WO2018042005A1

WO2018042005A1 - Intelligent energy charging system

Info

Publication number: WO2018042005A1
Application number: PCT/EP2017/071991
Authority: WO
Inventors: Laurent DE VROEY; Bertrand HAUT; Damien DE PRA
Original assignee: Laborelec Cvba
Priority date: 2016-09-02
Filing date: 2017-09-01
Publication date: 2018-03-08
Also published as: EP3507130A1; CN109996698A

Abstract

A system capable of energy efficient charging of a plurality of battery powered devices (300, 310, 320) is provided, the system (1000) comprising: means (2100) for determining the actual energy demand (110) of the devices (300, 310, 320; means (2000) for determining the available energy (100) to be used; means (2200) for determining information (120) related to users, associated to the connected devices (300, 310, 320) and representative to their behaviour in relation to actual demand and available energy mismatches; and means (2300) for computing based on the above, the to be delivered energy (200, 210, 220), considering a particular preference based on information (120) related to the users. A corresponding method for energy efficient charging of the devices by said system (1000) is also provided.

Description

INTELLIGENT ENERGY CHARGING SYSTEM

Field of the invention

The invention relates to the field of energy charging systems adapted for energy charging at least in principle a plurality of devices, more particularly a plurality of battery-powered devices, even more in particular a plurality of electric vehicles.

Background of the invention

Energy charging systems, adapted for charging a vehicle or a plurality of vehicles exist. Such energy charging systems or stations are operating in a very dynamic context, in that the energy available for the supply, in particular for supplying the vehicles, may be variable; and/or the demand is also varying, as the energy request per vehicle may be variable too (as it is depending on the type of vehicle and/or the status of the energy storage of the vehicle at a certain time) while the come and go of such vehicles, which is a consequence of the owners or users thereof their decision taking only, also implies an important source of variability. Control methods and energy charging systems adapted therewith, trying to optimize the overall energy charging to a certain extent, in one or another way, in view of such context exist.

However such systems are not sufficiently addressing the very dynamic context, in particular the come and go of vehicles as a consequence of the owners or users thereof their decision taking is not taken into account.

Aim of the invention

It is the aim of the invention to provide further optimized control methods and energy charging systems adapted therewith, for optimizing the energy charging, by taking the owners or users of the vehicles their decision taking, more explicitly into account. The methods can be employed separate or in combination with state of the art systems addressing other aspects.

Summary of the invention

In a first aspect of the invention is provided, a system capable of energy efficient charging of a plurality of battery-powered devices, the system comprising: means for determining the actual energy demand of the devices connected - or planned to be connected - to the system; means for determining the available energy to be used by the system; means for determining information related to users, associated to the connected devices and representative to their behaviour in relation to actual demand and available energy mismatches; means for computing, based on the actual energy demand, the available energy and the information related to the users, for each of the connected devices, the to be delivered energy, wherein the computing comprises a certain preference to users based on information related to the users. In practice these mismatches are related to inefficient use of the charging system, meaning that for instance the charging takes place at a bad timing, i.e. when there is not a lot of energy available and therefore related users will pay a high price for this - figuratively and/or literally. While also mentioning devices planned to be connected, a kind of reservation system is referred to. The information related to users, refers to data captured from one or more users using one or more particular devices that are connected to the system. Moreover, the information takes into account the users' behaviour based on previous flexibility or actions performed. The system's devices connected as described are for example vehicles. The preference to users as mentioned is for instance provided in accordance with a guaranteed energy delivery.

The system may further comprise means for determining information representative for the actual behavior of a user, associated to a connected device in relation to actual demand and available energy mismatches; including means for computing - based on the information representative for the actual behavior of a user - updated information representative to the behavior in relation to actual demand and available energy mismatches, related to the corresponding user; and means for providing the updated information. According to an embodiment, this means for computing updated information exploits information, that is derived from the means for computing the to be delivered energy. Moreover, this means for computing updated information and/or the means for providing updated information can be adapted for executing an optimization method. As an example of such method or algorithm, a few sequences and corresponding flow-charts are given in the detailed description of this invention.

The system may also comprise an energy delivery portion, connectable to the system's devices. In an embodiment even, the actual energy demand is derived from information provided by this energy delivery portion.

According to a second aspect of the invention is provided, a method for energy efficient charging of a plurality of battery-powered devices by a system, the method comprises the steps of: determining the actual energy demand of the devices, connected to said system; determining the available energy for use by said system; determining information related to users, associated to said connected devices and representative to their behavior in relation to actual demand and available energy mismatches; computing, based on said actual energy demand, said available energy and said information related to the users, for each of said connected devices, the to be delivered energy, said computing giving preference to users based on information related to the users. The method further includes delivering said energies in accordance with said computations to the corresponding devices.

According to an embodiment, these steps are executed at least upon connection of a device to the system. Moreover, these steps are for example executed upon receipt of a new status of the available energy for use by the system.

Further provided is a method for determining updated information suitable for use in the method for energy efficient charging, wherein the method comprises the steps of determining information representative for the actual behavior of a user, associated to a connected device, in relation to actual demand and available energy mismatches; computing, based on the information representative for the actual behavior of a user, updated information, representative to the behavior in relation to actual demand and available energy mismatches, related to the corresponding user.

According to a third aspect of the invention is provided related software, a computer program product as well as a non-transitory machine-readable storage medium for storing such computer program product.

Description of the drawings

Figure 1 illustrates schematically an embodiment of the smart charging system in accordance with the present invention.

Figure 2 illustrates schematically a further embodiment of the smart charging system in accordance with the present invention.

Figure 3 illustrates schematically a further embodiment of the smart charging system in accordance with the present invention.

Figure 4 illustrates schematically a further embodiment of the smart charging system in accordance with the present invention.

Figure 5 illustrates schematically a further embodiment of the smart charging system in accordance with the present invention.

Figure 6 shows a flow chart of a practical example of how the smart charging system operates in accordance with the present invention. Figure 7 shows a flow chart of a variant embodiment of the example of Figure 6, in accordance with the present invention.

Figure 8 shows a flow chart of another practical example of how the smart charging system operates in accordance with the present invention.

Detailed description

Before elaborating on the invented control methods and energy charging systems adapted therewith, it is worth noting that according to the art most often the energy charging systems, while being adapted for energy charging at least in principle a given plurality of vehicles (by foreseeing an amount of connection points), in practice (due to the high cost of the high power electronics) are not designed to deliver the full theoretic power supply, i.e. maximum energy per connection point multiplied with the amount of connection points. Moreover, and often in combination with the above, the amount of connection points is typically less than the amount of targeted vehicles in the region of interest.

In the invention the issue of dynamic supply described earlier (especially in view of the aspects just discussed) is tackled by taking the entirety of the combined system defined by the energy charging system and its users (and more in particular their vehicles' status including historical charging, and positions) in consideration by enabling an interplay between those. Indeed, given the dynamics, introduced by such users, steering such dynamics, in that a more optimal performance of the energy charging system is achieved, is here implemented. The invention is moreover specified by the fact that a warranty on the received energy can be given, even if the come-and-go of vehicles can be very dynamic and unpredictable.

The above requires as technical requirement a need to communicate the favorable conditions, such as e.g. energy efficient charging, to such users and further ensuring to the users that any engagement of such users in the required direction results in a more optimal energy charging experience for each of them individually, hence the underlying control methods must be adapted to ensure this, and hence the underlying decision making implemented therein, has to take this into account.

Recall that energy charging systems are operating in a very dynamic context, in that the energy they receive for the supply may be variable, e.g. due to dynamics on the power supply net or grid and/or the use of local energy generating systems like solar power and/or wind power systems, while the supply for the vehicles dedicated therefore may also be variable, e.g. due to a local to be ensured energy consumer, such as for instance a building. As indicated yet above, the demand per vehicle is also varying in itself. However the theoretical demand (e.g. the amount of energy needed to have full storage) is not necessarily the true demand of the user, who at least in theory requires only an energy level sufficient to achieve the mobility target he or she has in mind (most likely with some safety margin). True, why would a user deviate from the full storage approach (as it requires him or her to come back earlier and more often), unless such user gets incentives to do so, in that his behavior is recorded, he or she gets in one or another way appraised and/or receives negative feedback and finally this up-dated behavior is used within the energy charging system to his or her benefit.

Hence the announced interplay is realized by recording actual behavior (such as leaving earlier or later than the requested time), taking into account this actual behavior and hence adapting a sort of behavioral metric (for instance expressed in a number of points), and communicating this to the corresponding user. Moreover, when a user - with his particular behavioral metric - requires use of the energy charging system, its control method will take the user's behavior metric into account, by enhancing the energy charging experience that he or she receives, e.g. ensuring an improved minimum service (for instance providing a minimal energy level charging within a minimum of time) despite the entire system variability. The above requires, besides the communication means with the user (to input his or her request) and means to determine the actual vehicle status (referring to the corresponding energy storage status), further means for detecting actual behavior (like earlier or delayed departure), means for computing (and the corresponding rules and information used therewith) and finally storing each user's behavioral metric (like in databases), and means for determining for each user an appropriate service level, taking into account the up-to-date stored behavioral metric.

The invention relates to energy charging systems adapted for realizing the above, the related data storage systems and databases, the required (graphical) interfaces and related software and the corresponding methods for executing the control methods as the engaging of the user with the system. Finally the total system of energy charging systems and the entirety of the to-be served vehicles is considered.

The above has been described in the context of a single energy charging system with its local ecosystem of the to-be served vehicles but is definitely not limited thereto. Considering optimized control of a plurality of such energy charging systems with their local ecosystem (that might overlap, which can be also exploited by the control method) is also possible.

Detailed description of the drawings

The invention is now further described with a few schematic drawings as depicted in Figure 1 to 5, of the overall system 1000, used for charging or loading a plurality of devices 300, 310, 320. These devices 300, 310, 320 can be of any type, in fact may be anything that needs to be charged, hence battery powered devices included. In essence the invention is targeting vehicles but is not limited thereto. The amount of devices in the Figures 1 to 5 is per example chosen to be three, but is of course not limited to three and can be any amount of devices. The devices 300, 310, 320 are connected or connectable to the system 1000, more in particular to the energy delivering part 2400 thereof and hence the connecting lines 900, 910, 920 indicate power lines, contrary to the other arrowed lines in the Figures 1 to 5, representing information flow. Besides the energy delivering part 2400, the system 1000 comprises of an information handling part 2900 (indicated with dotted line), in itself comprising of determining or inputting means 2000, 2100, 2200 (detailed further) and a computation means 2300, making use of the respective determined or inputted information 100, 110, 120 and provides to the energy delivering part 2400 information 200, 210, 220 on the amount of energy to be delivered to each of the devices 300, 310, 320. The amount of available energy 100 is the first necessary information. This information could be inputted (e.g. by an operator) but is more likely determined via other means (e.g. derived from other information sources). Figure 3 shows an embodiment wherein part of this information is provided from outside the system 1000 while another part is available in a memory 2500. The amount of energy required by the connected devices, or so-called energy demand 110 is the second necessary information. Again this information could be inputted (e.g. by an operator) but is more likely it is determined via other means (e.g. derived from other information sources). Figure 2 shows an embodiment where the energy delivering part 2400 provides this information. Finally the information associated to each user described above or so- called user information 120 is the third necessary. Again this information could be inputted (e.g. by an operator) but is more likely determined via other means (e.g. derived from identification of a user in combination with a database storing the associated information). Note that the embodiments of Figure 2 and 3 can be combined. As explained before the system 1000 is responsive to the information associated or related to a user and hence preferably can also adapt such information. Figure 4 indicates further information handling components within the information handling part 2900 of the system 1000, comprising of a further input or determining means 2600 a further computation means 2700 and an output means 2800. The actual behavior of the user in relation to the system or so-called behavior information 130 is a fourth type of information. Again this information could be inputted (e.g. by an operator) but is more likely determined via other means. Figure 5 shows an embodiment wherein the energy delivering part 2400 provides, based on the connecting of devices 300, 310, 320 to it, such information 130. Based thereon the further computation means 2700 determines new computation information 410 to be associated to the user. Figure 5 shows also another embodiment, in that the computation means 2700 optionally exploits computation information 500 from the computation means 2300. Finally, by means of the output 2800, new information 400 is delivered. This outputted new information 400 can go out of the system 1000 but can also reflect a local storage in the system 1000 or combinations thereof. Note that the embodiments of Figure 5 can be combined and can also be combined with any of the embodiments of Figures 1 to 3. Further Figure 5 illustrates the realized feedback coupling 600 indicated by the dashed line, in that the new information 400 is fed back in a new cycle of use of the system 1000 as input information 120.

Before further detailing the invention it is worth noting that hence the invention provides a system 1000 (or a super system comprising the system 1000) and the connected devices 300, 310, 320) and handling of information 100, 110, 120, 130, 200, 210, 220, 400, 500 related thereto. More in particular the system 1000 relates its energy delivering function (provided by subsystem 2400) to each of said devices 300, 310, 320 to the connection status defined by connecting lines 900, 910, 920 of the devices/vehicles to the energy delivery function, in combination with the energy delivery context as defined by the available energy 100 and the required energy 110 accordingly. Therefore, in essence the computation means 2300 link the connection status (via the subsystem 2400) and the energy delivery context, more in particular by using or being responsive to the information associated with a user or so-called user information 120 while computation means 2700 (as closely linked to computation means 2300) adapts such information. As depicted in Figure 5, in essence the use of system 1000 enables closing the loop of information flow of the information 120, 500, 410, 400, 600 and makes it responsive (adapting its operations and/or act on the environment by changing the information associated to the user) to the physical changes in its environment (the amount and type of devices connected to it and their energy demands).

One can state that a system 1000 is provided that is capable of energy efficient loading of a plurality of battery-powered devices 300, 310, 320. With energy efficient is not only meant in relation to each of these devices 300, 310, 320 separately, but in relation to the ensemble of it and even beyond, as the notion of use of the available energy bring in the entire environment where the system 1000 and the devices 300, 310, 320, or more in particular the vehicles and their users operate. The methods used by the system 1000 can be denoted smart as information is used in a particular way of user behavior information use, adaptation and hence steering thereof. The methods used by the system 1000 can be denoted intelligent as the context is captured in a quantitative way (hence in a numeric way) for instance reflected in representing said user behavior information e.g. by a number of points. These smart and/or intelligent methods can be called optimized energy loading methods, which are user-aware as they are responsive to the information associated with a user, while ensuring that the actual delivered energy remains below the available energy. Examples of sequences

A few practical examples of how the system 1000 operates, are now described into more detail.

Example A: User constraints are specified at the charging point, when arriving

Referring to Figure 6, a first example in accordance with the present invention is given, wherein a practical application of the system 1000 is illustrated, that is to be connected to an electric vehicle (or a plurality thereof) being used by an electric vehicle user or EV user (or a plurality thereof). For simplicity reasons, the example is further described referring to one EV user and one corresponding car. Of course, the example may be extrapolated to a plurality of cars and their users.

By means of a flow chart representation the application starts with block 1, indicating that an EV user arrives at the charging site where N charging points are equipped with the smart charging solution, with N being an integer number. Subsequently within step 2 the EV user chooses an available charging point and connects his car. Further, the EV user identifies himself as illustrated by block 3. The EV user then indicates his constraints in step 4, by determining e.g. latest departure time and minimum requested energy. Next, represented by block 5, the smart charging solution being part of the system 1000 will start an evaluation process, evaluating (in dashed lines):

· The priority level 51 of the EV user, for instance based on his historical use of the smart charging points. This level can impact the following computations; and

• The globally available energy 52 for electric vehicle charging during the period between now and the indicated departure time of the EV user; and

• The cost 53 of the charging cycle based on the EV user's constraints.

Based on the priority level 51 of the EV user, the smart charging system will then evaluate if EV user's constraints can be respected, as illustrated by means of the dotted line 13, whereas block 6 represents the evaluation of the EV user constraints as such. Hence, as a result of the evaluation in step 6, either the smart charging system confirms in step 61 that EV user's constraints can be respected, and charging 8 can follow, or else the smart charging system continues with step 62 by

• Limiting the minimum available energy for the EV user to a value depending on the user's priority level (as an example, a lower energy will be available for a less priority user, and vice versa); and

• Making an adjusted proposal of energy to the EV user (departure time is considered as not negotiable) in step 63. This energy is guaranteed, meaning that at the foreseen departure time, at least this amount of energy will be delivered to the EV user.

By default, and as illustrated with following block 7, the adjusted proposal of energy needs to be validated by the EV user. In case of approval 72, charging 8 can follow. However, it is always possible for the EV user to refuse 71 the adjusted proposal. In this latter case, the procedure can be restarted from step 4.

Referring to the charging process 8 itself, it is noted that, in relation to the optimization algorithm outputs, the current evolves in time, based on:

• The actual and forecasted available energy evolution (e.g. repeated every 15 minutes)

• Events (e.g. new car arriving, interruption of another charging cycle...)

During the charging cycle 8, the EV user can ask for a change as illustrated by block 9, and consequently indicating new constraints (e.g. need to leave earlier or later, need for more or less energy than initially expected... including complete stop of the charging sequence) in step 4'. The smart charging system will then evaluate if EV user's new constraints can be respected, as depicted by block 6. Hence, as a result, either the smart charging system confirms in step 61 that EV user's new constraints can be respected, and either charging 8 or a stop (not shown) can follow, or else the smart charging system continues with step 62 by

• Making an adjusted proposal of energy to the EV user in step 63. This energy is guaranteed, meaning that at the foreseen departure time, at least this amount of energy will be delivered to the EV user.

The adjusted proposal of energy again needs to be validated by the EV user, this time referring to block 10 in order to do so. In case of approval 11, charging 8 can follow. However, it is always possible for the EV user to refuse 12 the adjusted proposal. In this latter case, the previous validated energy and departure time (as from step 4) remain valid. Optionally, in case of refusal, previous constraints are neither reconsidered and the procedure is restarted from step 4.

With the embodiment as described above, the priority level 51 remains the same during the entire charging cycle, and will be updated or refreshed once the charging cycle has been ended. According to another embodiment, as shown in Figure 7, the priority level 51 may change during the charging cycle, whereas in this case, new constraints will require new evaluation of the priority level 51. Here, step 4' will coincide with step 4.

Example B: Charging point reservation

Referring now to Figure 8, a second example in accordance with the present invention is given, wherein a practical application of the system 1000 is illustrated, that is to be connected to an electric vehicle (or a plurality thereof) being used by an electric vehicle user or EV user (or a plurality thereof). Again, for the sake of simplicity, the example is further described referring to one EV user and one corresponding car. Of course, the example may be extrapolated to a plurality of cars and their users.

By means of a flow chart representation the application starts with block 101, indicating that an EV user selects a charging site where he/she has the right to charge his/her car. By means of example, the charging site is equipped with M charging points with the smart charging solution, whereas M being an integer number. Subsequently within step 102 the EV user asks for a charging point for connecting his/her car, and the EV user also identifies him/her as illustrated by block 103. The EV user further indicates his/her constraints in step 104, by determining e.g. his/her arrival, departure time and his/her energy needs. Next, represented by block 105, the smart charging solution being part of the system 1000 will start an evaluation process, evaluating:

• The priority level 51 of the EV user, for instance based on his historical use of the smart charging points. This level can impact the following computations; and

• The globally available energy 52 for electric vehicle charging during the period between the indicated arrival time and the indicated departure time of the EV user; and

• The cost 53 of the charging cycle based on the EV user's constraints.

Based on the priority level 51 of the EV user (as illustrated by means of the dotted line 14), the smart charging system will then evaluate if EV user's constraints can be respected, as depicted by block 106. Hence, as a result of the evaluation 106, either the smart charging system confirms in step 61 that EV user's constraints can be respected, and reservation 108 is set, or else the smart charging system continues with step 62 by

In the extreme case (not shown), the EV user has no right to make a reservation (e.g. because of a too low priority level).

By default, and as illustrated with following block 107, the adjusted proposal of energy needs to be validated by the EV user. In case of approval 72, reservation 108 is fixed. However, it is always possible for the EV user to refuse 71 the adjusted proposal. In this latter case, the procedure can be restarted from step 104.

With the reservation 108 made, the charging point is reserved for the EV user, meaning that no other car can use this charging point during the period of reservation. After reservation 108, either the charging 8 (possibly followed by step 9...) as in Example A, depicted in Figure 6 or 7, is executed or else a cancellation 109 may take place.

Whenever the EV user arrives too late after the expected arrival time, the reservation may be cancelled by the smart charging system. Cancellation of the reservation is possible remotely.

Flex points and flex stars

According to an embodiment of the invention, flex points are introduced, with the system 1000 comprising the smart charging solution, as a sort of payment mechanism, whereas flex stars represent a certain label or ranking associated with the EV user.

In an embodiment, flex points are calculated for each charging cycle based on:

The user's constraints in terms of energy needs and departure time. Typically, a higher energy need will cost more flex points than a lower energy need. For charging sites where a maximum of different cars must be able to charge on a minimum number of charging points, an early departure time will cost less flex points than a late departure time. Oppositely, for sites where the power peak should be limited, a late departure time will cost less flex points than an early departure time.

The respect of the schedule. Leaving after the programmed departure time will induce an additional cost in flex points. To the contrary, moving the car before the programmed departure time will induce a gain of flex points. In case of a reservation, a too late arrival will also induce a cost in flex points.

Each EV user having access to a site for charging his car, has a certain amount of flex points allowing him to benefit or to buy some priority (having more energy charged costs more); flex points can be gained or lost depending on the user's constraints and the respect of the schedule.

Further according to an embodiment, on a regular base, a benchmark is performed of the EV users related to a site, based on their flex points. The priority levels are distributed between the users, based on the benchmark, under the form of flex stars. Hence flex stars can be interpreted as a kind of label or ranking varying in number for instance from 0 to 5. As an example, an EV user with 5 flex starts has then highest priority of use of the system 1000.

Depending on his number of flex stars, more or less energy will be guaranteed to each EV user and more or less facility will be offered for booking a charging point.

In summary the invention relates to a system 1000 capable of energy efficient charging of a plurality of battery powered devices 300, 310, 320, the system 1000 comprising: an energy delivery portion 2400, connectable to said devices 300, 310, 320; means 2100 for determining the actual energy demand 110 of said devices 300, 310, 320, connected to said system 1000; means 2000 for determining the available energy 100 for use by said system 1000; means 2200 for inputting information 120 related to users, associated to said connected devices 300, 310, 320 said information being representative to their come and go behaviour with respect to energy charging in relation to actual demand and available energy mismatches in terms of efficient use (charging) of the system; means 2300 for computing, based on said actual energy demand 110, said available energy 100 and said information 120 related to the users, for each of said connected devices 300, 310, 320, the to be delivered energy 200, 210, 220, said computing giving preference to users based on information 120 related to the users. Moreover as the invention introduces a in computer readable information exchange feedback loop, the means 2700 for computing, based on said information 130 representative for the actual behavior of a user, updated information 400, representative to the behavior in relation to actual demand and available energy mismatches, related to the corresponding user, provides this update information is a computer readable information, actually in the same format as the means 2000 for inputting information 120 requires. For sake of clarity, the various means for inputting and means for computing are (computing) processing engines (such as but not limited microprocessors, microcontrollers, FPGA's and relates structures or combinations thereof) executing computer implementable methods steps, as stored as computer program.

In a preferred embodiment the system provides priority management of the charging cycles, based on the car's minimum energy needs and car immobilization time.

Note that the system is preferably connected to the electrical grid of the site wherein it resides. Moreover preferably such site or building has its own electric energy generating capability (like photovoltaic systems). Hence means for fetching information on available energy can be meters and/or other type of data loggers connected at the suitable places of the electrical grid of the site/building and connected to the smart charging platform. Preferably building information (e.g. synchro of charging with PV production, optimization based on other, real time electrical consumption of the site/building) is used also. Said computing is then giving preference to users based on information 120 related to the users and such building/site information.

While the information about users, user preferences, in relation to their charging requirements can in an exemplary embodiment being provided with an user input interface at or on the charging point, in alternative embodiments uses mobile apps with the same functionality can be used. The system will use then access to charging point data through the OCPP protocol for communication between charging point and charging points platform and through web services for communication between the charging points platform and the smart charging platform.

Claims

1. A system (1000) capable of energy efficient charging of a plurality of battery powered devices (300, 310, 320), the system (1000) comprising: an energy delivery portion (2400), connectable to said devices (300, 310, 320); means (2100) for determining the actual energy demand (110) of said devices (300, 310, 320), connected to said system (1000); means (2000) for determining the available energy (100) for use by said system (1000); means (2200) for inputting information (120) related to users, associated to said connected devices (300, 310, 320) said information being representative to their come and go behaviour with respect to energy charging in relation to actual demand and available energy mismatches in terms of efficient use of the system; means (2300) for computing, based on said actual energy demand (110), said available energy (100) and said information (120) related to the users, for each of said connected devices (300, 310, 320), the to be delivered energy (200, 210, 220), said computing giving preference to users based on information (120) related to the users.

2. The system of claim 1, wherein said devices (300, 310, 320) being (battery electric) vehicles.

3. The system of claim 1 or 2, further comprising: means (2600) for determining information (130) representative for the actual behavior of a user, associated to a connected device (300, 310, 320), based on the connecting of the device to the system and in relation to actual demand and available energy mismatches; means (2700) for computing, based on said information (130) representative for the actual behavior of a user, updated information (400), representative to the behavior in relation to actual demand and available energy mismatches, related to the corresponding user; and means (2800) for providing said updated information (400).

4. The system of any of the previous claims, wherein said means (2700) for computing updated information (400) exploits information (500), derived from said means (2300) for computing the to be delivered energy (200, 210, 220).

5. The system of claim 4, wherein said actual energy demand (110) being derived from information provided by the energy delivery portion (2400).

6. The system of any of the previous claims, wherein said means (2700) for computing updated information (400) and/or said means (2800) for providing said updated information (400) is computing updated information while executing an optimization method.

7. The system of any of the previous claims, wherein said preference to users being provided in a guaranteed energy delivery, being at the foreseen departure time, at least this amount of energy will be delivered.

8. A method for energy efficient charging of a plurality of battery powered devices (300, 310, 320) by a system (1000), comprising an energy delivery portion (2400), connectable to said devices (300, 310, 320), the method comprises the steps of: determining the actual energy demand (110) of the devices (300, 310, 320), connected to said system (1000); determining the available energy (100) for use by said system (1000); determining information (120) related to users, associated to said connected devices (300, 310, 320) and said information representative to their come and go behavior with respect to energy charging in relation to actual demand and available energy mismatches; computing, based on said actual energy demand (110) in terms of efficient use of the system, said available energy (100) and said information (120) related to the users, for each of said connected devices (300, 310, 320), the to be delivered energy (200, 210, 220), said computing giving preference to users based on information (120) related to the users.

9. The method of claim 8, wherein said steps are executed at least upon connection of a device (300, 310, 320) to said system (1000).

10. The method of claim 8 or 9, wherein said steps are executed upon receipt of a new status of the available energy (100) for use by said system (1000).

11. A method for determining updated information (400) suitable for use in the methods of claim 8 to 10, the method comprising the steps of determining information (130) representative for the actual behavior of a user, associated to a connected device (300, 310, 320), based on the connection of said device to the system and in relation to actual demand and available energy mismatches; computing, based on said information (130) representative for the actual behavior of a user, updated information (400), representative to the behavior in relation to actual demand and available energy mismatches, related to the corresponding user.

12. A computer program product, operable on a processing engine, for executing any of the steps of the methods 8 to 11.

13. A non-transitory machine-readable storage medium storing the computer program product of claim 12.