Control and scheduling of electric vehicle charging, and related systems and methods.

Abstract

The present invention provides a method, apparatus, and computer program for controlling and scheduling the charging of electric vehicles. [Solution] The method determines a charging profile for charging the at least one electric vehicle via at least one charger, based on the battery characteristics of at least one electric vehicle, information on available power, and the availability of the at least one electric vehicle, and provides an output to control charging by each charger according to the determined charging profile.

Description

Technical Field

[0001] Field The present disclosure relates to a method, an apparatus, and a computer program for controlling and scheduling the charging of electric vehicles.

Background Art

[0002] Background The drive towards clean energy is promoting the adoption of distributed energy resources (DERs), the application of demand-side management, and growth in the electrification of urban transportation. This growth has been driven, at least in part, by challenging environmental and economic goals set by government policies around the world. There are technical issues associated with the integration and use of small DERs such as renewable energy, on-site generators, energy storage devices, controllable loads, and electric vehicles (EVs).

[0003] Many transport companies are typically replacing existing vehicles that run on diesel engines with cleaner electric vehicles. These electric vehicles have the potential to utilize more environmentally friendly energy sources. In this context, energy management systems need to address the challenges of modern power systems caused by integrating and aggregating DERs. In particular, the large-scale penetration of such EVs without an appropriate management system can lead to technical problems such as a non-uniform and unpredictable aggregated demand profile with high power absorption peaks. This can lead to technical problems such as potential bottlenecks in supply capacity, exposing the operators of electric vehicle fleets to equipment sizing issues.

[0004] There are technical problems in managing the charging of electric vehicle fleets at shared charging locations such as parking lots. For example, there may be challenges in managing power usage.

[0005] The objective of some embodiments is to address, or at least mitigate, one or more of the aforementioned problems. [Overview of the project] [Means for solving the problem]

[0006] overview According to one embodiment, a method is provided for controlling the charging of at least one electric vehicle, particularly two or more electric vehicles, via at least one charger, particularly two or more chargers, as described in claim 1.

[0007] The method includes determining a charging profile for charging at least one electric vehicle via at least one charger, based at least on the battery characteristics of at least one electric vehicle, information on available power, and the availability of at least one electric vehicle, and providing an output for controlling charging by each charger according to the determined charging profile.

[0008] Further embodiments and aspects of the method may address one or more of the problems described above. According to a further embodiment, the method involves a computer using at least one integrated circuit. The method may be implemented and / or performed by a device such as an industrial controller, a computing device, or a controller for an energy management system (EMS).

[0009] In a further embodiment, the availability of at least one electric vehicle may be based on at least one of the following: an operating schedule, location information of at least one electric vehicle, and real-time location information of at least one electric vehicle.

[0010] The operation schedule may provide information about when one or more electric vehicles will arrive at and / or depart from a charging location equipped with one or more chargers.

[0011] The determination of a charging profile for charging at least one electric vehicle may further be based on the requirement of pre-configuration of at least one electric vehicle.

[0012] In a further embodiment, the method includes providing an output for controlling the pre-adjustment of at least one electric vehicle.

[0013] Information on available power may include information on the power that can be supplied by the power grid and / or one or more local power sources.

[0014] A local power source may be a power source located within the same microgrid as at least one of the chargers.

[0015] In a further embodiment, determining a charging profile for charging at least one electric vehicle includes an optimization process.

[0016] The optimization process may include a primary objective of reducing variations in charging and / or pre-adjustment operations provided by the charging profile.

[0017] This may be to avoid or reduce interruptions in the battery charging process and / or to avoid current peaks. This may reduce stress on the battery that could reduce its effective lifespan.

[0018] The optimization process may include a minimum charge level constraint relating to the minimum charge level of each battery in at least one electric vehicle.

[0019] The optimization process may include a maximum charge level constraint relating to the maximum charge level of each battery in at least one electric vehicle.

[0020] The optimization process may include a second objective of maximizing the charge level of each battery beyond a minimum charge level, particularly up to a predetermined charge level.

[0021] The optimization process may include available power constraints regarding information on available power. The optimization process may include electric vehicle availability constraints regarding the availability of at least one electric vehicle.

[0022] The optimization process may include battery maximum load constraints regarding the maximum load applicable to each battery.

[0023] The determination may further be based on power supply limitations associated with one or more of the chargers. The characteristics of the battery may include the state of charge of the battery.

[0024] The characteristics of the battery may include at least one of one or more state of charge limitations and one or more charge rate limitations.

[0025] The determination of the charging profile for charging at least one electric vehicle may further be based on information regarding the current period and information from one or more future periods.

[0026] The determination of the charging profile for charging at least one electric vehicle may be repeated at a subsequent time to update the charging profile.

[0027] The output for controlling charging may control one or more of when at least one electric vehicle is charged and the rate at which at least one electric vehicle is charged.

[0028] In another embodiment, a device is provided configured to control the charging of at least one electric vehicle, particularly two or more electric vehicles, via at least one charger, particularly two or more chargers, the device comprising at least one integrated circuit configured to cause the device to determine a charging profile for charging the at least one electric vehicle via at least one charger, based at least on the characteristics of each battery of the at least one electric vehicle, information on available power, and the availability of the at least one electric vehicle, and to provide outputs for controlling charging by each charger according to the determined charging profile.

[0029] The device may address or mitigate one or more of the aforementioned problems. The availability of at least one electric vehicle may be based on at least one of the following: an operating schedule, location information of at least one electric vehicle, and real-time location information of at least one electric vehicle.

[0030] The operation schedule may provide information about when one or more electric vehicles will arrive at and / or depart from a charging location equipped with one or more chargers.

[0031] At least one integrated circuit may be configured to cause the device to determine a charging profile for charging at least one electric vehicle, further based on the pre-adjustment requirements of at least one electric vehicle.

[0032] At least one integrated circuit may be configured to cause the device to provide an output for controlling the pre-tune of at least one electric vehicle.

[0033] Information on available power may include information on the power that can be supplied by the power grid and / or one or more local power sources.

[0034] A local power source may be a power source located within the same microgrid as at least one of the chargers.

[0035] At least one integrated circuit may be configured to cause the device to determine a charging profile for charging at least one electric vehicle using an optimization process.

[0036] The optimization process may include a primary objective of reducing variations in charging and / or pre-adjustment operations provided by the charging profile.

[0037] This may be to avoid or reduce interruptions in the battery charging process and / or to avoid current peaks. This may reduce stress on the battery that could reduce its effective lifespan.

[0038] The optimization process may include a minimum charge level constraint relating to the minimum charge level of each battery in at least one electric vehicle.

[0039] The optimization process may include a maximum charge level constraint relating to the maximum charge level of each battery in at least one electric vehicle.

[0040] The optimization process may include a second objective: to maximize the charge level of each battery beyond a minimum charge level, particularly up to a predetermined charge level.

[0041] The optimization process may include available power constraints related to information on available power. The optimization process may include an electric vehicle availability constraint relating to the availability of at least one electric vehicle.

[0042] The optimization process may include a maximum battery load constraint relating to the maximum load applicable to each battery.

[0043] At least one integrated circuit may be configured to cause the device to determine a charging profile based further on power supply limitations associated with one or more chargers.

[0044] Battery characteristics may include the battery's charge state. The battery characteristics may include at least one of one or more charge state limits and one or more charge level limits.

[0045] At least one integrated circuit may be configured to cause the device to determine a charging profile for charging at least one electric vehicle, further based on information about the current period and information from one or more future periods.

[0046] At least one integrated circuit may be configured so that the device repeats the determination of a charging profile for charging at least one electric vehicle at a later time in order to update the charging profile.

[0047] The output for controlling charging may control one or more of the following: when at least one electric vehicle is charged, and the rate at which at least one electric vehicle is charged.

[0048] According to one embodiment, a computer program is provided which includes a computer-executable instruction that, when executed on at least one processor, causes one of the above methods to be performed.

[0049] According to one embodiment, a computer-readable medium is provided that includes program instructions stored for performing at least one of the above methods.

[0050] According to one embodiment, a non-temporary computer-readable medium is provided that includes program instructions stored for performing at least one of the above methods.

[0051] According to one embodiment, a non-volatile tangible storage medium is provided that includes program instructions stored for performing at least one of the above methods.

[0052] The above describes many different embodiments. It should be understood that further embodiments may be provided by any combination of two or more of the embodiments described above.

[0053] Various other embodiments are also described in the following detailed description and attached claims.

[0054] Explanation of the diagram Next, with reference to the attached diagrams, I will explain a few examples as mere illustrations. [Brief explanation of the drawing]

[0055] [Figure 1] Several embodiments of the system are shown. [Figure 2] A schematic representation of the rolling time horizon used in several embodiments is shown below. [Figure 3] An exemplary apparatus according to one embodiment of the present invention is shown. [Figure 4] Several exemplary methods of embodiments are schematically shown. [Figure 5] Several other exemplary methods of embodiments are schematically shown below. [Modes for carrying out the invention]

[0056] Detailed explanation Next, various exemplary embodiments of the present invention will be described. Some embodiments relate to the control of at least one electric vehicle. Some exemplary embodiments include a method for controlling the charging of at least one electric vehicle. Some exemplary embodiments include an apparatus for controlling the charging of at least one electric vehicle. The apparatus may comprise an integrated circuit and / or a device. The device may be a computer device, an industrial controller, or any other suitable device. Some embodiments relate to a system for controlling the charging of at least one electric vehicle.

[0057] Charging at least one electric vehicle may be done via at least one charger. Refer to Figure 1, which shows a system of several embodiments. The system comprises several chargers 2. The chargers 2 are connected to a device 4 for controlling the charging.

[0058] Some embodiments may be for managing the charging of a set or group of electric vehicles (EVs) 6. Each EV 6 to be charged is plugged into its respective charger 2. This charging may be provided by at least one charger 2. If two or more chargers 2 are provided, the chargers 2 may be located at one or more charging locations.

[0059] The set of electric vehicles 6 may include electric buses, delivery vehicles, taxis, utility vehicles, boats, factory vehicles, aircraft, drones, or any other vehicles.

[0060] Each charging location may be a parking lot, a garage, or any other charging location that is suitable for the vehicle being charged and has one or more chargers. In some embodiments, there may be two or more charging locations, for example, two bus stops.

[0061] Several embodiments may be used in which a relatively large number of electric vehicles 6 need to be charged at a charging station having a relatively large number of chargers 2. The number of chargers may be, for example, 10, 50, or more than 100 chargers. These numbers of chargers are merely examples, and in other embodiments, any other suitable number of chargers may be used.

[0062] The following describes an example of a group of electric vehicles including one or more vehicles 6. The vehicles are charged at charging stations. It will be electrified.

[0063] As described above, the electric vehicle group may include a bus group or any other electric vehicle group. The charging location may be any suitable charging location, such as a parking lot or any other suitable charging location. There may be two or more charging locations. One or more chargers 2 are provided at each charging location.

[0064] In some embodiments, different types of electric vehicles 6 may be used in combination. Some embodiments address the technical challenge of ensuring that each electric vehicle 6 in a group of one or more electric vehicles is charged. Some embodiments may address the technical challenge of ensuring that each electric vehicle 6 in a group of one or more electric vehicles is charged when it needs to operate according to a timetable or delivery schedule or operational schedule, etc.

[0065] Some embodiments may offer a “smart charging” strategy that enables the planning and execution of EV charging operations by leveraging both system and user flexibility as a way to cut peak loads and / or recharge vehicle batteries within a given timetable. Such mechanisms may range from simply turning the charging process on and off, and in some cases increasing or decreasing the charge rate, i.e., one-way vehicle control (V1G), to challenging two-way vehicle-to-grid (V2G) that allows vehicles to back-service the grid in discharge mode.

[0066] In some embodiments, pre-adjustment of the electric vehicle 6 may be required. This is optional in some embodiments.

[0067] By pre-conditioning the electric vehicle 6, the electric vehicle's battery may be heated or cooled to an optimal operating temperature while connected to a power source. This can improve battery life and / or the electric vehicle's range. This may not be necessary if the electric vehicle is charged before departure and the battery is still at or near its normal operating temperature when the electric vehicle is scheduled to depart. This may not be necessary in some embodiments.

[0068] Pre-adjustment may allow the internal temperature of the electric vehicle 6 to be adjusted to a desired temperature, either alternatively or additionally, while connected to a power source. For example, the bus may be heated in winter and cooled in summer. Pre-adjustment may reduce the amount of battery charge required to control the temperature inside the electric vehicle 6 when the vehicle is not connected to a power source. Pre-adjustment may need to be performed immediately before the vehicle departs. For example, it may be desirable to ensure that pre-adjustment is completed as close as possible to the vehicle's departure.

[0069] Pre-adjustment may be used in embodiments where the vehicle's battery is used for functions in addition to driving the vehicle, and the electric vehicle can provide at least partially of those functions in advance before leaving the charger.

[0070] Some embodiments may address the technical challenge of controlling the energy profile required at a charging station to support one or more chargers 2. One or more chargers 2 are used to charge one or more electric vehicles 6.

[0071] For example, some embodiments can avoid problems such as a non-uniform and unpredictable aggregated energy demand profile with high power absorption peaks. A non-uniform and unpredictable aggregated energy demand profile with high power absorption peaks can impair energy supply capacity. This could lead to a power bottleneck, which could reduce the vehicle's travel capacity. In some situations, it may not be able to cope with high power absorption peaks, potentially causing some batteries to stop charging before reaching the desired charge level.

[0072] Some charging locations may have their own local power sources, such as microgrids, at least partially. These local power sources may be provided by one or more renewable energy sources, e.g., PV (photovoltaic) installations or wind farms, local energy stores, energy stores, and / or local generators. Some embodiments may manage the use of these resources so that the use of one or more of these resources takes precedence over (or vice versa) the use of the grid. Some embodiments may manage electric vehicle (EV) charging to match the availability of renewable resources where possible. Some embodiments may use local power sources in the event of a blackout or problem with the primary power grid.

[0073] Some embodiments may manage the peak load of a charging station. In some embodiments, peak load reduction (power consumption) and / or energy consumption (power over time) are considered. In some embodiments, the goal may be to keep the energy load at the charging station below a threshold level. In some embodiments, this threshold level may be below the maximum peak load that can be supplied. In some embodiments, this threshold is a static threshold. In other embodiments, this threshold may be variable.

[0074] Some embodiments may aim to ensure that EVs are available for use when needed. In the context of a bus fleet, this may be to ensure that buses can operate according to the required timetable. Electric buses can serve daily driving missions on predetermined routes according to predetermined timetables.

[0075] Some embodiments may control the charging process for one or more electric vehicles. This may be individual vehicle control. Some embodiments may control when the charging process is turned on and / or turned off for each electric vehicle. Some embodiments may control the charge rate of a particular vehicle, i.e., increase or decrease the charge rate as needed. Thus, some embodiments may individually control each charger at a charging location.

[0076] In some embodiments, the electric vehicle 6 may support only so-called V1G operation. V1G operation is when there is one-way charging from the power grid (and / or other power source) to the electric vehicle 6.

[0077] In other embodiments, some or all of the electric vehicle may support so-called V2G operation. V2G operation provides bidirectional vehicle-to-grid (V2G) operation, which allows the electric vehicle to be charged from the grid and also allows the vehicle to provide back service to the grid in discharge mode. In some embodiments, it may be possible to control when a V2G vehicle is charged from the grid and, if discharging, when the V2G vehicle discharges to the grid. The grid may be a main grid and / or a microgrid.

[0078] The following examples assume unidirectional V1G operation, but please understand that some embodiments may accommodate V2G vehicles, particularly discharges returning to the grid.

[0079] Here, device 4 manages the charging and optional pre-adjustments performed at the charging location. The following describes an embodiment. Device 4 may be equipment that provides at least a part of an EMS (Energy Management System). In some embodiments, device 4 may be an EMS controller. Device 4 manages the scheduling of charging and pre-adjustment (if provided).

[0080] Device 4 may manage so-called slow charging. However, in other embodiments, so-called fast charging may be managed alternatively or additionally.

[0081] Device 4 may control the rate at which charger 2 is charging and / or the rate at which the EV's battery is being charged.

[0082] Device 4 may be located at the charging location and / or operated at a location away from the charging location. This device may comprise at least one integrated circuit.

[0083] In some embodiments, the device 4 is configured to receive information used to provide a charging profile, as will be described in more detail later. This information may be provided by one or more of the following: Responses to user input, automatic responses via APIs (Application Programmable Interfaces), and automatic detection of each piece of information (e.g., detecting when an EV is in or near a charging station, or en route to a charging station).

[0084] In some embodiments, some of the information may be provided in "real time." For example, the current location of an electric vehicle may be provided in real time.

[0085] Device 4 may determine a charging profile, which may be based on information received or obtained by other means.

[0086] Device 4 is configured to provide an output according to a determined charging profile. The output provided by device 4 may be presented on a graphical user interface. The output may include one or more control signals provided directly or indirectly to charger 2. The output from device 4 is used to control charging of the electric vehicle by the charger.

[0087] In some embodiments, the electric vehicle 6 can be automatically charged by its respective charger 2 as a result of the output from the device 4.

[0088] Therefore, charger 2 may be configured to receive control signals from device 4, which controls how each charger charges its respective vehicle. The time it takes for the charger to start and stop charging may be controlled by device 4. The rate at which charging is performed may also be controlled by device 4.

[0089] The charger 2 may be configured to provide data to the device 4. This data will be described in more detail later. The data may be provided directly to the device 4 by the charger 2, or it may be provided to the device through one or more other entities.

[0090] The charger 2 may be connected to the device 4 via a wired and / or wireless connection 8. The EV6 to be charged is plugged into its respective charger 2. Information from the EV6 is provided to the device 4. This can be done directly and / or via the respective charger. If the EV6 is connected directly to the device 4, this may be via a wired or wireless connection.

[0091] Device 4 is connected to each EV6 and / or each charger 2, which EV Identification information may be provided to indicate whether it is connected to a specific charger.

[0092] Identification information associated with an EV can be used by the device to determine information about that EV's battery. For example, identification information is used by device 4 to retrieve battery information in database 7. If database 7 is provided, it may be a database that is equipped with the device and / or a database that may be accessible via the internet, etc. Database 7 may be part of the device in some embodiments. Alternatively or additionally, EV 6 may provide at least some of the battery information.

[0093] Charger 2 is connected to the main grid 10 and / or any other suitable power source such as a microgrid or other local power source (not shown).

[0094] Refer to Figure 3, which shows an example of the apparatus 4 in several embodiments. In this example, the apparatus 4 is provided by a computer or server. It should be understood that in other embodiments, the apparatus may comprise two or more servers, two or more computers, or a combination of one or more computers and one or more servers. In some embodiments, the apparatus may comprise an industrial controller. The apparatus 4 may execute computer programs or algorithms.

[0095] The device shown in Figure 3 includes an integrated circuit (IC) 40. The integrated circuit includes one or more processors 36 and one or more memories 38. The memories can store computer code that defines a computer program or algorithm that can be executed on at least one processor. In some embodiments, one or more integrated circuits may be provided.

[0096] A display 30 may be provided to display information to the user. This may be optional in some embodiments. In some embodiments, a user interface 32 may be provided. This may be optional in some embodiments. In some embodiments, the user interface 32 may provide a display. This may be the case when the user interface is a touchscreen.

[0097] The device has a communication interface 34. This allows the device to communicate with the charger 2, the electric vehicle 6 (if communication with the EV does not go through the charger), and an external data source. The external data source may provide the device with data from, for example, a power grid supplier and / or availability information such as timetable information. The communication interface may support wireless and / or wired communication. In some embodiments, the communication interface may support one or more different communication standards (protocols). In some embodiments, there may be multiple different communication interfaces.

[0098] An internal communication network 36, such as a bus configuration, may be provided within the device to enable data communication between the integrated circuit 40, the display 30, the user interface 32, and the communication interface 34.

[0099] The data that device 4 requires from charger 2 may be transmitted directly from charger to device 4 via a communication network. The output that device 4 supplies to charger 2 may also be transmitted directly to charger via a communication network.

[0100] In some embodiments, the data required by the device from the charger may be transmitted from the charger to the device via one or more data hubs (not shown). The power may be transmitted to the charger via one or more data hubs (not shown).

[0101] In some embodiments, the device 4 may be located away from the charging location, for example, on one or more remote servers. This may optionally be supported by one or more data hubs in the charging location that collect information and transmit the data to the device 4. Similarly, control commands from the device may be distributed by one or more data hubs to, for example, the charger 2.

[0102] In another embodiment where the device 4 is located remotely, communication between the device 4 and the charger 2 may be via the Internet and / or other communication networks.

[0103] As stated above, in some embodiments, the apparatus 4 may be provided simply by an integrated circuit or by two or more integrated circuits.

[0104] Device 4 may be configured to determine a charging profile and provide an output for controlling charging. To achieve this, device 4 may calculate an active power setpoint by evaluating future and current information and distribute it to all electric vehicles 6 attached to charger 2. Alternatively or additionally, a reactive power setpoint may be calculated and distributed. In some embodiments, active current and voltage setpoints and / or reactive current and voltage setpoints may be distributed alternatively or additionally.

[0105] Therefore, the current charging profile is determined using information associated with future events. This future information may include one or more of the following: vehicle arrival, vehicle departure, information associated with energy supply, predicted initial battery charge state, and target battery charge state.

[0106] Information associated with energy supply may be provided by energy availability information or grid load measurement forecasts. Energy suppliers can use pricing to help control the use of energy supply, and this is one way in which grid load measurement forecast information can be provided.

[0107] The device may use this future information to schedule EV charging and manage energy use.

[0108] Some embodiments may use a rolling horizon framework to address uncertainties related to future information. For example, there are uncertainties related to the battery charge state (SoC) of the e-bus and the actual arrival time of the e-bus. Therefore, for a given time interval, a decision is made about how charging should be controlled based on current and future information. For the next time interval, the current and future information is updated, and the decision about how charging should be controlled is re-evaluated or updated. This is schematically illustrated in Figure 2. At the current time t+1, data for the next n time slots are considered. Each time slot has the same length Δt. In the example shown in Figure 2, Δt is 15 minutes. In this example, n=96, so the time considered is 24 hours. However, it should be understood that n may be greater than or less than 96.

[0109] Δt can be any appropriate value. Δt may depend on the application of the device. For example, Δt could be around 15 minutes for a bus stop, as shown in Figure 2.

[0110] At the next current time t+2, the algorithm will be re-evaluated for the next n time slots. It is worth.

[0111] In some embodiments, the value of n may vary. For example, n may be shorter during peak operation than during quiet operation, or vice versa. Alternatively or additionally, it should be understood that Δt may vary over time. Δt may be longer during quieter periods and shorter during busier periods, for example.

[0112] The sampling time and / or optimization horizon (i.e., the future distance at which the decision is taken into account) can be set as needed. For example, this may be based on data granularity and / or data availability. Just as an example, this may span a period of 12 or 24 hours. Of course, in other embodiments, longer or shorter horizons may be used.

[0113] Device 4 may collect data from the EV and charger. The collected data may include one or more of the following: Which electric vehicle 6 is attached to each charger 2 (for example, EV identification information), The charge status of the battery of the electric vehicle 6, and The charge / discharge power rate of the battery electric vehicle 6 (this may be obtained alternatively, or, for example, from a database as described above).

[0114] Device 4 may receive data from the power grid supplier. This may be a response to a request from Device 4. This data may include one or more of the following: The amount of electricity the system can supply to the power grid supplier (if supported), The amount of active power that can be received from the main grid, Absorption of maximum power peak from the main grid, Grid load information, Information indicating when electricity is likely to be available, and Information indicating when electricity availability is low.

[0115] Device 4 may receive timetables, operating schedules, or other vehicle availability information. This may come from the operator of a charging station. In the case of a bus operator, this information may include bus arrival and departure time data.

[0116] Device 4 may receive data relating to the microgrid and / or local power sources. The device may receive data from one or more microgrid entities and / or from one or more microgrid controllers. A microgrid may comprise one or more local power sources. Local power sources may include one or more of the following: generators, renewable energy sources, local energy storage units, wind farms, and solar panel installations.

[0117] Data received from one or more microgrid entities may contain information about energy availability in both the current and future horizons. Microgrid entities may include meters, controllers, computers, monitoring devices, or any other suitable entities.

[0118] In some embodiments, at least one of the chargers 2 may be located on a microgrid. In some embodiments, such a charger 2 may be further connected to a main grid.

[0119] Device 4 may be provided with information defining one or more of the following: power limits for battery charging / discharging, battery charging / discharging efficiency, and charge state limits. This information may be obtained from a database 7 or the like that stores this information. In some embodiments, alternatively or additionally, this information may be provided by EV6 itself that stores this information.

[0120] Device 4 may control the display 30 to display information. For example, the display 30 may be controlled to display information regarding energy use. This information may include one or more of the following: energy consumption, values ​​associated with the energy used, and / or any other appropriate information.

[0121] In some embodiments, the device 4 may be configured to display information indicating when each vehicle 6 should be charged and for how long it will be charged.

[0122] Some embodiments may be implemented on an application platform that relies on microservices and container technologies. In some embodiments, the device supports a set of services. Each service may run separately, for example, in its own dedicated container. Services may be relatively small. One or more services may be configured to cooperate with one or more other services. One or more services may be loosely coupled. The use of a microservices platform may provide one or more of the following advantages: flexibility, scalability, maintainability, portability, deployability, test stability, and cybersecurity.

[0123] The device may be configured to provide one or more APIs (Application Programmable Interfaces). One or more APIs may be web APIs. One or more APIs may be used to publish and / or provide information to and / or from third-party systems. For example, the information may include timetables, pre-adjustment data, EV battery SoC information, etc. One or more third-party systems may have one or more databases. In some embodiments, information may be obtained from one or more third-party systems and stored in a database 7 used by the device 4.

[0124] Device 4 may support one or more different protocols. For example, the device may support Modbus TCP / IP (Transmission Control Protocol / Internet Protocol), IEC 60870-104 (International Electrotechnical Commission standard) used to control, for example, power grids, and / or OCCP (Open Charge Point Protocol) to support electric vehicle charging. These protocols are merely examples, and other protocols may be used alternatively or additionally. The device may be configured to support one or more protocols used by devices with which it communicates.

[0125] In some embodiments, data exchange with external sources and / or data exchange for parameter configuration within the system may be via the JSON format and / or using any other suitable data format.

[0126] Device 4 may determine a charging profile that schedules charging activities while meeting the physical constraints of the equipment and / or the operator's needs at the charging site. Examples of equipment constraints include the capacity of the charging site and / or the capacity of the charger. Examples of operator needs may include power peaks and / or site efficiency.

[0127] Some embodiments provide a computer program that controls energy management at a charging location by determining a charging profile. The computer program is a mo It can be based on programming techniques using Dell. Some embodiments may use mathematical models. The models may include one or more constraints and / or one or more objectives.

[0128] Optimization can be performed using the model. However, other embodiments may use any other suitable programming techniques. The computer program may run on the apparatus or any other suitable one or more computing devices.

[0129] Therefore, some embodiments may provide a device 4 that determines a charging profile for controlling the charging of an electric vehicle, taking into account both the need for battery recharging and pre-adjustment.

[0130] Therefore, some embodiments may provide a device 4 that determines a charging profile to control the timing of charging, how much charging is performed, and optionally the charge rate of the electric vehicle, in order to control battery recharging. Some embodiments may provide a device 4 that determines a charging profile to control the pre-adjustment strategy of each electric vehicle.

[0131] Device 4 may provide an output that controls the charging of each EV6 plugged into its charger by each charger 2. The device may provide an output that controls the behavior of the chargers at a given charging time interval. An output may be provided for each charging time interval. This may be updated based on subsequent iterations of the rolling horizon.

[0132] Alternatively or additionally, device 4 may provide charger 2 with a full set of charging commands that control how the charger should charge the EV. This may include when charging should start, when it should end, and / or one or more rates to be used and when they should be used. This may be updated based on subsequent iterations of the later rolling horizon.

[0133] Device 4 may, alternatively or additionally, send a command to charger 2 when charging should begin. This may include information indicating the charge level. If the charge level should be changed or stopped, device 4 sends an update command to charger.

[0134] Some embodiments provide a scalable device 4 that can be modified to take into account a different number of chargers 2 installed at different charging locations.

[0135] Some embodiments may use specific hardware constraints that take into account the maximum number of chargers 2 available for a given charging location.

[0136] Some embodiments provide a device 4 aimed at reducing power peaks and / or controlling the timing of energy use. This may have further advantages in terms of efficient energy use.

[0137] Some embodiments provide a device 4 that can take into account distributed energy resources or local power sources, such as one or more of the following: renewable energy sources, generators, energy storage, PV (photovoltaic) units, (controllable) loads, V2G electric vehicle operation, etc.

[0138] Some embodiments may use computer programming methods to manage power consumption operations. EV6 may represent an asset modeled by a computer programming method.

[0139] In embodiments of the present disclosure, determining the charging profile may include an optimization process. Possible embodiments of this optimization process are described below.

[0140] The charging location can be assumed to be connected to the main external grid 10 and consume (and optionally supply – if the charging location has either renewable energy or conventional generators) power according to the local network power demand.

[0141] Distribution infrastructure (e.g., power lines, transformers, etc.) can be ignored, and distributed energy resources can be considered connected to the utility grid via a single connection point, i.e., a common connection point (PCC).

[0142] The following explains the limitations of PCC, EV, and chargers (CS). In these embodiments using a microservices architecture, the data used by constraints is generated by microservices that leverage information stored in a database. The charging location is n c It may have n chargers. There may be 6 groups or sets of EVs, and the number of vehicles in that group or set of EVs is n. E It is possible.

[0143] In this example, charger 2 is assumed to be identical. In some embodiments, the device 4 may be controlled to take into account two or more different types of charger 2. Different chargers may have, for example, different charging characteristics.

[0144] In some embodiments, the charging location is connected to the main grid 10 by a PCC. However, in some embodiments, there may be two or more PCCs.

[0145] One constraint PCC1 is a limit on the active power that is exchanged with the main grid 10. Limitations on power exchange can stem from the physical characteristics of transformers.

[0146] As a result of the agreed-upon constraints between the main grid 10 and the microgrid MG, limitations may exist.

[0147] There may be an upper limit to the amount of available electricity a system can obtain from the power company. Alternatively, or additionally, the amount of available electricity the system must obtain from the power company may be less or a minimum.

[0148] If a system generates power, there may be an upper limit to the amount of power it can supply to the grid. Alternatively, there may be a requirement that there is a minimum amount of power that must be supplied to the grid, either separately or additionally.

[0149] When a system generates power, there may be a requirement that more power is supplied from the grid than is supplied to the grid.

[0150] If a microgrid exists and the microgrid MG is used as the primary power source, power exchange with the grid may be zero under certain circumstances. However, any shortages can be compensated for by the main grid.

[0151] In other embodiments, the main grid is used as the primary power source. However, any shortages can be compensated for by a microgrid.

[0152] In some embodiments, the limit on the active power exchanged with the main grid is determined by the power capacity. It may be defined as follows.

[0153] In some embodiments, a microgrid or similar is not necessary, and all electricity may be supplied by a power company.

[0154] This information regarding this constraint with respect to the main grid 10 may be provided to the device 4 by the power supplier of the main grid.

[0155] A constraint PCC2 may exist, which is the maximum peak import power from the main grid 10. This can be considered a constraint on power peaks over a certain period. This may change over time or remain constant.

[0156] This information may be provided by the power supplier of the main grid 10. PCC1 and / or PCC2 can be considered examples of available power constraints.

[0157] The EV6 is a vehicle that uses chemical energy stored in a rechargeable battery to power an electric motor. When pre-calibration is required, the energy in the rechargeable battery is also used to supply power for this purpose. For example, the battery can power auxiliary loads such as the HVAC (heating, ventilation, and air conditioning) systems in some vehicles.

[0158] In the following, the EV6, or more specifically, the battery of an EV, will be modeled using a combination of the following two elements.

[0159] 1) Battery energy storage unit 2) Controllable load: This is optional and used when prior adjustment is required.

[0160] In some embodiments, EV6 supports V1G operation only. This means that the EV consumes power at the charging station to 1) recharge the battery, 2) control the temperature by pre-calibration (if necessary), or both. The controlled temperature may be the battery and / or vehicle (e.g., HVAC) temperature.

[0161] Pre-adjustment can be modeled by a simple, controllable load with a predetermined consumption profile. This profile may be defined according to the vehicle's timetable and pre-adjustment requirements. Pre-adjustment requires that certain conditions be met at a specific time. For example, the bus temperature must be at a specific value before the bus departs.

[0162] The number of arrivals may be equal to the number of departures. Logically, a vehicle only departs after it has arrived.

[0163] However, in different embodiments, different assumptions may be made regarding the number of arrivals compared to the number of departures.

[0164] For energy storage systems, constraints on power and energy boundaries must be considered.

[0165] An EV6 battery may have one or more constraints regarding power limits, charge states, and charge state limits. It should be understood that references to EV6 charging naturally refer to charging one or more batteries in an EV.

[0166] EVb1 - Charging and / or discharging of a given vehicle battery storage unit (battery) An EV power limit may be imposed on the effective output rate. There may be a maximum and / or minimum charge rate. There may also be a maximum and / or minimum discharge rate. This may apply when the battery is discharged to the grid.

[0167] EVb2 - EV Battery Dynamics - State of Charge (SoC). The state of charge for the next period may be equal to the current state of charge plus the measure of charge achieved during that period. The measure of charge achieved may depend on the power supplied, battery capacity, and storage charge and / or discharge efficiency.

[0168] In some embodiments, information about the number of times the battery has been charged may be used to determine the charge and / or discharge characteristics.

[0169] EVb3 EV SoC (Charging State) Limitations: These may be predefined limits to avoid extreme SoC levels (full charge / discharge). These boundaries are generally recommended by the respective manufacturers. Generally, it is best not to fully discharge the battery to avoid both rapid degradation issues and potential permanent damage. The values ​​used may vary over the battery's lifespan. There may also be minimum charge level constraints regarding the minimum charge level of the battery and / or maximum charge level constraints regarding the maximum charge level of the battery.

[0170] If pre-adjustment is supported, EV load modeling may be provided. Load absorption for pre-adjustment operations may be considered in the total charger power consumption. In this example, EV power absorption to supply the internal load is modeled as a continuously controllable load. The initial EV demand profile may be shifted or reshaped to reduce peak demand and / or to meet the maximum number of available chargers.

[0171] EV1 controllable load power - This can be expressed as a fraction (less than or equal to 1) of the nominal value of the HVAC or other pre-adjusted load.

[0172] EV12 load modulation constraints are obtained based on load demand within a range defined by the vehicle's load demand program prediction over time, by adding or subtracting a value between 0 and 1 multiplied by the nominal power. This latter value can be considered a measure of its maximum modulation rate.

[0173] EV13 Load gradient constraint - This represents the maximum gradient that can increase and / or decrease the vehicle's load power demand over a given time interval.

[0174] EV14 Load Energy Constraint – This ensures that the planned energy is supplied during the time between the vehicle 6 arriving at and leaving the charging station. Load energy may be shifted or reshaped during the time the vehicle 6 is in or at the charging station. For example, pre-adjustment can be performed for a period of time after the battery has been charged. In some embodiments, the charging profile supporting pre-adjustment may be smoothed to avoid peaks and troughs in load energy. The charging profile may be extended as needed.

[0175] When determining the charging profile, the device 4 may take into account the constraints of one or more chargers 2. For example, this could be one or more of the following: the availability of chargers, the number of chargers, and the charging profile capabilities of the chargers.

[0176] As mentioned above, the total power consumption of a single EV6 consists of the sum of battery recharge and pre-adjusted power provided. This is the constraint of the charger's total power exchange - CS1. In other words, there may be limitations to the charging provided by a given charger.

[0177] Second charger CS constraint - CS2. This charger power constraint is the total amount of power absorbed by a given vehicle, subject to the charger capacity. When the vehicle is attached to the charger, it may or may not be able to draw power from the grid.

[0178] In the case of the third charger constraint CS3, namely the charger availability constraint, the number of EVs simultaneously drawing power from the grid at a charging location cannot exceed the number of available chargers.

[0179] In the case of the PB1 active power balance constraint, the total active power demand (for all vehicles being charged) must meet the power supplied by the utility grid at each time interval.

[0180] In this example, only unidirectional V1G operation is considered. However, in the case of V2G operation, the supplied power may also take into account the power supplied by the vehicle.

[0181] Some embodiments may use a cost function. The objective of some embodiments is for device 4 to provide smart charging (and pre-coordination strategies, if used) to EVs at charging locations without placing an excessive burden on the grid.

[0182] In some embodiments, the objective of device 4 is to control the charging of EV batteries to a target value while minimizing energy and power consumption when power availability is low. For example, in some embodiments, energy consumption during peak demand times is reduced as much as possible. The objective may be to keep the variation in the charging operation of each battery within one or more defined limits. The objective may be to minimize the variation in the charging operation of each battery in each vehicle.

[0183] The objective function in time can be considered to be the sum of the following values: a) A value associated with the use of electricity supplied by or from the grid. This value can be a measure of energy availability. This value can be higher when available energy is low. It can be associated with predicted and / or current availability.

[0184] b) Value associated with the EV. The value associated with the EV may be higher if the battery is not charged within its defined limit or is not charged suboptimally.

[0185] The values ​​associated with the EV may represent a measure of the penalty for one or more of the following: the level of the EV SoC that differs from the target value at departure time, the pre-adjusted rescheduling cost (if provided), and setpoint variations.

[0186] The measure of the penalty for EV SoC costs may include a value associated with the EV in the event of a mismatch between the SoC level at the time of departure and its desired value.

[0187] The penalty for EV load setpoint rescheduling discomfort may be measured by quantifying the load discomfort caused by rescheduling prerequisite power consumption from its initial power program profile.

[0188] The penalty for EV charging setpoint fluctuations may be measured by the variability of battery charging operation. good.

[0189] The penalty for EV load setpoint variation may also be measured by the variation in the pre-adjusted setpoint.

[0190] In alternative embodiments, the objective function may include values ​​associated with the use of power supplied by or from the grid, or values ​​associated with the EV.

[0191] Some embodiments may aim to optimally charge each individual battery, if possible. For example, battery characteristics may be considered when determining the charge profile. Some embodiments aim to take into account the number of years and / or cycles when determining the charge profile, because optimal charge characteristics may change with the number of years and / or cycles. Some embodiments may, alternatively or additionally, take into account environmental conditions such as temperature when selecting the charge profile. This may extend battery life and / or provide better battery performance during the battery's lifespan.

[0192] Device 4 may be configured to define optimal charging and pre-adjustment setpoints for all EV6. This may be power availability information.

[0193] Some embodiments may provide a device 4 configured to provide an output for controlling the charging of an EV at a charging station. The device is configured to provide an output using an optimization process. The optimization process aims to provide a solution that satisfies one or more of the constraints and / or objectives. If there is one or more solutions that satisfy one or more constraints and / or one or more objectives, the solution associated with the lowest objective function value may be used. This solution may be considered the optimal solution. In practice, the so-called optimal solution may not be the best solution, but may be a solution that satisfies one or more constraints and / or one or more objectives as much as possible.

[0194] In other embodiments, multiple solutions may be found for charging, and solutions that do not satisfy the necessary constraints are discarded.

[0195] There are several different computer programming techniques that can be used in some embodiments. As just one example, some embodiments may use MILP (Mixed Integer Linear Programming). One technique for solving MILP is tree search using the Branch & Bound algorithm with linear programming relaxation. This can have exponential complexity (NP-hard). In the best case, the complexity of the Branch & Bound algorithm is linear in terms of the number of binary variables bin, i.e., O(bin), and in the worst case, a complete tree, i.e., O(2bin), must be searched.

[0196] Device 4 may use one or more power constraints. These power constraints may be as described above. There may be limitations on how much power can be purchased at a given time and / or on peak power usage.

[0197] Device 4 may use one or more EV6 or battery constraints as described above. There may be one or more constraints regarding power limits, charge states, and charge state limits.

[0198] Device 4 may use one or more load constraints associated with the pre-adjustments (if provided) as described above. There may be one or more constraints regarding when the pre-adjustments should be made, the maximum gradient at which the power demand can increase or decrease, and the pre-adjusted power demand.

[0199] Device 4 may use one or more charger constraints as described above. There may be one or more constraints regarding the number of chargers, the total power consumption of the EV, and the charger capacity.

[0200] Device 4 may use an active power balance constraint. In other words, the power used over a given period cannot exceed the total available power. This power may be supplied from the main grid 10 and / or local power sources.

[0201] Device 4 may use information regarding whether or not the vehicle 6 is available for charging as an availability constraint. The availability of EV6 may be provided by one or more of the following: an operating schedule, location information of EV6, and real-time location of EV6. The operating schedule may be a timetable or the like. The location information may be the real-time location of EV6, such as the GPS location of EV6, or it may be the predicted location of EV6. This predicted location may be based on the EV's previous location and may take into account elapsed time and / or traffic information.

[0202] Device 4 may use information regarding the SoC for EV6 upon arrival. This could be the actual SoC of the arriving vehicle, or a predicted SoC if the vehicle has not yet arrived but is expected to arrive within the considered horizon.

[0203] Various examples of constraints and objectives are provided. One or more of these constraints and / or objectives may be omitted from the determination of the charging profile.

[0204] The constraints used may depend on the available information and / or the limitations of energy resource availability. For example, if there are many different consumers (who may be at different charging locations or other energy users) sharing a common energy resource, it may be desirable to optimize the use of energy resources at the charging location as much as possible. This can be achieved by using a variety of constraints.

[0205] Alternatively or additionally, constraints may depend on their relative importance to a particular use case. For example, the constraint of EV availability may be less important for a group of delivery vehicles compared to a group of buses.

[0206] Refer to Figure 4, which illustrates the methods of several embodiments. In A1, the method includes determining a charging profile for charging at least one electric vehicle via at least one charger, based at least on the characteristics of each battery of at least one electric vehicle, information on available power, and the availability of at least one electric vehicle.

[0207] You may use any one or more of the battery characteristics examples mentioned above. You may use any one or more of the aforementioned examples of the availability of at least one electric vehicle.

[0208] You may use any one or more of the examples of available power information mentioned above. In A2, the method includes providing an output for controlling charging by each of the chargers according to a determined charging profile.

[0209] Refer to Figure 5, which schematically illustrates a more detailed method of several embodiments. In B1, the method is based at least on the characteristics of the battery of at least one electric vehicle, information on available power, and the availability of at least one electric vehicle, This also includes determining candidate charging profiles for charging at least one electric vehicle via a single charger.

[0210] Candidate charging profiles can define when each electric vehicle should be charged, and optionally, the rate at which each EV should be charged.

[0211] The decision takes into account EV scheduling information or other availability information. This scheduling information provides information about when EVs will arrive and when they will depart. This may be for a specific vehicle, or it may be for any EV that must be ready to depart at a specific time.

[0212] The determination of candidate charging profiles takes into account one or more constraints. In the method shown in Figure 5, the constraints are one or more power constraints, one or more EV constraints, and one or more charger constraints. These constraints can be any one or more of the constraints mentioned above. One or more of these constraints may be omitted.

[0213] The decision will also take into account power balance requirements. It may be impossible to determine a solution that satisfies all constraints. In such a scenario, one or more constraints may take precedence over others. For example, the power balance requirement may take precedence.

[0214] Some of the limitations are quite strict, such as the number of chargers available. If the maximum peak input power is one of the constraints, exceeding this constraint for a relatively short period and below a threshold amount may be permitted.

[0215] If pre-adjustments are provided, and no charging profile is found that satisfies those constraints (and other constraints), one or more relevant constraints may be ignored, at least partially.

[0216] The priority of EV availability information may depend on the nature of each charging location. For example, in the case of a bus stop, the requirement that there is a bus ready to depart at a specific time may take precedence over other constraints. However, in other scenarios, EV availability information may be given a lower priority compared to other constraints.

[0217] In B2, the value of the objective function associated with each candidate charging profile is determined. This could be the objective function mentioned above.

[0218] In B3, one or more charging profiles are selected depending on the value of the objective function. The selected charging profiles may be associated with the minimum value of the associated objective function.

[0219] In B4, an output is provided to control the charging of the EV by each charger based on the selected charging profile.

[0220] It should be understood that the method in Figure 5 may be repeated over subsequent time horizons. In some embodiments, the method in Figure 5 may be repeated entirely over subsequent time horizons. Other embodiments may determine whether the solution provided for the previous time horizon can still be used, while still satisfying the necessary constraints. If so, the charge profile determined in the previous iteration of the method is continued to be used. In some embodiments, the previously determined charge profile is continued to be used only if one or more values ​​are within a defined threshold.

[0221] Figures B1-B3 of Figure 5 should be understood to provide an example of how the method in Figure 4 A1 may be performed. In other embodiments, any other suitable optimization process may be performed to determine the charging profile to be used.

[0222] The device may take into account a scenario in which the EV discharges power to the system. In this embodiment, the discharge behavior of the battery may also be taken into account.

[0223] Some embodiments may take into account additional power sources as described above, as well as bidirectional V2G operation that enables the vehicle to provide back service to the grid in discharge mode.

[0224] In the example described above, it is assumed that the electric vehicle is charged only at its base or charging station. In other embodiments, it should be understood that one or more vehicles may be charged at one or more points along the route. This can be taken into consideration when determining the level to which the battery should be charged when the electric vehicle is charged at a charging station.

[0225] In the example described above, it was assumed that the charging station provides slow charging for the vehicle. In other embodiments, partial or exclusive fast charging may be supported.

[0226] Some embodiments are described as being implemented on application platforms that rely on microservices and container technologies. These are merely examples, and other embodiments may be implemented using any other suitable computer programming techniques.

[0227] In at least one embodiment, the method may be a computer implementation method. This method may be performed by one or more integrated circuits. Furthermore, the method may be performed on a computing device or on an industrial controller.

[0228] Accordingly, the embodiments may vary within the scope of the appended claims. Generally, some embodiments may be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. For example, some embodiments may be implemented in hardware, while others may be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device, but the embodiments are not limited thereto.

[0229] Computer software or programs, also called program products, which include software routines, applets, and / or macros, may be stored on any device-readable data storage medium and contain program instructions for performing a particular task. A computer program product may include one or more computer executable components configured to perform embodiments when the program is executed. One or more computer executable components may be at least one piece of software code or a portion thereof.

[0230] Various embodiments may be illustrated and described using block diagrams, flowcharts, or some other graphical representations, but it should be understood that these blocks, apparatus, systems, techniques, or methods described herein may be implemented, in non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or any combination thereof.

[0231] The above description has provided a complete and useful description of exemplary embodiments of the present invention, as non-limiting examples. However, various modifications and adaptations will become apparent to those skilled in the art when read in conjunction with the accompanying drawings and claims, taking the foregoing description into consideration. However, all such and similar modifications of the teachings of the present invention fall within the scope of the invention as defined in the accompanying claims. In fact, there are further embodiments, including combinations of one or more embodiments with any of the other embodiments described above.

Claims

1. A method for controlling the charging of at least one electric vehicle (6), particularly two or more electric vehicles, via at least one charger (2), particularly two or more chargers, Determining a charging profile for charging the at least one electric vehicle (6) via at least one charger (2), based at least on the battery characteristics of the at least one electric vehicle (6), information on available power, and the availability of the at least one electric vehicle (6), To provide an output for controlling charging by each of the chargers (2) according to the determined charging profile, Methods that include...

2. The method of claim 1, wherein the availability of the at least one electric vehicle (6) is based on at least one of the following: an operating schedule, location information of the at least one electric vehicle (6), and real-time location information of the at least one electric vehicle (6).

3. The method of any one of the preceding claims, wherein the determination of the charging profile for charging the at least one electric vehicle (6) is further based on the requirement of pre-adjusting the at least one electric vehicle (6).

4. The method of claim 3, further comprising providing an output for controlling the pre-adjustment of the at least one electric vehicle (6).

5. The method according to any one of the preceding claims, wherein the information on available power includes information on power that can be supplied by one or more of the power grid and one or more local power sources.

6. The method according to any one of the preceding claims, wherein the determination of the charging profile for charging the at least one electric vehicle (6) includes an optimization process.

7. The method of claim 6, wherein the optimization process includes a first objective of reducing variations in the charging operation and / or pre-adjustment operation provided by the charging profile.

8. The method according to any one of claims 6 and 7, wherein the optimization process includes a minimum charge level constraint relating to the minimum charge level of each of the batteries of the at least one electric vehicle (6).

9. The method according to claim 8, wherein the optimization process includes a second objective of maximizing the charge level of each battery beyond the minimum charge level, particularly up to a predetermined charge level.

10. The method according to any one of the preceding claims 6 to 9, wherein the optimization process includes available power constraints relating to the information of the available power.

11. The method according to any one of the preceding claims 6 to 10, wherein the optimization process includes an electric vehicle (6) availability constraint relating to the availability of the at least one electric vehicle (6).

12. The method according to any one of the preceding claims 6 to 11, wherein the optimization process includes a maximum battery load constraint relating to the maximum load applicable to each of the batteries.

13. The method of any one of the preceding claims, wherein the determination is further based on a power supply limitation associated with one or more of the at least one charger (2).

14. The method according to any one of the preceding claims, wherein the characteristics of the battery include the charge state of the battery.

15. The characteristics of the aforementioned battery are One or more charging state restrictions, One or more charge level limits and The method of any one of the prior claims, comprising at least one of the following.

16. The method according to any one of the preceding claims, wherein the determination of the charging profile for charging the at least one electric vehicle (6) is further based on information about the current period and information from one or more future periods.

17. The method according to any one of the preceding claims, wherein the determination of the charging profile for charging the at least one electric vehicle (6) is repeated at a subsequent time to update the charging profile.

18. The method according to any one of the preceding claims, wherein the output for controlling charging controls one or more of the following: when the at least one electric vehicle (6) is charged and the rate at which the at least one electric vehicle (6) is charged.

19. A computer program comprising computer executable code configured to perform the method of any one of the preceding claims when executed on at least one processor (36).

20. A device configured to control the charging of one or more electric vehicles via one or more chargers, comprising at least one integrated circuit (40) configured to cause the device to perform the method according to any one of claims 1 to 18.

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