Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
  • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
  • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L1/00—Supplying electric power to auxiliary equipment of vehicles
  • B60L1/003—Supplying electric power to auxiliary equipment of vehicles to auxiliary motors, e.g. for pumps, compressors
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L1/00—Supplying electric power to auxiliary equipment of vehicles
  • B60L1/02—Supplying electric power to auxiliary equipment of vehicles to electric heating circuits
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
  • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
  • B60L15/2045—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed for optimising the use of energy
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L2240/00—Control parameters of input or output; Target parameters
  • B60L2240/10—Vehicle control parameters
  • B60L2240/12—Speed
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L2240/00—Control parameters of input or output; Target parameters
  • B60L2240/10—Vehicle control parameters
  • B60L2240/34—Cabin temperature
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L2240/00—Control parameters of input or output; Target parameters
  • B60L2240/60—Navigation input
  • B60L2240/62—Vehicle position
  • B60L2240/622—Vehicle position by satellite navigation
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L2240/00—Control parameters of input or output; Target parameters
  • B60L2240/80—Time limits
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L2260/00—Operating Modes
  • B60L2260/40—Control modes
  • B60L2260/50—Control modes by future state prediction
  • B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L2260/00—Operating Modes
  • B60L2260/40—Control modes
  • B60L2260/50—Control modes by future state prediction
  • B60L2260/54—Energy consumption estimation
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T10/00—Road transport of goods or passengers
  • Y02T10/60—Other road transportation technologies with climate change mitigation effect
  • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T10/00—Road transport of goods or passengers
  • Y02T10/60—Other road transportation technologies with climate change mitigation effect
  • Y02T10/72—Electric energy management in electromobility
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02T90/10—Technologies relating to charging of electric vehicles
  • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

Definitions

• 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.
• batteries embedded in electric vehicles have a finite energy capacity.
• 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.
• auxiliary management strategy 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.
• EMS Energy Management Systems
• 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.
• patent application EP1462300 A1 In the field of energy management of vehicles with purely electric engines, mention may be made of patent application EP1462300 A1.
• 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.
• 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:
• 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.
• 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.
• the instruction to control the drive member is the engine torque demanded said vehicle.
• 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.
• objectives 01, 02, 03 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:
• 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.
• 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.
• 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.
• 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.
• 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.
• the heating position variable is an integer variable. Each position corresponds to a fixed power for heating the passenger compartment of the vehicle.
• 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.
• the trajectory of the engine torque is the value of the torque supplied by the motor at each position of the path.
• 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.
• 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.
• Xe 10 meters.
• 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.
• 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:
• objective 03 may be formulated as follows:
• the management strategy must satisfy several constraints, among which for example the following constraints C1, C2, C3, C4:
• the instantaneous charge of the batteries must always be higher than a fixed threshold, in order to preserve the life of the batteries;
• the travel time must be greater than a fixed threshold
• the vehicle speed must not exceed a certain threshold, in order to respect the speed limits along the route.
• the management strategy implemented by the invention notably performs an arbitration between these contradictory objectives while respecting the stated constraints.
• 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:
• 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.
• 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.
• 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.
• 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.
• 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.
• the path is sampled according to step Xe.
• 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.
• startup 30 in a preliminary step at point A, several operations are performed: - 301, entering the geographical coordinates of the destination;
• 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.
• Te temporal sampling step
• X (i) a sampled position according to Te.
• the position X (i) is compared with the final value Xf.
• the vehicle has reached the arrival point B, it is at its final destination 39.
• 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.
• 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
• ⁇ 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
• Refreshing, or updating, of the instructions is for example carried out
• the chosen optimization algorithm is for example particulate swarm. It is of course possible to use other meta-heuristics such as
• the optimization problem can be formulated by minimizing
• the single lens function is the
• Param_trajet parameters relating to the path (length, profile of road, external temperature, etc ..) under the following constraints:
• Min_state and max_state minimum and maximum vehicle status
• nPcMax Maximum power required for heating
• n corresponds to a discrete position of the
• the pair p is normalized and varies between -1, for the
• the particle swarm optimization 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.
• a 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.
• a particle corresponds to a set of system state variables.
• a particle corresponds to the requested motor torque Cp and to the heating position n.
• a particle corresponds to:
• V t (t + 1) V ⁇ t) + c 1 xr 1 x [(W) - P ⁇ (t)) + c 2 xr 2 x (P t (t) - 3 ⁇ 4 '(t))
• V ⁇ t velocity vector or displacement at iteration t
• 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.
• an initial step 41 the initialization of the particles is carried out.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 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.
• 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.
• 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.

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• 2011
  • 2011-05-25 FR FR1154555A patent/FR2975795B1/fr active Active
• 2012
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  • 2012-05-22 US US14/119,837 patent/US9278628B2/en not_active Expired - Fee Related
  • 2012-05-22 WO PCT/EP2012/059441 patent/WO2012160045A1/fr active Application Filing

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