EP4680484A1

EP4680484A1 - Systems and methods for controlling charging of electric vehicles for electric power grid services

Info

Publication number: EP4680484A1
Application number: EP23712494.6A
Authority: EP
Inventors: Brecht BAETEN
Original assignee: ABB E Mobility BV
Current assignee: ABB E Mobility BV
Priority date: 2023-03-16
Filing date: 2023-03-16
Publication date: 2026-01-21
Also published as: CN121100080A; WO2024188467A1

Abstract

A charging control computing device for controlling charging of electric vehicles for grid services is provided. The charging control computing device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to receive a charging request from an electric vehicle at a charging point. The at least one processor is also programmed to generate a command based on a parametric model between the command and output power, wherein the command is a maximum amperage of current output to the electric vehicle at the charging point, and the output power is power output to the electric vehicle from the charging point. The at least one processor is further programmed to output the command to the charging point.

Description

(42005-2451)

SYSTEMS AND METHODS FOR CONTROLLING

CHARGING OF ELECTRIC VEHICLES FOR ELECTRIC

POWER GRID SERVICES

BACKGROUND

[0001] The field of the disclosure relates generally to systems and methods of a power grid, and more particularly, to systems and methods of controlling charging of electric vehicles for grid services.

[0002] Electric vehicles have become popular. Charging electric vehicles imparts high electrical demands on an electric power grid, which may stress the grid. Therefore, it would be desirable to control the charging of electric vehicles. Known system and methods are disadvantaged in some aspects and improvements are desired.

BRIEF DESCRIPTION

[0003] In one aspect, a charging control computing device for controlling charging of electric vehicles for grid services is provided. The charging control computing device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to receive a charging request from an electric vehicle at a charging point. The at least one processor is also programmed to generate a command based on a parametric model between the command and output power, wherein the command is a maximum amperage of current output to the electric vehicle at the charging point, and the output power is power output to the electric vehicle from the charging point. The at least one processor is further programmed to output the command to the charging point.

[0004] In another aspect, a method of controlling charging of electric vehicles for grid services is provided. The method includes receiving a charging request from an electric vehicle at a charging point. The method also includes generating a command based on a parametric model between the command and output power, wherein the command is a maximum amperage of current output to the electric vehicle at the charging point, and output power is power output to the electric vehicle from the charging point. The method further includes outputting the command to the charging point.

DRAWINGS

[0001] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings.

[0002] FIG. 1 is a schematic diagram of a charging control system for controlling charging of electric vehicles.

[0003] FIG. 2 is an example method of controlling charging of electric vehicles.

[0004] FIG. 3 shows an example parametric model.

[0005] FIG. 4 is a block diagram of an example computing device.

[0006] FIG. 5 is a block diagram of an example server computing device.

DETAILED DESCRIPTION

[0007] The disclosure includes systems and methods for controlling charging of electric vehicles. An electric vehicle is a vehicle that operates on an electric motor, and may be a battery electric vehicle (BEV), a plug-in hybrid electric vehicle (PHEV), or a hybrid electric vehicle (HEV). Method aspects will be in part apparent and in part explicitly discussed in the following description.

[0008] In known systems, when an electric vehicle is plugged in a charging station, the electric vehicle is in control and determines the amount of power that the electric vehicle draws from the charging station. The charging station typically does not have control on the power being drawn from the charging station. The lack of control by the charging station pose threat to the stability of the grid, especially as electric vehicles have become popular and the demand for power from charging has significantly increased. In a power grid, the frequency of the power needs to be maintained. A 0.2 Hz deviation from the nominal frequency of alternate current (AC), such as 60 Hz in the U.S. or 50 Hz in Europe, may be referred to as a severe grid condition. A 0.2 Hz deviation may cause appliances in the grid to malfunction. A 0.5 Hz deviation may result in blackout of the entire grid. Frequency deviation is typically caused by imbalance between consumption and production of electricity in the grid. Accordingly, the lack of control by the charging station may cause severe problems to the grid, even resulting in blackouts.

[0009] An industrial standard in control is to use a feed-back control mechanism such as a PID (proportion, integral, and derivative) controller to manage the problem of deviation in system parameters. In the feed-back control mechanism, deviations from desired signals are monitored and a PID controller is used to adjust the output of the system. A feed-back control mechanism is reactive and has a delay in the response. Because of the delay, the system may have already failed before a responsive action can be implemented. For example, if the deviation is large to a point of being dangerous, the system will not be corrected before severe consequences occur. Further, a large amount of data of car charging sessions from actual systems and experiments are needed to adjust and tune the PID parameters in the PID controller. A charging session is a session where an electric vehicle is charged at a charging point. Such data are often unavailable and unpredictable because it is typically uncertain when and how long a car will be present at a charging point. Further, in tuning PID parameters, the system is experimented by adjusting the parameters to a point the system becomes unstable or dangerous. Such an approach is risky to apply in the utility applications.

[0010] A regular machine learning model such as a neural network model is not a good option to solve the problem of controlling charging of electric vehicles either. Training a neural network model requires a relatively large amount of data. Trained neural network models also requires a relatively large storage. Due to the sheer number of electric vehicles, charging points, and the different combinations of electric vehicles and charging points, the amount of data and storage required to train and use a regular machine learning model that suits every charging point and every electric vehicle would be cost prohibitive. [0011] In contrast, systems and methods described herein use a feed forward mechanism, instead of a feedback mechanism. Systems and methods described herein use a parametric model in determining output power at a specific charging point to a specific electric vehicle. A parametric model is a model that captures the relationship between an input and an output with a finite number of parameters. The parameters may be parameters of a function that expresses the relationship between the input and the output. Little data and storage is needed because only parameters of the parametric model may need to be stored. The parametric model is customized and specific to every combination of a charging point and an electric vehicle. In addition, the systems and methods are self-learning or self-optimized. Historical data may be used to optimize the parameters of the parametric model. Further, the load patterns or power profile may be forwarded some time ahead such as one day ahead so that the grid may be controlled and the load and supply are matched. The systems and methods described herein also provides real-time control of the energy system, thereby maintaining the stability of the grid.

[0012] FIG. 1 is a schematic diagram of an example charging control system 100. In the example embodiment, charging control system 100 includes a charging control computing device 102. Charging control computing device 102 may be a computing device 800 (see FIG. 4 described later). Charging control computing device 102 may be a server computing device 1001 (see FIG. 5 described later). Charging control system 100 further includes charging points 104 in communication with charging control computing device 102 via wired or wireless communication. Charging point 104 may be a point that an electric vehicle 106 is charged by receiving electric power from a power grid 108. Charging point 104 may be a charging station, or a charging outlet at home, office, or a facility such as a parking lot. A battery of electric vehicle 106 may be recharged at charging point 104. Charging control computing device 102 and/or charging point 104 may communicate with electric vehicle 106 via wired or wireless communication.

[0013] FIG. 2 is a flow chart of an example method 200 of controlling charging of electric vehicles. In the example embodiment, method 200 includes receiving 202 a charging request from an electric vehicle at a charging point. When an electric vehicle 106 is plugged in a charging point 104, electric vehicle 106 sends a charging request. The charging request is relayed to charging control computing device 102. Method 200 further includes generating 204 a command based on a parametric model between the command and output power. The command is the maximum amperage specific to the electric vehicle that the electric vehicle draws from a charging point. The command is used to control the charging of electric vehicle 106. Output power is the amount of power output to the electric vehicle. Because charging point 104 is connected to grid 108, the amount of power output to charging points 104 is limited by the demand of the loads in grid 108. As a result, the grid controls the charging of electric vehicles in the grid, thereby maintaining the balance between demand and supply in the grid and significantly reducing risks of blackouts. Method 200 also includes outputting 206 the command to the charging point. The command is further communicated to electric vehicle 106 via charging point 104.

[0014] FIG. 3 shows an example parametric model 300. In the example embodiment, parametric model 300 includes a truncated linear relationship between a command 302 and output power 304. When command 302 is less than a threshold command 302-th corresponding to a maximum output power 304-max, output power 304 is a linear function of command 302 along a linear line 312. When command 302 is equal to or greater than threshold command 302-th, output power 304 is constant. FIG. 3 shows parametric model 300 fits well with measurements or data 306. Threshold command 302-th may be derived based on the linear function using maximum output power 304-max as the input of the linear function. Parametric model 300 may be described by three parameters of an offset 308, a slope 310, and maximum output power 304-max. For example, offset 308 is the intercept of linear line 312 with the axis of output power 304. Slope 310 is the slope of linear line 312. With only three parameters, parametric model 300 needs little storage. The simple parametric model of the truncated linear model has been derived by inspection of data and power profiles of charging sessions. Default settings of parameters in parametric model 300 may be determined by fitting existing data, such as available data of charging points and electric vehicles to parametric model 300. Alternatively, default parameters may be derived based on data from a pool of charging points and electric vehicles. Default settings may be adjusted or fine-tuned to individual charging points and/or individual electric vehicles. Maximum output power 304-max may play a bigger role than other parameters of offset 308 and slope 310 in affecting the fitting of parametric model 300 with data because a charging point and/or an electric vehicle may place a certain constraint on the charging of the electric vehicle and maximum output power 304-max reflects the constraint. [0015] In the example embodiment, system 100 is a self-learning or selfoptimizing system. Parametric model 300 may be fine-tuned to correspond to a specific charging point and a specific electric vehicle. When a vehicle arrives at a charging point to be charged for the first time, a default parametric model is used in charging the vehicle. A default parametric model 300 is provided with default parameters of parametric model 300. Data of the vehicle are saved as historical data. Historical data may include tuples of historical commands and corresponding historical output power of charging sessions of the electric vehicle at the charging point in the past. After the vehicle has been charged at the charging point for multiple times, the historical data are used to optimize the parameters in parametric model 300. Historical data may be pre-processed before being used to update parametric model 300. Irrelevant historical data may be deleted. Irrelevant historical data are data of time points where the relationship between the command and the output power is not reflected, for example data of time points when the electric vehicle is not being charged or the commands are not communicated to the electric vehicle. In one example, a tuple that includes a historical command having an interval from a prior historical command shorter than a threshold value may be deleted. An electric vehicle typically should follow a command within 10 seconds (s). Charging control computing device 102 may send a burst of requests of measurements to the electric vehicle or the charging point every 5 s for 30 seconds and afterwards return to a sampling interval of around 1-5 minutes. An example threshold value of the interval may be multiple of the initial sampling interval, e.g., the threshold value being 10 s. The interval lasting shorter than the threshold value may indicate commands being sent too often, where the electric vehicle does not have sufficient time to follow the command or the behavior of the electric vehicle was not measured properly. In another example, a tuple that includes a historical command of a session that the electric vehicle has already been fully charged may be deleted. When little output power is drawn from the charging point with the associated command being at a relatively large amperage such as 30 A, the electric vehicle may have been fully charged or is not being charge, both of which indicate the data are irrelevant and do not reflect relationship between the command and the output power and therefore the tuple should be discarded. After historical data is preprocessed, the preprocessed data are used to update parametric model 300 by fitting the historical data with parametric model 300 to update offset 308, slope 310, and/or maximum output power 304- max. [0016] Systems and methods described herein are advantageous in increasing the speed and accuracy of controlling power demand of a grid, compared to known methods using feel-back control mechanisms. Parametric model 300 instantaneously provides adjustments of output power based on the commands using a feed-forward mechanism, instead of relying on delayed and complex responses from a feed-back control system.

[0017] Systems and methods described herein are advantageous in requiring relatively little data or storage in providing control of charging at every combination of a charging point and an electric vehicle. The fine -turning of the model also requires relatively little computational load because optimizing of only a finite number of parameters, such as three, is needed.

[0018] Systems and methods described herein are advantageous also in reducing measurement frequencies of charging data. Reduced measurement frequencies save cost from a relatively high network bandwidth associated with a relatively high measurement frequencies. Because in the systems and methods described herein, the output power is predictable from parametric model 300, a relatively high measurement frequencies would not be necessary.

[0019] Charging control computing device 102 described herein may be any suitable computing device 800 and software implemented therein. FIG. 4 is a block diagram of an example computing device 800. In the example embodiment, computing device 800 includes a user interface 804 that receives at least one input from a user. User interface 804 may include a keyboard 806 that enables the user to input pertinent information. User interface 804 may also include, for example, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad and a touch screen), a gyroscope, an accelerometer, a position detector, and/or an audio input interface (e.g., including a microphone).

[0020] Moreover, in the example embodiment, computing device 800 includes a presentation interface 817 that presents information, such as input events and/or validation results, to the user. Presentation interface 817 may also include a display adapter 808 that is coupled to at least one display device 810. More specifically, in the example embodiment, display device 810 may be a visual display device, such as a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED) display, and/or an “electronic ink” display. Alternatively, presentation interface 817 may include an audio output device (e.g., an audio adapter and/or a speaker) and/or a printer.

[0021] Computing device 800 also includes a processor 814 and a memory device 818. Processor 814 is coupled to user interface 804, presentation interface 817, and memory device 818 via a system bus 820. In the example embodiment, processor 814 communicates with the user, such as by prompting the user via presentation interface 817 and/or by receiving user inputs via user interface 804. The term “processor” refers generally to any programmable system including systems and microcontrollers, reduced instruction set computers (RISC), complex instruction set computers (CISC), application specific integrated circuits (ASIC), programmable logic circuits (PLC), and any other circuit or processor capable of executing the functions described herein. The above examples are example only, and thus are not intended to limit in any way the definition and/or meaning of the term “processor.”

[0022] In the example embodiment, memory device 818 includes one or more devices that enable information, such as executable instructions and/or other data, to be stored and retrieved. Moreover, memory device 818 includes one or more computer readable media, such as, without limitation, dynamic random access memory (DRAM), static random access memory (SRAM), a solid state disk, and/or a hard disk. In the example embodiment, memory device 818 stores, without limitation, application source code, application object code, configuration data, additional input events, application states, assertion statements, validation results, and/or any other type of data. Computing device 800, in the example embodiment, may also include a communication interface 830 that is coupled to processor 814 via system bus 820. Moreover, communication interface 830 is communicatively coupled to data acquisition devices.

[0023] In the example embodiment, processor 814 may be programmed by encoding an operation using one or more executable instructions and providing the executable instructions in memory device 818. In the example embodiment, processor 814 is programmed to select a plurality of measurements that are received from data acquisition devices. [0024] In operation, a computer executes computer-executable instructions embodied in one or more computer-executable components stored on one or more computer- readable media to implement aspects of the invention described and/or illustrated herein. The order of execution or performance of the operations in embodiments of the invention illustrated and described herein is not essential, unless otherwise specified. That is, the operations may be performed in any order, unless otherwise specified, and embodiments of the invention may include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the invention.

[0025] FIG. 5 illustrates an example configuration of a server computer device 1001 such as charging control computing device 102. Server computer device 1001 also includes a processor 1005 for executing instructions. Instructions may be stored in a memory area 1030, for example. Processor 1005 may include one or more processing units (e.g., in a multi-core configuration).

[0026] Processor 1005 is operatively coupled to a communication interface 1015 such that server computer device 1001 is capable of communicating with a remote device or another server computer device 1001. For example, communication interface 1015 may receive data from charging control computing device 102 or a charging point 104, via the Internet.

[0027] Processor 1005 may also be operatively coupled to a storage device 1034. Storage device 1034 is any computer-operated hardware suitable for storing and/or retrieving data. In some embodiments, storage device 1034 is integrated in server computer device 1001. For example, server computer device 1001 may include one or more hard disk drives as storage device 1034. In other embodiments, storage device 1034 is external to server computer device 1001 and may be accessed by a plurality of server computer devices 1001. For example, storage device 1034 may include multiple storage units such as hard disks and/or solid state disks in a redundant array of independent disks (RAID) configuration, storage device 1034 may include a storage area network (SAN) and/or a network attached storage (NAS) system. [0028] In some embodiments, processor 1005 is operatively coupled to storage device 1034 via a storage interface 1020. Storage interface 1020 is any component capable of providing processor 1005 with access to storage device 1034. Storage interface 1020 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any component providing processor 1005 with access to storage device 1034.

[0029] At least one technical effect of the systems and methods described herein includes (a) using a parametric model to control output power; (b) a model tuned to a specific combination of a charging point and an electric vehicle; (c) forward feeding control of the system; (d) a parametric model that includes a truncated linear relationship between the command and the output power; and (e) only three parameters of an offset, a slope, and a maximum output power needed to define the parametric model.

[0030] Example embodiments of systems and methods of controlling charging of electric vehicles are described above in detail. The systems and methods are not limited to the specific embodiments described herein but, rather, components of the systems and/or operations of the methods may be utilized independently and separately from other components and/or operations described herein. Further, the described components and/or operations may also be defined in, or used in combination with, other systems, methods, and/or devices, and are not limited to practice with only the systems described herein.

[0031 ] Although specific features of various embodiments of the invention may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the invention, any feature of a drawing may be referenced and/or claimed in combination with any feature of any other drawing.

[0032] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

Claims

WHAT IS CLAIMED IS:

1. A charging control computing device for controlling charging of electric vehicles for grid services, comprising at least one processor in communication with at least one memory device, and the at least one processor programmed to: receive a charging request from an electric vehicle at a charging point; generate a command based on a parametric model between the command and output power, wherein the command is a maximum amperage of current output to the electric vehicle at the charging point, and the output power is power output to the electric vehicle from the charging point; and output the command to the charging point.

2. The charging control computing device of claim 1, wherein the at least one processor is further programmed to: generate the command based on the parametric model that includes a truncated linear relationship between the command and the output power, wherein in the truncated linear relationship, the output power is a linear function of the command when the command is less than a threshold command corresponding to a maximum output power, and the output power is constant when the command is equal to or greater than the threshold command.

3. The charging control computing device of claim 2, wherein parameters of the parametric model include an offset, a slope, and/or the maximum output power.

4. The charging control computing device of claim 1, wherein the at least one processor is further programmed to: retrieve historical data corresponding to the electric vehicle at the charging point, wherein the historical data include tuples of historical commands and historical output power; and update parameters of the parametric model based on the historical data.

5. The charging control computing device of claim 4, wherein the at least one processor is further programmed to: update an offset, a slope, and/or a maximum output power in the parametric model based on the historical data.

6. The charging control computing device of claim 4, wherein the at least one processor is further programmed to: preprocess the historical data by deleting irrelevant historical data; and update the parameters based on the preprocessed historical data.

7. The charging control computing device of claim 6, wherein the at least one processor is further programmed to: preprocess the historical data by deleting a tuple that includes a historical command having an interval from a prior historical command shorter than a threshold value.

8. The charging control computing device of claim 6, wherein the at least one processor is further programmed to: preprocess the historical data by deleting a tuple that includes a historical command that was sent when the electric vehicle has already been fully charged.

9. The charging control computing device of claim 1, wherein the at least one processor is further programmed to: derive default parameters of the parametric model based on data of a plurality of vehicles at a plurality of charging points; and generate the command based on the parametric model having the default parameters.

10. A method of controlling charging of electric vehicles for grid services, the method comprising: receiving a charging request from an electric vehicle at a charging point; generating a command based on a parametric model between the command and output power, wherein the command is a maximum amperage of current output to the electric vehicle at the charging point, and output power is power output to the electric vehicle from the charging point; and outputting the command to the charging point.

11. The method of claim 10, wherein generating a command further comprises: generating the command based on the parametric model that includes a truncated linear relationship between the command and the output power, wherein in the truncated linear relationship, the output power is a linear function of the command when the command is less than a threshold command corresponding to a maximum output power, and the output power is constant when the command is equal to or greater than the threshold command.

12. The method of claim 1 1, wherein parameters of the parametric model include an offset, a slope, and/or the maximum output power.

13. The method of claim 10, wherein the method further comprises: retrieving historical data corresponding to the electric vehicle at the charging point, wherein the historical data include tuples of historical commands and historical output power; and updating parameters of the parametric model based on the historical data.

14. The method of claim 13, wherein updating parameters further comprises: updating an offset, a slope, and/or a maximum output power in the parametric model based on the historical data.

15. The method of claim 13, wherein updating parameters further comprises: preprocessing the historical data by deleting irrelevant historical data; and updating the parameters based on the preprocessed historical data.

16. The method of claim 15, wherein preprocessing the historical data further comprises: preprocessing the historical data by deleting a tuple that includes a historical command having an interval from a prior historical command shorter than a threshold value.

17. The method of claim 15, wherein preprocessing the historical data further comprises: preprocessing the historical data by deleting a tuple that includes a historical command that was sent when the electric vehicle has already been fully charged.

18. The method of claim 10, wherein the method further comprises: deriving default parameters of the parametric model based on data of a plurality of vehicles at a plurality of charging points; and generating the command based on the parametric model having the default parameters.