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    • 专利摘要:
    The present invention relates to a method of determining a route (ITI) minimizing the energy expenditure of a vehicle, the method is based on the use of a dynamic model (MOD) of the vehicle depending on the speed and the acceleration of the vehicle, the construction of an adjoint graph (GA), and an algorithm (ALG) of shorter path adapted to negative energies.
    公开号:FR3057951A1
    申请号:FR1660326
    申请日:2016-10-25
    公开日:2018-04-27
    发明作者:Giovanni DE NUNZIO；Laurent Thibault；Antonio Sciarretta
    申请人:IFP Energies Nouvelles IFPEN；
    IPC主号:
    专利说明:

    ® FRENCH REPUBLIC
    NATIONAL INSTITUTE OF INDUSTRIAL PROPERTY © Publication number:
    (to be used only for reproduction orders)
    ©) National registration number
    057 951
    60326
    COURBEVOIE © Int Cl 8 : G 01 C21 / 34 (2017.01), B 60 W 40/04, 40/13, 20/14, G 06 F 17/10
    A1 PATENT APPLICATION
    ©) Date of filing: 25.10.16. © Applicant (s): IFP ENERGIES NOUVELLES Etablis- (© Priority: public education - FR. @ Inventor (s): DE NUNZIO GIOVANNI, THIBAULT LAURENT and SCIARRETTA ANTONIO. (43) Date of public availability of the request: 04.27.18 Bulletin 18/17. ©) 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 ROUTE MINIMIZING THE ENERGY EXPENDITURE OF A VEHICLE BY MEANS OF AN ASSISTANT GRAPH.
    The present invention relates to a method for determining a route (ITI) minimizing the energy expenditure of a vehicle, the method is based on the use of a dynamic model (MOD) of the vehicle depending on speed and vehicle acceleration, construction of an assistant graph (GA), and a shortest path algorithm (ALG) suitable for negative energies.
    FR 3 057 951 - A1
    The present invention relates to the field of vehicle navigation, and in particular the field of eco-navigation (from English "eco-routing"), which determines a route minimizing the energy consumed by the vehicle for a route .
    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 fossil or electric. To achieve this, driver assistance systems (ADAS, for “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 does not need to make any changes to the mechanical components of the vehicle).
    Among the driver assistance systems intended to improve driving energy efficiency, there are mainly two strategies which can and should be complementary: eco-driving and eco-navigation (or “eco-friendly” routing ”). Eco-driving consists in optimizing in terms of energy expenditure a speed profile along a route. This speed profile is then suggested to the driver, who can reduce consumption on his route by following the recommended route. An example of a process relating to eco-driving is described in patent application FR 2994923 (US 9286737). Eco-navigation consists in identifying the optimal route to go from a point of origin to a point of destination by minimizing energy expenditure, and by taking into account a multitude of parameters such as the characteristics of the vehicle, the topological characteristics of the road network, traffic conditions, etc.
    Eco-navigation has been envisaged in the following patent applications: US 2012123657, US 2012179315, US 2012066232, US 9091560. However, these patent applications do not specify how the route which minimizes energy consumption is determined, nor how the speed used for these methods is determined. It is therefore not possible to know the precision obtained by the methods described in these patent applications.
    Other eco-navigation methods are based on Dijkstra's shortest path algorithm, to determine the route that minimizes energy expenditure. However, this algorithm does not take into account negative values. Therefore, this algorithm can be used only for thermal vehicles, and cannot be used for electric vehicles, for which energy recovery is possible (for example with regenerative braking). Therefore, these methods are not adaptable to any type of vehicle. Such methods are described in particular in the following documents:
    - Andersen O, Jens CS, Torp K, Yang B (2013), "EcoTour: Reducing the environmental footprint of vehicles using eco-routes", Proc. 2013 IEEE 14th Int. Conf. on Mobile Data Management, Milan, Italy, 3-6 June 2013,
    Boriboonsomsin K, Barth MJ, Zhu W, Vu A (2012), "Eco-routing navigation System based on multisource historical and real-time traffic information", IEEE Trans. on Intelligent Transportation Systems, vol. 13, no. 4, p. 1694-1704,
    Ben Dhaou I, "Fuel estimation model for ECO-driving and ECO-routing", Proc. 2011 IEEE Intelligent Vehicles Symposium, Baden-Baden, Germany, 5-9 June 2011, p. 37-42,
    Ericsson E, Larsson H, Brundell-Freij K (2006), "Optimizing route choice for lowest fuel consumption - Potential effects of a new driver support tool", Transportation research Part C, vol. 14, p. 369-383.
    To overcome these drawbacks, the present invention relates to a method for determining a route minimizing the energy expenditure of a vehicle, the method is based on the use of a dynamic model of the vehicle dependent on speed and acceleration. of the vehicle, the construction of an assistant graph, and a shorter path algorithm adapted to negative energies. 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. The algorithm adapted to negative energies makes the process adaptable to any type of vehicle, including electric vehicles.
    The method according to the invention
    The invention relates to a method for determining a route minimizing the energy expenditure of a vehicle traveling within a road network. For this process, the following steps are carried out:
    a) the position and the destination of 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;
    c) an assistant graph of said road network is constructed between said identified position of said vehicle and said identified destination of said vehicle;
    d) the energy consumed by said vehicle is determined for each arc of said assistant graph by means of said dynamic model of the vehicle and of an average speed of said vehicle on said arc considered, and of an acceleration of said vehicle to reach said average speed over said arc considered;
    e) said route is determined between said identified position of said vehicle and said identified destination of said vehicle by means of a shortest path algorithm which minimizes on said attached graph said energy consumed, said shortest path algorithm being able to take take into account, if necessary, a negative energy consumed on at least one arc of said adjoining graph.
    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 the infrastructures of said road network.
    According to a variant, 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.
    According to one characteristic, said assistant graph is constructed by means of the topology of said road network.
    According to an embodiment option, said topology of said road network is determined by means of geolocation.
    According to one embodiment, 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 route is displayed on an autonomous device or on the dashboard of said vehicle.
    Advantageously, said dynamic model of said vehicle depends on the power demand of at least one auxiliary system of said vehicle.
    Preferably, said demand for power from at least one auxiliary system is a function of the outside temperature.
    According to one implementation, said shortest path algorithm is a Bellman-Ford algorithm.
    According to an alternative embodiment, said assistant graph 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).
    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 description below of nonlimiting examples of embodiments, with reference to the appended figures and described below.
    FIG. 1 illustrates the steps of the method according to an 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 illustrates the average speed and the speeds measured on a first trip.
    FIG. 5 illustrates the cumulative energy consumption measured, the cumulative energy consumption determined by a model according to the prior art, and the cumulative energy consumption determined by a dynamic model according to the invention, for the example of Figure 4.
    Figure 6 illustrates the average speed and the measured speeds for a second trip.
    FIG. 7 illustrates the cumulative energy consumption measured, the cumulative energy consumption determined by a model according to the prior art, and the cumulative energy consumption determined by a dynamic model according to the invention, for the example of Figure 6.
    Detailed description of the invention
    The present invention relates to an eco-navigation method, that is to say a method of determining a route minimizing the energy expenditure of a vehicle traveling within a road network. The route a vehicle should travel from a starting point (the vehicle's current position) to an ending point (the vehicle's destination) is called a route.
    The method according to the invention is suitable for any type of vehicle: thermal vehicles, hybrid vehicles, electric vehicles.
    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] p 1 w Tractive effort from vehicle to wheel [NOT] p 1 areo Aerodynamic force on the vehicle [NOT] P friction Friction force undergone by the vehicle [NOT] Fslope Normal force undergone by the vehicle (gravity) [NOT] p 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] CD Coefficient of aerodynamic resistance [-] c r Rolling resistance coefficient [-] g Gravitational acceleration [m 2 / s] Ωθ, ci- £ and ci 2 Vehicle settings [-] r Wheel radius [m] Pt Vehicle transmission report [-] Pt Vehicle transmission efficiency [-] T Engine couple [Nm] T 1 m, max Maximum engine torque [Nm] T 1 m, min Minimum engine torque [Nm] Pm Power available on the motor shaft [W]
    P b Power required from the battery [W]
    T] b Aggregate efficiency of the electric traction chain [-]
    P aux Power of vehicle auxiliaries [W]
    T'amb Ambient temperature [K] i Route segment i [-] i-1 Route segment, preceding route segment i [-] v Average traffic speed [m / s]
    K Function
    Ëi Energy consumed in segment i [Wh]
    E b Energy consumed by the battery [Wh]
    Pi Vehicle power in segment i at average speed [W]
    Pi Vehicle power on segment i at variable speed [W]
    Τι Travel time on segment i [s] li Length of segment i [m]
    Ejump.i Energy consumed associated with the speed variation for the segment i [Wh] tjump.i Time to achieve the speed variation for the segment i [s]
    HÇ Arc weight of the adjoining graph [Wh]
    For these notations, the derivative with respect to time is noted or by a point above the variable considered.
    The method according to the invention comprises the following steps:
    1) identification of the position and destination of the vehicle
    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) determining the route.
    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.
    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 and destination of the vehicle (D / A)
    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) determining the eco-navigation route (ITI) using a shortest path algorithm (ALG) applied to the assistant graph (GA) weighted by the energy consumed.
    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 intrinsic vehicle parameters (PAR), these parameters can be used to build 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 vehicle's position and destination
    During this step, the current position and the destination of the vehicle are identified. In other words, we identify the start and finish of the route to be traveled.
    The current position of the vehicle can be identified by means of 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).
    The destination of the vehicle can be indicated by a user by means of an interface with this. Alternatively, the destination can be stored in a database, for example if it is a previously known destination (for example a smartphone, the dashboard, or a geolocation system).
    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) rtî dt ~ ~ Faero ~ P’friction ~ ^ slope
    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) = r (t) = 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:
    F-res Faero T Fp r i c i : i on Ο 2 ν {Ρ) + d 4 v {t) + CIq Where the parameters a 0 , a 4 and a 2 can be identified for the vehicle considered from a standard test called “coast down”, meaning descending slope.
    So the force at the wheel can be expressed as:
    F w = mv (t) + ct 2 v (û) 2 + apiflt) + a 0 + mg sin (cr (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"):
    F v r w '
    T m =
    Ptm
    F w rg t v Pt if F w > 0 if F w <0
    Where r is the wheel radius, p t and p 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:
    1 m, max ω (0, if T m > T m; max
    Pm = S if T m , mi n <T m <f min if Pm - Pm;
    m, max
    Where <O t is the engine speed which is defined as:
    rn ν ^ Ρΐ ω (0 =
    The demand for battery power is expressed as follows:
    P 6 = fr ifP ^ ° PmBb> if Pm <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:
    Paux = K (P a mb)
    Therefore, for this embodiment, the energy consumption at the battery over a time horizon T can be defined as:
    Pb = f Pb i Paux dt J 0
    The model described above requires an instantaneous speed signal. This information is not available a priori on the road segments (portions of road in 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.
    Alternatively, the average speed over a segment can be obtained in real time by communication with online data services, which acquire traffic information on the road network in real time. 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 storage of historical data, which stores 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 speed of the traffic v. It is therefore assumed that all of the vehicles on the road segment i travel at speed v t . So the expression of the force at the wheel is modified for each road segment i:
    F W; i = α 2 ϋι + + a 0 + m 9 sin (ai (x))
    Where the term acceleration disappears. The engine torque becomes:
    1 m, i
    Fw.iT p t m 'F w , i.rih v Pt if F w , i> 0 if F w , i <0
    The engine speed is also constant over time since we assume a constant speed v t :
    ViPt ω;
    The mechanical power available to the electric machine is rewritten as follows:
    1 m, max 'o) i, if T mi > T mr
    Fm, i -) j ω ,, if T mm i n <T mi <T mr (bp if T mi <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:
    if P mii > 0 if P m , i <0
    The energy consumption of the battery is therefore:
    Ëb, i = (Pb, i + PauxFi
    Where f = li / vt is the journey time on the road segment i if you are traveling at the average speed of traffic ν ,.
    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 preceding 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 Ej Umpi associated with the speed variation between two road segments is defined as:
    tjump.i
    Pjump.i ~ (Pb, i + dt 0
    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 + a ^ Çt) + a 0
    Where the variable speed in time v (t) in each transient can be linearly modeled here as:
    v (t) = v i _ 1 + signfvi - v ^ f) 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-i signed - a
    Total energy consumption in segment i is defined as follows:
    Eb, i ~ Eb, i + Ejump, i
    Taking into account interface accelerations makes the model more precise. 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 which has the same starting and ending point with a negative total cost. In the specific case of a weighted graph with energetic weights, this represents a situation of infinite energy recovery if the loop is continuously traversed, 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 which 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 speed variation between the two segments can be modeled as two transients: the first to go from the speed v i _ 1 to 0 (stopping of the vehicle, for example at a traffic light ), and the second to go from 0 to speed v ,. Therefore, the energy consumption, linked to the variation of the speed can be described as the sum of two contributions:
    tjumpi, i tjump2, i
    E ltm pi = f (P „1J + dt + f (Pb2, i + False) dt
    0
    Where the speed variation in the first term is modeled as:
    vflt) = v i _ 1 - a -1
    And the time to make the first variation:
    tjumpi, i = v i — i / a
    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 = v i! at
    Consequently, according to the invention, the dynamic model of the vehicle can be written (for any type of vehicle):
    tjumpi
    Et = PiTi + J Pidt o
    With Ei the energy consumed in segment i, A 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, Ti the time during which the vehicle is considered at constant speed on segment i, P t the power requested from the vehicle energy storage system when the vehicle is considered to have a speed variation (speed variation between segment i-1 and the 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.î
    Ei = (fli + ΡαιιχΧΓί + J (fi + Paux) dt 0
    With E t the energy consumed in segment i, P t 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 the power demand of at least one auxiliary system, T t the time during which the vehicle is considered at constant speed in segment i, P t the power requested from the vehicle energy storage system when the vehicle is considered having a speed variation (speed variation between segment i-1 and segment i), and t jump i the time to realize 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. We call, in theory of graphs, an adjoining 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 as follows: 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.
    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 eco-navigation, 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 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 ajx) 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 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 includes five nodes N, corresponding to the four ends of the roads and to the intersection of these. 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 building the associated graph GA from the direct graph GD. The associated graph GA includes eight nodes N corresponding to each arc of the direct graph GD. In addition, the associated 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 during each arc of the associated 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 adjoining 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 adjoining 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 the stops / restarts induced by the U jump infrastructure, i tjumpi.i tjump2, i
    I (P bi , 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) = (7 ‘, A‘) of a graph G has as its nodes the arcs of the graph G, so 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:
    f Ë bi + E jumv i, if i - 1 EV * has incoming arcs W = (E bi + Ej ump i + Eh i-'L, if i - 1 GV * has no incoming arcs We recall 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 route
    During this step, the route is determined which minimizes the energy expenditure of the vehicle between the identified position and the identified destination. This step is carried out by taking into account the energy consumed on each arc of the associated graph. The determination of the eco-navigation route is implemented by a shortest path algorithm. The shortest path algorithm determines the route on the adjoining graph taking into account the consumed energy determined for each arc. 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.
    Once the algorithm restores the optimal sequence of nodes in the associated graph, this result can be easily transferred to the original graph, by generating the sequence of nodes in the original graph between origin and destination corresponding to the optimal path, i.e. the optimal route in terms of energy expenditure.
    According to an implementation of the invention, the approach presented may include a step of recording offline historical global 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, destination and departure time. The N-best eco-routes are calculated from historical data. Their total cost is then updated based on current traffic conditions, and compared to determine the best current route in terms of energy consumption. This solution allows traffic conditions to be taken into account in real time.
    Indeed, the traffic conditions are very variable during the day and to have an optimal global solution of eco-routing, it would be necessary to update the energy cost of all the arcs of the graph according to the time of desired departure for navigation. The size of the graph can be large and the calculation time for updating all the weights is not suitable for use in real time.
    Furthermore, according to an implementation of the invention, the calculated route can be compared with other routes obtained by means of different performance indices, in particular the travel time. Thus, the user can choose the most interesting compromise between energy consumption and journey time as required.
    An optional step of the method according to the invention may consist in displaying the determined route, 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 eco-navigation route. It is also possible to display the energy consumed for the route, which is estimated using the model and the associated graph.
    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.
    The invention also 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.
    Examples
    The two examples presented below show the good consistency of the dynamic model according to the invention with measured values. The dynamic model used for these two examples is the model exemplified in the application (step 2).
    The examples were carried out with an electric vehicle having the characteristics indicated in table 1.
    Table 1 - vehicle characteristics
    Characteristics Symbol value Vehicle mass m 1190 kg Wheel radius r 0.2848 m Transmission report Pt 5.763 Transmission efficiency Pt 0.95 Acceleration at 1.5 m / s 2 Coefficient 125.73 N Coefficient have 1.72 N / (m / s) Coefficient θ-2 0.58 N / (m / s) 2 Minimum engine torque T 1 m, min -50 Nm Maximum engine torque T 1 m, max 200 Nm Electric motor efficiency Pb 0.85
    The experimental results for these examples were obtained during open road tests by recording the position and the speed by a GPS sensor. The tests were carried out in town, on different types of roads to verify the robustness and the accuracy of predicting consumption by the dynamic model of the process according to the invention. The repetitions of the same course were carried out always at the same departure time over several days. The energy consumption used as a reference subsequently was calculated using the dynamic model described above from the real instantaneous speed profile recorded. The estimation of the energy consumption by the macroscopic model object of the invention was carried out starting from average speeds of traffic obtained by the online services of cartography (traffic).
    Two validation results are presented here. These results illustrate the improvement in the estimation of energy consumption compared to the standard technique used in the state of the art.
    First case study
    The first validation study was carried out on a route that presents a combination of urban and motorway roads.
    FIG. 4 illustrates, for this first case study, the different speeds measured
    Vmes (km / h) as a function of the distance traveled D (m) on this route. Figure 4 also illustrates the average speed Vmoy on each segment of the route. The average speed Vmoy is obtained as a function of traffic conditions by communication with online services. The actual speed profiles Vmes show good repeatability characteristics, even if they correspond to different days. The macroscopic average speed Vmoy data fairly well represent the traffic conditions at the time of the tests.
    FIG. 5 illustrates, for this first case study, the cumulative energy consumed E (Wh) over the distance traveled D (m) on this route. This graph shows the cumulative energy consumed for the measured values MES (obtained with the measured speeds), the cumulative energy consumed estimated with the average speed with a model according to the prior art AA not taking into account acceleration, and the cumulative energy consumed estimated with the model according to the invention INV taking acceleration into account. The model based on average speeds AA without taking acceleration into account results in significant errors of underestimation of real consumption. The error of this type of model compared to the average of the final energy values of the reference, in this first case study, is around 30%. The dynamic model proposed in the INV invention, which also takes acceleration into account, is able to follow more precisely the trends in consumption variation. The error in estimating the energy consumed compared to the reference is approximately 7%.
    Second case study
    The second validation study was carried out on a route with only secondary urban roads.
    FIG. 6 illustrates, for this second case study, the different measured speeds Vmes (km / h) as a function of the distance traveled D (m) on this route. Figure 4 also illustrates the average speed Vmoy on each segment of the route. The average speed Vmoy is obtained as a function of traffic conditions by communication with online services. The actual speed profiles Vmes show good repeatability characteristics, even if they correspond to different days. The macroscopic average speed Vmoy data fairly well represent the traffic conditions at the time of the tests.
    FIG. 7 illustrates, for this second case study, the cumulative energy consumed E (Wh) over the distance traveled D (m) on this route. On this graph, are represented, the cumulative energy consumed for the measured values MES (obtained with the measured speeds), the cumulative energy consumed estimated with the average speed with a model according to the prior art AA not taking into account counts the acceleration, and the cumulative energy consumed estimated with the model according to the invention INV taking acceleration into account. In this second case study, the models based on average speeds according to the prior art AA without taking acceleration into account make errors of even greater underestimation of real consumption. The error compared to the reference is approximately 38%. This behavior is due to the fact that on secondary urban roads the accuracy and reliability of the macroscopic data of average speed are much lower. Therefore, average speeds are less representative of real traffic conditions, which can also pose problems for models that integrate interface accelerations. In particular, if the macroscopic data provide average speeds which do not vary very little between the different road segments, taking into account interface accelerations is no longer sufficient for a correct prediction 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.
    The dynamic model proposed in the invention is able to follow more precisely the trends in consumption variation. The error in estimating the energy consumed compared to the reference is approximately 9%.
    权利要求:
    Claims (14)
    [1" id="c-fr-0001]
    Claims
    1) Method for determining a route minimizing the energy expenditure of a vehicle traveling within a road network (RR), characterized in that the following steps are carried out:
    a) the position and the destination of 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;
    c) an assistant graph (GA) of said road network is constructed between said identified position of said vehicle and said identified destination 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 arc considered, and of an acceleration of said vehicle to reach said average speed on said arc considered;
    e) determining said route (ITI) between said identified position of said vehicle and said identified destination of said vehicle by means of a shortest path algorithm (ALG) which minimizes on said assistant graph (GA) said energy consumed, said algorithm shortest path being able to take into account, if necessary, a negative consumed energy on at least one arc of said assistant graph.
    [2" id="c-fr-0002]
    2) Method according to 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 route (ITI) 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 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) said assistant graph (GA) of said road network (RR) is constructed 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).
    [14" id="c-fr-0014]
    14) 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 implementing the method according to the 'One of the preceding claims, when said program is executed on a computer or on a mobile phone.
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    优先权:
    申请号 | 申请日 | 专利标题
    FR1660326A|FR3057951B1|2016-10-25|2016-10-25|METHOD FOR DETERMINING A ROUTE MINIMIZING THE ENERGY EXPENDITURE OF A VEHICLE BY MEANS OF AN ASSISTANT GRAPH|
    FR1660326|2016-10-25|FR1660326A| FR3057951B1|2016-10-25|2016-10-25|METHOD FOR DETERMINING A ROUTE MINIMIZING THE ENERGY EXPENDITURE OF A VEHICLE BY MEANS OF AN ASSISTANT GRAPH|
    EP17306325.6A| EP3315913B1|2016-10-25|2017-10-04|Method for determining an itinerary minimising the energy consumption of a vehicle by means of an adjunct graph|
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    US15/792,942| US11215469B2|2016-10-25|2017-10-25|Method of determining a route minimizing the energy consumption of a vehicle using a line graph|
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