EP2872358A1

EP2872358A1 - Method of managing the energy consumed by an automotive vehicle and system implementing such a method

Info

Publication number: EP2872358A1
Application number: EP13739633.9A
Authority: EP
Inventors: Mathieu Grossard; Neil ABROUG; Guillaume HAMON
Original assignee: Commissariat a lEnergie Atomique et aux Energies Alternatives CEA
Current assignee: Commissariat a lEnergie Atomique et aux Energies Alternatives CEA
Priority date: 2012-07-12
Filing date: 2013-07-10
Publication date: 2015-05-20
Also published as: FR2993213A1; US20150202990A1; WO2014009405A1; US9616771B2; FR2993213B1

Abstract

The invention relates to a method of managing the energy consumed by an automotive vehicle, in particular an electric vehicle. The method uses at least: - a simulation unit incorporating a vehicle model predicting the behaviour of said vehicle and a driver model predicting the behaviour of the driver of said vehicle, said driver model receiving as input a speed setpoint to be attained (31) and the speed of said vehicle measured (32) at successive instants and providing a motor torque setpoint (33) to said vehicle model (3) dependent on said speeds (31, 32) and on the behaviour of the driver modelled; - an optimization algorithm cooperating with said simulation unit; said method presenting a set of trajectories which is composed of the trajectory of said speed setpoint and of at least the trajectory of a setponit to control an auxiliary item of equipment, the trajectory of a setpoint describing the evolution of said setpoint as a function of the position of the vehicle, said trajectories being calculated with respect to given objectives according to said optimization algorithm whose variables are formed of said setpoints.

Description

Method of managing the energy consumed

by a motor vehicle

and system implementing such a method

The present invention relates to a method for managing the energy consumed by a motor vehicle. It also relates to a system implementing such a method. It applies especially for electric vehicles and more.

More and more fully electric powered vehicles are being used, especially in urban areas. The use of electric vehicles offers many advantages. Batteries are critical components for these types of vehicles. More generally, the management of energy for these vehicles is a totally different issue from that of fossil-fueled thermal vehicles.

In particular, batteries embedded in electric vehicles have a finite energy capacity. Moreover, the electric recharging of a battery requires a very important time. Therefore, it is essential for the driver of such a vehicle to be assured that the amount of energy stored in the batteries is sufficient to travel a desired path while activating ancillary equipment that ensures passenger comfort.

For thermal vehicles, the question of the management of auxiliary equipment (heating, air conditioning, etc ..) does not arise since fossil energy is available on the road network at many points of refueling. Thus, the auxiliary management strategy is reduced to meeting the demands of the driver. In the case of electric vehicles, this simple strategy can quickly become unfeasible. Storage capacities are limited and refills are currently absent. Satisfying at all costs the requested comforts (via heating auxiliaries, car radio, ...) can quickly deplete the energy resources of the battery. This can be done to the detriment of the goal of the mission which is to arrive at the course.

The implementation of an energy management strategy taking into account the minimization of the energy consumed, the constraint of arrival at destination and the satisfaction of the comforts, can become binding for the driver. Indeed, these criteria can impose a very slow driving mode and a non-satisfaction of driver speed requests.

Many articles present solutions for the realization of energy management systems in hybrid vehicles, with thermal and electric motorization. These systems are still called EMS, the acronym for the English expression "Energy Management Systems". The term EMS can be used later.

In general, these articles present energy management strategies aimed at finding the best scenario for activation of the thermal and / or electric engine at a given moment with respect to consumption criteria and / or or pollutant emission of a vehicle. These strategies do not make it possible to manage at the same time the satisfaction of the comfort indices of the vehicles, in particular the requests of the auxiliary equipments, the battery power consumption and the indices of performances of the vehicles, such as the time of travel for example, in the case of a purely electric motor.

In the field of energy management of vehicles with purely electric engines, mention may be made of patent application EP1462300 A1. In this document, the purpose is to allow the management of the level of charge and discharge of the battery by the driver through some information given to the driver of the vehicle. A disadvantage of the proposed solution is that it requires the use of a battery charger, which is a strong constraint.

These solutions of the prior art therefore do not respond to the problem faced by a driver, especially an all-electric vehicle. It is indeed essential for this driver to be assured that the amount of energy stored in the batteries is sufficient to make the journey. In the case where the quantity of energy can not reliably guarantee the arrival at destination, it is necessary to propose to the driver a mode of driving allowing him to reach his destination. This objective must be respected regardless of the driver of the vehicle. In addition, the maximum capacity of current batteries remaining very inadequate, it is necessary to ensure not to waste and therefore minimize the energy consumed.

An object of the invention is therefore in particular to provide optimal instructions that a driver must apply to minimize both travel and energy consumption while responding best to the activation requests of auxiliary equipment, and this regardless of the driver.

For this purpose, the subject of the invention is a method for managing the energy consumed by a motor vehicle, for a given path between a starting point A and an arrival point B, said method using at least:

a simulation unit incorporating a vehicle model predicting the behavior of said vehicle and a driver model predicting the behavior of the driver of said vehicle, said driver model receiving as input a target speed to reach and the speed of said vehicle measured at successive times and providing an engine torque setpoint to said vehicle model function of said speeds and behavior of the modeled driver;

an optimization algorithm cooperating with said simulation unit; said method having a set of trajectories composed of the trajectory of said speed setpoint and at least the trajectory of a setpoint for controlling an auxiliary equipment, the trajectory of a setpoint describing the evolution of said setpoint as a function of the position of the vehicle, said trajectories being calculated with respect to objectives given according to said optimization algorithm whose variables are formed from said setpoints, said method comprising a preliminary step comprising:

a sub-step of storing an approximated profile (2) of said path in the form of straight line segments XL, forming a first sequence of sampled positions XL (k) along the path, a sampled position XL (k) corresponding to the transition from one segment to the next segment;

a substep of sampling said profile according to a space step

Xe, forming a second sequence of sampled positions Xe (j) along the path, the sampled positions Xe (j) being located within the segments XL;

the trajectories of said setpoints being recalculated at each sampled position XL (k) of the first sequence according to the optimization algorithm, a simulation predicting the energy environment of the vehicle and the behavior of said driver to the arrival point B according to said setpoints and at least the approximate profile of the remaining path, the optimization algorithm taking into account the result of the simulation to calculate the instructions of the trajectories.

The vehicle is for example towed from a single source of energy. In a particular mode of implementation, the optimization algorithm is a particle swarm meta-heuristic, a particle being composed of said instructions.

The conductive model can be an Integral Proportional Corrector (P.I.D.) in speed. For example, it comprises a correction representative of the driver's anticipation action in the face of a type of event.

The type of event is for example a change of slope on said path.

The conductor model is for example calculated on at least one section of said given path, said model being calculated by performing a linear regression from the value of the applied engine torque, the measured vehicle speed and the target speed in points. of said given section.

The segments of the approximated profile are for example a function of the elevation of the path and / or the speed limit changes.

For example, a segment represents a path section of constant slope and / or constant speed limitation.

The predicted energy environment may include the state of the energy resource.

The energy environment comprises, for example, the speed of the vehicle, the remaining travel time and at least one output variable of an auxiliary equipment.

The simulation is for example performed according to the traffic conditions on the remaining path.

Since several objectives are each composed of a combination of one or more objectives taken from a set of objectives, several sets of trajectories are for example presented for the same instruction, a trajectory being calculated with respect to a combination of objectives.

The vehicle using electrical energy, the energy resource being electric batteries, the combinations of objectives are for example created among the following objectives 01, 02, 03:

- 01: to minimize the total electrical charge consumed by the batteries, load transmitted to the electric motor and energy transmitted to the auxiliary equipment; - 02: minimize the travel time between the starting point A and the arrival point B;

- 03: minimize the difference between the output of the requested auxiliary equipment and the actual output of the equipment.

The subject of the invention is also a system for managing the energy consumed by a motor vehicle on a given path, characterized in that said system comprises at least:

means for sensing the position of said vehicle on said path;

means for measuring the torque applied by the engine of said vehicle and its speed;

means for measuring the state of the energy resource of said vehicle;

means providing output information of at least one auxiliary equipment;

a calculator adapted to be embedded in said vehicle and interfaced with said means, said calculator incorporating

a simulation unit incorporating a vehicle model predicting the behavior of said vehicle and a driver model predicting the behavior of the driver of said vehicle, said driver model receiving as input a target speed to reach and the speed of said vehicle measured at successive times and providing an engine torque setpoint to said vehicle model which is a function of said speeds and a function of the behavior of the modeled driver;

an optimization algorithm cooperating with said simulation unit;

and implementing the method as described above.

Other characteristics and advantages of the invention will become apparent with the aid of the following description made with reference to appended drawings which represent:

- Figure 1, the actual profile of an example of a path to be traveled by a vehicle, and its approximate profile;

- Figure 2, an illustration of the integration of the method or a device according to the invention in the power train of an electric vehicle; FIG. 3, an illustration of the integration of the speed corrector associated with an identified conductor and the resulting torque command generation; FIG. 4, an example of identification of a corrector to a given driver

FIG. 5, an illustration of spatial sampling examples along the approximate path;

FIG. 6 is a flowchart of an example of a general algorithm implementing an energy management method according to the invention;

FIG. 7, an example of an optimization algorithm;

FIG. 8, an illustration of the cooperation between the optimization algorithm and a simulation of the state of a vehicle on a path remaining to be traveled;

FIG. 9, an example of a simulation algorithm;

FIG. 10, an example of a result of an energy management according to the invention in the form of the presentation of three trajectories, giving speed instructions to be applied.

FIG. 1 shows the profile of an example of a path, intended to be traveled by a vehicle, between a starting point A and an arrival point B. In particular, it presents the elevation of the road according to the position of a vehicle along this path. The real profile 1 of the road is approximated by a linear function 2 by a set of segments.

The invention is described for application to a vehicle, in particular towed from a single source of energy. Thus, the invention can be applied to a fully electric propulsion vehicle via a battery providing only energy to the electric traction motor.

The implementation of the invention requires the use of an electronic computer within the vehicle capable of collecting, via a communication protocol, of the CAN type for example, a set of signals representative of the level of charge of the vehicle. the battery, the speed of advance of the vehicle, and the level of use of the auxiliary equipment in particular. This calculator loads a simulator of the vehicle in order to be able to predict the energy consumption on the course. The invention also uses for example a GPS to know the future road information on the inclination of the road. GPS knowledge of road traffic information can also be advantageously used. In the remainder of the description, by way of example, heating is considered as the only auxiliary equipment in the vehicle. Other auxiliaries could be taken into account, especially audio equipment, air conditioning or interior lighting for example. In general, the invention at least takes into account the electric motor and the entire chain of traction of the vehicle, as well as at least one auxiliary comfort equipment. Two control variables X, or instructions, will be taken into account later.

- the speed desired by the driver;

- the position of the requested heating.

The invention takes into account the conduct of the driver in the EMS strategy embedded in the aforementioned calculator. For this purpose, a model of the driver is defined. The model of the driver can be extracted from rolling profiles known a priori and used for other routes. It can also be identified online at the beginning of each road trip or at specific times of the course.

In previous solutions, most of the embedded strategies only take into account the mechatronic components of the vehicle, for example engine performance mapping, a simplified model of the battery or a model of loss of service auxiliaries.

The modeling of the behavior of the driver used by the invention is as follows: the driver wishes to reach a reference speed at a given moment of the course. Depending on the road profile and certain information relating to his vehicle, the driver will more or less press the accelerator pedal, that is to say, provide a set torque of the drive chain. This, in order to reach the reference speed. Thus the driver can be considered as a speed corrector. The method according to the invention then directly takes into account the torque setpoint resulting from this corrector, representative of the behavior of the driver.

FIG. 2 illustrates the integration of the method or device according to the invention into the traction system of an electric vehicle.

The traction chain conventionally comprises an electric motor 21 transmitting a torque to a transmission member 22 which transmits a torque to the dynamic members of the vehicle 23, in particular the wheels drive. A set of power electronics 24 forms the interface between the control and the motor.

According to the invention, a regulation loop is added at the same time as the addition of a speed correction module 25, the correction provided being a function of the behavior of the driver. For this purpose, the speed of the vehicle is measured at times t and sent to a comparator delivering an AV value corresponding to the difference between the desired speed and the measured speed. The speed correction module 25 models in particular how to respond to this AV value by the driver.

A control member 27 of the vehicle, said EGV member, makes a prediction of the state of the vehicle at the future times of the course. This member comprises a signal acquisition module 271. Via this module, the controller acquires the battery charge level as a function of time, called SOC (t) according to the English expression "State Of Charge". It also acquires the profile of the road between the points A and B as illustrated by FIG. 1, that is to say between the initial moment t ₀ and the final instant t _f . It also acquires all the information provided by the GPS 29.

The EGV member also comprises a module 272 performing the online optimization steps, all along the path, from the model of the vehicle and the driver model and a heuristic which will be described later, this heuristic also applying to the driver model.

A module 273 applies the EMS strategy by providing the vehicle control information to the driver, both with respect to the speed 200 and the control of the auxiliaries 201.

The introduction of the behavior of the driver makes it possible to predict, on the future instants of the route A-B, the transient state that would have been carried out by the driver thus simulated to reach the target speed. Advantageously, this makes it possible to accurately predict the energy consumption of the vehicle associated with a given driver.

FIG. 3 illustrates the integration of the speed corrector associated with an identified conductor and the generation of the resulting speed reference. In the diagram of FIG. 3, the speed correction includes the corrector module 25 and the comparator 26 of FIG. 2. The corrector thus receives, as input, the desired speed set point 31, desired by the driver, as well as the value of the real speed 32 of the vehicle at a time t. The corrector provides output a reference torque value 33 as setpoint for the motor. This corrector is of integral proportional type and derivative (PID) in speed. In addition, this corrector contains for example an anticipatory action, called "feed-forward" in the Anglo-Saxon literature, which is representative of the anticipatory action of the driver facing the inclination of the road. Indeed, if it notices that it is about to climb, it will anticipate the change of altitude profile by pressing more strongly on the acceleration pedal, and conversely when negotiating a descent. It is a mechanism close to that of speed control on motor vehicles. The addition of these two values, correction P1 then "feed-forward" action, forms the value 33 of the torque applied to the motor and therefore the entry of the model 3 of the vehicle from which the control member 27 performs the EMS strategy. of the vehicle. Anticipation may concern another type of event than a change in altitude profile.

A speed corrector is therefore adapted to each user. Several solutions are possible to determine the characteristics of a speed controller characterizing a conductive model. FIG. 4 illustrates an example of identification of a corrector to a given driver enabling the creation of a driver model.

In this example, the behavior of the driver is extracted from a rolling profile on a given course. The speed controller is developed by performing a linear regression between the torque applied, the speed desired by the driver and the actual speed of the vehicle. The corrector representing the behavior of the driver has been integrated into the simulation model incorporated in the correction module 25.

FIG. 4 shows the real speed of the vehicle, measured speed, on a given course by a first curve 41. A second curve 42 represents the associated speed setpoint generated automatically. This speed instruction 42 corresponds to the speed desired by the driver, that is to say the speed he wanted to achieve. A third curve 43 represents the value of the motor torque actually applied. All these values 41, 42, 43 can be acquired by known means throughout the given course and more particularly on the sections of this course. Using the two speed curves 41, 42 and the torque curve 43, a conductor behavior composed of a linear dynamics of speed correction and anticipation of the slope of the road. When this corrector 25, 26 is applied to the system and the input speed command 31 is injected, as is the measured speed 32, a fourth curve 44 is obtained representing the estimated torque from the learned driver model. It is these estimated torque values 44 that will be the torque setpoints applied to the motor, in the consideration of the speed corrector, and therefore the behavior of the driver. The estimated torque curve 44 closely resembles the engine torque represented by the third curve 43, confirming that this method of representing the driver through this speed corrector is effectively identified.

It should be noted that the speed corrector is completely defined by a finite set of numerical parameters. These define the way the driver drives, for example the application of brief or slow accelerations, the anticipation action more or less pronounced ... Thus, for each given corrector, the corrector must be able to adapt this set of parameters. According to the invention, this set of parameters is for example identified in an embedded manner throughout the course at different times. Thus, the invention makes it possible to take into account any driver, and if a driver changes his driving behavior, when passing to an urban area to a peri-urban area for example, the invention also makes it possible to take into account Changing behaviour.

As indicated above, two control variables X, or instructions:

- the speed instruction;

- the position of the requested heating, as an example.

The torque requested from the motor is then a function of the difference between the set speed V _CO nsign _e (desired by the driver) and the current speed of the vehicle V _V eco-friendly ■ By noting f this function and Cp the engine torque, it is Cp = f (V _CO NSIGN _e, V éhicuie _V), the function f translating driver behavior. It is defined by the speed corrector 25. In the electric vehicle in question, it is possible to perform regenerative braking energy phases. Thus the requested motor torque can be positive, in the case of propulsion, or negative, in the case of deceleration. The corresponding speed variable V is for example indicated as a percentage of its maximum allowable value. The heating position variable is an integer variable. Each position corresponds to a fixed power for heating the passenger compartment of the vehicle.

In the context of the invention, the trajectory of a variable X corresponds to the evolution of this variable as a function of the position, from the starting point A to the arrival point B. Thus the trajectory of the speed is the value of the speed at each position of the path. The description of the trajectories is therefore made with respect to a spatial reference rather than a temporal reference, in particular for the following two reasons:

the path is known a priori via position coordinates, GPS information, the arrival time being unknown and constituting an optimization parameter;

- Some variables of the model of the vehicle for the simulation are function of the position or the elevation, for example the requested torque, thus the speed, varies essentially with the elevation.

For the prediction, the two variables, requested speed and requested heating position, are calculated over the entire path. For this purpose, the path is sampled according to a spatial period Xe, tests being carried out at each of the sampled positions according to a management algorithm, an example of which will be described later. It should be noted that a large number of samples can be considered over a relatively long journey. As an example we can take Xe = 200 meters.

A second category of spatial samples XL is introduced, the samples XL being for example defined by segments 2 approximating the profile of the path, each segment corresponding to a sample XL. In particular, the spatial samples XL correspond to locations of the route where the optimization algorithm, which will be described later, is restarted to refresh all the setpoints, both in terms of speed and of the heating position. Between two instants XL and XL + 1 one oversamples with the pitch Xe, Xe being very inferior to XL. At each interval Xe is assigned a speed and heating setpoint.

An overall objective of the strategy for managing energy within a vehicle is to determine the optimal values of these two variables over the entire sampled path, vis-à-vis for example the following three objectives O1 , O2, 03: - 01: to minimize the total electrical charge consumed by batteries or any other type of energy resource, charge transmitted to the electric motor and energy transmitted to the heating;

- 02: minimize the travel time between the departure point and the arrival point;

- 03: minimize the difference between the passenger compartment temperature requested by the driver and the actual temperature in the passenger compartment under the action of the heating system.

For auxiliary equipment other than heating, objective 03 may be formulated as follows:

In addition to these objectives, the management strategy must satisfy several constraints, among which for example the following constraints C1, C2, C3, C4:

- C1: the instantaneous charge of the batteries must always be higher than a fixed threshold, in order to preserve the life of the batteries;

- C2: the travel time must be less than a fixed threshold;

- C3: the difference between the requested and actual temperature must not exceed a fixed threshold;

- C4: The vehicle speed must not exceed a certain threshold, in order to respect the speed limits along the route.

It should be noted that in the case of a thermal vehicle, the last two objectives can be easily achieved since the energy reservoir is of infinite capacity, fast charging and available in fuel. In the case of an electric vehicle, these two objectives are not so easily achieved. The management strategy implemented by the invention notably performs an arbitration between these contradictory objectives while respecting the stated constraints.

Between a starting point A and an arrival point B, all the trajectories considered are the mechanical torque supplied by the motor and the heating position, for example that the state of charge of the batteries (SOC), the vehicle speed, travel time and cabin temperature. The trajectory of each of its information may be represented by a curve representing their value as a function of the position of the vehicle along the path between point A and point B. One objective of the energy management strategy according to the invention is to provide three sets of trajectories between the points A and B, a set of low trajectories, a set of high trajectories and a set of so-called pseudo-optimal trajectories, these trajectories can be defined as follows:

- Low trajectories: these trajectories are obtained taking into account objectives 01 and 03, we use the term "low" because the speed of the vehicle resulting from this optimization is theoretically lower than those obtained by considering the other sets of objectives;

- High trajectories: these trajectories are obtained taking into account only the objectives 02 and 03, in these trajectories the speed of the vehicle should be greater than that obtained by the low trajectories;

- Pseudo-optimal trajectories: these are the trajectories obtained taking into account the three objectives 01, 02, 03 simultaneously.

Preferably, these trajectories are proposed to the driver of the vehicle, in a given ergonomic form. The driver then always has the possibility to decide to accelerate or brake, and to change the heating power setpoint. The three sets of trajectories serve in particular to assist the driver and reassure him about the possibility of arriving at the destination of his journey with the amount of electrical energy stored. The invention makes it possible to propose to the driver the optimal trajectories according to his preferences and his mode of driving for example.

FIG. 5 illustrates the spatial Xe and XL sampling previously defined for a given path profile represented by a curve 51. The samples Xe are represented inside a segment 52 flanked by two values of samples XL. A segment 52 represents for example a path section of constant slope and / or constant speed limitation.

At starting point A, the three sets of trajectories are calculated and determined based on the known information on the path. These trajectories are updated at particular points corresponding to the times of spatial sampling. At an update point, the three sets of trajectories are recalculated from the history, the remaining path profile, the outside temperature, and the measurements collected in this way. point. These measurements indicate, for example, the state of charge of the batteries, the temperature of the passenger compartment and the travel time to this point. The history notably includes the records of the trajectories calculated at the instants of previous samplings.

Optimal trajectories are computed from a formalization of the EMS management problem into a constrained single-objective optimization problem containing several decision variables.

Referring to FIG. 5. At the k-th run update point, sampled position XL (k) of the XL series, the objective is to determine the optimal trajectories to the end position Xf. The previous update took place at the point XL (k-1), the points XL (k-1) and XL (k) framing a segment 52. In the example of Figure 5, two consecutive segments are not collinear, which means in practice that the passage from one segment to another is at a change of inclination of the slope of the road. However, we could consider cases where two consecutive segments are collinear, without change of inclination, for example in the case where a segment is too long, it can be subdivided. Inside the segments, to the end position Xf, the path is sampled according to step Xe.

For the update at point XL (k), segment breaking point, it is a question of determining the requested speeds, as well as the positions of setpoint of the heating, envisaged until the final position Xf, in point B. These variables, speed and heating position, are determined so as to minimize a criterion involving the three objectives 01, O2 and O3 of the previous section 52 XL (k-1), XL (k), and respecting the four constraints C1, C2, C3 and C4.

FIG. 6 presents the flowchart of an example of a general algorithm implementing an exemplary EMS strategy according to the invention since starting the vehicle at a point A to an arrival point B, final destination.

At start 60 in a preliminary step, at point A, several operations are performed for example:

- 601, entering the geographical coordinates of the destination;

- 602, reading of the path by the GPS device, the path profile can be approximated during this step;

- 603, calculation of the temperature gradient in the path according to available weather information; - 604, determination of the two sets of sampled positions XL and Xe from the approximate profile 2 as illustrated for example in Figure 1;

- 306, starting order of the vehicle resulting in particular by the activation of the engine torque;

- Initialization of the indices i, j and k to 1, indices respectively corresponding to a temporal step Te of sampling along the path, to the spatial sampling step Xe, and to the spatial sampling step XL of updating of the instructions optimal.

Once started, the GPS regularly transmits the current position of the vehicle on the course.

The departure of the vehicle is followed by a step 600 of identification of the behavior of the driver leading to the creation of the speed controller. During this identification step, the EMS system identifies, for example, the driver model using the torque and speed measurement data returned by the electric vehicle control unit (UCVE). This unit has the particular function of controlling all the low-level electrical functions and of acquiring the course data, such as speed or torque measurements. The identification phase ends when it has been decided that the input / output signals (speed / torque) of the future corrector are sufficiently rich from a frequency point of view to design an effective corrector. A linear regression on the values of torque and speed is then for example used to define the coefficients of the speed corrector, the latter being integrated into the simulation model of the EMS which will be described later. As soon as this identification step 600 is completed, the algorithm can be executed. The identification of the driver model can also be established again throughout the journey.

The algorithm begins and then continues with a series of two tests 61, 62. These tests are performed according to the temporal sampling step Te, that is to say that all Te, these tests are carried out. We denote X (i) a sampled position according to Te.

In a first test 61, the position X (i) is compared with the final value Xf. When the value X (i) is substantially equal to the value Xf, otherwise stored, the vehicle has reached the end point B, it is at its destination final 69. In the opposite case, the position X (i) and compared, in a second test 62, to a sampled position XL (k) of setpoint change. If the value X (i) is not equal to XL (k), the optimal setpoints to be applied between the point XL (k-1) and the end point Xf are maintained 63 for all the positions Xe (j). If the value X (i) is substantially equal to XL (k), the refreshing of the optimal setpoints for each position Xe (j) between the position XL (k) and Xf 64 is applied. The position XL (k) is incremented by a step XL, XL (k + 1) for the next test 62. After this test, after which there is maintenance 63 or refresh instructions 64, the algorithm is looped back on the first test 61 where the not X (i + 1) is compared with the position Xf, then if Xf is not reached X (i + 1) is compared with XL (k) or XL (k + 1) depending on whether XL has been incremented or not .

The positions X (i) and XL (k) do not necessarily coincide, so an interval of distance LE such that | XL (k) - X (i) | <LE means that the position XL (k) is reached. It's the same with Xf. The positions of the vehicle are detected by position sensors, for example using a GPS system, the distance LE taking into account the uncertainties of measurements.

For example, the refresh or update of the setpoints is performed by an optimization algorithm.

The chosen optimization algorithm is for example particulate swarm. It is of course possible to use other meta-heuristics such as genetic algorithms or ant colony algorithms for example. The optimization problem can be formulated by minimizing a constrained mono-objective function. The mono-objective function is the weighted sum of the objectives 01, 02, 03. This problem is thus formulated in the following table for a position X (i), denoted X _t , coinciding with a position XL (k):

(XX Ol (Cp, Pc, State _ i, Param _ path)]

minimize + βχ 02 (Ορ, State _ i, Param _ path)

+ γχ 03 (Pc, State _ i, Param _ path)

or

Param_trajet: parameters relating to the path (length, road profile, outside temperature, etc.) under the following constraints:

1 <Cp≤ 1

40 <Pc≤ nPcMax

State _ min <State≤ State _ max

or

Min_state and max_state: minimum and maximum vehicle status

nPcMax: Maximum power required for heating

In the table above, n corresponds to a discrete position of the heating setpoint. The speed Cp is normalized and varies between -1, for the minimum speed, and +1, for the maximum speed.

It should be noted that the transition from one optimal trajectory to another is done by weighting differently the objective function to be minimized according to the values α, β, γ.

This optimization problem is constrained mono-objective with a very large search space. It must also take into account variables with real values, such as the value of the requested speed, and integers, such as the heating position.

This optimization problem can be difficult to solve by common optimization techniques. The use of a meta-heuristic overcomes the difficulty. The particle swarm optimization algorithm, iterative algorithm, has the particular advantage of being simple to implement in a computer embedded in a vehicle. It is a method based on the existence of a population of particles, corresponding to the solutions, which move in the search space of the admissible solutions. Each particle has a memory that allows it to find its best position, according to the optimization criterion. She has also access to the best positions of its neighbors. The particle in a flight plan that allows him to know his future destination in the search space. This flight plan is calculated from its best position in the past, the best position of all particles and its last vector of displacement, called by language abuse speed. In the present invention, a particle corresponds to a set of system state variables. In the present example, a particle corresponds to the requested speed and the heating position n. For example, a particle corresponds to:

Vp = 10% of the maximum speed;

- n = 4.

The future position of a particle i is determined by means of the two equations presented in the following table: (ΐ))

or

V ^. (t): velocity vector or displacement at iteration t

P _t (t): position vector at iteration t

P ^ ^is (t): best position vector of the particle i

P _gio l (0 ^: best position vector of all particles

rr ₂ : random numbers between 0 and 1

û), cc ₂ , χ: setting parameters of the algorithm

To make this meta-heuristic more robust and to allow convergence towards the global optimum, the following operations can be performed:

- deterministic or random dynamic variation of certain parameters of the algorithm;

- limitation of the displacement vector to prevent excessive displacements, which may tend to cause the particles to leave the domain of the admissible solutions or to confine them to the boundaries of the search space;

- introduction of the mutation operator, already used in genetic algorithms, to avoid particle stagnation or premature convergence towards a local optimum. For integer variables, such as heating positions, a simple method is to relax the integrity constraint by allowing the use of real variables. To pass these variables to evaluation via a simulation model, we approximate the real value by the nearest integer.

FIG. 7 presents the optimization algorithm 65, in which the steps described above are found in particular. All particles, or solutions, are evaluated with respect to the criterion to be minimized, optimization, and constraints. This criterion and these constraints make use of a simulator of the vehicle, integrating the conductor behavior, having its own algorithm 70, capable of determining the state of the system from a position X (i) to a position Xf. The simulator 70 includes the behavioral model of the driver.

The use of the simulator inside the optimization algorithm is illustrated in particular in FIG. 7. In an initial step 71, the initialization of the particles is carried out. This step is followed by a step 72 of evaluation of the particles initialized according to the optimization criterion and the constraints, using the simulator 70. This step is followed by a step 73 of updating the particles according to the previous equation system (Eq1). It is followed by a step 74 evaluation. This step performs the evaluation of the new particles according to the optimization criterion and according to the constraints, using the simulator 70. This evaluation step is followed by a step 75 of updating the best position of each particle, itself followed by a step 76 of updating the best particle of the swarm. After this step 76, we proceed to the next iteration 77 by looping back on the step 73 of updating the particles. When the maximum iteration is reached 78, the algorithm stops. FIG. 8 illustrates the cooperation between the meta-heuristic 81, for example corresponding to the optimization algorithm of FIG. 7, and the simulator 70. The function of the simulator 70 is notably to predict the energy consumption of the power train. and heating, and more generally all auxiliary, on the remaining course, involving conductor behavior. This simulator is intended to be called as many times as there are particles at each iteration of the particle swarm algorithm. The total number of calls from the simulator can thus reach a few thousand for a given scenario. The cycle time of the simulation must be compatible with the different sampling parameters. For the sake of simplification, modeling can be limited in first approximation to the behavioral model of the vehicle and the only organs consuming most of the battery energy, that is to say electric traction and heating, and to the driver model whose characteristics are those of the speed corrector 25. In a more general context taking into account other auxiliaries, the energy consumed by them can be neglected.

In the following paragraphs, we consider the synthesis of a simulator of the power train. For the analytical expression of the vehicle model, the following simplifying assumptions are used:

• Longitudinal dynamics: An exhaustive modeling of the chain of actuation of a vehicle takes into account the 6 degrees of freedom of the vehicle and decouples the dynamics of the vehicle of that of the 4 wheels. This leads to a differential equation of degree 10. In order to simplify the model for embedding it in an EGV vehicle calculator, only the longitudinal dynamics of the vehicle are modeled. · No slippage on the pavement: The distinction between vehicle and wheel dynamics (taking wheel-road slip into account) is only relevant if there is a need to model the ABS.

Following the simplifying assumptions above, we can identify 4 subsystems of the power train:

• The motor and its drive

• The mechanical transmission

• The longitudinal dynamics of the vehicle

• A model of the battery

With regard to the equations governing these different subsystems:

Servomotor: The power losses of an asynchronous motor are not stationary, they are functions of torque and engine speed. Static mapping represents the motor behavior and its performance. This makes it possible to identify the electrical power consumed by the engine at each moment. Mechanical transmission: The transmission is modeled by a speed gain corresponding to the ratio noted N of the engine (rd / s) and vehicle (m / s) speeds, and a gain in torque, engine torque ratio (Nm) and vehicle effort ( NOT). The gain in effort and speed is assumed to be identical, the losses being modeled at the motor level.

Vehicle dynamics: Following the reduction of the vehicle dynamics to its single longitudinal component without slip, this can be described by the following first-order nonlinear differential equation (quadratic dynamics):

MX = ± F _t - f _s ∞s) sign (x) - f _am X \ x \ - Mg ùn)

with:

- M: Sum of vehicle mass and rotational inertia

(engine, transmission, wheel) reduced to a total mass in translation

- F _t : Traction or braking force according to sign

- f _s : Dry friction torque

- f _aero : Coefficient of aerodynamic resistance

- β: slope of the road at the current time

Battery: The state of charge of the battery, also called SOC, is the difference between the total charge stored and the charge consumed by the different organs connected to it:

SOC (t) = SOC _Q - \ ldt where l (t) is the current flowing through the motor, and U (t) is the voltage across the motor, and E ₀ is a function of the temperature in particular. However, in a first step, it can be considered constant while reserving the possibility of introducing the characteristic SOC ₀ = f (T °) later. When the equations of the different equations governing the subsystems are established, the parameterization of the model is carried out for these subsystems.

Motor: As an example, we consider an asynchronous motor ABM with a power of the order of 15 kW. A series of measurements make it possible to provide a map of the power losses as a function of the speed and the engine torque. This map is a 3D surface that is a function of engine speed and torque, digitized and stored in the system. To reduce calculation times, the map can be interpolated as polynomial equations to describe the 3D surface.

Mechanical transmission: The following ratio parameter N:

Τ ForceTraction SpeedMotor

N = =

CoupleEngine SpeedVehicle

defines the transmission gain and corresponds to the ratio of the gain of the gearbox by the radius of the driving wheel.

Vehicle dynamics:

• Mass: M = M _B¾ + M _{PackBatteries} + M _{chm emle} = 500 + 140 + 200 = 840kg

f = 0 3

· Coefficient of dry friction: ^Js '

• Aerodynamic coefficient: ^f ° <"> ^{= C} * ^{S =} ° ^{<3 xl <5} = 0, 45N.m ^{" 2} .

Battery: The battery held is for example made up of 10 cells of 1, 766 KWh each, E ₀ = 10 x 1, 766 = 17.66 kWh = 63576 mega joules.

Returning to FIG. 8, the output of the simulator 70 constitutes an input of the optimization algorithm 51 in the sense that the simulator calculates a state of the system (Velocity V (x), temperature t (x), SOC (x ) in particular) for X (i) positions sampled up to Xf, this state represents the environment vehicle energy. This state is used for the evaluation of the updated particles. Similarly, the output of the optimization algorithm constitutes an input of the simulator in that the algorithm provides the speed and the optimum heating position to the simulator to perform the simulation of the vehicle, this state (vehicle speed, position of heating) being defined in step 76 of updating the best particle. In applications taking into account other auxiliary equipment than heating, this state or energy environment would take into account the output variable of these equipment, the sound volume of a car radio or the outlet temperature of an air conditioning for example. The simulation is also carried out according to the traffic conditions on the remaining route, such as climatic conditions or the intensity of road traffic.

The behavior of the vehicle is governed by nonlinear time differential equations. These time equations are sampled in a time step Ts of simulation before proceeding to their numerical integration. Ts is for example of the order of 2 seconds. The particle swarm optimization algorithm samples, for its part, the different states (torque, speed and SOC in particular) at the pitch Xe, the instructions being refreshed at the pitch of the samples XL. The meta-heuristic 81 and the simulator 70 exchange input and output data. The same states are thus expressed in two different spaces, temporal and spatial. It is necessary that the inputs / outputs of a module 81 are compatible with the inputs / outputs of the other module 70.

The passage of data expressed in time space to a space space does not pose any particular problem. After having obtained all the states in the time domain, we have the position vector as a function of time, the correspondence between the spatial and temporal information then being defined. The different values of the state vectors calculated at successive instants are thus interpolated as a function of the position Xe (j). The inverse passage, from space space to time space, is necessary in order to determine the speed and heating setpoint to be considered at each time iteration of the simulator. It is assumed that at time zero, the position is also zero, V _V éhicui _e (x = 0) = V _V éhicui _e (t = 0), being véhicuie vehicle speed. At each iteration, the new vehicle position is calculated and compared to the spatial samples corresponding to setpoint changes. In the case of a match, the set speed for the next time iteration of the simulator is re-estimated by assigning the torque corresponding to this critical position in the reference vector v = f (x).

Since the objective of the simulator is to estimate velocities and SOC on a path whose final position is known, the simulator output condition is spatial and not temporal. It should be noted that, in some atypical cases envisaged by the stochastic optimization method, the vehicle may not reach the destination, it is therefore necessary to add an exit condition to a maximum number of iterations. Figure 9 shows the operating algorithm of the simulator 70 according to the above description. In particular, at the instantaneous sampling time of order i, the simulator calculates in a first step 91 the position X (i) of the vehicle, the speed V (i) of the vehicle, the state of charge of the SOC batteries. (i) and the temperature T ° (i) inside the passenger compartment, by means of sensors known to those skilled in the art. The data V (i), SOC (i) and T ° (i) are transmitted to the optimization algorithm.

X (i) is then compared to Xf to determine whether the vehicle has arrived at destination 90. If not, X (i) is compared to the next sampled position Xe (j). If X (i) is different from Xe (j), the setpoint is maintained. In the opposite case, X (i) is substantially equal to Xe (j), the setpoint is incremented 95, it is maintained until the next space step. The index j is then incremented by a unit 1 so that the next comparison 93 will be made with Xe (j + 1). At the time of following temporal sampling 97, one loops back on the first step 91 of calculation of position, speed, state of charge and temperature, ie X (i + 1), V (i + 1), SOC (i + 1) and T ° (i + 1).

The time sampling period is the sampling period mentioned above, it can be equal to the period Te used for the general algorithm presented in Figure 6.

Figure 10 shows an example of a final result of the optimization strategy at a given moment or at a given position, in this example at the position XL (k). This result presents the three optimal trajectories, the average trajectory 101, the high trajectory 102 and the low trajectory 103. These trajectories represent the value of the requested speed as a function of the position. Each path predicts the optimal speed values from position XL (k) to the end of the path at position Xf, the optimal speed values being refreshed, that is, recalculated, all XL. They are calculated to minimize the consumption, the time traveled and / or allow the maximum comfort, taking into account the behavior of the driver, using the strategy implemented by the method according to the invention as described above. Depending on the objectives initially set and the constraints related to the course.

These three sets of trajectories are returned to the driver so that he adapts his driving style. Preferably they are not restored in a raw form as illustrated by FIG. 10. They can be rendered in an ergonomic form adapted to the situation of an automobile driver, for example in the form of simple voice or visual instructions. to read in the visual case.

The algorithms implementing the method according to the invention are for example implemented in a computer embedded in the vehicle, for example in the UCVE, this computer being interfaced with the various sensors providing the necessary input data such as the positions in particular , the speed or the internal and external temperatures for example, as well as the measurements of the state of the batteries.

Claims

1. A method of managing the energy consumed by a motor vehicle for a given path between a starting point A and an end point B, characterized in that said method uses at least:

a simulation unit (70) incorporating a vehicle model (3) predicting the behavior of said vehicle and a driver model (25) predicting the behavior of the driver of said vehicle, said driver model receiving as input a speed setpoint to be reached (31) and the speed of said measured vehicle (32) at successive instants and providing a motor torque setpoint (33) to said vehicle model (3) according to said speeds (31, 32) and the behavior of the modeled conductor;

an optimization algorithm (65) cooperating with said simulation unit (70);

said method having a set of trajectories composed of the trajectory of said speed setpoint and at least the trajectory of a setpoint for controlling an auxiliary equipment, the trajectory of a setpoint describing the evolution of said setpoint as a function of the position of the vehicle, said trajectories being calculated with respect to objectives given according to said optimization algorithm (65) whose variables are formed of said setpoints, said method comprising a preliminary step comprising:

a substep (601, 602) for storing an approximated profile

(2) of said path in the form of line segments (XL, 52), forming a first sequence of sampled positions XL (k) along the path, a sampled position XL (k) corresponding to the transition of one segment to the next segment ;

a substep (604) for sampling said profile in a spatial step Xe, forming a second sequence of sampled positions Xe (j) along the path, the sampled positions Xe (j) being located inside the segments (XL, 52);

the trajectories of said setpoints being recalculated at each sampled position XL (k) of the first sequence according to the optimization algorithm (65), a simulation (70) predicting the energy environment of the vehicle and the behavior of said driver to the point arrival B depending said setpoints and at least the approximate profile of the remaining path, the optimization algorithm taking into account the result of the simulation to calculate the instructions of the trajectories.

2. Method according to claim 1, characterized in that said vehicle is towed from a single source of energy.

3. Method according to any one of the preceding claims, characterized in that the optimization algorithm (65) is a particulate swarm meta-heuristic, a particle being composed of said instructions.

4. Method according to any one of the preceding claims, characterized in that said conductive pattern is a corrector integral proportional type (P.I.D.) speed.

5. Method according to any one of the preceding claims, characterized in that said conductive model comprises a correction representative of the anticipatory action of the driver facing a type of event.

6. Method according to claim 5, characterized in that the type of event is a change of slope in said path.

7. Method according to any one of the preceding claims, characterized in that said conductive pattern (25) is calculated on at least one section of said given path, said model being calculated by performing a linear regression from the value of the driving torque. applied, the measured vehicle speed and the target speed at points of said given section.

8. Method according to any one of the preceding claims, characterized in that the segments (52) of the approximated profile are a function of the elevation of the path and / or changes in speed limitation.

The method of claim 8, characterized in that a segment (52) represents a path section of constant slope and / or constant speed limitation.

10. Method according to any one of the preceding claims, characterized in that the predicted energy environment comprises at least the state of the energy resource.

1 1. Method according to claim 10, characterized in that the energy environment comprises the vehicle speed, the remaining travel time and at least one output variable of an auxiliary equipment.

12. Method according to any one of the preceding claims, characterized in that the simulation (70) is also performed according to the traffic conditions on the remaining path.

13. Method according to any one of the preceding claims, characterized in that several given objectives being each composed of a combination of one or more objectives taken in a set of objectives (01, 02, 03), several sets of trajectories are presented (101, 102, 103) for the same instruction, a trajectory being calculated with respect to a combination of objectives.

14. Method according to claim 13, characterized in that the vehicle using the electrical energy, the energy resource being electric batteries, the objective combinations are created among the objectives following 01, 02, 03:

- 01: to minimize the total electrical charge consumed by the batteries, load transmitted to the electric motor and energy transmitted to the auxiliary equipment;

- 02: minimize the travel time between the starting point A and the arrival point B;

15. System for managing the energy consumed by a motor vehicle on a given path, characterized in that said system comprises at least:

means (271) for sensing the position of said vehicle on said path;

means for measuring the state of the energy resource of said vehicle;

means providing output information of at least one auxiliary equipment;

a simulation unit (70) incorporating a vehicle model (3) predicting the behavior of said vehicle and a driver model (25) predicting the behavior of the driver of said vehicle, said driver model receiving as input a speed setpoint to be reached (31) and the speed of said measured vehicle (32) at successive instants and providing a motor torque setpoint (33) to said vehicle model (3) according to said speeds (31, 32) and function of the behavior of the modeled conductor;

an optimization algorithm (65) cooperating with said simulation unit (70);

and implementing the method of any one of the preceding claims.