapper 2.3 :

For fuzzy implication, min or product operators may be used be used (Pedrycz 1989). For example, for rule 2.1:

min-operator: product-operator: μsplit = min (μ _{r, μ} Δ _P) μsplit = μ _r × μ _{Δ P}

For the final decision about the situation recognition, the maximum of all rules, which is formed in block 15 applies.

j = curve, split, even,. , ,  

The following criteria can be used to secure the decision to be introduced:

with 0 <Δ <1: identification distance.

Claims (1)

1. A method for detecting a situation in which a braked equipped with an antilock system vehicle is, characterized in that the lateral acceleration b _y and the difference of the braking pressures .DELTA.P the front wheels are determined that from the lateral acceleration b _y a size α abge is passed, which is at low lateral accelerations 0.5, with increasing lateral acceleration increases to 1 and then remains 1, that in accordance with the relationship γ = abs (1 / α - 1), a size r is determined that, in accordance with the relationships and Quantities μ _by , μ _{Δ P} and μ _{r are} obtained, where w _by , w _{Δ P} and w _{r are} constant, that at μ _by big a curve brake signal μ _KI , at μ _Δ P big and μ _r big a μ-split Brake signal μ _split and at μ _{Δ P} small and μ _r big a Geradeausbremssignal μ₆ is obtained and that the maximum value of the signals μ _K , μ _split and μ₆ is selected to identify the situation.