EP2714460A1 - Method of managing the energy consumed by a mobile system, in particular a motor vehicle, on-board device implementing such a method - Google Patents

Abstract

The method presents at least one set of trajectories which is composed of the trajectory of a setpoint for controlling the motive unit and of the trajectory of a setpoint for controlling at least one auxiliary item of equipment, the trajectory of a setpoint describing the evolution of said setpoint as a function of the position of the mobile system, said trajectories being calculated with respect to objectives given according to an optimization algorithm whose variables are formed of said setpoints, said method comprising: - a preliminary step comprising itself: - a substep (301, 302) of storing an approximate profile of said path in the form of straight line segments, forming a series of sampled positions XL(k) along the path, a sampled position XL(k) corresponding to the passage from one segment to the next segment; - a substep (304) of sampling said profile according to a spatial interval Xe, forming a series of sampled positions Xe(j) along the path; the trajectories being set to recalculated at each sampled position XL(k) according to the optimization algorithm (35), the setpoints being constant over a given segment, a simulation predicting the energy environment of the mobile system up to the point of arrival B being performed at each sampled position Xe(j) as a function of said setpoints and at least of the approximate profile of the remaining path, the optimization algorithm taking account of the result of the simulation to calculate the setpoints of the trajectories.

Description

 METHOD FOR MANAGING ENERGY CONSUMED BY A MOBILE SYSTEM, IN PARTICULAR A MOTOR VEHICLE, ON-BOARD DEVICE IMPLEMENTING SUCH A METHOD

The present invention relates to a method for managing the energy consumed by a mobile system. It also relates to a device implementing such a method. It applies in particular for electric vehicles.

More and more purely electric vehicles are 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 indeed impose a very slow driving mode and a non-satisfaction of the speed demands of the driver.

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. An object of the invention is in particular to provide optimum instructions for a driver, or more generally a control member, to apply to minimize both travel times and energy consumption while responding best to requests for activation of ancillary equipment.

For this purpose, the subject of the invention is a method of managing the energy consumed by a mobile system, for a given path between a starting point A and an arrival point B, said method having at least one set of trajectories composed of the trajectory of a setpoint for controlling the driving member and the trajectory of a setpoint for controlling at least one auxiliary equipment, the trajectory of a setpoint describing the evolution of said setpoint as a function of the position of the mobile system, said trajectories being calculated with respect to objectives given according to an optimization algorithm whose variables are formed from said instructions, said method comprising:

 a preliminary stage comprising itself:

 a substep of storing an approximated profile 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 passage of a segment to the next segment;

 a substep of sampling said profile at a spatial pitch Xe, forming a second sequence of sampled positions Xe (j) along the path;

the trajectories being recalculated at each sampled position XL (k) of the first sequence according to the optimization algorithm, the setpoints being constant over a given segment XL, a simulation predicting the energy environment of the mobile system up to the point of arrival B being performed at each sampled position Xe (j) of the second sequence 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.

In one possible embodiment, the optimization algorithm is a particulate swarm meta-heuristic, a particle being composed of said instructions.

 The segments of the approximated profile are for example a function of the elevation of the path, a segment representing a path section of constant slope.

The predicted energy environment comprises for example at least the state of the energy resource.

 In a particular mode of implementation where the mobile system is a vehicle, the instruction to control the drive member is the engine torque demanded said vehicle. In this case, the energy environment may further comprise the vehicle speed, the remaining travel time and at least one output variable of an auxiliary equipment.

The simulation is for example also 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 01, 02, 03, several sets of trajectories may be presented, a trajectory calculated with respect to a combination of objectives. The vehicle using electric energy, the energy resource being electric batteries, the objective combinations are 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 instructions defined by the trajectories can be presented to the driver of the vehicle in the form of visual or voice instructions.

The invention also relates to a device for managing the energy consumed by a mobile system. The device being able to be embedded in a mobile system, it comprises at least one computer, means for sensing the positions of said system, sensors for measuring the state of the energy resource of said system, and sensors giving information. output of the auxiliary equipment, said means and said sensors being interfaced to the computer, the computer 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;

 FIG. 2, an illustration of spatial sampling examples along the approximate path;

 FIG. 3 is a flowchart of an example of a general algorithm implementing a method of managing the energy according to the invention;

 FIG. 4, an example of an optimization algorithm;

 FIG. 5, 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. 6, an example of a simulation algorithm; FIG. 7, an example of a result of an energy management according to the invention in the form of the presentation of three trajectories, giving 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. It applies to all types of mobile systems traveling on a given route. The invention is advantageously applied to a mobile system 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.

In the example that follows, two control variables X are chosen, instructions, on which the driver can act:

 - the torque requested from the engine;

- the position of the requested heating. In the electric vehicle considered, 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 torque variable 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 engine torque is the value of the torque supplied by the motor 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 vehicle model for the simulation are a function of position or elevation, for example the requested torque varies mainly with the elevation.

The two variables, the requested motor torque and the 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. For example we can take Xe = 10 meters.

However, it is possible to simplify by introducing a second category of spatial samples XL, the samples XL being for example defined by segments 2 approximating the profile of the path, each segment corresponding to a sample XL. These XL samples correspond to the refresh rate of the instructions. Indeed, on a fixed slope path segment under stationary traffic conditions, a typical driver requests a first approximation of the same motor torque set point throughout this segment, corresponding to a sample XL. The actual torque variations around this average setpoint can be omitted on such a road segment. In addition, a driver changes the heating demand setpoint to a smaller number of space steps than the Xe steps.

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 01 , 02, 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:

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

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 greater 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 the state of charge of the batteries, the speed of the vehicle , the travel time and the temperature of the passenger compartment. 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. 2 illustrates the spatial Xe and XL sampling previously defined for a given path profile represented by a curve 21. The Xe samples are represented inside a segment 22 flanked by two XL sample values. 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 at that 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 Figure 2. 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 occurred at the point X (k-1), the points X (k-1) and X (k) framing a segment 22. In the example of Figure 2, 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 torques required to the motor, propulsion and braking, as well as the positions of the heating setpoint, provided up to the final position. Xf, at point B. These variables, torque and heating position, are determined so as to minimize a criterion involving the three objectives 01, O2 and O3 of the previous section 22 X (k-1), X (k) , and respecting the four constraints C1, C2, C3 and C4.

FIG. 3 presents the flowchart of an example of a general algorithm implementing an exemplary EMS strategy according to the invention since the vehicle was started at a point A to an arrival point B, final destination. At startup 30 in a preliminary step, at point A, several operations are performed: - 301, entering the geographical coordinates of the destination;

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

 - 303, calculation of the temperature gradient in the path according to available weather information;

 304, determining the two sets of sampled positions XL and Xe from the approximated profile 2 as illustrated, for example, in FIG. 1;

 - 305, 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.

The algorithm begins and then continues with a series of two tests 31, 32. These tests are performed according to the temporal sampling step Te, that is to say that all Te, these tests are performed. We denote X (i) a sampled position according to Te.

 In a first test 31, the position X (i) is compared with the final value Xf. When the value X (i) is substantially equal to the value Xf, moreover stored, the vehicle has reached the arrival point B, it is at its final destination 39. In the opposite case, the position X (i) and compared, in a second test 32, 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 33 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 34 is applied. The position XL (k) is incremented by a step XL, XL (k + 1) for the next test 32. After this test, after which there is maintenance 33 or refreshing instructions 34, the algorithm is looped back on the first test 31 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 system

GPS, the distance LE taking into account the uncertainties of measurements.

Refreshing, or updating, of the instructions is for example carried out

not 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

of a mono-objective function under constraint. The single lens function is the

weighted sum of objectives 01, 02, 03. This problem is formulated

in the following table for a position X (i), denoted X _t , coinciding with a

XL position (k):

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

minimize <^ + β ^χ 02 {Ορ, State _ i, Param _ way)

+ γ ^χ 03 (Pc, State _ i, Param _ way)

or

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

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 pair p is normalized and varies between -1, for the

minimum braking torque, and +1, for the maximum propulsion torque. 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 motor torque, 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 also has access to the best positions of her 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 motor torque Cp and to the heating position n. For example, a particle corresponds to:

 - Cp = 10% of the maximum torque;

 - n = 4.

The future position of a particle i is determined using the two equations presented in the following table: V _t (t + 1) = V ^ t) + c ₁ xr ₁ x [(W) - P ^ (t)) + c ₂ xr ₂ x (P _t (t) - ¾ '(t))

 Eq 1

P _i (t + \) = P _i (t) + ZXV _i (t + \)

or

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

P _t (t): position vector at iteration t

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

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

r _x , r 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.

Figure 4 shows the optimization algorithm where we find in particular the steps described above. All particles, or solutions, are evaluated with respect to the criterion to be minimized, optimization, and constraints. This criterion and these constraints use a simulator of the vehicle, having its own algorithm 40, capable of determining the state of the system from a position X (i) to a position Xf. The use of the simulator within the optimization algorithm is illustrated in particular in FIG. 4. In an initial step 41, the initialization of the particles is carried out. This step is followed by step 42 of evaluation of the particles initialized according to the optimization criterion and the constraints, using the simulator 40. This step is followed by a step 43 of updating the particles according to the preceding system of equation (Eq1 ). It is followed by a step 44 of evaluation. This step performs the evaluation of the new particles according to the optimization criterion and according to the constraints, using the simulator 40. This evaluation step is followed by a step 45 of updating the best position of each particle, itself followed by a step 46 of updating the best particle of the swarm. After this step 46, we go to the next iteration 47 by looping back on the step 43 of updating the particles. When the maximum iteration is reached 48, the algorithm stops.

FIG. 5 illustrates the cooperation between the meta-heuristic 51, for example corresponding to the optimization algorithm of FIG. 4, and the simulator 40. The function of the simulator 40 is notably to predict the energy consumption of the power train. and heating, and more generally all auxiliary, on the remaining course. 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 simulator calls 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, the modeling can be limited in first approximation to the behavior of the vehicle and the only organs consuming most of the battery energy, that is to say the electric traction and heating. In a more general context taking into account other auxiliaries, the energy consumed by these 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.

 • Neglect of the dynamics of the engine: Given the very short response times of the engine compared to the dynamics of the vehicle, the dynamics of the engine is not modeled. The motor being controlled in torque, the engine torque is almost equal to the target torque.

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 vehicle dynamics to its single longitudinal component without slip, this can be described by the following first-order non-linear differential equation (quadratic dynamics): MX = ± F _t - f _s ∞s) sign (x) - f _am X \ x \ - Mg ùn)

with: - M: Sum of the vehicle mass and rotating 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

It should be noted that the braking force is due to the engine brake only and corresponds to a negative torque setpoint, while the traction force corresponds to a positive setpoint.

Battery: The state of the charge of the battery, also called SOC according to the English expression "State Of charge", is the difference between the total energy stored and the energy consumed by the different organs connected to it :

SOC (t) = E ₀ - j lUdt where l (t) is the current flowing through the motor, and U (t) is the voltage across the motor, and E ₀ depends on the temperature in particular. However, at first, it can be considered constant while reserving the possibility of introducing the characteristic E 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 about 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 ration 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 _empty + M _PackBattenes + M _chMgemle = 500 + 140 + 200 = 840

 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. 5, the output of the modulator 40 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 the X (i) positions sampled up to Xf, this state represents the energy environment of the vehicle. This state is used for the evaluation of the updated particles. Likewise, the output of the optimization algorithm constitutes an input of the simulator in the sense that the algorithm provides the engine torque and the optimum heating position to the simulator to perform the simulation of the vehicle, this state (engine torque, engine position). heating) being defined in step 46 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 various states (torque, speed and SOC in particular) at the pitch Xe, the instructions being refreshed in step with XL samples. The meta-heuristic 51 and the simulator 40 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 51 are compatible with the inputs / outputs of the other module 40.

 The passage of data expressed in time space to a space space does not pose any particular problem. After obtaining all the states in the time domain, the position vector is available 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 torque and heating setpoint to be considered at each time iteration of the simulator. It is assumed that at zero, the position is also zero, Cm (x = 0) = Cm (t = 0), where Cm is the engine torque. At each iteration, the new vehicle position is calculated and compared to the spatial samples corresponding to setpoint changes. In the case where there is a match, the setpoint torque for the next time iteration of the simulator is re-estimated by affecting the torque corresponding to this critical position in the reference vector c = 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 6 shows the operating algorithm of the simulator 40 according to the above description. In particular, at the instant of time sampling of order i, the simulator calculates in a first step 61 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 62 to Xf to determine if the vehicle has arrived at destination 60. If it is not, X (i) is compared 63 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 65, it is maintained until the next space step. The index j is then incremented by a unit 1 so that the next comparison 63 will be made with Xe (j + 1). At the next time sampling time 67, the first step 61 for calculating position, speed, state of charge and temperature is looped back, 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 FIG.

FIG. 7 shows an example of the 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 71, the high trajectory 72 and the low trajectory 73. These trajectories represent the value of the engine torque demanded as a function of the position. The values of the pairs are constant on the segments 22 whose pitch XL is variable, since these segments do not all have the same length. They correspond for example to sections of constant slope as indicated above. Within these segments, the torque setpoint is therefore constant. Each path predicts the optimal torque values from position XL (k) to the end of the path at position Xf. They are calculated to minimize consumption, time and / or allow maximum comfort 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 in FIG. 7. 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, this computer being interfaced with the various sensors providing the necessary input data such as positions including speed, internal and external temperatures, for example, as well as battery condition measurements. The invention has been described for a motor vehicle taking into account a single auxiliary. It can be applied to driving an unmanned vehicle. The recommendations or proposals for driving and control of the auxiliary drivers to the driver from the trajectories of Figure 7 are then used as instruction signals to control members of the engine torque and auxiliaries. It is then necessary to provide interfaces between the on-board computer and the various control members.

 The invention is also adapted for the energy management of robots, the latter having a battery as a source of energy. In this case, an example of an auxiliary is the control of a robot member, an arm in particular, adding to the main control for moving the robot.

Claims

1. A method for managing the energy consumed by a mobile system, for a given path between a starting point A and an arrival point B, characterized in that said method has at least one set of trajectories composed of the trajectory of a setpoint for controlling the motor unit and the trajectory of a setpoint for controlling at least one auxiliary equipment, the trajectory of a setpoint describing the evolution of said setpoint as a function of the position of the mobile system, said trajectories being calculated with respect to given objectives according to an optimization algorithm (35) whose variables are formed of said instructions, said method comprising:

 a preliminary stage comprising itself:

 a substep (301, 302) for storing an approximated profile (2) of said path in the form of segments (XL, 22) of lines, forming a first sequence of sampled positions XL (k) along the path, a sampled position XL (k) corresponding to the passage of a segment to the next segment;

 a sub-step (304) of sampling said profile in a spatial pitch Xe, forming a second sequence of sampled positions Xe (j) along the path, the sampled positions Xe (j) being located inside the segments (XL, 22);

the trajectories of said setpoints being recalculated at each sampled position XL (k) of the first sequence according to the optimization algorithm (35), the setpoints being constant over a given segment (XL, 22), a simulation (40) predicting the energy environment of the mobile system to the arrival point B being performed at each sampled position Xe (j) of the second sequence according to said setpoints and at least the approximate profile of the remaining path, the optimization algorithm taking into account of the result of the simulation to calculate the instructions of the trajectories.

2. Method according to claim 1, characterized in that the mobile system is towed from a single source of energy.

3. Method according to any one of the preceding claims, characterized in that the optimization algorithm (35) is a meta- particulate swarm heuristic, a particle being composed of said instructions.

A method according to any one of the preceding claims, characterized in that the segments (22) of the approximated profile are a function of the elevation of the path, a segment (22) representing a path section of constant slope.

5. 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.

6. Method according to any one of the preceding claims, characterized in that the mobile system being a vehicle, the instruction to control the drive member is the engine torque requested said vehicle.

7. Method according to claim 6, characterized in that the energy environment comprises the vehicle speed, the remaining travel time and at least one output variable of an auxiliary equipment.

8. Method according to any one of claims 6 or 7, characterized in that the simulation (40) is performed further according to the traffic conditions on the remaining path.

9. 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, a trajectory being calculated with respect to a combination of objectives.

10. Method according to claim 9, characterized in that the vehicle using the electrical energy, the energy resource being electric batteries, the combinations of objectives are created among the objectives according to 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.

1 1. Method according to any one of claims 6 to 10, characterized in that the instructions defined by the trajectories are presented to the driver of the vehicle in the form of visual or voice instructions.

12. Device for managing energy consumed by a mobile system, characterized in that said device is adapted to be embedded in a mobile system, said device comprising at least one computer, means for sensing the positions of said system, sensors for measuring the state of the energy resource of said system, and sensors giving output information of auxiliary equipment, said means and said sensors being interfaced to the computer, the computer implementing the method according to any one of the claims preceding.