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    • 专利摘要:
    The invention relates to a method for preserving the state of health of a traction battery of a hybrid motor vehicle. According to the invention, the method comprises steps of: a) acquisition, by means of a navigation system embedded in the hybrid motor vehicle, of a path to be made, b) division of said path into successive sections, c) assignment of attributes characterizing each section, d) determining, for each of said sections, a curve or a cartography linking each fuel consumption value of the hybrid motor vehicle on the section to a value of charge or discharge of the traction battery , e) determining an optimum point of each curve or mapping to minimize the aging of the traction battery over the entire path and to obtain a complete discharge of the traction battery at the end of said path, and f) development of an energy management directive according to the coordinates of said optimum points.
    公开号:FR3061471A1
    申请号:FR1750109
    申请日:2017-01-05
    公开日:2018-07-06
    发明作者:Abdel-Djalil OURABAH；Atef Gayed；Benjamin QUOST；Thierry Denoeux
    申请人:Renault SAS；
    IPC主号:
    专利说明:

    Holder (s): simplified.
    RENAULT S.A.S. Joint-stock company
    O Extension request (s):
    ® Agent (s): RENAULT SAS.
    ® PROCESS FOR OPTIMIZING THE ENERGY CONSUMPTION OF A HYBRID VEHICLE.
    FR 3,061,471 - A1 (57) The invention relates to a method for preserving the state of health of a traction battery of a hybrid motor vehicle.
    According to the invention, the method comprises steps:
    a) acquisition, by means of a navigation system on board the hybrid motor vehicle, of a journey to be carried out,
    b) dividing said path into successive sections,
    c) assignment of attributes characterizing each section,
    d) determination, for each of said sections, of a curve or of a map connecting each value of fuel consumption of the hybrid motor vehicle on the section to a charge or discharge value of the traction battery,
    e) determination of an optimal point of each curve or map making it possible to minimize the aging of the traction battery over the entire path and to obtain a complete discharge of the traction battery at the end of said path, and
    f) development of an energy management instruction as a function of the coordinates of said optimal points.

    Technical field to which the invention relates
    The present invention relates generally to plug-in hybrid vehicles.
    It relates more particularly to a method for optimizing the energy consumption of a hybrid vehicle comprising an internal combustion engine supplied with fuel and an electric motor supplied by a traction battery.
    The invention finds a particularly advantageous application in hybrid vehicles with large electric range, that is to say in vehicles capable of driving with their only electric motor over a distance greater than ten kilometers.
    Technological background
    A plug-in hybrid vehicle comprises a conventional thermal traction chain comprising an internal combustion engine and a fuel tank, and an electric traction chain comprising an electric motor and a traction battery which can in particular be charged on an electrical outlet.
    Such a hybrid vehicle is capable of being towed by its only electric traction chain, or by its only thermal traction chain, or even simultaneously by its two electric and thermal traction chains, which corresponds to a hybrid mode of operation of the vehicle. . The choice to use one or both traction chains simultaneously is made by an energy management system (EMS).
    Due to the ignorance of the future path of the vehicle, the strategy currently implemented to use one or the other of the traction chains consists in systematically starting by discharging the traction battery at the start of the journey until reaching a level of minimal energy, then use the thermal traction chain. In this way, when the driver makes short trips and regularly has the possibility of recharging the traction battery, he makes maximum use of the electric traction chain, which reduces the vehicle's polluting emissions.
    Consequently, the energy management systems implement a strategy known as "discharge-maintenance", consisting in favoring a complete discharge of the traction battery without taking into account the nature and the reliefs of the path. Thus, the “discharge-maintenance” strategy involves stresses on the traction battery which can be extreme and likely to impair its performance prematurely.
    Indeed, the traction battery is designed to operate over a defined energy state range (SOE), which differs according to the intrinsic characteristics of the battery. For example, for a Lithium-ion battery, the most used in electric and hybrid vehicles, this operating range is generally between 15% and 95% of the energy state range. It is defined by making a compromise between the usable capacity and the life of the battery. There are several factors that degrade battery performance and reduce its capacity, such as temperature, high current for an extended period of time, overvoltage, undervoltage, etc.
    To this end, the document FR2995859 discloses an energy management system making it possible to limit the aging of the traction battery. For this, this document proposes an energy management system enlarging the range of use of the battery in hybrid mode when the battery ages.
    However, this solution has the disadvantage of being applied regardless of the distance traveled by the vehicle. Thus, the vehicle can be brought to operate in hybrid mode all along the journey while the autonomy of the traction battery would have enabled it to complete the entire journey without consuming gasoline.
    According to another drawback, the optimal usage range is predefined in advance and does not take account of the running profile on the journey. The energy management system can thus impose charging or discharging instructions causing premature aging of the traction battery when the driving conditions are not favorable for its use.
    Object of the invention
    In order to remedy the aforementioned drawbacks of the prior art, the present invention provides a method for optimizing the energy consumption of a hybrid vehicle as defined in the introduction, which comprises the following steps:
    a) acquisition, by means of a navigation system, of a journey to be made;
    b) dividing said path into successive sections;
    c) acquisition, for each section, of attributes characterizing said section;
    d) for each of said sections and taking into account its attributes, selection, from a plurality of predetermined relationships relating values of fuel consumption to values of electrical energy consumption, of a relationship relating to the fuel consumption of the motor vehicle hybrid on the section to its electric energy consumption;
    e) determining an optimal point for preserving the state of health of the traction battery in each of the relationships selected, so that all of the optimal points minimize the aging of the traction battery over the whole of the path and maximize the discharge of the traction battery at the end of said path; and
    f) preparation of a directive for managing the fuel consumption and electric current of the motor vehicle, all along the journey, according to the coordinates of said optimal points.
    Thus, thanks to the invention, it is possible to determine at what times it is better to use the electric motor or rather the internal combustion engine, in order to best reduce the aging of the traction battery on the path taken by the hybrid vehicle. More specifically, the invention makes it possible to favor the use of the traction battery in an optimal and restricted operating range, taking into account the nature and the reliefs of the journey. Thus, the traction battery is used in conditions more respectful of its state of health, that is to say, in a range of electrical voltage allowing it to deliver a current intensity which is neither too high nor too weak. Advantageously, the invention therefore makes it possible to increase the life of the traction battery, thereby limiting the maintenance costs of a hybrid motor vehicle, by avoiding early replacement of the traction battery.
    According to another characteristic of the invention, in step e) the determination of an optimal point in each of the relationships selected for each section depends on the fuel consumption over the entire section, weighted by a relationship of preservation of the state of health of the traction battery. In other words, the weighting relationship makes it possible to privilege the operation of the electric motor when the traction battery is in its optimal range of use. Conversely, when the charge of the traction battery is lower or higher than this optimal state of charge, the weighting relationship favors the use of the heat engine in order to reduce the stresses exerted by the electric motor on the traction battery. . However, it should be noted that the weighting relationship does not prevent the use of the traction battery outside of its optimal use range.
    Other advantageous and non-limiting characteristics of the process for preserving the state of health of a traction battery according to the invention are the following:
    the value of the preservation relationship decreases when the energy state of the traction battery is within an optimal range of use, so that the management setpoint favors the use of the traction battery in its optimal range of use during the journey;
    - the value of the preservation relationship decreases, when the distance to travel to destination increases, preferably when the maximum electric range of the vehicle decreases and is less than the distance remaining to travel to reach the destination, the management instruction favors the use of the electric motor so as to discharge the traction battery at the end of the journey;
    the preservation relationship depends on a product between an activation function and a weighting function, the value of the activation function being minimum when the maximum electrical autonomy of the vehicle can for example be less than twice the distance it still has to travel to reach its destination, so as to minimize the influence of the preservation relationship in determining an optimal point for preserving the state of health of the traction battery in each of the relationships selected;
    the preservation relationship depends on a product between an activation function and a weighting function, the value of the weighting function being minimal when the energy state of the traction battery is outside its range d optimal use, so as to minimize the influence of the preservation relationship in determining the optimal point, preferably the preservation relationship tends towards the value 1;
    - the value of the activation function is maximum when the distance to travel to reach the destination decreases, preferably the value of the activation function is maximum when the maximum electric range of the vehicle is less than eight times the distance remaining to go to arrive at destination, so as to allow a complete discharge of the traction battery at the end of the journey;
    - the activation function can be maximum when the maximum electric range of the hybrid vehicle is less than the distance remaining to be traveled by the vehicle;
    - the activation function can be more than twice or preferably more than six times the maximum electric range of the vehicle;
    - the value of the activation function varies linearly between its minimum value and its maximum value;
    the value of the weighting function is maximum when the energy state of the traction battery is at the center of its optimal range of use, so that the management instruction favors the use of the electric motor when its battery traction operates within its optimal range of use;
    the value of the weighting function varies symmetrically on either side of the center of the optimum range of use of the traction battery;
    - for example, the weighting function can have a maximum value over more than 10% of the optimum range of use of the traction battery, preferably over 50%;
    - the optimal use range of the traction battery is between 60% and 80% of its maximum charge;
    - the preservation relationship includes a maximum weighting value, so as to control the amplitude of the variation in the value of the preservation relationship;
    - The maximum weighting value is preferably constant is between 0.1 and 1;
    - the preservation relationship is proportional to a product between the activation function, the weighting function and the maximum weighting value.
    Detailed description of an exemplary embodiment
    The description which follows with reference to the appended drawings, given by way of nonlimiting example, will make it clear what the invention consists of and how it can be carried out.
    In the accompanying drawings:
    - Figure 1 is a table illustrating the attribute values characterizing sections of a journey that a vehicle must perform;
    FIG. 2 is a table illustrating the parameters of reference curves characterizing the sections of the path to be performed;
    - Figure 3 is a graph illustrating the distribution of specific consumption curves acquired during test runs;
    - Figure 4 is a graph illustrating several reference curves;
    - Figure 5 is a table associating with each attribute value assigned to a section, a probability that this section is associated with one or the other of the reference curves of Figure 4;
    - Figure 6 is a graph illustrating the corrections to be made to a reference curve, taking into account the electrical consumption of auxiliary devices of the vehicle;
    - Figure 7 is a graph illustrating the corrections to be made to a reference curve, taking into account the slope of the section of the corresponding path;
    - Figure 8 is a graph illustrating an example of a step of calculating an algorithm for finding the optimal trajectory by an optimization algorithm;
    - Figure 9 is a graph illustrating an example of the form of an activation function according to the invention;
    - Figure 10 is a graph illustrating two examples of the form of a weighting function according to the invention;
    - Figure 11 is a graph illustrating an example of variation of the energy state of a traction battery during a journey greater than its maximum electrical autonomy according to a method according to the invention (curve A) and according a discharge-maintenance method (curve B).
    Conventionally, a motor vehicle comprises a chassis which in particular supports a powertrain, bodywork elements and cabin elements.
    In a plug-in hybrid vehicle, the powertrain includes a thermal traction chain and an electric traction chain.
    The thermal traction chain notably comprises a fuel tank and an internal combustion engine supplied with fuel by the tank.
    The electric traction chain comprises a traction battery and one or more electric motor (s) supplied with electric current by the traction battery.
    The motor vehicle here also includes a power outlet for locally charging the traction battery, for example on the electrical network of a home or on any other electrical network.
    The motor vehicle also includes auxiliary devices, which are here defined as electrical devices supplied with current by the traction battery.
    Among these auxiliary devices, mention may be made of the air conditioning motor, the electric window motors, or even the geolocation and navigation system.
    This geolocation and navigation system conventionally comprises an antenna making it possible to receive signals relating to the geolocated position of the motor vehicle, a memory making it possible to store a cartography of a country or a region, and a screen making it possible to illustrate the position of the vehicle on this map.
    Here, we consider the case where this screen is touchscreen to allow the driver to enter information. It could of course be otherwise.
    Finally, the geolocation and navigation system includes a controller for calculating a route to be taken taking into account the information entered by the driver, the map stored in his memory, and the position of the motor vehicle.
    The motor vehicle 1 also comprises an electronic control unit (or ECU for Electronic Control Unit), here called a computer, making it possible in particular to control the two aforementioned traction chains (in particular the powers developed by the electric motor and by the combustion engine internal).
    In the context of the present invention, this computer is connected to the controller of the geolocation and navigation system, so that these two elements can communicate information.
    Here, they are connected together by the main inter-organ communication network of the vehicle (typically by the CAN bus).
    The computer comprises a processor and a storage unit (hereinafter called memory).
    This memory stores data used in the process described below.
    In particular, it records a table of the type illustrated in FIG. 5 (which will be detailed later in this presentation).
    It also records a computer application, consisting of computer programs comprising instructions whose execution by the processor allows the implementation by the computer of the method described below.
    As a preliminary point, several concepts used here will be defined in the description of the process described below.
    We can thus define the term "journey" as a path that the motor vehicle must take from a departure station to get to an arrival station.
    This arrival station, the end of the journey, will be considered to be equipped with a charging station allowing the traction battery to be recharged via the socket fitted to the vehicle.
    Each route can be divided into "adjacent segments" and "adjacent sections".
    The concept of segments will be that natively used by the controller equipping the geolocation and navigation system.
    In practice, each segment can for example correspond to a part of the journey which extends between two road intersections. To define the shortest or fastest route, the controller will therefore determine by which road segments the route must pass.
    The concept of sections is different. It will be well detailed in the rest of this presentation. To simplify, each section of the route corresponds to a part of the route over which the characteristics of the road do not change significantly. For example, the route could thus be divided into several sections on each of which the maximum authorized speed is constant.
    These sections are characterized by parameters called “attributes” here. Examples of attributes used to characterize each section are as follows.
    A first attribute will be the “FC road category”. The controllers equipping the geolocation and navigation systems generally use this kind of categories to distinguish the different types of roads. Here, this category could take an integer value between 1 and 6 for example. An attribute equal to 1 could correspond to a motorway, an attribute equal to 2 could correspond to a national road, ...
    A second attribute will be the “GR slope” of the section, expressed in degrees or in percentage.
    The third, fourth, fifth and sixth attributes will relate to the speeds characteristic of the vehicles using the section.
    The third attribute will be the “SC speed category” of the segment. The controllers equipping the geolocation and navigation systems generally also use this kind of categories to distinguish the different types of roads. Here, this category could take an integer value between 1 and 6 for example. An attribute equal to 1 could correspond to a high speed road (greater than 120 km / h), an attribute equal to 2 could correspond to a high speed road (between 100 and 120 km / h), ...
    The fourth attribute will be the “maximum authorized speed SL” on the section.
    The fifth attribute will be the “average SMS speed” observed on the section (the value of which comes from a statistical measurement carried out on each route).
    The sixth attribute will be the "instantaneous TS speed" observed on the segment (the value of which comes from a real-time traffic information system).
    The seventh attribute will be the "LL length" of the section.
    The eighth attribute will be the “average radius of curvature LC” of the section.
    The ninth attribute will be the "number of lanes NL" of the section in the direction of travel taken by the vehicle.
    In the following discussion, we will use these nine attributes to characterize each section of the journey.
    As a variant, each section of the journey can be characterized by a limited or greater number of attributes.
    We will also define the SOE energy state of the traction battery as being a parameter used to characterize the energy remaining in this traction battery. As a variant, it is possible to use another parameter such as the state of charge SOC of the battery (or “other state of charge”) or any other parameter of the same type (internal resistance of the battery, voltage across the terminals of the drums, ...).
    The charge or discharge ASOE of the traction battery will then be considered equal to the difference between two energy states considered at two distinct times.
    The “specific consumption curve” of the vehicle is then defined over a section considered to be a curve which associates with each value of DC fuel consumption of the vehicle a charge or discharge value ASOE of the traction battery. Indeed, on a given section, it is possible to estimate what will be the DC fuel consumption of the vehicle (in liters per kilometer traveled) and the ASOE charge or discharge of the traction battery in (Watt-hours per kilometer). These two values will be linked by a curve, since they will vary depending on whether the electric powertrain or rather the thermal powertrain is used to drive the vehicle.
    Since there are infinitely many specific consumption curves, we finally define "reference curves" as being specific specific consumption curves, whose characteristics we will know well and which will allow us to approximate each specific consumption curve. Otherwise formulated, as will appear more clearly in the rest of this presentation, we will associate with each section of journey not a specific consumption curve, but rather a reference curve (the one that will constitute the best approximation of the specific consumption curve) .
    The method, which is implemented jointly by the geolocation and navigation system controller and by the vehicle computer, is a method for calculating a setpoint for managing the fuel consumption and electric current of the vehicle.
    This process consists more precisely in determining how, on a predefined path, it will be necessary to use the electric traction chain and the thermal traction chain so as to best preserve the state of health of the traction battery.
    According to a particularly advantageous characteristic of the invention, the method comprises the following six main steps:
    - acquisition of a journey to be made,
    - dividing said path into successive adjacent sections T,
    - acquisition, for each section Tj, of attributes FC, SC, SL, TS, RG, LL NL, SMS characterizing this section Tj,
    - determination, for each of the sections T ,, taking into account the attributes FC, SC, SL, TS, RG, LL NL, SMS of this section T, of a relation (here called reference curve CEj) connecting each consumption value in DC fuel of the hybrid motor vehicle on the section at an ASOE charge or discharge value of the traction battery,
    - determination of an optimal point Pj of each reference curve CEj making it possible to best preserve the state of health SOUND of the traction battery and to obtain a complete discharge of the traction battery at the end of said journey, and
    - development of an energy management instruction based on the coordinates of said optimal points Pj.
    It will be recalled here that during its lifetime, a battery exhibits performances which tend to deteriorate progressively due to irreversible chemical changes which take place during use. This deterioration is quantified using an indicator called "SON health status", which defines the capacity of the battery to provide specific services, compared to the services it was able to provide in new condition. As is well known, this state of health SON has a very strong correlation with the internal resistance of the battery and with the voltage at its terminals (in the charged state).
    These six successive stages are detailed in the rest of this presentation.
    The first step is to acquire the route that the motor vehicle must complete.
    This step can be carried out by the on-board controller in the geolocation and navigation system.
    This step is then implemented in a conventional manner.
    Thus, when the driver uses the touch screen of the geolocation and navigation system to define an arrival station, the controller of this system calculates the route to be taken, depending in particular on the routing parameters selected by the driver (route on faster, shortest route ...).
    At this point, it will be noted that the process will have to be reset as soon as the vehicle takes a route different from that defined by the geolocation and navigation system.
    As a variant, this first step could be carried out otherwise.
    Thus, it will be possible to dispense with the input by the driver of the arrival station on the touch screen. For this, the controller will be able to detect the habits of the driver and automatically deduce the arrival station.
    For example, when the driver takes the same trip every day of the week to go to work, this trip can be automatically acquired without the driver having to enter any information on the touch screen of the geolocation and navigation system.
    At the end of this first step, the on-board controller in the geolocation and navigation system knows the path of the vehicle, which is then composed of a plurality of adjacent segments, which we recall that they each extend between two road intersections.
    The second step consists in dividing the path into sections Tj.
    The advantage of re-dividing the journey no longer into segments, but into sections is first of all to reduce the number of subdivisions of the journey. Indeed, it often happens that the attributes of two successive segments are identical. If these two successive segments were treated separately, the duration of the calculations would be unnecessarily multiplied. By bringing together identical segments within the same segment, we will be able to reduce the duration of the calculations.
    Another advantage is that the characteristics of the road on the same segment can vary appreciably (part of the segment can correspond to a road of zero slope and another part of this segment can correspond to a road of significant slope). Here, we wish to divide the journey into sections on each of which the characteristics of the road remain homogeneous.
    Each section Tj will be defined here as a portion of the path which includes at least one invariable attribute over its entire length.
    This attribute could be made up of the slope RG and / or the speed category SC and / or the road category FC.
    Here, this step will be implemented by the on-board controller in the geolocation and navigation system. To this end, it will cut the path into sections T, of maximum lengths over which the three aforementioned attributes (RG, SC, FC) are constant.
    At the end of this second stage, the controller thus defined N sections.
    The third step is to acquire the attributes of each section T ,.
    When one of the attributes will be variable on the considered section, it is the average value of this attribute on the whole of the section which will be considered.
    In practice, this third step is carried out in the following manner.
    First, the on-board controller in the geolocation and navigation system informs the computer that a new route has been calculated. The computer then requests the sending of the attributes of each section, in the form for example of a table of the type of that illustrated in FIG. 1.
    The controller then acquires the attributes of each section as follows.
    It calculates part of it, in particular the length LL of the section.
    It reads another part of it in the memory of the geolocation and navigation system, in particular the road category FC, the slope RG, the speed category SC, the maximum authorized speed SL, the average speed SMS, the average radius of curvature LC, and the number of NL channels.
    A last part of these attributes is communicated by another device, in particular the instantaneous speed TS communicated to it by the information system on the state of traffic in real time.
    The controller then transmits all of this information to the vehicle's main computer, via the CAN bus.
    The advantage of using the on-board controller in the geolocation and navigation system rather than the main computer of the vehicle to operate the first three steps is to reduce the number of information to be transmitted to the computer by the CAN bus. Indeed, by merging the adjacent segments of the path which have the same attributes, the volume of the transmitted data is reduced, which accelerates the data transmission by the bus.
    CAN.
    Upon receipt of the information, the computer implements the following steps.
    The fourth step then consists, for each of the sections Tj, in determining from the reference curves CE] recorded in the computer memory which one will allow the best estimate of the energy consumption (in fuel and in current) of the vehicle on the section Tj considered.
    This step then makes it possible to pass from a characterization of each section by attributes, to a characterization by an energy cost.
    During this fourth step of the present exemplary embodiment, the computer will use the table TAB illustrated in FIG. 5, which is stored in its memory.
    As shown in Figure 5, this TAB table has rows that each correspond to a value (or range of values) of an attribute. It has columns each corresponding to one of the CEj reference curves. In the example illustrated, we will consider that the computer memory stores M reference curves CEj, with M here equal to eleven.
    In Figure 5, the boxes in the TAB table are left empty since the values they contain will depend on the characteristics of the vehicle.
    In practice, this table TAB will be stored in the computer memory with values in each of these boxes.
    These values will be probability values (between 0 and 1) corresponding to the probability that each attribute value corresponds to one or the other of the reference curves CEj.
    For example, if the road category FC of a section Tj has a value equal to 2, we can read in the table that the probability that this section is well characterized in terms of energy cost by the reference curve CE1 will be equal to ai, that the probability that this section is well characterized in terms of energy cost by the CE2 reference curve will be equal to a 2 , ...
    It should be noted that the values of the slopes RG and of length LL were not intentionally used in this table TAB.
    At this stage, the computer can then read each probability value corresponding to the value of each attribute of the section T, considered.
    In the example illustrated, where we consider that the attribute FC is equal to 2, that the attribute SC is equal to 6, that the attribute SL is equal to 30, that the attribute NL is equal to 2, that the SMS attribute is between 60 and 80 and that the TS attribute is between 40 and 60, the computer reads the values noted ai to an, bi to bu, ci to en, d-ι to d-ι 1, θι to en, and f 1 to fn
    The computer then adds up the probabilities that the section Tj considered is well characterized in terms of energy cost by each of the eleven reference curves CEj.
    In the example illustrated, the computer sums for this purpose the values noted ai to f-ι, then a 2 to f 2 , ...
    Finally, the calculator determines which of the eleven sums gives the highest result.
    Then, he considers that the reference curve CEj with which this high probability sum is associated is the one which best characterizes the section Tj in terms of energy cost.
    The computer can then acquire in its memory the values of the parameters characterizing this reference curve CEj.
    At this stage of the presentation, we can focus more specifically on the way in which these reference curves are obtained and modeled.
    For each vehicle model (or for each engine model, or for each set of car models, or for each set of engine models), it is necessary to perform a large number of test runs (or test run simulations). ) on different sections of geolocated road.
    These test runs make it possible to determine the fuel and electric current consumption of the vehicle on different sections whose attributes are known. To do this, the vehicle is made to evolve several times on each section, each time increasing the share of traction developed by the electric motor.
    It is then possible to generate a specific CCS consumption curve for each section. These specific consumption curves are of the type of curves illustrated in FIG. 4.
    It can be observed on each of these curves that the more electrical energy is used (ie an ASOE <0) the more the fuel consumption decreases until reaching 0 when driving using exclusively the electric traction chain. Conversely, the more you try to recharge the battery via the internal combustion engine (ASOE> 0), the more fuel consumption increases. Finally, it will be recalled that each CCS specific consumption curve describes the average energy consumption of the vehicle for the situation of running on a horizontal road (zero slope), without the electrical consumption of the auxiliary devices.
    These test runs make it possible to find as many CCS specific consumption curves as there are sections tested.
    Each CCS specific consumption curve can be modeled by a second-order polynomial for which the variations in charge and discharge ASOE of the traction battery are limited between a minimum threshold ASOE min and a maximum threshold ASOE max , which can be to write :
    (m FC = Ψ 2 .ΔΞΟΕ 2 + Ψ ^ ΔΞΟΕ + Ψ ο [ASOE e [ASOEmin ASOEmax] with Ψο, Ψ-ι, Ψ 2 the coefficients of the polynomial.
    As the curves in FIG. 4 show, to simplify this modeling, we can estimate that the two coefficients Ψ-ι, Ψ 2 are identical from one curve to another. We can also observe that the minimum threshold ASOEmin depends on the three coefficients of the polynomial. Thus, only vary the coefficient Ψ ο and the maximum threshold max ASOE · It is these two values that are characteristic of each specific consumption curve CCS.
    Figure 3 illustrates by an example points whose coordinates correspond to these two variables Ψ ο and ASOE max . It shows the distribution of the CCS specific consumption curves obtained during the test runs carried out. Here, it is considered that these points are distributed in eleven distinct zones. Each zone is then defined by its barycenter.
    Thus, as explained above, in the process, we do not acquire the specific consumption curve which would correspond exactly to the section considered, but rather consider one of the eleven reference curves whose variables Ψ ο and ASOE max correspond to barycenter of one of these eleven zones.
    At this stage of the process, each section T, is then defined as shown in FIG. 2 by the parameters Ψ ο , Ψ-ι, Ψ 2 , ASOE min , ASOE max mentioned above, as well as by the length LL, of each section T , and by its slope RG ,.
    As explained above, the energy curve CE, selected does not take into account either the slope of the section T ,, or the consumption of electric current of the auxiliary devices (air conditioning motor, etc.).
    In order to take account of the slope of each section T ,, there is provided a step for correcting each reference curve CEj as a function of the slope RG ,.
    As FIG. 7 clearly shows, this correction step simply consists in shifting the reference curve CEj associated with the section T, upwards or downwards (that is to say at constant ASOE charge or discharge), d 'a value depending on the slope RG ,.
    It is in fact understood that when the section of road considered goes up, the fuel consumption will be higher than that initially planned. Conversely, when the road section considered goes down, the fuel consumption will be lower than that initially planned.
    In addition, during braking phases, it will be possible to recover more electrical energy downhill than uphill.
    In practice, the correction step will consist of correcting the parameter Ψ ο according to the following formula:
    Ψ ο '= Ψ ο + K. RGl [2] with K a coefficient in the value depends on the vehicle model considered and on its characteristics (for example, we can here consider K = 0.01327 l. Km- 1 ) .
    In order to take account of the electrical current consumption of the auxiliary devices, a second step of correcting each reference curve CEj is provided as a function of the electrical power P aux consumed by these auxiliary devices.
    It will be noted here that the value of electrical power P aux considered is the value which can be measured at the time of the calculations. In this process, we therefore assume that the electrical power consumed will remain substantially constant during the journey. If the computer ever detects a large variation in this electrical power over a significant period (for example because the air conditioning is on), it could be programmed to restart the process at this stage in order to take into account the new power value electric P aux .
    More precisely, the process could be reinitialized at this second correction step if the difference between the electrical power considered in the calculations and that measured were to remain above a threshold (for example 10%) over a period greater than a threshold ( for example 5 minutes).
    As Figure 6 clearly shows, the second correction step simply consists in shifting the reference curve CE, associated with the section T, to the left (that is to say at constant fuel consumption), by a value function of the electrical power P aux .
    It is in fact understood that when the electrical appliances are used, the charge of the battery will be slower than expected and the discharge of this battery will be faster than expected.
    In practice, the correction step will consist in shifting the reference curve CEj by a Water value calculated from the following formula:
    Waters = ““ [3] where v represents the average speed over the section (in km / h). This value can be supplied directly by the geolocation and navigation system, estimating that it will be equal to the value of the traffic speed or the statistical average speed or the maximum authorized speed.
    The invention aims to propose an energy management system (EMS) capable of limiting the aging of the traction battery, in particular when the total energy required to reach the final destination of the hybrid vehicle is much greater than electrical energy. contained in the traction battery. In this case, a large part of the energy required to reach the final destination is of a thermal nature and the traction battery saves a small part of this energy. In view of this small saving in economy, it is therefore preferable to preserve the state of health (SON) of the traction battery, by promoting its use under optimal conditions of use.
    Indeed, for the same supply voltage delivered by the traction battery, the value of the electric current that it generates varies according to its state of charge (SOC). Thus, the value of the current generated by the traction battery can be very low or very high, when its charge is respectively high or low. In these specific cases, the components of the battery are subjected to too slow or too rapid dynamics, causing premature wear of its components. To prevent this phenomenon of premature aging, battery manufacturers recommend ranges of optimal battery usage values, between a minimum threshold (SOE mjtl ', for example 60% charge) and a maximum threshold (SOE maX ' , for example 80% charge) of the traction battery charge, between a minimum charge state value (SOE min , for example 10% charge) and a maximum charge state value (SOE max , for example example 90% charge) when in use.
    The invention specifically aims to promote the operation of the traction battery within its range of optimal values of use and for as long as possible, during the journey of the hybrid vehicle, while providing for its complete discharge at the final destination of the vehicle. By the term "full discharge" is meant the fact that the battery charge is less than a rest charge value. For example, this resting charge value can be less than 10% or less than 5% of its capacity of the total charge of the traction battery. Preferably, the resting charge value corresponds to the recommendations of the battery manufacturer concerning its optimal conditions for empty storage.
    The invention therefore proposes the use of an optimization algorithm of the energy management system of the hybrid vehicle, favoring the use of the traction battery in its range of optimal use values [SOE min ', SOE max ] for each section covered by the vehicle, and a complete discharge of the traction battery at the end of its journey.
    The optimization algorithm is implemented by the computer according to a fifth step of the method described above, which it is recalled that it consists in determining an optimal point P, of each reference curve CEj selected for each section of the path .
    More precisely, the optimization algorithm firstly aims to minimize, at the start of each section to be covered, the value of an energy cost function /, so that the energy consumption is as low as possible over the whole journey.
    This energy cost function f corresponds to the sum of the energy consumed by the vehicle to reach a new section i and an estimate of the energy to be spent to reach the final destination corresponding to a section N.
    More precisely, the energy cost function f is defined as follows:
    f (di, SOEt) = gÇd ^ SOEt) + to (d (iJV) , SOE (ÜV) ) [4] with:
    - the function g (d it SOEi) represents the cumulative energy cost SOEi to cover the distance d ^ in order to reach the node i from an initial node (corresponding to the start of the journey), and passing through all the nodes precedents; and
    - the function hÇd ^ ySOE ^ ') represents an estimate of the remaining energy cost SOEç iN) to cover the remaining distance d ( ijW ) to wait for the final node N (corresponding to the final destination) from node i.
    The calculation of the values of the function f at the start of each section i therefore involves the calculation of the value of the functions gid ^ SOEi) and h (d ( iN ySOE ( itr> ) defined as follows:
    gÇd ^ SOEt) = gCdi ^ SOEi ^) + M ^ ÇASOE ^^ xl i _ i [5] and h (d (kN) , SOE ^ y) =% j = i M J FC (AS0E ( iJ ) ) X lj [6] with:
    li the length of the section i;
    - ASOEç ^ q the variation of the state of charge of the traction battery on the section preceding node i;
    - M FC the fuel consumption of the hybrid vehicle on the section preceding node i.
    In order to facilitate the understanding of the invention by the reader, FIG. 8 shows an example of calculations of values of the energy cost function f by the computer. More specifically, in FIG. 8, the route of the hybrid vehicle is divided into N sections up to its final destination, symbolized by the letter T. Each section is characterized by its own distance Ι χ plotted on the abscissa axis. The ordinate axis indicates the state of charge (SOE) of the traction battery along the route. According to the present example, the hybrid vehicle approaches a second section (i = 2) of its route. The computer then performs the calculation of the value of the energy cost function f by varying the value of the function h, more precisely as a function of varying the value of the fuel consumption necessary to reach the final destination of the journey, according to the variation of the state of charge of the traction battery. According to the present example, five values of the function h are calculated, making it possible to obtain five values of the function f reported in FIG. 8 at the level of an axis delimiting the first and the second section. Of course, the computer can carry out a greater or lesser number of calculations of values of the function f.
    As a reminder, the invention aims to limit the aging of the traction battery, in particular when the energy required to reach the final destination of the vehicle is much greater than the electrical energy available in the traction battery. For this, the invention proposes to weight the fuel consumption values used in equations [5] and [6], by a preservation value (r pre ) of the state of health (SON) of the traction battery. .
    The objective of this weighting is globally to make the output that the choice of each node of the journey is made not only according to the energy consumption of the vehicle over the entire journey, but that it is also done so that , when the journey is long and the contribution of the electric traction chain will be negligible, the stresses which are exerted on the battery and which are likely to make it age, remain limited.
    The fuel consumption values are more precisely weighted as follows:
    = r pre (ΔΞΟΕ ^, ΞΟΕ ^, R x ) X m ^ ÇASOE ^) [7]
    With:
    - ÂSOEçx ^ representing the variation of the state of charge of the traction battery per kilometers carried out, in the section delimited by the nodes x and y;
    - SOEçx ^ representing the average value of the state of charge of the traction battery between the nodes x and y;
    - R x representing the distance between the node x and the final node N; and nfpc representing the function as defined in equation [1] above for node i.
    The preservation relationship (r pre ) depends on the following parameters:
    r pre Ç ^ SOE SOE (xyy Rx) = 1 - f act (Rx) X fpOnd ^^^^ x ' y ^ ~ $ OErec ^ X p max [8]
    With:
    fact (. R x) representing an activation function with R x the distance to be traveled by the hybrid vehicle to reach its final destination;
    ZponC-SOF ^ y) - S0E rec ) representing a weighting function;
    - S0E rec representing the median value of the range of optimal values for use of the recommended traction battery,. mr · $ OE max l + SOE m t nl in with: S0E rec = -;
    - Pmax representing a maximum weighting value.
    In particular, the range of optimum values for using the traction battery depends on the nature of the battery and the recommendations of its manufacturer. For example, this range of optimum values for using the traction battery can be between 60% and 80% of its maximum electrical charge. Of course, these values can vary depending on the intrinsic characteristics of the battery used.
    The activation function f ad depends on the distance R T that the motor vehicle must travel before reaching its final destination. The activation function aims to make it possible to apply a significant weighting (that is to say a significant weight) to the value of fuel consumption m fc when the vehicle is at a distance still far from its final destination, then reduce this weight in order to allow a complete discharge of the battery once it reaches its destination.
    The following distance thresholds can be defined for this:
    - R min representing the minimum distance below which no weighting is applied (ff ac (R x ) = O with Rr ^ Rmin), for example the value of R min can correspond to twice the autonomy vehicle maximum electric power in kilometers (l AER ); R max representing the distance beyond which 100% of the weighting is applied (ff ac (R x ) = 1 with R- ^ Rmax), as an example the value of R max can correspond to six times the maximum electric range of the vehicle in kilometers (AER).
    It should be noted that the weighting function can vary linearly between the values R mi n and R max as shown in FIG. 9. Of course, other variation profiles are possible.
    The weighting function f pon depends on the one hand on the average value of the state of charge of the traction battery between the nodes x and y; and on the other hand, the value S0E rec representing the median value of the recommended SOE interval in the optimal range of use of the traction battery. This weighting function thus aims to ensure that the SOE energy state remains as far as possible within the optimal use range (as long as the vehicle is distant from the arrival of the journey). By way of example, the weighting function can be defined so as to reproduce one or the other of the traces (I) and (II) shown in FIG. 10. Of course, other trace profiles are possible. .
    The maximum weighting value p max defines the maximum degree of weighting of the nodes with an SOE energy state in the optimal use range. For example, the maximum weighting value can be equal to 0.1 in order to favor 10% of the nodes in the optimal use range.
    Thus, the use of the weighting relationship described above makes it possible to modify the calculated values of the energy cost function f, so that the optimization algorithm then favors the values corresponding to a fuel consumption making it possible to discharge or to recharge the traction battery, so that its state of charge is within its range of optimal values of use, as long as possible during the journey, and to ensure the complete discharge of the traction battery by end of journey. Thus, according to the present example, the value of the function f is minimized by the weighting relation so that its calculated values are the lowest in the middle of the interval of the optimal range of use of the traction battery, corresponding for example at the value 3 in FIG. 8.
    As a function of the minimum value of the function f determined by the optimization algorithm, the computer deduces therefrom an optimal point (Pj) on the reference curve CEj associated with the section Tj, making it possible to favor the use of the traction battery. within its range of optimal use values.
    According to a sixth step of the method described above, once the optimal path found (passing through the optimal points of the reference curves CEj), the computer develops an energy management instruction as a function of the coordinates of the optimal points Pj. This energy management instruction is then used during the journey by the computer in order to follow the trajectory.
    Several methods make it possible to carry out such monitoring. An example is particularly well illustrated in patent application FR2988674 filed by the applicant, or in documents WO2013150206 and WO2014001707.
    FIG. 11 shows an example of an energy management instruction according to the invention, for a journey of approximately 800 km on the highway, with the hypothesis of a hybrid vehicle having a maximum electric autonomy of the AER of 30 km . Curve A illustrates an energy management instruction according to a discharge-maintenance strategy, known from the state of the art, with respect to an energy management instruction according to the invention represented by curve B. According to this For example, the invention makes it possible to increase by more than 600% the distance during which the traction battery operates within its optimum operating range, this distance passing from 10km to 600km. In addition, the invention makes it possible to ensure the complete discharge of the traction battery at the end of the journey, which maximizes the use of the electric potential of the vehicle and makes it possible to reduce fuel consumption.
    The present invention is not limited to the embodiment described and shown, but the skilled person will be able to make any variant according to his spirit.
    In particular, instead of storing the parameters Ψ ο , Ψ-ι, Ψ 2 , ASOE min , ASOE max of the reference curves, provision could be made for the computer to store points characterizing the shape of each reference curve overall. We will then speak of cartography.
    According to another variant of the invention, in the case where the geolocation and navigation system does not know the value of an attribute of a section of the journey, one can provide:
    - either that the calculation of the sums of probabilities does not take into account the values of the probabilities assigned to this attribute,
    - or that the calculation replaces the unknown value with a predetermined value.
    In conclusion, the invention proposes a new method for calculating instructions for managing the fuel and electric current consumption of a hybrid motor vehicle, reducing the aging of the traction battery during journeys greater than its maximum electric autonomy, in 5 ensuring that the traction battery is discharged when the hybrid vehicle arrives at its final destination. In other words, the invention proposes an optimization algorithm comprising a weighting function penalizing the calculations of fuel consumption when the battery is not operating in its optimal operating state, while ensuring that the energy state of the battery 10 reaches a minimum recommended threshold when the vehicle arrives at its destination.
    权利要求:
    Claims (10)
    [1" id="c-fr-0001]
    1. Method for optimizing the energy consumption of a hybrid vehicle comprising an internal combustion engine supplied with fuel and an electric motor supplied by a traction battery, characterized in that it implements the following steps:
    a) acquisition, by means of a navigation system, of a journey to be made;
    b) dividing said path into successive sections (Tj);
    c) acquisition, for each section (Tj), of attributes (FC, SC, SL, TS, RG, LL, NL, SMS) characterizing said section (Tj);
    d) for each of said sections (Tj) and taking into account its attributes (FC, SC, SL, TS, RG, LL, NL, SMS), selection from a plurality of predetermined relationships (CEj) connecting consumption values in fuel (CC) to electrical energy consumption values (ASOE), a relation (CEj) relating the fuel consumption (CC) of the hybrid motor vehicle on the section (Tj) to its electrical energy consumption ( ASOE);
    e) determining an optimal point (P,) for preserving the state of health (SOH) of the traction battery in each of the relationships (CEj) selected, so that the set of optimal points (P s ) minimize the aging of the traction battery over the entire path and maximize the discharge of the traction battery at the end of said path; and
    f) drawing up of a directive for managing the fuel consumption and electric current of the motor vehicle, throughout the journey, as a function of the coordinates of said optimal points (Pi).
    [2" id="c-fr-0002]
    2. Method for optimizing the energy consumption of a hybrid vehicle according to the preceding claim, in which, in step e), the determination of the optimal point (P,) in each of the relationships (CEj) selected for each section (Tj) depends on the fuel consumption over the entire section (Tj), weighted by a preservation relationship (r pre ) of the state of health (SOH) of the traction battery.
    [3" id="c-fr-0003]
    3. Method for optimizing the energy consumption of a hybrid vehicle according to the preceding claim, in which the value of the preservation relationship (r pre ) decreases when the energy state (SOE) of the traction battery is included in an optimal use range [SOEmin-; SOE maX '].
    [4" id="c-fr-0004]
    4. Method for optimizing the energy consumption of a hybrid vehicle according to claim 2 or 3, in which the value of the preservation relationship (r pre ) decreases when the distance to be traveled to arrive at the destination increases.
    [5" id="c-fr-0005]
    5. Method for optimizing the energy consumption of a hybrid vehicle according to one of claims 2 to 4, in which the preservation relationship (r pre ) depends on a product between an activation function (f ac t ) and a weighting function (f pon d), the value of the activation function (f act ) being minimum when the distance remaining to be traveled is less than a first threshold which is determined as a function of the maximum electrical autonomy ( AER) of the vehicle, so as to minimize the influence of the preservation relationship (r pre ) in determining the optimal point (Pj).
    [6" id="c-fr-0006]
    6. Method for optimizing the energy consumption of a hybrid vehicle according to one of claims 2 to 5, in which the preservation relationship (r pre ) depends on a product between an activation function (f act ) and a weighting function (f pon d), the value of the weighting function (f pon d) being minimal when the energy state (SOE) of the traction battery is outside of its optimum operating range [SOEmirr; SOE maX '], so as to minimize the influence of the preservation relation (r pre ) in determining the optimal point.
    [7" id="c-fr-0007]
    7. Method for optimizing the energy consumption of a hybrid vehicle according to claim 5 or 6, in which the value of the activation function (f ac t) is maximum when the distance to be traveled to arrive at the destination decreases.
    [8" id="c-fr-0008]
    8. Method for optimizing the energy consumption of a hybrid vehicle according to one of claims 5 to 7, in which the value of the weighting function (f pon d) is maximum when the energy state (SOE ) of the traction battery is at the center of its optimal operating range [SOE mjn '; SOE m a X '] ·
    [9" id="c-fr-0009]
    9. Method for optimizing the energy consumption of a hybrid vehicle according to one of claims 5 to 8, in which the weighting function (f pon d) has a maximum value over more than 10% of the range of optimal use [SOE m in '; SOE max ] of the traction battery.
    [10" id="c-fr-0010]
    10. Method for optimizing the energy consumption of a hybrid vehicle according to one of claims 3 to 9, in which the optimal range of use [SOEmirï; SOE maX '] of the traction battery is between 60% and 80%.
    1/4
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    法律状态:
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    2018-07-06| PLSC| Publication of the preliminary search report|Effective date: 20180706 |
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    优先权:
    申请号 | 申请日 | 专利标题
    FR1750109A|FR3061471B1|2017-01-05|2017-01-05|PROCESS FOR OPTIMIZING THE ENERGY CONSUMPTION OF A HYBRID VEHICLE|
    FR1750109|2017-01-05|FR1750109A| FR3061471B1|2017-01-05|2017-01-05|PROCESS FOR OPTIMIZING THE ENERGY CONSUMPTION OF A HYBRID VEHICLE|
    EP17837974.9A| EP3565748A1|2017-01-05|2017-12-20|Method for optimising the energy consumption of a hybrid vehicle|
    US16/475,910| US20190344777A1|2017-01-05|2017-12-20|Method for optimising the energy consumption of a hybrid vehicle|
    JP2019536312A| JP2020505263A|2017-01-05|2017-12-20|Methods for optimizing the energy consumption of hybrid vehicles|
    CN201780082290.0A| CN110139789A|2017-01-05|2017-12-20|Method for optimizing the energy consumption of hybrid vehicle|
    PCT/FR2017/053742| WO2018127645A1|2017-01-05|2017-12-20|Method for optimising the energy consumption of a hybrid vehicle|
    KR1020197020733A| KR102213021B1|2017-01-05|2017-12-20|A method for optimizing the energy consumption of hybrid vehicles|
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