• 专利附录
  • 专利说明
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    • 专利摘要:
    The present invention relates to a method for determining at least one zone (ZON) of a road network reachable by a vehicle circulating within the road network. The method is based on the use of a dynamic model (MOD) of the vehicle depending on the speed and acceleration of the vehicle, the construction of an adjoint graph (GA), and a shorter path algorithm (ALG ).
    公开号:FR3062834A1
    申请号:FR1751262
    申请日:2017-02-16
    公开日:2018-08-17
    发明作者:Giovanni DE NUNZIO；Laurent Thibault
    申请人:IFP Energies Nouvelles IFPEN；
    IPC主号:
    专利说明:

    ® FRENCH REPUBLIC
    NATIONAL INSTITUTE OF INDUSTRIAL PROPERTY © Publication number:
    (only to be used for reproduction orders) © National registration number
    062 834
    51262
    COURBEVOIE © Int Cl 8 : B 60 W 40/02 (2017.01)
    PATENT INVENTION APPLICATION
    A1
    ©) Date of filing: 16.02.17. © Applicant (s): IFP ENERGIES NOUVELLES Etablis- (© Priority: public education - FR. @ Inventor (s): DE NUNZIO GIOVANNI and THIBAULT x ~ x LAURENT. (43) Date of public availability of the request: 17.08.18 Bulletin 18/33. ©) List of documents cited in the report preliminary research: Refer to end of present booklet (© References to other national documents ® Holder (s): IFP ENERGIES NOUVELLES Etablisse- related: public. ©) Extension request (s): © Agent (s): IFP ENERGIES NOUVELLES.
    METHOD FOR DETERMINING A ZONE REACHABLE BY A VEHICLE USING A DYNAMIC MODEL AND AN ASSISTANT GRAPH.
    The present invention relates to a method for determining at least one zone (ZON) of a road network reachable by a vehicle traveling within the road network. The method is based on the use of a dynamic model (MOD) of the vehicle depending on the speed and acceleration of the vehicle, the construction of an assistant graph (GA), and a shortest path algorithm (ALG ).
    FR 3 062 834 - A1
    Illllllllllllllllllllllllllllllllllllllllllllll
    The present invention relates to the field of vehicle navigation, and in particular to the field of predicting the radius of action of a vehicle, which corresponds to the definition of the positions attainable by the vehicle as a function of the stored energy remaining at within the vehicle.
    According to the International Energy Agency, more than 50% of the oil used in the world is intended for the transport sector, including more than three quarters for road transport. According to the same agency, the transport sector was responsible for almost a quarter (23.8%) of greenhouse gas emissions and more than a quarter (27.9%) of CO 2 emissions in Europe in 2006.
    It is therefore increasingly important to increase the energy efficiency of road trips, to reduce energy consumption, whether it is fossil or electric energy. To achieve this, driver assistance systems (ADAS, “Advanced Driver Assistance Systems”) represent a promising solution, because it is economical (because you can simply use the driver's smartphone) and not intrusive (because there is no need to make any changes to the mechanical components of the vehicle).
    In addition, increasing the autonomy of electric vehicles represents a major challenge for their development. Indeed, the low storage capacities of the batteries and the high recharging times make the use of electric vehicles complex and slow their diffusion. In addition, these problems tend to further limit the use of electric vehicles, because often the remaining range in kilometers of road actually displayed to the user is not reliable. Consequently, electric vehicles with a low remaining range are often underused.
    The patent application US 2015/127204 A1 relates to a device for predicting the radius of action of a vehicle. However, the device described in this document does not describe the method of calculating the autonomy of the vehicle. It is therefore not guaranteed that the device presented in this document presents the range of action of the vehicle in a reliable and precise manner.
    The patent application US 2016/129803 A1 relates to a method for predicting the radius of action of a vehicle. However, the method described in this document does not take into account the impact of roads and infrastructure on energy expenditure. The range obtained by the method described in this document is therefore not reliable and precise.
    The patent application US 2014/0278038 A1 relates to a method of calculating the energy radius by means of an approach to modeling energy expenditure, and by taking into account the maneuvers and the impact of the infrastructure. However, this method requires significant memory to perform the calculation. In fact, this document proposes a solution for saving cartographic data in the form of an adjacency list, which requires a large memory. In addition, the method described in this patent application US 2014/0278038 A1 overestimates the energy during the acceleration phases by assuming that the engine power reaches its maximum value. In addition, the method described in this document does not give high precision. Indeed, this method makes the assumption of a constant slope on an arc, which is a simplifying assumption which decreases the precision. In addition, this method uses a macroscopic approach based on the variation of kinetic energy with the assumption of a flat efficiency.
    To overcome these drawbacks, the present invention relates to a method for determining at least one area (a radius of action) of a road network reachable by a vehicle traveling within the road network. The method is based on the use of a dynamic model of the vehicle depending on the speed and acceleration of the vehicle, the construction of an assistant graph, and a shortest path algorithm. The use of such a dynamic model and the construction of an associated graph allow better precision of the energy consumed, in particular by taking acceleration into account, which makes it possible to determine the radius of action precisely. In addition, the use of an assistant graph makes it possible to significantly reduce the memory necessary to carry out the determination of the area. Thus, the method according to the invention allows more efficient management of energy, in particular more efficient management of the battery of an electric vehicle.
    The method according to the invention
    The invention relates to a method for determining at least one area of a road network reachable by a vehicle traveling within said road network. For this process, the following steps are carried out:
    a) the position of said vehicle and the amount of energy stored in said vehicle are identified;
    b) a dynamic model of said vehicle is constructed which relates the energy consumed by said vehicle to the speed and acceleration of said vehicle, as well as to the slope profile of the road;
    c) an assistant graph of said road network is constructed around said identified position of said vehicle;
    d) the energy consumed by said vehicle for each arc of said assistant graph is determined by means of said dynamic model of the vehicle and of an average speed of said vehicle on said arc considered, of the slope profile on said arc considered and of an acceleration of said vehicle to reach said average speed on said arc considered; and.
    e) at least one zone of said road network reachable by said vehicle is determined with said quantity of energy stored in said vehicle by means of a shortest path algorithm which minimizes on said attached graph said energy consumed, said path algorithm the shortest being constrained by said amount of energy stored in said vehicle.
    According to one embodiment, said average speed and said acceleration of said vehicle are determined by means of traffic conditions and / or the topology and / or infrastructures of said road network.
    Advantageously, said traffic conditions are obtained in real time by communication with online data services.
    Alternatively, said traffic conditions are stored in historical data storage means.
    In accordance with an implementation, said assistant graph is constructed using the topology of said road network.
    Advantageously, said topology of said road network is determined by means of geolocation.
    According to an embodiment option, said dynamic model of the vehicle depends on intrinsic parameters of said vehicle.
    Preferably, said intrinsic parameters of said vehicle are obtained from a database, or are indicated by a user.
    According to one embodiment, said zone is displayed on an autonomous device or on the dashboard of said vehicle.
    According to one characteristic, said dynamic model of said vehicle depends on the power demand of at least one auxiliary system of said vehicle.
    Advantageously, said demand for power from at least one auxiliary system is a function of the outside temperature.
    Advantageously, said shortest path algorithm is a
    Bellman-Ford.
    According to one implementation, the consumption of said vehicle is determined for at least one point in said zone.
    According to one embodiment, said assistant graph is constructed by implementing the following steps:
    i) a direct graph of said road network is constructed with nodes and arcs, said nodes of said direct graph corresponding to the intersections of said road network, and said arcs of said direct graph corresponding to the roads connecting said intersections; and ii) said assistant graph of said road network is constructed with nodes and arcs, said nodes of said assistant graph corresponding to the arcs of said direct graph and said arcs of said assistant graph corresponding to the adjacency of said arcs of said direct graph.
    According to one characteristic, said vehicle is an electric vehicle, and the amount of energy stored in the vehicle corresponds to the state of charge of the battery of said vehicle.
    In addition, the invention relates to a method for managing a battery of a vehicle traveling within a road network. For this process, the following steps are implemented:
    a) at least one area of said road network reachable by said vehicle within said road network is determined by means of the determination method according to one of the preceding characteristics; and
    b) the use and / or charge of said battery is managed according to said reachable area.
    Furthermore, the invention relates to a computer program product downloadable from a communication network and / or recorded on a computer-readable medium and / or executable by a processor or a server, comprising program code instructions for setting implementing the method according to one of the preceding characteristics, when said program is executed on a computer or on a mobile telephone.
    Brief presentation of the figures
    Other characteristics and advantages of the method according to the invention will appear on reading the following description of nonlimiting examples of embodiments, with reference to the appended figures and described below.
    FIG. 1 illustrates the steps of the method according to a first embodiment of the invention.
    FIG. 2 illustrates the steps of the method according to a second embodiment of the invention.
    FIG. 3 illustrates the construction of an assistant graph according to an embodiment of the invention.
    Figure 4 is a curve illustrating the power demand of an auxiliary system as a function of the outside temperature.
    FIG. 5 is a first example of a card obtained by the method according to an embodiment of the invention and by a method according to the prior art.
    FIG. 6 is a second example of a card obtained by the method according to an embodiment of the invention.
    FIG. 7 is a third example of a card obtained by the method according to an embodiment of the invention, respectively for a one-way journey, and for a round-trip journey.
    Figures 8a and 8b illustrate two examples of the road network.
    Detailed description of the invention
    The present invention relates to a method for predicting at least one area of a road network by a vehicle traveling within the road network. In other words, the method according to the invention makes it possible to determine the radius of action of a vehicle.
    The method according to the invention is suitable for any type of vehicle: thermal vehicles, hybrid vehicles, electric vehicles, etc.
    Ratings
    In the following description, the following notations are used:
    V Vehicle speed [m / s] X Vehicle position [m] m Vehicle mass [kg] ω Vehicle engine speed [tr / s] F w Tractive effort from vehicle to wheel [NOT] F 1 areo Aerodynamic force on the vehicle [NOT] Friction Friction force undergone by the vehicle [NOT] Fslope Normal force undergone by the vehicle (gravity) [NOT] F 1 res Resulting from aerodynamic and rolling losses [NOT] at Road tilt angle [rad] Pa Air density [kg / m 3 ] A f Vehicle front surface [m2]
    c d c r
    Oq, ct 4 and ci2 r
    Pt
    9t
    T
    T 1 m, max
    T 1 m, min Pm Pb 9b
    Tamb i
    i - 1 v
    K
    Ëi
    E b
    Pi
    Pi
    All k
    F U jump, i tjump, i
    W
    Coefficient of aerodynamic resistance [-]
    Rolling resistance coefficient [-]
    Gravitational acceleration [m 2 / s]
    Vehicle parameters [-]
    Wheel radius [m]
    Vehicle transmission report [-]
    Vehicle transmission efficiency [-]
    Motor torque [Nm]
    Maximum engine torque [Nm]
    Minimum engine torque [Nm]
    Power available on the motor shaft [W]
    Power required from the battery [W]
    Aggregate efficiency of the electric traction chain [-]
    Vehicle auxiliary power [W]
    Ambient temperature [K]
    Route segment i [-]
    Route segment, preceding route segment i [-]
    Average traffic speed [m / s]
    Function
    Energy consumed in segment i [Wh]
    Energy consumed by the battery [Wh]
    Vehicle power in segment i at average speed [W]
    Vehicle power on segment i at variable speed [W]
    Travel time on segment i [s]
    Length of segment i [m]
    Energy consumed associated with the speed variation for the segment i [Wh] Time to achieve the speed variation for the segment i [s]
    Weight of the arc of the assistant graph [Wh]
    The method according to the invention comprises the following steps:
    For these notations, the derivative with respect to time is noted or by a point above the variable considered.
    1) identification of the vehicle position and the amount of energy stored
    2) construction of a dynamic model of the vehicle
    3) construction of an assistant graph
    4) determination of the energy consumed on the attached graph
    5) determination of the reachable area.
    The stages of construction of the dynamic model of the vehicle and construction of the associated graph can be carried out in this order, simultaneously or in reverse order.
    The method according to the invention can be carried out before the departure of the vehicle or when the vehicle is moving. Preferably, the method according to the invention is executed in real time.
    Thus the method according to the invention allows more efficient management of energy, in particular more efficient management of the battery of an electric vehicle.
    FIG. 1 illustrates, diagrammatically and in a nonlimiting manner, the steps of the method according to an embodiment of the invention.
    1) identification of the position (D) and the quantity of stored energy (Es)
    2) construction of the dynamic vehicle model (MOD)
    3) construction of the assistant graph (GA)
    4) determination of the energy consumed on the assistant graph, by means of the dynamic model (MOD) and the assistant graph (GA)
    5) determination of the reachable area (ZON) by means of a shortest path algorithm (ALG) applied to the assistant graph (GA) constrained by the quantity of stored energy (Es).
    FIG. 2 illustrates, schematically and without limitation, the steps of the method according to a second embodiment of the invention. In addition to the steps described in relation to FIG. 1, the method includes the following optional steps:
    - determination of the topology of the road network (TOP), the topology of the road network can be used for the construction of the dynamic model (MOD) and for the construction of the assistant graph (GA),
    - determination of road traffic (TRA), the determination of traffic can be used for the construction of the dynamic model (MOD) and for the construction of the assistant graph (GA),
    - determination of the intrinsic parameters of the vehicle (PAR), these parameters can be used for the construction of the dynamic model (MOD),
    - construction of a direct graph (GD) of the road network, the direct graph can be obtained by means of the topology (TOP) of the road network, and can be used for the construction of the assistant graph (GA).
    The steps for determining the topology of the road network (TOP), road traffic (TRA) and intrinsic vehicle parameters (PAR) are independent. It is therefore possible to carry out only part of these steps.
    All the process steps, including their variants proposed in FIG. 2 are described below.
    1) Identification of the position and the amount of energy stored
    During this step, the current position is identified on the one hand. In other words, we identify the starting point of the vehicle.
    The current vehicle position can be identified using a geolocation system (for example of the GPS or Galileo type). Alternatively, the current position can be indicated by a user by means of an interface with this (for example a smartphone, the dashboard, or a geolocation system).
    In this step, we also identify the amount of energy stored in the vehicle. The amount of energy stored in the vehicle corresponds to the remaining energy that can be used by the vehicle. It can be the total amount of energy remaining. Alternatively, the user or the designer of the vehicle can define an energy threshold to be kept (for example a minimum level of security), in this case, the amount of energy stored corresponds to the difference between the remaining energy and the energy threshold to conserve.
    For an electric vehicle, the amount of energy stored can correspond to the state of charge of the battery (noted SOC for "State of Charge") of the vehicle. The state of charge can be measured by any known means.
    For a thermal vehicle, the amount of energy stored can correspond to the amount of fuel in the vehicle's tank. The amount of fuel can be measured by any known means.
    For a hybrid vehicle, the amount of energy stored can correspond to a conversion in the same energy unit of the state of charge of the battery (SOC) and the amount of fuel of the vehicle. The state of charge and the amount of fuel can be measured by any known means.
    2) Construction of the dynamic model of the vehicle
    During this step, we build a dynamic model of the vehicle. The dynamic vehicle model is a model that relates the energy consumed by the vehicle to the speed and acceleration of the vehicle. The dynamic model of the vehicle can be built using the fundamental principle of dynamics, combined with an energy model of the engine.
    According to an implementation of the invention (cf. step of determining the intrinsic parameters of the vehicle in FIG. 2), the model can be constructed from macroscopic parameters of the vehicle, for example: motorization of the vehicle, mass of the vehicle, maximum power, maximum speed, type of transmission, aerodynamic parameters, etc. Thus, the dynamic model is representative of the vehicle, and takes into account its specific characteristics.
    According to an alternative embodiment, the macroscopic parameters can be obtained from a database, which lists the various vehicles in circulation. For example, the macroscopic parameters can be obtained by indicating the vehicle registration number, the database associating the plate number with its design (make, model, engine ...), and including the macroscopic parameters of the vehicle.
    Alternatively, the macroscopic parameters can be manufacturer data entered by the user, in particular by means of an interface (for example a smartphone, the dashboard, or a geolocation system).
    The dynamic model of the vehicle may also depend on road parameters, such as the slope of the road. Such data can be obtained from a topology (see step of determining the topology in Figure 2) or from a map of the road network.
    The dynamic model of the vehicle takes into account the dynamics of the vehicle. It can be built from the application of the fundamental principle of vehicle dynamics applied to its longitudinal axis, and can be written in the following form:
    dv (t) r- r- r m - Fv Faero Ff r i c ti on F s i O p e
    Where m is the mass of the vehicle, v (t) its speed, F w the wheel force, F aero the aerodynamic force, F friction the rolling resistance force, F slope the gravitational force.
    So the model can be rewritten:
    x (t) = ν (ί) (zni> (C) = F w - ^ p a A f c d v (t) 2 - mgc r - mg sin (a (x))
    Where p a is the air density, A f the front surface of the vehicle, c d the aerodynamic resistance coefficient, c r the rolling resistance coefficient, a (x) the slope of the road as a function of the position , and g the gravitational acceleration. The sum of the aerodynamic and rolling losses are generally approximated with a second order polynomial as a function of the speed v:
    Eres E aero + Ff riction a 2 v (t) + ûqv (t) + Ωθ
    Where the parameters a 0 , α Ύ and a 2 can be identified for the vehicle considered from a standard test called "coast down", meaning downward slope.
    So the force at the wheel can be expressed as:
    F w = mv (t) + a 2 v (f) 2 + cpvÇt ') + a 0 + mg sin (a (x))
    In the following, the dynamic model of the vehicle is described for a non-limiting embodiment of an electric vehicle. The electric vehicle comprises at least one electric machine, at least one means for storing electric energy (such as a battery) to supply the electric motor or to be supplied by the electric machine (in the case of regenerative braking) and energy recovery means, in particular regenerative braking means. However, the model is adaptable to any type of motorization (thermal, hybrid, electric).
    The torque required from the electric machine to achieve the required force at the wheel is defined as (in the equations "if" is the English translation of "if"):
    j F w r T 'in le
    T w n t V Pt if F w > 0 if F w <0
    Where r is the wheel radius, p t and g t are the transmission ratio and the transmission efficiency. An electric machine is generally a reversible machine, so it behaves like a motor when T m is positive and like a generator (energy recovery) when T m is negative. The torque generated by the electric machine is saturated by T mmax and T m, min · In particular, during the braking phases, if the engine torque is less negative than the saturation value T mmin , then the vehicle is only braked by the regenerative braking system. Otherwise, the mechanical brake intervenes by adding its action to regenerative braking.
    The power available at the motor shaft, in the presence of a regenerative braking system, can be defined as:
    l'm, max ^ (t '), if T m > T m , r p m = <T m rn (tf ifT m , <T <T min' m l m, max fPm, min < - i> ( t '), if T m <T m , m in
    Where <O t is the engine speed which is defined as:
    v ^ pt
    The demand for battery power is expressed as follows:
    i / p ^ °
    VmÎJb, if <θ
    Where η „is the aggregate efficiency of the electric traction chain (inverter, battery, etc.).
    According to one embodiment of the invention, in order to improve the accuracy of the model and of the estimation of the energy consumption of a journey, it is possible to take into account the power demand of at least one auxiliary system in the model of vehicle dynamics. Indeed, the power required by the driver for comfort, in particular for heating the passenger compartment or for air conditioning, is particularly expensive in terms of energy consumption, in particular for an electric vehicle on which the heating can have an impact. very strong on autonomy. The term of power requested by the auxiliaries can be expressed as a function of the ambient temperature:
    P aux = K (Tamb)
    A non-limiting example of this function which links the auxiliary power P aux to the ambient temperature T amb is illustrated in FIG. 4. For the example illustrated, the function is a piecewise refined function, which decreases for low temperatures ( passenger compartment heating) then increasing for high temperatures (passenger compartment air conditioning).
    Therefore, for this embodiment, the energy consumption at the battery over a time horizon T can be defined as:
    Pb = f Pb + Paux dt Jo
    The model described above requires an instantaneous speed signal. This information is not available a priori on the road segments (road sections of the road network), on which the only information available is average speeds.
    According to the invention, we first consider an average speed on each road segment, then we consider the vehicle acceleration to reach this average speed from the previous segment. Preferably, the average speed can be obtained from information on road traffic on the road network.
    According to a variant, the average speed over a segment can be obtained in real time by communication with online data services, which acquire real-time information on traffic on the road network. This optional step of determining the traffic is described for the embodiment of FIG. 2.
    Alternatively, the average speed can be stored by means of historical data storage, which store traffic data on the road network, in particular for different days, different times, etc.
    Thus, if we assume that the average speed v due to traffic in a road segment is known, we can modify the model described above to estimate the energy consumption of the vehicle to cover the road segment considered. Subsequently, in the dynamic model, the speed v (t) is replaced by the average traffic speed v. It is therefore assumed that all of the vehicles on the road segment i travel at speed v t . Therefore, the expression of the force at the wheel is modified for each road segment i:
    F wi = a 2 ^ i + a Ni + a o + m 9 sin (aj (x))
    Where the term acceleration disappears. The engine torque becomes:
    (fi
    Fw.ri Prih ’
    Pt if F wi > 0 if Ë wi <0
    The engine speed is also constant over time since we assume a constant speed v t :
    _ = ÿjpt
    I r
    The mechanical power available to the electric machine is rewritten as follows:
    (Εη, ηιαχ 'if T m i> T mmax l'm.i' if T mm i n <T mi <T mmax
    Tm.min 'if T m i <T mm i n
    In what follows, it is assumed that the torque saturation values are independent of the engine speed. However, other embodiments are valid, in particular the maximum and minimum torques can be dependent on the engine speed.
    The demand for power from the electric vehicle battery can be defined as:
    The energy consumption of the battery is therefore:
    Where Ti = li / vi is the journey time on the road segment i if one is traveling at the average speed of traffic Vj.
    The use of average speed in energy consumption models represents a standard approach in the prior art. The method according to the invention proposes taking acceleration into account in the dynamic model of the vehicle for a more precise and reliable estimate of the real consumption. To take into account acceleration phenomena, the route on each road segment is divided into two phases: a phase at constant cruising speed Vj, and a speed variation phase (i.e. d 'acceleration or deceleration) to go from speed v i _ 1 , that is to say the average speed of the previous segment, to speed Vj, that is to say the average speed of the current segment. Preferably, a constant acceleration (or deceleration) is considered to reach the speed Vj. Therefore, even if the macroscopic information available does not make it possible to know the temporal information, the spatial acceleration taking place at the interface between two road segments is considered. The energy consumption E jumpi associated with the speed variation between two road segments is defined as:
    F- = b jump, i
    With P bi the power demand from the battery for the acceleration phase to go from speed v ^, to speed Vj.
    Such a request for battery power can be obtained, as seen above, from a force at the interface wheel defined as:
    F w = m - a + a 2 v (t) 2 + α ± ν (ί) + a 0
    Where the variant speed in time v (t) in each transient can be linearly modeled here as:
    v (t) = v i - 1 + sigm (Vi - ν ^) a -1
    Where r; -! is the speed on the upstream segment, v t is the speed on the downstream segment, a is the constant acceleration to achieve the speed change. The speed variation is therefore carried out by:
    Vi - Vi-1
    Sign ^ - v ^) a
    Total energy consumption in segment i is defined as follows:
    Kb, i Eh, + Ej umpi
    Taking into account interface accelerations improves the accuracy of the energy estimate, and therefore the accuracy and reliability of determining the reachable areas.
    However, the information available a priori is not always complete or up to date. In particular, it is unlikely to have precise information on average traffic speeds for secondary streets. Therefore, it is possible to have long stretches of road on which the traffic speed will simply be a constant nominal value. In this case, taking into account only data from the road network would consist in supposing that there is no acceleration which would generate large errors in the estimation of energy consumption. This is the reason why the invention also makes it possible to enrich the data on the road network by integrating the speed disturbances induced by critical elements of the road infrastructure, in particular traffic lights, intersections and turns. For example, if we know that a traffic light is located at the interface between two segments, we take its impact into account when estimating consumption, taking into account the variation in speed between the two segments.
    Taking these accelerations into account makes it possible not only to obtain more realistic and precise energy costs, but also to avoid negative loops in the routing graph which models the road network. Indeed, the negative loop represents a sequence of road segments that has the same starting and ending point with a negative total cost. In the specific case of a graph weighted with energetic weights, this represents a situation of infinite energy recovery if we travel the loop continuously, which is impossible in reality. This criticality is easily verified if we consider electric vehicles and if the estimate of consumption on a road segment and its neighbors does not take into account important elements such as the slope and / or the accelerations to transit from one segment to the next. The presence of negative loops in the routing graph prevents finding a path that minimizes consumption overall, because the search algorithm would trivially converge on these loops to reduce consumption.
    In accordance with an implementation of the invention, the variation in speed between the two segments can be modeled as two transients: the first to go from speed to 0 (stopping the vehicle, for example at a traffic light), and the second to go from 0 to speed v t . Therefore, the energy consumption, linked to the variation of the speed can be described as the sum of two contributions:
    F-. = L-'jump.i
    Where the speed variation in the first term is modeled as:
    v ± (t) = Vi-i - a -1
    And the time to make the first variation:
    tjumpi, i ~ Ü-lA 1
    Similarly, the speed variation in the second term is modeled as: v 2 (t) = a t
    And the time to realize this variation:
    tjump2, i ~ Vi! d
    Consequently, according to the invention, the dynamic model of the vehicle can be written (for any type of vehicle):
    Ei - PAi +1 Pi dt
    o
    With Ei the energy consumed in segment i, Pi the power required from the vehicle's energy storage system (fuel tank, battery, etc.) when the vehicle is considered at constant speed in segment i, 7) the time during which the vehicle is considered at constant speed on segment i, P t the power required from the vehicle energy storage system when the vehicle is considered to have a speed variation (speed variation between segment j - 1 and segment i), and t jump i the time to achieve the speed variation. The first term of the model corresponding to the energy consumed in the segment due to the average speed, and the second term corresponds to the energy consumed due to the speed variation to reach the average speed.
    For the embodiment, according to which the power demand of at least one auxiliary system is taken into account, the dynamic model of the vehicle can be written (for any type of vehicle):
    tjump.i E i = (fi + Paux) T i + J (Pi + P aux) dt 0
    With Ei the energy consumed in segment i, Pi the power required from the vehicle's energy storage system (fuel tank, battery, etc.) when the vehicle is considered at constant speed in segment i, P aux la power demand of at least one auxiliary system, Ti the time during which the vehicle is considered at constant speed on segment i, P ^ the power requested from the vehicle energy storage system when the vehicle is considered to have a variation speed (speed variation between segment i -1 and segment i), and t jump i the time to achieve the speed variation. The first term of the model corresponding to the energy consumed in the segment due to the average speed, and the second term corresponds to the energy consumed due to the speed variation to reach the average speed.
    Remember that for an electric vehicle, the energy consumed can be negative. Braking can recover energy from the battery.
    3) Construction of the assistant graph
    During this step, an assistant graph of the road network is constructed. In theory of graphs, we call an assistant graph of a graph G (in this case the road network), a graph which represents the adjacency relation between the edges of G. The adjoining graph of a graph can be defined in the following way: each vertex of the adjoining graph represents an edge (also called an arc) of the graph G, and two vertices of the adjoining graph are adjacent (i.e. connected) if and only if the corresponding edges share one end common in graph G. Thus, the assistant graph is an equivalent representation of the road network where all the maneuvers are correctly decoupled and distinguished, which allows a precise determination of the energy costs. The use of an assistant graph allows a reduction of the memory necessary for the determination of the reachable zone compared to the use of a direct graph. Indeed, the data structure is less complex for an assistant graph.
    For the methods according to the prior art, the road network can be modeled as an oriented graph ("Directed Graph"). Consider the graph G = (V, A), where V is the set of nodes and A is the set of connections between the nodes, i.e. the arcs. Let w-.A-> W be a function which assigns a weight to each arc of the graph. In the graphs used for conventional navigation, the weight associated with the arcs represents either the length or the travel time. For the method according to the invention, each weight represents the energy consumption for traversing the arc.
    According to one embodiment of the invention, the objective of this work may be to design a strategy based solely on statistical and topological information from the road network, without any use of actual driving data. This type of information, very often incomplete and / or imprecise, is generally available on chargeable cartographic web-services (online services). For each arc ie A of the graph, it is possible to know the length, the average speed of the current traffic v t which depends on the hour of the day, and the slope of afx) which varies inside the arc considered according to the position. In addition, some mapping web services provide a degree of importance for each road segment, specifying whether it is a highway, a major urban axis, or a secondary urban street. In addition, the position of certain traffic lights may be available.
    By means of the method according to the invention, it is possible to considerably improve the accuracy of the estimation of the energy consumption and of the navigation taking into account the accelerations induced by the different speeds in the road segments and / or by elements of known infrastructure.
    The taking into account of the interface accelerations between the adjacent arcs poses a problem in the modeling of the road network as a direct graph (prior art) and especially in the assignment of the weights to each arc. In particular, each node of the graph with two or more incoming arcs is critical because v i _ 1 and therefore E jump i are not unique. Obviously, this fact prevents an unambiguous assignment of the weights on the arcs. Therefore, the direct graph G is not adequate for the proposed energy consumption model. This ambiguity can be resolved by using the attached graph as a graph for the proposed navigation strategy.
    According to one embodiment of the invention, the assistant graph of the road network is constructed by implementing the following steps:
    i) a direct graph of said road network is constructed with nodes and arcs (also called segments or edges), the nodes of the direct graph corresponding to the intersections of the road network, and the arcs of the direct graph corresponding to the roads connecting the intersections; and ii) the assistant graph of said road network is constructed with nodes and arcs, the nodes of the assistant graph corresponding to the arcs of the direct graph and the arcs of the assistant graph corresponding to the adjacency of said arcs of the direct graph.
    FIG. 3 illustrates schematically and without limitation these stages of construction of the assistant graph. The RR road network concerns an intersection between two roads. The first step is to build the direct GD graph from the road network. The direct graph GD comprises five nodes N, corresponding to the four ends of the roads and to the intersection thereof. In addition, the direct graph GD comprises eight arcs A connecting the nodes and corresponding to the roads of the road network RR. The second step consists in constructing the associated graph GA from the direct graph GD. The associated graph GA comprises eight nodes N corresponding to each arc of the direct graph GD. In addition, the assistant graph GA includes twenty arcs A corresponding to the adjacency of the nodes N of the direct graph GD.
    4) Determination of the energy consumed for each arc of the adjoining graph
    During this step, a weight is determined for each arc of the adjoining graph. The weight corresponds to the energy consumed by the vehicle on this arc. For this, we apply the dynamic model of the vehicle for each arc of the adjoining graph, considering the average speed of the vehicle on this arc, and the acceleration of the vehicle to reach the average speed. Thus, it is possible to know precisely the energy consumed on an arc, which makes it possible to determine an optimal route in terms of energy expenditure.
    The use of the associated graph L (G) as a routing graph makes it possible to assign a unique weight to each arc of the graph, by decoupling all the possible maneuvers modeled in the original graph G. Each arc of the associated graph represents a path on two adjacent arcs of the direct graph G, and therefore each arc of the associated graph L (G) contains information on an arc of the original direct graph G and also on its upstream arc.
    This intrinsic property of the associated graph allows not only to correctly consider the interface accelerations between adjacent arcs, but also to model in a more realistic way the impact of the infrastructure on energy consumption. More specifically, according to a proposed modeling approach, the energy term which takes into account infrastructure-induced shutdown / restart
    F ^] ump, i tjumpi.i tjump2, i
    I (P b i, i + P aux ) dt + I (P b2 , i + P aux ) dt oo
    This consideration can be introduced only on the arcs of the associated graph which represent the following situations:
    • a traffic light or stop sign is located at the junction between an upstream road of lower priority and a downstream road of higher priority.
    In this way, the green waves on the major axes are not penalized.
    • the upstream and downstream arcs are connected by a maneuver with a wider turning angle than an adjustable threshold.
    The associated graph L (G) = (F *, 4 *) of a graph G has as its nodes the arcs of the graph G, therefore i e A but also i e V *. So let w *: A * -> W * be a new function for assigning weights to the arcs of the associated graph. The weight for each arc k e A * is defined as follows:
    (Ë bi + E jumv i, if i - 1 e V * has incoming arcs
    W = (E bi + Ej Ump i + E bi _ 1 , if i - 1 EV * has no incoming arcs Remember that for an electric vehicle, the energy on an arc can be negative.
    Consequently, the weight of this arc of the associated graph can be negative. Braking can recover energy from the battery.
    5) Determination of the reachable area
    During this step, at least one area of the road network is determined which can be reached by the vehicle with the amount of energy stored starting from the identified position. This reachable area is determined by a method which minimizes energy consumption. The reachable area can be defined by a graphic representation on a road map. For example, the graphic representation can consist of all the points (positions) of the road network that the vehicle can reach, these points can be inscribed in a polygon. The reachable area may not be a "full" area: within the perimeter delimiting the area, critical areas (or points) that cannot be reached may exist. It can for example be critical zones (or points) corresponding to hills, mountains, etc. Thus the reachable area can be a polygon, within which the critical areas are excluded.
    This step is carried out taking into account the energy consumed on each arc of the associated graph. The determination of the reachable area is implemented by a shortest path algorithm. The shortest path algorithm determines the reachable area on the attached graph taking into account the consumed energy determined for each arc. Preferably, the optimal algorithm that calculates the shortest path in an oriented and weighted graph from a source vertex is the Bellman-Ford algorithm. The algorithm chosen is able to take into account a negative weight (that is to say an energy consumed) on at least one arc of the associated graph, unlike other algorithms like that of Dijkstra which, although more fast, is not optimal in the presence of arcs with negative weights. The use of a shortest path algorithm makes it possible to determine the widest achievable area possible (in a less conservative manner than the methods according to the prior art). Thus, the autonomy of the vehicle is increased.
    Once the algorithm restores the optimal sequence of nodes of the associated graph, this result can be easily transferred to the original graph, by generating the set of nodes of the original graph surrounding the origin of the displacement, i.e. - say the reachable area.
    According to an implementation of the invention, the method may include a step of recording offline global historical information on the traffic conditions of different days of a chosen week at different times of the day. Real-time adaptation is implemented only after the driver selects the starting point, and the departure time.
    An optional step of the method according to the invention may consist in displaying the determined reachable area, for example on a screen of a geolocation system (GPS, Galileo), of a smart phone, on the dashboard of the vehicle, on a website, etc. Thus, it is possible to inform the user or any other person (for example a vehicle fleet manager, a road infrastructure manager, etc.) of the reachable area. It is also possible to display the energy consumed for the points in the reachable area.
    The display of the reachable area can consist of representing on a map of the road network, all the points (positions) that the vehicle can reach.
    In addition, the method according to the invention can be used to determine the reachable area by providing a round trip. Indeed, an extension of the method according to the invention which is particularly attractive in the case of electric vehicles, may consist in calculating the radius of action when round trip journeys are envisaged. For an electric vehicle, it is interesting to know not only the possibility of reaching or not a destination, but also the capacity to return to the point of origin to recharge.
    This result can be obtained using the shortest path algorithm, for example the Bellman-Ford algorithm, on the "inverted" attached graph. This graph is obtained simply by reversing the direction of the arcs of the original assistant graph. Then, the shortest path algorithm (Bellman-Ford) can be executed on this graph always having as source node the same origin. This makes it possible to obtain all the optimal paths to reach the starting position from all the other nodes of the associated graph.
    According to one embodiment of the invention, the energy consumption of the vehicle is estimated from the start to at least one point in the reachable area. Preferably, the energy consumption for all points in the reachable area is determined. Indeed, the fact of using a dynamic model and a shortest path algorithm, in particular the Bellman Ford algorithm, makes it possible to estimate the energy consumption from the origin to all the destinations within the zone attainable. So, in the reachable area, one can know how energy consumption varies according to the different possible destinations. Thus, it is possible to optimize the management of the vehicle battery charge depending on the destination and the reachable area. According to an alternative embodiment of this embodiment, it is also possible to display the estimated energy consumption, for example on a screen of a geolocation system (GPS, Galileo), of a smart phone, on the dashboard of the vehicle. , on a website, etc.
    According to an embodiment according to the invention, the method according to the invention can be used as a tool for calibrating a simplified approach of the isodistance type. The iso-distance approach is a known approach which consists in determining a distance which the vehicle can travel according to the amount of energy stored, and in applying this distance to the starting point of the vehicle. This iso-distance approach therefore determines a substantially circular area of constant radius around the starting point. Such an application makes it possible to operate with very limited resources, both in terms of requests for cartographic data and computing power.
    In such an approach, the present invention is used once to design the simplified approach, which will then be used independently. The invention then makes it possible to calibrate the range per kilometer corresponding to a given vehicle and to a given geographical area. After this offline calibration of the iso-distance algorithm with the method according to the invention, the iso-distance algorithm calibrated online is applied to determine the reachable areas. In this application, the resulting autonomy is no longer optimal but will correspond to the worst case of the optimal approach and can therefore have the maximum admissible value with an iso-distance approach, more reliable and more precise than an iso- distance without calibration.
    The method according to the invention can be used for motor vehicles. However, it can be used in the field of road transport, the field of two-wheelers, etc ...
    In addition, the method according to the invention relates to a method for managing a battery of a vehicle, electric or hybrid, circulating within a road network. Battery management can include the following steps:
    - at least one area of the road network reachable by the vehicle is determined by means of the method of determination according to one of the variants described above, as a function of the starting position and the amount of energy stored in the battery, and
    - we manage the use and / or the charge of the battery according to the reachable area. Managing the use and / or charge of the battery can consist of determining a destination for the vehicle within the reachable area. The destination can be the final destination for the vehicle or a vehicle charging station, if the final destination desired by the user is outside the reachable area. Alternatively, managing the use and / or charging of the battery may consist in delaying and / or advancing its route so that the battery is fully charged. For example, if the final destination desired by the user is not in the reachable area, the user can charge the battery until his desired final destination is in the reachable area. Alternatively, if the user finds that he cannot make the desired journey, he can advance and / or modify his journey to charge the battery as soon as possible.
    For the embodiment for which the energy consumption for the reachable area is determined, the management of the use and / or the charge of the battery can be carried out as a function of the determined energy consumption, so as to optimize this management.
    Thanks to the precision and reliability of the method for determining the reachable area, the management of the vehicle battery is more efficient than with the methods according to the prior art. Thus, the battery life of the vehicle is increased.
    The invention further relates to a computer program product downloadable from a communication network and / or recorded on a computer-readable medium and / or executable by a processor or a server. This program includes program code instructions for implementing the method as described above, when the program is executed on a computer or a portable telephone or any similar system.
    Application examples
    The characteristics and advantages of the process according to the invention will appear more clearly on reading the examples of applications below.
    Example 1
    This example 1 relates to a comparison of the method according to the invention with a method according to the prior art of the iso-distance type.
    It is recalled that the iso-distance approach is a known approach which consists in determining a distance which the vehicle can travel as a function of the amount of energy stored, and in applying this distance to the starting point of the vehicle. This isodistance approach therefore determines a substantially circular area of constant radius around the starting point.
    For this comparison, we estimate a common starting point and a quantity of energy remaining of 1kWh. For the iso-distance approach according to the prior art, this amount of energy remaining corresponds to a distance to be covered of 5.5 km.
    Figure 5 illustrates, on a map of the Paris region, the results obtained for the AA iso-distance process. FIG. 5 illustrates, on the same map, the region reachable from the origin O for the method according to the invention INV.
    Thus, the comparison between the energy approach of the present invention INV and the state of the art AA makes it possible to show on the one hand that the method according to the invention makes it possible to give a less pessimistic prediction: it is possible to reach points located 11 km from the origin, twice the distance provided by the process according to the prior art. On the other hand, the polygon obtained with the energy approach (according to the invention) is not as symmetrical as that of the iso-distance approach (according to the prior art), this shows the impact of all the factors which impact energy consumption (slope, traffic, signaling, etc.) which are not taken into account in the approach of the prior art. Thus, the method according to the invention makes it possible to determine a reliable energy radius and in agreement with the reality of the road network.
    Example 2
    This example 2 also relates to a comparison of the method according to the invention with a method according to the prior art of the iso-distance type.
    For this comparison, we estimate a common starting point and a quantity of energy remaining of 1kWh. For the iso-distance approach according to the prior art, this amount of energy remaining corresponds to a distance to be covered of 5.5 km.
    FIG. 6 illustrates, on a map of the Paris region, the points which can be reached from the outset for the method according to the invention INV. It is noted that critical zones ZC cannot be reached by the vehicle. These are points corresponding to hills, for which an additional amount of energy is required.
    Example 3
    This example 3 relates to a comparison of the method according to the invention for a one-way journey and for a round-trip journey.
    For this comparison, we estimate a common starting point and a quantity of energy remaining of 1kWh for the outward journey, and an amount of remaining energy of 2 kWh for a return journey.
    Figure 7 illustrates, on a map of the Paris region, the reachable points P-A for a one-way journey from origin O, and the limits of the reachable zone ZON-AR for the return journey from origin O.
    We note that these two zones defined by the points P-A and ZON-AR are not identical. In the case of a round trip, the symmetry of the reachable area with respect to the point of origin is increased because the high energy costs in one direction (for example due to a positive elevation) are partially recovered during the journey in the opposite direction. In addition, points that cannot be reached in the case of a one-way journey become admissible if we consider the round-trip radius of action.
    Example 4
    This example 4 presents the advantages in terms of memory of the method according to the invention.
    With respect to the complexity of the data and algorithmic structure, modeling as an assistant graph, and using a list of arcs to represent this graph, has advantages.
    In terms of complexity of the data structure, for a given road network of n nodes and m arcs, an adjacency list (for example as used in patent application US 2014/0278038 A1) representing the assistant network graph contains V nodes and E arcs:
    V = m n
    E = ID (ni) * 0D (ni) i = t
    Where n t is the node i, lD (ni) is the number of incoming arcs in the node i, 0D (ni) is the number of outgoing arcs of the node i.
    The occupation in memory of this data structure is equal to V + E.
    The invention presented here, for a given road network of n nodes and m arcs, keeps in memory a list of arcs of dimension E:
    not
    E = ID (ni) * 0D (ni) i = t
    The occupation in memory of this data structure is equal to E, therefore less than that of the data structure of the prior art.
    In terms of algorithmic complexity, the shortest BellmanFord path algorithm has a complexity equal to O (V * E) in both cases. With the modification proposed in the invention, the algorithm converges with a complexity O (k * E) or k "V.
    To better understand the differences, two examples of road networks are shown in Figures 8a and 8b.
    - For the example of figure 8a:
    We have a number of nodes n equal to 4, and a number of arcs m equal to n (n-1) = 12.
    The method according to the invention manages a data structure of size n (n − 1) A 2 = 36, while in the case of an adjacency list of arcs (prior art) the size of the structure is η (η-1) + η (η-1) Λ 2 = 48.
    Therefore, the shortest path algorithm has a quadratic complexity for the method according to the invention, on the contrary, for the method according to the prior art, the complexity is of order 3.
    - For the example of figure 8b:
    We have a number of nodes n equal to 4 and a number of arcs equal to 5.
    The method according to the invention manages a structure of size 4, while the adjacency list of arcs (of the prior art) manages a number of arcs equal to 9. Consequently, in a case where the number of operations is comparable to the number of arcs of the road network, the complexity of the data structure is double for the prior art, compared to the method according to the invention. Thus, the invention requires less material (computer) resources, in particular in terms of memory, and has reduced computation time.
    权利要求:
    Claims (17)
    [1" id="c-fr-0001]
    Claims
    1) Method for determining at least one zone (ZON) of a road network reachable by a vehicle traveling within said road network, characterized in that the following steps are carried out:
    a) the position (D) of said vehicle and the quantity of energy stored (Es) in said vehicle are identified;
    b) a dynamic model (MOD) of said vehicle is constructed which relates the energy consumed by said vehicle to the speed and acceleration of said vehicle, as well as to the slope profile of the road;
    c) an assistant graph (GA) of said road network is constructed around said identified position (D) of said vehicle;
    d) the energy consumed by said vehicle for each arc of said assistant graph (GA) is determined by means of said dynamic model (MOD) of the vehicle and of an average speed of said vehicle on said considered arc, of the slope profile on said arc considered and of an acceleration of said vehicle to reach said average speed on said arc considered; and.
    e) at least one zone (ZON) of said road network reachable by said vehicle is determined with said quantity of energy stored (Es) in said vehicle by means of a shortest path algorithm (ALG) which minimizes on said graph adjoins (GA) said energy consumed, said shortest path algorithm being constrained by said amount of energy stored (Es) in said vehicle.
    [2" id="c-fr-0002]
    2) The method of claim 1, wherein said average speed and said acceleration of said vehicle are determined by traffic conditions (TRA) and / or topology (TOP) and / or the infrastructures of said road network.
    [3" id="c-fr-0003]
    3) Method according to claim 2, wherein said traffic conditions (TRA) are obtained in real time by communication with online data services.
    [4" id="c-fr-0004]
    4) Method according to claim 2, wherein said traffic conditions (TRA) are stored in historical data storage means.
    [5" id="c-fr-0005]
    5) Method according to one of the preceding claims, in which said assistant graph (GA) is constructed by means of the topology (TOP) of said road network.
    [6" id="c-fr-0006]
    6) Method according to one of claims 2 to 5, wherein said topology (TOP) of said road network (RR) is determined by geolocation means.
    [7" id="c-fr-0007]
    7) Method according to one of the preceding claims, wherein said dynamic model (MOD) of the vehicle depends on intrinsic parameters (PAR) of said vehicle.
    [8" id="c-fr-0008]
    8) The method of claim 7, wherein said intrinsic parameters (PAR) of said vehicle are obtained from a database, or are indicated by a user.
    [9" id="c-fr-0009]
    9) Method according to one of the preceding claims, wherein said zone (ZON) is displayed on an autonomous device or on the dashboard of said vehicle.
    [10" id="c-fr-0010]
    10) Method according to one of the preceding claims, wherein said dynamic model (MOD) of said vehicle depends on the power demand of at least one auxiliary system of said vehicle.
    [11" id="c-fr-0011]
    11) The method of claim 10, wherein said power demand of at least one auxiliary system is a function of the outside temperature.
    [12" id="c-fr-0012]
    12) Method according to one of the preceding claims, wherein said shortest path algorithm (ALG) is a Bellman-Ford algorithm.
    [13" id="c-fr-0013]
    13) Method according to one of the preceding claims, in which the consumption of said vehicle is determined for at least one point in said zone (ZON).
    [14" id="c-fr-0014]
    14) Method according to one of the preceding claims, in which said assistant graph (GA) is constructed by implementing the following steps:
    i) a direct graph (GD) of said road network (RR) is constructed with nodes (N) and arcs (A), said nodes (N) of said direct graph (GD) corresponding to the intersections of said road network, and said arcs (A) of said direct graph corresponding to the roads connecting said intersections; and ii) constructing said assistant graph (GA) of said road network (RR) with nodes (N) and arcs (A), said nodes (N) of said assistant graph (GA) corresponding to the arcs (A) of said direct graph (GD) and said arcs (A) of said assistant graph (GA) corresponding to the adjacency of said arcs (A) of said direct graph (GD).
    [15" id="c-fr-0015]
    15) Method according to one of the preceding claims, wherein said vehicle is an electric vehicle, and the amount of energy stored in the vehicle corresponds to the state of charge of the battery of said vehicle.
    5
    [16" id="c-fr-0016]
    16) Method for managing a battery of a vehicle traveling within a road network, in which the following steps are implemented:
    a) at least one zone (ZON) of said road network reachable by said vehicle is determined within said road network by means of the determination method according to one of the preceding claims; and
    10 b) the use and / or charge of said battery is managed as a function of said reachable area (ZON).
    [17" id="c-fr-0017]
    17) Product computer program downloadable from a communication network and / or recorded on a medium readable by computer and / or executable by a processor
    15 or a server, comprising program code instructions for implementing the method according to one of the preceding claims, when said program is executed on a computer or on a mobile telephone.
    1/4
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    同族专利:
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    FR3062834B1|2019-04-12|
    CN108446448A|2018-08-24|
    EP3363707A1|2018-08-22|
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    法律状态:
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    2018-08-17| PLSC| Publication of the preliminary search report|Effective date: 20180817 |
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    优先权:
    申请号 | 申请日 | 专利标题
    FR1751262A|FR3062834B1|2017-02-16|2017-02-16|METHOD FOR DETERMINING AN AREA REACHABLE BY A VEHICLE USING A DYNAMIC MODEL AND AN ADDITIONAL GRAPH|
    FR1751262|2017-02-16|FR1751262A| FR3062834B1|2017-02-16|2017-02-16|METHOD FOR DETERMINING AN AREA REACHABLE BY A VEHICLE USING A DYNAMIC MODEL AND AN ADDITIONAL GRAPH|
    EP18305051.7A| EP3363707A1|2017-02-16|2018-01-22|Method for determining an area reachable by a vehicle using a dynamic model and an associated graph|
    CN201810150760.9A| CN108446448A|2017-02-16|2018-02-13|The method for determining the reachable region of vehicle using kinetic model and line chart|
    US15/896,232| US10690506B2|2017-02-16|2018-02-14|Method of determining an area reachable by a vehicle using a dynamic model and a line graph|
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