DE102014210539A1 - Method for forecasting a drive load - Google Patents

Abstract

In the method according to the invention, a drive load of a motor vehicle is predicted. An electronic horizon drive load forecast considers both a most likely path (10, 11, 12) and one or more alternative paths (19) as a route and weighting the paths according to multiple criteria. The advantage here is that already alternative paths (19) are included, which may differ greatly from characteristics of the most likely path (10, 11, 12). Traditionally, only this most probable path (10, 11, 12) is considered.

Description

The present invention relates to a method for predicting a drive load of a motor vehicle. Moreover, the present invention relates to a computer program that performs each step of the method according to the invention when it runs on a computing device, as well as a machine-readable storage medium that stores this computer program. Finally, the invention relates to an electronic control device which is set up to carry out the method according to the invention.

State of the art

Legislative CO ₂ limits and related penalties, as well as increasing environmental awareness among customers, are encouraging automakers to step up development and deployment of automotive components and functions to reduce fuel consumption. Since separate optimization of individual motor vehicle components is already well advanced and remaining potentials are therefore relatively expensive, work is currently being done in particular on novel driver assistance functions ("Advanced Driver Assistance Systems", ADAS) in support of an efficient, forward-looking driving style. Foresighted driver assistance systems use an electronic horizon which describes the route ahead as a function of the respective current vehicle position on the basis of characteristic, in particular geometric and traffic-technical properties and serves as the basis for the energetic evaluation of possible operating strategies of the motor vehicle and its components. A transmission of the predictive route data takes place in a motor vehicle system in principle by a provider ("Electronic Horizon Provider", EHP) - preferably the navigation unit - via a bus system - usually a CAN ("Controller Area Network") - to receiver control units. Due to the large quantity as well as demanded topicality and quality of the described data and a limited data rate of the bus system, this transmission of the data reaches its limits even in simple functions. Complex ADAS, however, also require dynamic additional information, such as traffic conditions and weather conditions, beyond stationary route attributes. In addition, several alternative routes must be taken into account that arise during the course of a journey.

The following describes services that provide these fixed link attributes and additional dynamic information.

The electronic horizon today is understood to mean, in particular, road gradient and curve curvature and the statutory speed limit. These are determined depending on the current vehicle position as stationary attributes along a route ahead. Without route guidance, a single Most Probable Path (MPP) is also usually transmitted based on the underlying road classes.

The traffic message channel (TMC) service transmits traffic impairments in the inaudible range of an FM signal in digital form. Modern navigation systems can receive traffic jams via TMC and thus create routes for avoiding congestion and disabilities (dynamic route guidance). The broadcast of TMC is now happening in many European countries of numerous radio stations.

"Floating Car Data" (FCD) refers to data that is generated from a motor vehicle that is currently involved in the traffic. By using the FCD method, motor vehicles become mobile sensors. Currently, taxi fleets are the main source of FCD generation.

"Extended Floating Car Data" (XFCD) is an extension of FCD, in which data from driver assistance systems such as ABS, ASR, ESP and rain sensors are processed as FCD. For example, on the basis of the ABS and ESP, an icy road section can be recognized in order to warn subsequent drivers.

The term TPEG "Transport Protocol Experts Group" refers to a method for the digital transmission of language-independent and multimodal traffic and travel information. In particular, TPEG offers TMC advantages over a more detailed description of traffic events, as well as parking and weather information, and a more precise location of each traffic point in a digital map.

For transmission of data from a navigation system to other components in the motor vehicle, an industry standard is available. This industry standard is the Advanced Driver Assistance System Interface Specification (ADASIS) standard, which is continually evolving, with this development being organized as part of an ADASIS forum. Initial product implementations of an ADASIS interface have been announced by automakers, such as Integrated Predictive Cruise Control (IPCC). However, ADASIS leaves room for interpretation.

The data that can be provided by the navigation device in the sense of a look ahead to other components in the motor vehicle are dependent on map data, which are available from card suppliers and may differ depending on the card supplier both in scope and in quality. In the summer of 2012, it was assumed that information relevant to energy, such as in particular height, slope profiles, curvatures and speed profiles, would not be available across the board. In particular, speed information and elevation and elevation information within Europe are essentially limited to high-level road classes. High-ranking street classes in Germany are to be understood as motorways and federal highways. According to roadmaps of the map suppliers, the area-wide availability of the elevation, the slope profiles, the curvatures and the speed information for the high-ranking road classes is planned. However, the availability of, for example, the slope information for residential streets in the foreseeable future is not expected.

Disclosure of the invention

In the method according to the invention, a drive load of a motor vehicle is predicted. An electronic horizon drive load forecast considers both a most likely path and one or more alternate paths as a route and weighting the paths according to several criteria. The alternative paths can be very different from characteristics of the most likely path. By taking into account the alternative paths, a more robust drive load prognosis is achieved than without consideration of alternative paths.

Preferably, driving resistances, such as rolling resistance, air resistance, gradient resistance and acceleration resistance, are taken into account for the drive load prognosis. Particularly preferably, the gradient resistance is determined on the basis of slope and / or height information of digital maps. Likewise particularly preferably, vehicle-specific data including a rolling resistance coefficient, an air resistance coefficient and an end face of the vehicle are taken into account for calculating the rolling and / or air resistance. The correct consideration of the driving resistances by means of digital maps ensures that the drive load prognosis is optimally calculated.

Particularly preferably, at least one of the parameters rolling resistance, aerodynamic drag and acceleration resistance is calculated on the basis of digital maps and an expected speed profile of the motor vehicle is predicted. The prediction of the speed profile of the motor vehicle allows the calculation of a drive load prognosis.

Most preferably, at least one of the parameters statutory road classes, speed limits, curve bends, traffic forecasts, weather forecasts, preferred driving routes, driving style and comfort settings are taken into account when forecasting the speed profile. This has the advantage that the prediction of the velocity profile is predicted as precisely as possible. The more parameters that are taken into account, the more precise the prediction of the speed profile becomes.

It is furthermore very particularly preferred that an energy requirement of a route is calculated numerically by means of the course of the speed. By means of the energy demand, the power for the drive load prognosis can be determined easily.

Preferably, a desired battery charge state is generated as the basis of predictive hybrid operating strategies. This can save fuel and CO ₂ emissions.

Preferably, the plurality of criteria selected from the energy needs of the motor vehicle, height differences of the route, incline, gradient, road classes or traffic. By means of these criteria, alternative paths can be characterized very well.

The drive load prognosis is preferably generated on the basis of data of the motor vehicle, the travel route and environmental conditions, and is made available to other further-processing control devices via a bus system of the motor vehicle. The drive load prognosis for the most probable path and the alternative paths are particularly preferably distributed separately from one another via the bus system. As a result, the drive load forecast can also be used for other calculations.

The drive load prognosis is already aggregated in a control device of the motor vehicle, in particular in the navigation device, particularly preferably to an equivalence factor and communicated via the bus system to other control devices of the motor vehicle. An equivalence factor according to the invention is understood to mean a factor of an equivalence relationship between two forms of energy. The Equivalence Factor makes optimizing total energy consumption very easy.

In particular, the equivalence factor is dependent on a driving cycle, of a Route topography, which includes altitude differences of the route, incline and incline, and driving style. Furthermore, in particular, the equivalence factor is continuously adapted to the driving cycle, the route topography and the driving style. This ensures that the equivalence factor is optimized.

Very particularly preferably, alternative paths with gradients are assigned a high importance in comparison with alternative paths without gradients when the equivalence factor is adjusted. This ensures that no recuperation potential of a traction battery is given away.

When forecasting the drive load, a route characteristic of the preceding route can be aggregated in a single size. To implement the predicted drive load in a network of control units, this is preferably provided in a predefined data format. The data format can be chosen so that a further processing of the drive load forecast in all other control units of the motor vehicle is possible. There may optionally be a further conversion of the predicted drive load in a size that is required for further computing steps.

The computer program according to the invention makes it possible to implement the method according to the invention in an existing electronic control unit, without having to make any structural changes thereto. For this purpose, it performs each step of the method according to the invention, in particular if it runs on a computing device or electronic control unit. The machine-readable storage medium according to the invention stores the computer program according to the invention. By applying the computer program according to the invention to an electronic control unit, the electronic control unit according to the invention is obtained, which is set up to derive and implement a predicted drive load in the combination of control units of a motor vehicle by means of the method according to the invention.

Short description of the drawing

Embodiments of the invention are illustrated in the drawings and explained in more detail in the following description.

The figure shows the determination of a drive load prognosis of an embodiment of the method according to the invention.

Embodiment of the invention

In the following, a first embodiment of the method according to the invention will also be explained inter alia by means of the figure. Here, all relevant data of an electronic horizon are already summarized within the navigation unit to a forecast of a time-varying drive load of a motor vehicle and provided in this form the participating control units via a bus system. To implement the predicted drive load in the network of control units involved, the predicted drive load is provided in the form of vehicle acceleration, the required drive load over the line, or the required drive load over time. The summary is independent of variable motor vehicle parameters and thus represents the expected due to a motor vehicle environment performance requirements and potentials of the next route horizon that can be interpreted in individual driver assistance functions suitable, for example to select electrically covered travel sections or to operate ancillary components of the motor vehicle at the best possible time , Already included are alternative routes, which may differ greatly from a characteristic of the most probable path (MPP).

In principle, the drive load prognosis is based on a longitudinal dynamics equation of the motor vehicle, in which the occurring travel resistances F _W are calculated as the sum of a rolling resistance F _R , an air resistance F _L , a gradient resistance F _S and an acceleration resistance F _B : F _W = F _R + F _L + F _S + F _B. (Formula 1)

The grade resistance F _S may be determined based on slope or height information from digital map data: F _S = m · g · sin α. (Formula 2)

Here, m denotes the mass of the motor vehicle, g the gravitational acceleration and α a pitch angle. In order to calculate roll resistance and air resistance, motor vehicle-specific data, such as rolling resistance coefficient, drag coefficient and end face of the motor vehicle, are stored in a navigation unit and do not change during the driving operation. The total rolling and air resistance is also shown in the form of three coefficients a, b, c of a second degree polynomial in which v is the speed of the motor vehicle and v _{rel is} the relative speed of the motor vehicle Motor vehicle relative to the air flowing around: FR + FL = a + b * v + c * v _rel ² . (Formula 3)

This polynomial is used in a motor vehicle development process and is determined by Ausrollmessungen on flat test tracks. If these motor vehicle-specific coefficients are not available to the navigation unit, a calculation is based on standard values.

To calculate the rolling resistance, the air resistance and the acceleration resistance, an expected speed profile is predicted on the basis of digital map data. This velocity profile takes into account various stationary map attributes, such as legal road classes, speed limits and turns, but also dynamically varying additional information, such as traffic and weather forecasts, driver's preferred routes, driving habits and comfort settings. The result of a prediction of the velocity profile is the velocity profile v (s) outlined in the figure. 10 over the considered route horizon s. Furthermore, in the figure, the height profile h (s) 11 and the acceleration profile a (s) 12 shown. The outlined speed profile v (s) 10 is divided into four main sections. In the first main section 13 the motor vehicle drives in the local area and thus the speed of the motor vehicle is limited. In the second main section 14 The speed of the motor vehicle is again limited, but on a highway at a much higher speed than in the local area. The motor vehicle is in the third main section 15 in a traffic jam. Accordingly, its speed is lower than in the first and second main sections 13 . 14 , In the fourth main section 16 is the velocity profile v (s) 10 of the motor vehicle for a highway. Within this fourth main section 16 There are two minor sections 17 . 18 in which the motor vehicle is at a significantly reduced speed compared to the straight line in a curve. The height profile h (s) 11 and the acceleration profile a (s) 12 are shown accordingly in the figure. In the acceleration profile a (s) 12 is another acceleration profile a (s) 19 an alternative route shown. For the acceleration profiles a (s) 12 . 19 is valid that with positive acceleration values the motor vehicle is driven and with negative acceleration values recuperation takes place.

In order to ensure independence from a variable vehicle mass, a predicted drive and recuperation power is calculated by a predicted acceleration profile a (s). 12 expressed as a division of the acceleration resistance F _B (see formula 1) is interpreted by the vehicle mass. The energy demand to be expected in a considered route horizon is calculated by simple numerical integration in different receiver control devices of the motor vehicle. Likewise, in these receiver control units a desired battery charge state is generated as the basis of predictive hybrid operating strategies. In addition, the forecast of the velocity profile v (s) 10 used to obtain a drive load forecast and to account for drive and recuperation efficiencies, power electronics operating limits, traction battery operating limits, or ancillary power requirements. In addition, this is used to anticipate the operation of the components mentioned to the drive load forecast. If, for example, a potential thermal overload of a component is detected, this can be prevented by preventative cooling. If load requirements within certain limits are temporally displaceable - such as those of an air conditioning compressor - then they can be moved into time ranges that allow a particularly efficient operation of the components.

According to the first embodiment, the possibility advantageously results of deviating from a most probable path and of including alternative paths in the prediction of the speed profile. Such an alternative path is shown in the figure for the acceleration profile a (s) 19 shown.

When calculating the most likely path through the Electronic Horizon Provider (EHP), multiple paths are internally evaluated and tracked to a defined depth. Due to a limited data rate of a bus system of the motor vehicle, however, only the most probable path itself is shipped in a conventional manner, which can lead to considerable impairment of participating assistance functionaries in the case of short-term travel route changes. This becomes important in energy management functions.

An air-conditioning compressor, whose load requirement during the previous driving operation has been deliberately set back to work as efficiently as possible over the predicted gradient, would have to be operated more inefficiently in the event of an unforeseen detour over a small hill in order to ensure the required climate comfort. Such an unforeseen detour could also lead to a traction battery of the hybrid vehicle that has already been deliberately discharged to absorb braking energy in a predicted gradient in a hybrid vehicle with predictive energy management, ultimately having to be charged with poor efficiency to meet charging limits and To be able to supply auxiliary consumers.

In the method according to the invention according to the first exemplary embodiment, such situations are already deliberately included in a drive load prognosis with knowledge of the entire route network and of the alternative paths. If all alternative paths differing from the most probable path have a significantly higher energy requirement, this can already be included in the drive load prognosis, taking into account the different probabilities of the route selection, to a defined proportion (see the figure). The susceptibility of the assistance functions involved to short-term route changes can thereby be significantly reduced. The drive load forecast can be transmitted using the industry standard ADASIS Version V2.

Many hybrid operating strategies are based on minimizing a total energy consumption of the motor vehicle, consisting of the energy content of the spent fuel and the electrical energy extracted from the traction battery. In order to simplify the optimization of the hybrid operating strategy, an equivalence factor is used to convert from fossil to electrical energy. It is therefore spoken of an "Equivalent Consumption Minimization Strategy (ECMs)". According to the first exemplary embodiment of the invention, the drive load forecast described is distributed via a bus system of the motor vehicle to further processing control devices of the motor vehicle, which calculate an equivalence factor based on received data, which leads to optimum operation of the motor vehicle.

The equivalence factor itself, however, depends to a significant extent on the driving cycle, a route topography and driving style. If a route leads over many downhill grades, the electric energy from the traction battery will be cheap compared to the chemical energy of the fuel, as it can easily be recuperated. The equivalence factor thus also reflects to some extent the characteristics of the route. The first embodiment of the invention relates inter alia to the calculation and continuous adaptation of the equivalence factor or a suitable multiplier, already in a navigation system of the motor vehicle to which all relevant information is available, and its transmission to the further processing control devices, in addition to the information of the electrical horizon , If the alternative paths have a larger gradient than the most probable path, the equivalence factor is reduced according to the invention and the cost of electric driving decreases, which shifts a weighting to more electric driving, since the probability of an increased recuperation potential increases. Conversely, if the alternate paths have a greater slope than the most likely path, the equivalence factor would be increased and the cost of electric driving further increased, shifting the emphasis to less electric driving and more IC driving, as the likelihood of increased energy expenditure on uphill driving increases. If the alternative paths have different gradients and slopes, a weighting of the equivalence factor can be effected as a function of the priority path of the alternative path and of the gradient and the gradient. In order not to give away any recuperation potential, the alternative paths and the most likely path with a slope can be assigned a comparatively higher weighting. As a result, the equivalent factor is reduced, which reduces the cost of electric driving, resulting in a lower charge state of the traction battery, whereby more Rekuperationspotential is available. If, within the respective alternative path, the gradient is not monotonically increasing or decreasing, the gradients and gradients within this path can be cumulated for one another. The description of uphill and downhill gradients will be made via a fictitious accumulated uphill section and a cumulative fictitious downhill section. The difference will be considered in the weighting described above.

According to a second embodiment of the invention, the drive load prognosis for the most probable path is transmitted independently of the drive load forecasts of the alternative paths. The drive load forecasts for alternative paths are transferred separately in this case. Advantage of the inventive method according to the second embodiment is that the drive load forecast for the most likely path is derived only from properties of the most likely path and thus is not variable over time or the way. Furthermore, information for the alternative paths is transmitted only for short distances, which result in small amounts of data, since the drive load forecast for the alternative paths is aggregated. An aggregation of the drive load forecast for branching paths is made for adjustable lengths of the alternative paths. The aggregation of the drive load forecast takes place via a simple averaging of the load profile for the specified route.

Claims (14)

Method for forecasting a drive load of a motor vehicle, characterized in that a drive load forecast in the form of an electronic horizon both a most probable path ( 10 . 11 . 12 ) as well as one or more alternative paths ( 19 ) considered as a route and weight the paths according to several criteria. Method according to Claim 1, characterized in that driving resistances, such as the rolling resistance, air resistance, gradient resistance and the acceleration resistance, are taken into account for the drive load prognosis. Method according to one of claims 1 or 2, characterized in that at least one of the parameters rolling resistance, aerodynamic drag and acceleration resistance are calculated on the basis of digital maps and an expected speed course ( 10 ) of the motor vehicle is predicted. A method according to claim 3, characterized in that at least one of the parameters legal road classes, speed limits, curvatures, traffic forecasts, weather forecasts, preferred routes, driving style and comfort settings when predicting the velocity course ( 10 ) is taken into account. Method according to one of claims 3 or 4, characterized in that an energy requirement of a route by means of the velocity course ( 10 ) is calculated numerically. Method according to one of claims 1 to 5, characterized in that the plurality of criteria are selected from the energy needs of the motor vehicle, height differences of the route, incline, gradient, road classes or traffic. Method according to one of claims 1 to 6, characterized in that based on data of the motor vehicle, the route and environmental conditions, the drive load forecast is generated and is provided via a bus system of the motor vehicle other processing controllers available. Method according to claim 7, characterized in that the drive load prognosis for the most probable path ( 10 . 11 . 12 ) and the alternative paths ( 19 ) are distributed separately from one another via the bus system. Method according to one of claims 7 or 8, characterized in that the drive load forecast is already aggregated in a navigation device of the motor vehicle to an equivalence factor or a multiplier and is communicated via the bus system to other control devices of the motor vehicle. A method according to claim 9, characterized in that the equivalence factor or the multiplier is determined in dependence on a driving cycle, of a route topography, the height differences of the route, incline and inclination, and a driving style. A method according to claim 10, characterized in that the equivalence factor or a multiplier is continuously adapted to the driving cycle, the route topography and the driving style. A computer program adapted to perform each step of the method of any one of claims 1 to 11. A machine-readable storage medium on which a computer program according to claim 12 is stored. Electronic control unit, which is set up to predict drive load of a motor vehicle according to one of claims 1 to 11.

Images