Method and system for energy management in an automated storage and retrieval system

Abstract

A method is proposed for energy management in an automated storage and retrieval system (10), said system (10) comprising a plurality of autonomous vehicles (16), the method comprising: determining tasks (Lx) to be performed within a predetermined duration (tf); determining a fleet of autonomous vehicles (N) with furniture to perform the tasks within the predetermined duration (tf), operating parameters of the autonomous vehicles (Pj) to perform the tasks within the predetermined duration (tf) and / or an energy recharging strategy of the autonomous vehicles (Cuj) to perform the tasks (Lx) within the predetermined duration (tf), such that the amount of energy consumed to perform the tasks (Lx) within the predetermined duration (tf) is minimal. Abstract Figure: Figure 1

Description

Title of the invention: Method and system for energy management in an automated storage and retrieval system Technical field

[0001] The present disclosure relates to the field of automated storage and retrieval systems (ASRS) in warehouses. Prior art

[0002] Automated storage and retrieval systems (ASRS) include autonomous vehicles or AGVs (from the English "Auto Guided Vehicles"). Such AGVs are configured to navigate within a structure in which items are stored completely autonomously, that is to say without human intervention. The autonomous vehicles move within the structure to place or remove the items.

[0003] Conventionally, autonomous vehicles travel at a speed that allows them to complete a set of item placement or removal tasks as quickly as possible and connect to a charging station when necessary. However, autonomous vehicles quickly become uncharged, and the automated storage and retrieval system consumes considerable energy to recharge them.

[0004] Document WO 2020 / 200821 proposes a monitoring device configured to monitor the available energy, and to modify the charging strategy and / or the travel speed of autonomous vehicles when the available energy falls below a predetermined threshold. Furthermore, document WO 20215 / 8442 describes monitoring the price of energy, and adapting the charging strategy according to the energy costs.

[0005] These solutions effectively make it possible to temporarily limit the amount of energy consumed by the system, as well as the associated costs. However, these strategies are independent of the number of tasks that the autonomous vehicles must perform in a given time. When the system operates with reduced energy consumption, the time required to complete all the tasks can increase considerably. The productivity of the systems is then reduced. In addition, periods of excess energy consumption may appear to compensate for the phases of operation with reduced consumption, making the entire system energy-intensive over the long term. Summary

[0006] The present disclosure improves the situation.

[0007] There is provided a method for energy management in an automated storage and retrieval system, said system comprising a plurality of autonomous vehicles, the method comprising: - determine tasks to be completed within a predetermined time; - determining a fleet of autonomous vehicles to be mobilized to carry out the tasks within the predetermined duration, operating parameters of the autonomous vehicles to carry out the tasks within the predetermined duration and / or an energy recharging strategy for the autonomous vehicles to carry out the tasks within the predetermined duration, so that the quantity of energy consumed to carry out the tasks within the predetermined duration is minimal, at least when the tasks are less than a threshold number of tasks.

[0008] Thus, the energy consumption of the system can be adapted according to the number of tasks to be completed in a given time. The energy consumption can be reduced while ensuring that the tasks are completed in the given time.

[0009] The features set out in the following paragraphs may, optionally, be implemented independently of one another or in combination with one another.

[0010] Determining the fleet of autonomous vehicles, the operating parameters of the autonomous vehicles and / or the charging strategy of the autonomous vehicles can be carried out if the tasks are less than the threshold number of tasks, and can otherwise take pre-established default values.

[0011] Thus, the fleet of autonomous vehicles, the operating parameters of the Autonomous vehicles and / or the charging strategy can be determined to consume less energy only when conditions permit, i.e. when the number of tasks to be performed is sufficiently low and for a given time. Otherwise, the pre-established default values ​​can allow tasks to be completed as quickly as possible. The productivity of the system is not compromised.

[0012] The operating parameters may include at least one of: an acceleration rate, a deceleration rate, and a travel speed. These parameters make it possible to reduce the amount of energy consumed by each autonomous vehicle when traveling in the warehouse. In the case of the deceleration rate, it is further possible to regenerate energy during braking.

[0013] Each autonomous vehicle may be configured to move in a longitudinal direction, a lateral direction, and a vertical direction, and the operating parameters may include at least one of: a vertical ascent acceleration rate, a vertical ascent deceleration rate, a vertical descent acceleration rate, a vertical descent deceleration rate. Thus, the ascent function of autonomous vehicles can be exploited to further reduce the amount of energy consumed by autonomous vehicles. During descent, it is also possible to regenerate a considerable amount of energy.

[0014] Each rate can be a fixed value or a function of the travel speed. In the case of a fixed value, the acceleration and deceleration rates can be determined more easily (the calculations are simplified). This can in particular make it possible to achieve near real-time adaptation of the rates. In the case of a function of the travel speed, the acceleration and deceleration rates can be further optimized to further reduce the amount of energy consumed and regenerate more energy.

[0015] Defining the charging strategy may include selecting a charging current and a charging time. These parameters effectively reduce the amount of electrical energy used to recharge the fleet of autonomous vehicles, in particular by avoiding losses linked to charging faster than necessary.

[0016] The operating parameters of the autonomous vehicles and / or the charging strategy of the autonomous vehicles may be identical for each autonomous vehicle in the fleet of autonomous vehicles. Thus, the determination may be simplified. When the charging strategy is common, the autonomous vehicle may furthermore be charged indifferently at any charging station.

[0017] Determining the fleet of autonomous vehicles, the operating parameters of the autonomous vehicles and / or the charging strategy of the autonomous vehicles may include: - determining an energy loss, the energy loss being a difference between an amount of electrical energy used and an amount of mechanical energy produced during the predetermined duration for a plurality of combinations of autonomous vehicle fleet, operating parameters and / or charging strategy; and - select the combination with the lowest difference.

[0018] These steps of determining the fleet of autonomous vehicles, the operating parameters of the autonomous vehicles and / or the recharging strategy of the autonomous vehicles make it possible to evaluate a plurality of theoretical solutions and to compare them with each other according to the energy that they would consume in order to identify the most suitable one (i.e. the one consuming the least energy).

[0019] The amount of electrical energy used may be a function of the number of autonomous vehicles, the operating parameters and the charging strategy. Thus, the amount of electrical energy corresponds to the electrical energy consumed by the system as a whole to carry out the tasks. The optimization then becomes global and coordinated and allows energy economies of scale rather than temporary and local savings that would otherwise lead to overconsumption.

[0020] The amount of mechanical energy produced can be a function of the operating parameters. Thus, the amount of mechanical energy produced corresponds to the energy used (the effective energy) by the fleet of autonomous vehicles to move, as opposed to losses.

[0021] Energy loss can be determined by simulation or by experimentation. By simulation, energy loss can be determined using a model of the system. It is possible to test a large number of combinations. By experimentation, energy loss can be determined in a simpler and more accessible way. In this case, the system can be scalable: it can "learn".

[0022] The combination with the lowest energy loss can be stored in a database. Thus, the step of determining the fleet of autonomous vehicles, the operating parameters of the autonomous vehicles and / or the recharging strategy of the autonomous vehicles can be simplified by browsing the database.

[0023] When the predetermined duration is greater than a predetermined limit duration, a minimum autonomous vehicle fleet, extreme operating parameters and / or an extreme charging strategy can be selected. The amount of energy consumed to perform the tasks can be further reduced, since the tasks can be performed over a very long duration (equated to an infinite duration). There is no longer a time constraint for the performance of the tasks.

[0024] According to another aspect, there is provided a computer program comprising instructions for implementing the method when this program is executed by a processor.

[0025] According to another aspect, there is provided an automated storage and retrieval system comprising: - a plurality of autonomous vehicles configured to remove and / or store items; - at least one autonomous vehicle charging station; and - a processor configured to control the fleet of autonomous vehicles, the operating parameters of the autonomous vehicles and / or the charging strategy by implementing the method.

[0026] Each autonomous vehicle can be configured to move in a longitudinal direction, a transverse direction and a vertical direction. Thus, the autonomous vehicles can move in three dimensions in the warehouse, accessing all stored items efficiently. Brief description of the drawings

[0027] Other characteristics, details and advantages will appear on reading the detailed description below, and on analyzing the attached drawings, in which: Fig. 1

[0028] [Fig-1] schematically illustrates a storage and retrieval system at automated according to one embodiment. Fig. 2

[0029] [Fig.2] schematically illustrates an autonomous vehicle capable of being implemented in the system of [Fig.l] according to one embodiment. Fig. 3

[0030] [Fig.3] illustrates a flowchart of a method for energy management capable of being implemented in the system of [Fig.l] according to one embodiment. Fig. 4

[0031] [Fig.4] illustrates a task versus time graph according to one embodiment. Fig. 5

[0032] [Fig.5] schematically illustrates a comparison between two modes of operation of the automated storage and retrieval system of [Fig.l] according to one embodiment. Fig. 6

[0033] [Fig.6] illustrates a graph of the travel speed and energy consumption of the autonomous vehicle of [Fig.2] as a function of time according to one embodiment. Fig. 7

[0034] [Fig.7] illustrates a flowchart of a method for determining the fleet of autonomous vehicles, the operating parameters, and the charging strategy for consuming a minimum amount of energy according to one embodiment. Description of the embodiments

[0035] [Fig.l] schematically illustrates an automated storage and retrieval system ASRS 10 (from the English "Automated storage and retrieval System"). Such a system 10 is used in warehouses for the storage of articles.

[0036] The system 10 comprises a plurality of storage racks 12 intended to receive the articles for their storage. The storage racks 12 are spaced from each other in a longitudinal direction x and a lateral direction y to form a grid of aisles 14 between which autonomous vehicles 16 can circulate. The storage racks 12 are for example spaced by a distance of 800 mm, 600 mm or even 400 mm.

[0037] Each storage rack 12 comprises a plurality of storage columns 18 arranged in rows. Each storage rack 12 may comprise one or two rows of storage columns 18. The storage columns 18 receive pallets 20 (or other supports / containers such as "bins"), the pallets 20 being superimposed on top of each other along the storage column 18 (i.e., along the vertical direction z). The pallets 20 receive the articles, and their superposition allows high-density storage of the articles in the warehouse. The autonomous vehicles 16 can rely on the storage columns 18 to move in the vertical direction z and access the articles at height.

[0038] The system 10 further comprises a plurality of autonomous vehicles 16 or AGVs (from the English “Auto Guided Vehicles”). The plurality of autonomous vehicles 16 moves in the aisles 14 in the longitudinal and lateral directions x,y and in the vertical direction z in order to be able to access all the articles stored in the warehouse. The autonomous vehicles 16 can carry out tasks of placing and removing articles by moving in the longitudinal, lateral and vertical directions x,y,z.

[0039] [Fig.2] illustrates in more detail an autonomous vehicle 16.

[0040] The autonomous vehicle 16 comprises advancement means 22, for example under the shape of wheels 22, adapted to the movement of the autonomous vehicle 16 in the lateral and longitudinal directions y,x (i.e. movement on the ground). The autonomous vehicle 16 can thus move in the aisles 14 between the storage racks 12.

[0041] The autonomous vehicle 16 further comprises a lifting system 24 adapted to movement in the vertical direction z. In addition to the two planar dimensions generally associated with the ground on which the autonomous vehicle 16 moves, there is a third vertical dimension associated with the storage racks 12 on which the autonomous vehicle 16 is capable of ascending and descending. The lifting system 24 can engage with the storage columns 18 of two neighboring storage racks 12 and move along the storage columns 18.

[0042] The advancement means 22 and the ascent system 24 may be driven by a motor (not visible). The motor may for example be configured to reach a displacement speed Vj of 4 m / s. The displacement speed Vj may be the displacement speed Vj in the lateral and longitudinal directions y, x, or the displacement speed in the vertical direction z.

[0043] The autonomous vehicle 16 further comprises an interface 26 adapted to remove or insert a pallet 20 into a storage rack 12, and to support said pallet 20. The pallet 20 can be removed from the storage rack 12 and transported by the autonomous vehicle 16. The pallet 20 can also be transported by the autonomous vehicle 16 and inserted into the storage rack 12. The interface 26 can in particular be configured to support loads of up to 30 kg.

[0044] The autonomous vehicle 16 may comprise a guidance system 28, for example a laser guidance system 28. The autonomous vehicle 16 can thus locate itself in the warehouse and can assess its environment to avoid possible obstacles, including other autonomous vehicles 16.

[0045] The autonomous vehicle 16 may be powered by an on-board battery (or several; not visible) configured to supply the other elements of the autonomous vehicle 16 with electrical energy. Then, the system 10 further comprises at least one charging station (not illustrated). The autonomous vehicle 16 may connect to a charging station to recharge the on-board battery. The system 10 may comprise a single charging station to recharge all of the autonomous vehicles 16. Alternatively, the system 10 may comprise a plurality of charging stations to allow a plurality of autonomous vehicles 16 to recharge at the same time.

[0046] The system 10 further comprises a processor adapted to control the autonomous vehicles 16 and the at least one charging station to carry out tasks Lx in a predetermined duration tf. The tasks Lx correspond to a number of items X to be removed from (or inserted into) the storage racks 12. The tasks can for example be one task (X=1), 10 tasks (X=10), 100 tasks (X=100) or even 1000 tasks (X=1000). The predetermined duration tf corresponds to a duration in which the tasks Lx must be carried out. The predetermined duration tf can be in hours, days, weeks or months. The predetermined duration tf is measured from an initial time t0.

[0047] The processor is configured to select a fleet of autonomous vehicles N. The fleet of autonomous vehicles N corresponds to the number of active autonomous vehicles 16, i.e. the number of autonomous vehicles moving in the warehouse to carry out the tasks Lx. The fleet of autonomous vehicles N may for example be a number between zero autonomous vehicles 16 and a maximum number Nmax of autonomous vehicles 16. The maximum number may for example be 10, 20, 50 or even 100 autonomous vehicles 16. The processor may select the fleet of autonomous vehicles N adapted to carrying out the tasks in the predetermined duration tf.

[0048] It is noted that, in addition to their number, the processor can identify the autonomous vehicles 16 constituting the fleet, in particular when the latter are not all identical (different models or versions, different dimensions, wear of certain components including the battery, specific tools, etc.).

[0049] The processor is also configured to select operating parameters Pj of the fleet of autonomous vehicles N. The operating parameters Pj can be defined by the processor and transmitted to the autonomous vehicles 16. By operating parameters Pj, we mean the parameters allowing the autonomous vehicles 16 to move. For example, the parameters of operation Pj include a displacement speed Vj in the lateral direction y, longitudinal direction x and vertical direction z, an acceleration rate aj in the lateral and longitudinal direction y,x, a deceleration rate dj in the lateral and longitudinal direction y,x, an acceleration rate in vertical ascent caj, a deceleration rate in vertical ascent cdj, an acceleration rate in vertical descent faj and a deceleration rate in vertical descent fdj.

[0050] The processor can define operating parameters Pj for each of the autonomous vehicles j of the fleet of autonomous vehicles N. Advantageously, operating parameters Pj common to all the autonomous vehicles 16 of the fleet of autonomous vehicles N facilitate the control of the autonomous vehicles 16 to carry out the tasks Lx in the predetermined duration tf. Alternatively, operating parameters Pj can be defined for each autonomous vehicle j of the fleet of autonomous vehicles N. Each autonomous vehicle j of the fleet of autonomous vehicles N can have operating parameters Pj which are specific to it. This solution makes it possible in particular to optimize the use of each autonomous vehicle 16.

[0051] It is noted that the operating parameters of acceleration rate aj in the lateral and longitudinal directions, deceleration rate dj in the lateral and longitudinal directions x,y, vertical ascent acceleration rate caj, vertical ascent deceleration rate cdj, vertical descent acceleration rate faj and vertical descent deceleration rate fdj may be constant parameters. Alternatively, the acceleration and deceleration rates may be functions of the travel speed.

[0052] The processor is further configured to define the energy recharging strategy Cj of the autonomous vehicles 16. By recharging strategy Q, we mean in particular a recharging time tj and a charging current Ij. The recharging time tj corresponds to a duration, for example in minutes or hours, during which the autonomous vehicle 16 is connected to the charging terminal. The charging current Ij corresponds to the quantity of current used to charge the on-board battery of the autonomous vehicle 16 during the recharging time tj. Furthermore, when the system 10 comprises a plurality of charging terminals, the recharging strategy Cj. can also comprise a number of active charging terminals, that is to say a number of charging terminals available to recharge the autonomous vehicle 16.

[0053] The processor can define an energy recharging strategy Cj common to all the charging stations. Thus, the autonomous vehicle 16 can connect to any charging station. Alternatively, the processor can define an energy recharging strategy Cj specific to each charging station. Then, it is possible to operate several energy recharging strategies Cj simultaneously. Al Alternatively, the processor can define an energy charging strategy Q for each autonomous vehicle j of the fleet of autonomous vehicles N, independently of the charging station. In this case, the autonomous vehicle j can identify itself to the charging station, and the charging station can adopt the charging strategy Q specific to the identified autonomous vehicle j.

[0054] In a so-called “normal” operating mode, the processor can select a fleet of autonomous vehicles N, operating parameters of the autonomous vehicles Pj and a charging strategy Cj according to pre-established default values. For example, the fleet of autonomous vehicles N can be a number of active vehicles Nnormai, the operating parameters can be Pjnormal parameters and the charging strategy can be a Cjnormai strategy. “Normal” operation makes it possible to complete all of the tasks of placing or removing items as quickly as possible.

[0055] In a so-called “economic” operating mode, the processor can determine a fleet of autonomous vehicles N, operating parameters Pj of the fleet of autonomous vehicles N and a recharging strategy Q making it possible to carry out the tasks Lx in the predetermined duration tf while consuming a reduced amount of energy. The tasks Lx are carried out in a longer time (but less than the predetermined duration tf) while consuming a minimal amount of energy.

[0056] A method for energy management, implemented by the processor described above, is described below with reference to [Fig.3].

[0057] The method may be implemented periodically. For example, the method may be implemented each time a set of tasks has been completed by the system 10. The method may also be implemented each time new tasks to be performed are assigned to the system 10. The method could also be implemented hourly, daily, or weekly depending on the usual frequency of updating the tasks to be performed.

[0058] According to a first step 100, tasks Lx to be carried out in the predetermined duration tf are determined. The tasks Lx to be carried out are determined from the present instant tO. As visible in [Fig.4], when the tasks Lx are less than a threshold number of tasks LUm, it is determined that the fleet of autonomous vehicles N, the operating parameters Pj, and the charging strategy Cj can be modified. Advantageously, it is determined that the “economic” mode can be activated by ensuring that the tasks Lx will be carried out in the predetermined duration tf. The quantity of energy consumed is reduced when the number of tasks is rather low, such as for example at the end of the day or at the end of the week.

[0059] If the number of tasks Lx to be carried out is greater than the number of threshold tasks Liim, then the values ​​remain the pre-established default values ​​(Nnormai PjnormauxCjnormai). The system 10 can then continue to perform the tasks as quickly as possible ("normal" operation) when the number of tasks to be performed is high. This situation may, for example, correspond to a peak in activity. For example, in "normal" operation, the movement speed Vj may be 4 m / s.

[0060] According to a second step 200, when the number of tasks Lx is less than the threshold number of tasks LUm, the fleet of autonomous vehicles N is determined to carry out the tasks Lx. The fleet of autonomous vehicles N can be determined according to the tasks Lx to be carried out and the predetermined duration tf. The fleet of vehicles N corresponds to the optimal number of autonomous vehicles Nopti to carry out the tasks Lx in the predetermined duration tf. The optimal fleet of autonomous vehicles Nopti can for example correspond to the minimum number of autonomous vehicles allowing the tasks Lx to be carried out in the predetermined duration tf. The reduced number of autonomous vehicles 16 makes it possible to minimize the quantity of energy consumed.

[0061] Indeed, [Fig.5] compares the consumed electrical power Peiec used to carry out the tasks Lx in the predetermined duration tf as a function of the fleet of autonomous vehicles N. When the fleet of autonomous vehicles N is the fleet of autonomous vehicles in normal operation Nnormai (i.e. the fleet of autonomous vehicles pre-established by default), the tasks Lx are carried out in a short time but require electrical energy (solid line curve) and therefore a Peieciniportante electrical power. When the fleet of autonomous vehicles N is the optimal fleet of autonomous vehicles Nopti, the tasks Lx are carried out in a longer time but require lower electrical energy (dotted line curve) and therefore a lower electrical power Peiec. Thus, an energy loss E over the predetermined duration tf is reduced.Reducing the fleet of autonomous vehicles N increases the time to complete tasks Lx but reduces the amount of electrical energy consumed over the same duration.

[0062] According to a third step 300, operating parameters Pj of the fleet of autonomous vehicles N are determined. For example, compared to the pre-established default values, the travel speed Vj can be reduced. The acceleration rate aj in the longitudinal and lateral directions x,y can be reduced. The deceleration rate dj in the longitudinal and lateral directions x,y can be optimized to allow energy regeneration. Since the autonomous vehicle 16 can move in the vertical direction z, it is advantageous to further reduce the acceleration rate in vertical ascent caj. Indeed, the ascent function is particularly energy-intensive, and reducing the acceleration rate in vertical ascent caj allows a considerable reduction in the amount of energy consumed. The deceleration rate in vertical descent fdj can also be optimized.A considerable amount of energy can thus be regenerated when the autonomous vehicle. performs a descent. These modifications make it possible to minimize the amount of energy consumed by each autonomous vehicle j in the fleet of autonomous vehicles N during their travels to perform the tasks Lx.

[0063] For example, [Fig.6] illustrates the energy loss E by the autonomous vehicle 16 when the travel speed Vj is reduced. It is observed that a reduction in the travel speed Vj of 1.5 m / s allows almost a halving of the energy consumption for carrying out a task. In practice, in “economical” operation, the travel speed Vj can be 3 m / s. For a task of 120 s, the time to carry out the task then increases by 25%, but the quantity of energy saved can be reduced by 7%.

[0064] According to a fourth step 400, a recharging strategy Q is determined. For example, the recharging current Ij can be reduced compared to the pre-established default value. The charging time tj can be increased to enable the same amount of energy to be supplied to the autonomous vehicle 16 connected to the recharging terminal. The number of active recharging terminals can also be reduced.

[0065] As described above, the operating parameters Pj may be common to all the autonomous vehicles in the fleet of autonomous vehicles N, facilitating their determination. Alternatively, the operating parameters Pj may be determined for each autonomous vehicle j in the fleet of autonomous vehicles N. Furthermore, the charging strategy Q may be common to all the charging stations, specific to each charging station or specific to each autonomous vehicle 16.

[0066] It is noted that the steps of determining the fleet of autonomous vehicles 200, the operating parameters 300 and the recharging strategy 400 can be carried out simultaneously, or at least in any order and / or in several stages.

[0067] A method is subsequently described for determining the fleet of autonomous vehicles N, the operating parameters Pj, and the recharging strategy Q allowing a minimum quantity of energy to be consumed, with reference to [Fig.7].

[0068] The determination of the fleet of autonomous vehicles N, the operating parameters Pj and the charging strategy Q making it possible to consume the minimum amount of energy can be carried out upstream of the activation of the so-called “economical” operation. A database can be provided storing a plurality of tasks to be carried out in a plurality of predetermined durations. For each number of tasks and predetermined duration, a fleet of autonomous vehicles N, operating parameters Pj and a charging strategy Cj can be associated. Thus, the values ​​can be found by browsing the database according to the tasks Lx and the predetermined duration tf at the present time t0.

[0069] According to a first step 500, a plurality of vehicle fleets is defined.

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] autonomous N. The plurality of autonomous vehicle fleets can take the form of a vector ranging from zero autonomous vehicles to the maximum number Nmax of autonomous vehicles. According to a second step 600, a plurality of operating parameters Pj are defined. Each operating parameter Pj may be a vector comprising a travel speed Vj, an acceleration rate aP, a deceleration rate dj, a vertical ascent acceleration rate caj, a vertical ascent deceleration rate cdj, a vertical descent acceleration rate faj and a vertical descent deceleration rate fdj. The vectors may be associated with a particular autonomous vehicle j or be common to the fleet of autonomous vehicles N. The acceleration and deceleration rates may be constant or a function of the travel speed. ' v I 1 have CD] cd ; faj fd. According to a third step 700, a plurality of energy recharging strategies Q are defined. Each energy recharging strategy Q may be a vector comprising a charging current Ij and a charging time tj. The vectors may be associated with a particular autonomous vehicle j, with a particular charging station, be common to all the charging stations and / or all the autonomous vehicles 16. According to a fourth step 800, an energy loss E is determined for a plurality of combinations of autonomous vehicle fleets N, operating parameters Pj and energy recharging strategies Q. The energy loss E is determined as a function of the predetermined duration tf. The energy loss E corresponds here to a difference between the quantity of electrical energy used Eeiec and a quantity of mechanical energy produced Emech during the predetermined duration tf. The quantity of electrical energy used Eeiec is a function of the operating parameters Pjet of the charging strategy Q. In fact, the quantity of electrical energy used Eeiec corresponds to the electrical energy consumed by the system 10 for the completion of tasks Lx in the predetermined duration tf. The quantity of electrical energy used Eeiec can in particular be calculated according to the following equation.

[0078] , . A Eelec( Kf ) —

[0079] The quantity of mechanical energy produced Emech is a function of the operating parameters Pj. In fact, the mechanical energy produced Emech corresponds to the energy used by the fleet of autonomous vehicles to carry out the tasks (to move). The mechanical energy produced Emech can in particular be calculated according to the following equation.

[0081] It is noted that the energy loss E can be determined experimentally or by simulation.

[0082] According to a fifth step 900, the combination C of fleet of autonomous vehicles N, operating parameters Pj and recharging strategy Cj having the lowest energy loss E is selected. This combination C corresponds to the optimal fleet of autonomous vehicles Nopti, to the operating parameters Pj and to the recharging strategy Cj to be used to carry out all the tasks in the predetermined duration tf while consuming a minimum amount of energy. The combination can be grouped in a vector.

[0083] INoptj

[0084] The combinations determined above can be stored in a database and indexed according to the predetermined duration tf and the number of tasks Lx.

[0085] The invention is not limited to the examples described above but is on the contrary susceptible of numerous variants accessible to those skilled in the art.

[0086] For example, one or more of the fleet of autonomous vehicles N, the energy charging strategy Cj or the operating parameters Pj can be determined. Thus, one or more of the fleet of autonomous vehicles N, the charging strategy Cj or the operating parameters Pj can be modified with respect to the pre-established default values. The determination of the values ​​in “economic” operation can be simplified.

[0087] The method for determining the fleet of autonomous vehicles N, the operating parameters Pj, and the recharging strategy Q allowing the consumption of a minimum quantity of energy described above could be implemented each time it is determined that the tasks Lx are less than the number of threshold tasks Lseuii. The determined combination can then be stored in memory. When the tasks and the predetermined duration is encountered again, the processor can select the combination stored in memory. The system can learn and evolve over time.

[0088] In addition, the fleet of autonomous vehicles N, the energy recharging strategy Q or the operating parameters Pj can be determined regardless of the number of tasks to be performed. The system can then constantly seek to optimize energy consumption, without a separate “normal” operating mode.

[0089] Furthermore, when the predetermined duration tf is greater than a predetermined limit duration tinf, it can be considered that the predetermined duration is infinite. In this case, a minimum autonomous vehicle fleet Nmin, extreme operating parameters Pjextr and / or an extreme charging strategy Cjextr can be selected. The minimum autonomous vehicle fleet corresponds to the smallest number of autonomous vehicles allowing the tasks to be carried out. The extreme operating parameters are for example the slowest speed and the lowest acceleration rates allowing the least possible energy consumption. The charging strategy corresponds to the lowest current and the longest charging time. The amount of energy consumed to carry out the tasks can be further reduced, since the tasks can be carried out over a very long period.

[0090] The present invention is in no way limited to the type of autonomous vehicle implemented in the energy management method relating thereto.

[0091] From the point of view of the movement of said autonomous vehicles, these may be trajectories in 2 dimensions, i.e. on a plane (in the lateral and longitudinal directions x,y only). In this respect, said autonomous vehicles have means of advancement capable of making their movements possible in these two dimensions. The floor of the warehouse or more generally of the recovery and storage system may constitute the plane on which said autonomous vehicles move. An example illustrating this technology is accessible in patent application WO 2007 / 149712. According to another configuration, the storage racks arranged in the warehouse delimit at their top a flat surface on which the autonomous vehicles can move. Patent application WO 2015 / 104263 illustrates this technology for example.

[0092] When the autonomous vehicles have means of ascent, making them capable of moving in three dimensions, the autonomous vehicles may be constructed differently from the autonomous vehicles described above. Examples of autonomous vehicles are described in particular in documents WO 2018 / 189110, but also WO 2020 / 056175, EP 3 288 865 and WO 2022 / 089811.

[0093] Furthermore, regarding the guidance system of the autonomous vehicle, the system may be other than laser guidance. The guidance system may for example be wire guidance, laser guidance and optoguidance. Other technologies exist such as geosteering and ultrasonic guidance. Autonomous vehicles can also navigate using mapping and environmental recognition techniques.

Claims

Claims

1. Method for energy management in an automated storage and retrieval system (10), said system (10) comprising a plurality of autonomous vehicles (16), the method comprising: - determining tasks (Lx) to be performed within a predetermined duration (tf);- determining a fleet of autonomous vehicles (N) to be mobilized to carry out the tasks in the predetermined duration (tf), operating parameters of the autonomous vehicles (Pj) to carry out the tasks in the predetermined duration (tf) and / or an energy recharging strategy of the autonomous vehicles (Q) to carry out the tasks (Lx) in the predetermined duration (tf), by: - ​​determining an energy loss (E), the energy loss (E) being a difference between a quantity of electrical energy used (Eeiec) and a quantity of mechanical energy produced (Emech) during the predetermined duration (tf) for a plurality of combinations of fleet of autonomous vehicles (N), operating parameters (Pj) and / or recharging strategy (Q);and - selecting the combination having the smallest difference so that the amount of energy consumed to carry out the tasks (Lx) in the predetermined duration (tf) is minimal, at least when the tasks (Lx) are less than a threshold number of tasks (Liim).;

2. Method according to claim 1, in which determining the fleet of autonomous vehicles (N), the operating parameters of the autonomous vehicles (Pj) and / or the recharging strategy of the autonomous vehicles (Cj) is carried out if the tasks (Lx) are less than a threshold number of tasks (Liim), and otherwise take pre-established default values.

3. A method according to claim 1 or 2, wherein the operating parameters (Pj) comprise at least one of: an acceleration rate (aj), a deceleration rate (dj) and a travel speed (Vj).

4. Method according to any one of claims 1 to 3, wherein each autonomous vehicle (16) is configured to move in a longitudinal direction (x), a lateral direction (y) and a vertical direction (z), and the operating parameters (Pj) comprise at least one of: a vertical ascent acceleration rate (caj), a vertical ascent deceleration rate (cdj), an acceleration rate in vertical descent (faj), a rate of deceleration in vertical descent (fdj).

5. A method according to any preceding claim, wherein defining the charging strategy (Q) comprises selecting a charging current (Iju) and a charging time (tj).

6. Method according to any one of the preceding claims, in which the operating parameters of the autonomous vehicles (Pj) and / or the recharging strategy of the autonomous vehicles (Q) are identical for each autonomous vehicle (16) of the fleet of autonomous vehicles (N).

7. Method according to one of claims 1 to 6, in which the quantity of electrical energy used is a function of the number of autonomous vehicles (N), the operating parameters (Pj) and the recharging strategy (Cj), and in which the quantity of mechanical energy produced (Emech) is a function of the operating parameters (Pj).

8. Method according to one of claims 1 to 7, in which the combination having the lowest energy loss (E) is stored in a database.

9. A method according to any preceding claim, wherein, when the predetermined duration is greater than a predetermined limit duration (tinf), a minimum autonomous vehicle fleet (N min), extreme operating parameters (Pjextr) and / or an extreme charging strategy (Cjextr) are selected.

10. Computer program comprising instructions for implementing the method according to one of claims 1 to 9 when this program is executed by a processor.

11. An automated storage and retrieval system (10) comprising: - a plurality of autonomous vehicles (16) configured to retrieve and / or store items; - at least one autonomous vehicle charging station; and - a processor configured to control the fleet of autonomous vehicles (N), the operating parameters of the autonomous vehicles (Pj) and / or the charging strategy (Cj) by implementing the method according to one of claims 1 to 9.

12. The automated storage and retrieval system (10) of claim 11, wherein each autonomous vehicle (16) is configured to move in a longitudinal direction, a transverse direction, and a vertical direction.

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