Electric vehicle scheduled charging system and method

Abstract

The present application relates to an electric vehicle reservation charging system and method. The system includes: a communication controller that receives charging reservation setting information, discharging reservation setting information, and power rate information; a vehicle controller that uses the charging reservation setting information, the discharging reservation setting information, and the power rate information to determine a preset charging curve based on the input of a preset minimum cost charging mode, and performs charging state control based on an optimal charging state curve and an optimal charging power curve preset according to the charging curve; and a charger that performs power control for charging or discharging to correspond to a target charging and discharging power instruction determined by the charging state control.

Description

Technical Field

[0001] The present application relates to an electric vehicle scheduled charging technology, and more specifically, to an electric vehicle scheduled charging system and method, which uses hourly electricity cost information, the driver's charging time, and the target charging amount as input, calculates the optimal charging power that can minimize the charging cost during the entire charging interval on an hourly basis before charging, and then charges the vehicle battery based on this. Background Art

[0002] Recently, environmental and energy issues have increased, and electric vehicles are being developed to solve these problems as future transportation tools. Electric vehicles include pure electric vehicles, plug-in hybrid vehicles, etc. These vehicles require rechargeable batteries for operation, and for this purpose, the batteries can be charged at home or at a charging station. When slow charging is performed at a home charging facility, it takes about 5 hours or more to charge to a state of charge (SOC) of 90% or more.

[0003] At the same time, when such vehicles are used for commuting, the pattern of driving in the morning and charging in the evening will have an adverse effect on the power grid or distributed power source within a certain hour interval and increase the overall maximum consumption of the power grid. This is the amount that matches the maximum power consumption, and when more power is used, the current power system with a reserve ratio of about 10% requires additional power generation facilities and wastes a lot of power, thus reducing its energy efficiency. In addition, the general electricity rate varies with the hour interval, so even if the same amount of electricity is charged, the total charging cost may vary depending on the charging method.

[0004] Therefore, methods for minimizing the charging cost have been proposed. The following methods have been considered: a method for saving charging costs by inputting charging conditions by a user (e.g., using time settings, charging power settings, and charging operation division settings), a method for selecting an hour interval with a lower electricity rate for charging control in consideration of the charging rate of each hour interval, a method for dividing situations according to the time in the charging hour interval where the estimated completion time is located and the difference between the rates of each hour interval, a method for determining charging between a distributed power source and a commercial power source and performing a shift of the charging vehicle to the hour interval where the unused power is the largest, and the like.

[0005] However, even in these methods, there are problems that the user must set individual charging conditions to save costs, or it is closely related to the use of the power source of the grid (solar energy, wind energy, thermal energy, etc.), or the charging cost of each hour interval must be considered, and the amount of calculation to find all situations is too large, or it is necessary to offset the charging time, speed up and slow down the charging of a specific power, etc. Therefore, when charging the battery, a charging strategy that minimizes the overall charging cost is required.

[0006] The contents described in this section are merely for understanding the background of the present application and may include contents that are not previously known to those skilled in the art to which the present application belongs. Summary of the invention

[0007] The present application provides an electric vehicle scheduled charging system and method, which can charge the battery at a minimum cost even if the user does not set additional charging conditions to save costs.

[0008] In addition, the present application provides an electric vehicle reservation charging system and method, which can use the electricity rate information of the hourly interval to perform the optimization of the charging cost of the vehicle, regardless of the use of the power source of the power grid. Another object of the present application is to provide an electric vehicle reservation charging system and method, which can reduce the calculation time by applying a dynamic programming algorithm, so that the entire area can be optimized even at the calculation level of the vehicle's engine control unit (ECU). Another object of the present application is to provide an electric vehicle reservation charging system and method, which can charge at an optimal charging power that is variable every hour, rather than offsetting the charging time, or speeding up or slowing down the charging of a specific power.

[0009] To achieve these objectives, the present application provides an electric vehicle reservation charging system, which uses hourly electricity cost information, the driver's charging time and the target charging amount as input, calculates the optimal charging power hourly before charging, and this optimal charging power can minimize the charging cost during the entire charging interval, and then charges the vehicle's battery based on this.

[0010] An electric vehicle reservation charging system may include: a communication controller configured to receive charging reservation setting information, discharging reservation setting information, and electricity rate information; a vehicle controller configured to determine a preset charging curve based on an input of a preset minimum cost charging mode using the charging reservation setting information, the discharging reservation setting information, and the electricity rate information, and perform charging state control according to an optimal charging state curve and an optimal charging power curve preset based on the charging curve; and a charger configured to perform power control for charging or discharging to correspond to a target charging and discharging power instruction determined by the charging state control.

[0011] In addition, the charging curve may be a first charging curve preset based on input of a preset minimum cost charging mode, or a second charging curve determined using a preset dynamic programming method when there is no first charging curve. The charging reservation setting information may include a target charging amount, a scheduled charging completion time, and a charging mode. The power control may adjust the voltage and current to correspond to a target power instruction determined by an optimal state of charge curve and an optimal charging power curve.

[0012] Further, the optimal charging power curve is a curve indicating the relationship between time and power, and the optimal state of charge curve is a curve indicating the relationship between time and state of charge. When the minimum cost charging mode is not input, the first charging curve performs user condition charging control set by the user or general charging control. The vehicle controller may be configured to determine the charging curve again when the power rate information changes while the power control is being performed.

[0013] The second charging curve can be generated using the following information: an electricity rate mapping table based on electricity rate information, including the rate of electricity per hourly interval; charging constraint information based on the electricity rate mapping table, the charging constraint information including the starting charging state, the target charging state, the charging start time, the charging completion time and the system maximum charging power; and power search grid information, the power search grid information is generated to meet the charging constraint information, and the grid is generated at any interval between the starting charging state and the target charging state, at any interval between the charging start time and the charging completion time, and at any interval between 0kw and the system maximum charging power.

[0014] In addition, the second charging curve can be generated using the following information: an electricity rate mapping table based on electricity rate information, including the rates of electricity per hourly interval; charging constraint information based on the electricity rate mapping table, the charging constraint information including the starting charging state, the target charging state, the charging start time, the charging completion time and the system maximum charging power; and power search grid information, the power search grid information is generated to meet the charging constraint information, and the grid is generated at any interval between the starting charging state and the target charging state, at any interval between the charging start time and the charging completion time, and at any interval between the system maximum discharge power and the system maximum charging power.

[0015] The system maximum charging power reflects the characteristics of at least one of the battery, the charging device, and the charger. In addition, an intermediate map can be generated by calculating the charging cost per hour and in the charging state from the last hour in a reverse method, and the intermediate map can be used to calculate the second charging curve in a forward method. The second charging curve can be obtained using the intermediate map stored for each grid in each charging state, the intermediate map being obtained from the last hour in a reverse method.

[0016] According to another exemplary embodiment of the present application, an electric vehicle reservation charging system may include: a communication controller configured to receive charging reservation setting information, discharging reservation setting information, and electricity rate information; a vehicle controller configured to determine a preset charging curve based on an input of a preset minimum cost charging mode using the charging reservation setting information, the discharging reservation setting information, and the electricity rate information, and generate a charging curve; and a charger configured to perform charging state control according to an optimal charging state curve and an optimal charging power curve preset based on the charging curve, and to perform power control for charging and discharging to correspond to a target charging and discharging power instruction determined by the charging state control.

[0017] According to another exemplary embodiment of the present application, a method for reserving charging for an electric vehicle may include: receiving charging reservation setting information, discharging reservation setting information and electricity rate information by a communication controller; determining a preset charging curve by a vehicle controller based on an input of a preset minimum cost charging mode using the charging reservation setting information, discharging reservation setting information and electricity rate information, and performing charging state control according to an optimal charging state curve and an optimal charging power curve preset based on the charging curve; and performing power control for charging or discharging by a charger to correspond to a target charging and discharging power instruction determined by the charging state control.

[0018] According to another exemplary embodiment of the present application, a method for reserving charging for an electric vehicle may include: receiving charging reservation setting information, discharging reservation setting information and electricity rate information by a communication controller; determining a preset charging curve by a vehicle controller based on an input of a preset minimum cost charging mode using the charging reservation setting information, discharging reservation setting information and electricity rate information, and generating a charging curve; performing charging state control by a charger according to an optimal charging state curve and an optimal charging power curve preset based on the charging curve, and performing power control for charging or discharging to correspond to a target charging and discharging power instruction determined by the charging state control.

[0019] According to the present application, the vehicle battery can be charged at a minimum cost even if the user does not set additional charging conditions to save related costs. In addition, the electricity rate information of each hour interval can be used to perform optimization of the vehicle's charging cost, regardless of the use of the power source of the power grid. The calculation time can also be reduced by applying a dynamic programming algorithm, so that the entire area can be optimized even at the calculation level of the vehicle's engine control unit (ECU). In addition, it is possible to charge at an optimal charging power that is variable every hour, rather than offsetting the charging time, or speeding up or slowing down the charging of a specific power. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other features of the present invention will now be described in detail with reference to certain exemplary embodiments of the invention shown by the attached drawings, which are given hereinafter by way of illustration only and are therefore non-limiting to the present invention, in which:

[0021] Figure 1 A block diagram of an electric vehicle reservation charging system according to an exemplary embodiment of the present application;

[0022] Figure 2 According to the exemplary embodiment of the present application Figure 1 A detailed block diagram of a vehicle controller shown in FIG.

[0023] Figure 3 A block diagram of an electric vehicle reservation charging system according to another exemplary embodiment of the present application;

[0024] Figure 4A and Figure 4B A flowchart showing a minimum cost reservation charging control process according to an exemplary embodiment of the present application;

[0025] Figure 5A To illustrate an exemplary embodiment according to the present application Figure 4A and Figure 4B The flowchart of the optimization process in the optimization execution box of optimizing the charging cost of the grid-vehicle is shown in FIG.

[0026] Figure 5B To illustrate an exemplary embodiment according to the present application Figure 4A and Figure 4B The flowchart of the optimization process in the optimization execution box is shown as the optimization including the charging cost of the vehicle-grid;

[0027] Figure 6 To illustrate the exemplary embodiment according to the present application based on Figure 5A and Figure 5B Flowchart of dynamic programming (DP) of the optimization process;

[0028] Figure 7 To illustrate an exemplary embodiment according to the present application Figure 6 Flow chart of the detailed process in S610 shown in FIG.

[0029] Figure 8 To illustrate the exemplary embodiment of the present application Figure 6 A flowchart of the detailed process in S620 shown in FIG.

[0030] FIG. 9A to FIG. 15 To illustrate an example of battery charging schedule optimization when the electricity rate for each hour interval is subdivided according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0031] It should be understood that the term "vehicle" or "vehicle-based" or other similar terms used herein generally include motor vehicles, such as passenger cars including sport utility vehicles (SUVs), buses, trucks, various commercial vehicles, ships including various boats, vessels, aircraft, etc., and include hybrid vehicles, electric vehicles, plug-in hybrid electric vehicles, hydrogen-powered vehicles, and other alternative fuel vehicles (e.g., fuels derived from non-fossil energy sources). As referred to herein, a hybrid vehicle is a vehicle having two or more power sources, such as both gasoline-powered and electric-powered vehicles.

[0032] Although the exemplary embodiments are described as using multiple units to perform the exemplary processes, it should be understood that the exemplary processes can also be performed by one or more modules. In addition, it should be understood that the term controller / control unit refers to a hardware device that includes a memory and a processor. The memory is configured to store the modules, and the processor is specifically configured to run the modules to perform one or more processes described further below.

[0033] In addition, the control logic of the present invention can be implemented as a non-transitory computer readable medium on a computer readable medium, which contains executable program instructions executed by a processor, a controller / control unit, etc. Examples of computer readable media include, but are not limited to, ROM, RAM, compact disc (CD)-ROM, magnetic tape, floppy disk, flash drive, smart card, and optical data storage device. The computer readable recording medium can also be distributed on a network-connected computer system so that the computer readable medium is stored and executed in a distributed manner, for example, through a telematics server or a controller area network (CAN).

[0034] The terms used herein are only used for the purpose of describing specific embodiments and are not intended to limit the present invention. As used herein, the singular forms "one", "an" and "said" are intended to also include plural forms, unless the context clearly states otherwise. It will also be understood that when the terms "comprising" and / or "including" are used in this specification, it is indicated that there are the features, numerical values, steps, operations, elements and / or components, but the presence or addition of one or more other features, numerical values, steps, operations, elements, components and / or their groups are not excluded. As used herein, the term "and / or" includes any and all combinations of one or more related enumerated items.

[0035] Unless otherwise stated or apparent from the context, the term "about" as used herein is understood to be within the normal tolerance range in the art, such as within 2 standard deviations of the mean. "About" may be understood to be within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of a specified value. Unless the context clearly indicates otherwise, all numerical values ​​provided herein are modified by the term "about".

[0036] Various modifications and various exemplary embodiments can be made in the present application, so that specific exemplary embodiments are shown in the drawings and described in detail in the specification. However, it should be understood that it is not intended to limit the present application to a specific disclosed form, but rather includes modified forms, equivalent forms and alternative forms that fall within the spirit and technical scope of the present application.

[0037] When describing each of the accompanying drawings, the same reference numerals are used for the same elements. The terms "first", "second", etc. may be used to describe various components, but the components should not be limited by the terms. These terms are only used to distinguish one component from another component. For example, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component without departing from the scope of the present application. It should be further understood that those terms defined in, for example, commonly used dictionaries should be additionally understood to have meanings consistent with their meanings in the context of the relevant art, and are not understood as idealized or overly formal meanings unless clearly defined in this application.

[0038] Hereinafter, an electric vehicle reservation charging system and method according to an exemplary embodiment of the present application will be described in detail with reference to the accompanying drawings. Figure 1 FIG. 1 is a block diagram of an electric vehicle reservation charging system 100 according to an exemplary embodiment of the present application. Figure 1 The electric vehicle reservation charging system 100 may include a vehicle controller 110 , a communication controller 120 , a battery controller 130 , a charger 140 , and the like.

[0039] The communication controller 120 may be configured to receive charging reservation setting information and / or power rate information to send the information to the vehicle controller 110. The charging reservation setting information may be generated by the user 10, and the charging reservation setting information may include a target charge amount, a scheduled charging completion time, a charging mode, etc. The power rate information may be sent from the management server 20. The target charge amount may be described using the state of charge (SOC), but is not limited thereto, and the state of health (SOH), depth of discharging (DOD), state of function (SOF), etc. may also be used.

[0040] The management server 20 may be connected to a power grid (not shown) and may be configured to provide rate information about the amount of electricity used. The management server 20 may also be configured to calculate the rate of the total amount of electricity used for charging through a power supply unit (not shown) and charge the electric vehicle owner at the rate. For this purpose, as a database, the management server 20 may include electric vehicle information, user information, rate information, etc.

[0041] The communication controller 120 may be integrally composed of an audio-video navigation (AVN), a telematics unit (TMU), etc., and may be configured as a stand-alone type, respectively. In addition, the AVN may be a communication terminal such as a smart phone, a notepad, etc. The vehicle controller 110 may be configured to determine a preset charging curve based on entry or input of a preset minimum cost charging mode using charging reservation setting information and power rate information, and may be configured to generate a charging power instruction corresponding to the charging curve.

[0042] The battery controller 130 may be configured to optimize the battery management of the vehicle, thereby improving energy efficiency and extending the life of the battery. The battery controller 130 may also be configured to monitor the voltage, current and temperature of the battery in real time, and to prevent overcharging and overdischarging in advance, thereby improving the safety and reliability of the battery. Specifically, the battery controller 130 may be configured to detect the battery status in the charger 140, and send SOC information to the vehicle controller 110 based on the battery status.

[0043] The charger 140 may be configured to receive the maximum power information and power from the connector 30 of the charging station (not shown) to supply charging power to the battery (not shown) based on the charging power instruction of the vehicle controller 110. In addition, the charger 140 may include the maximum power information transmitted from the connector to provide the vehicle controller 110 with the maximum power information of the entire system that can be charged.

[0044] Figure 2 for Figure 1A detailed block diagram of the vehicle controller 110 is shown in FIG. Figure 2 The vehicle controller 110 may include: an information acquisition module 210, a condition determination module 220, a charging cost optimization module 230, a control module 240, a power control module 250, etc.; the information acquisition module 210 is configured to obtain charging reservation setting information and electricity rate information; the condition determination module 220 is configured to determine a preset first charging curve based on the entry or input of a preset minimum cost charging mode; the charging cost optimization module 230 is configured to determine a second charging curve by optimizing the charging cost using a preset dynamic programming (DP) when there is no determined first charging curve; the control module 240 is configured to perform charging control based on the first charging curve or the second charging curve; the power control module 250 is configured to send a charging power instruction to the charger based on the charging control.

[0045] In this article, the minimum cost charging mode refers to charging control that minimizes the total charging power rate when charging the battery. In addition, dynamic programming is an optimization technique that is used to search for control variables that minimize the defined cost in a reverse method through the "Principle of Bellman" to find the minimum solution for all situations.

[0046] The charging cost optimization module 230 performs DP optimization to minimize the charging fee and then performs charging control while satisfying the scheduled charging completion time and the target charging amount. Figure 2 In the above example, the charging cost is optimized as follows.

[0047] Equation 1

[0048]

[0049] Where J represents the total charging cost, P chg Indicates charging power, C chg represents the charging cost, P chg_MAX Indicates the maximum charging power, and k represents 0 to N-1.

[0050] Equation 2

[0051] P bat (k) = P c h a (k)×η eff (k)

[0052] Among them, P bat represents the battery input power, η eff Indicates charging efficiency.

[0053] SOC is controlled as follows.

[0054] Equation 3

[0055]

[0056] Among them, P chg_cmd Indicates power command, P* chg (k) represents the optimal power curve, SOC* chg (k) represents the optimal SOC curve. act (k) represents the current actual SOC curve, and f represents a function for outputting the difference between the two values. Power control is to follow the power command P chg_cmd (k) Voltage / current control.

[0057] The above equation represents charging only from the grid to the vehicle (grid→vehicle). Accordingly, when charging from the vehicle to the grid (vehicle→grid) is included, the equation is as follows.

[0058] Equation 4

[0059]

[0060] Among them, P dischg_MAX Indicates the maximum discharge power. Therefore, when the grid → vehicle, P chg The range of is limited to the maximum charging power of the system. On the contrary, when the vehicle → grid, P chg The range is limited to the maximum charging power P of the system chg_MAX and the maximum discharge power P dischg_MAX , and the system efficiency equation is divided into two according to the charging situation and the discharging situation. This situation can be expressed by the following equation.

[0061] Equation 5

[0062] P bat (k) = P chg (k)× chgeff (k) If charging

[0063] P chg (k) = P bat (k)×η disc h geff (k), if the discharge

[0064] Among them, η chgeff Represents the charging efficiency, η dischgeff Indicates the discharge efficiency.

[0065] Figure 3 FIG. 1 is a block diagram of an electric vehicle reservation charging system according to another exemplary embodiment of the present application. Figure 3 In, with Figure 1Differently, the charging state control (eg, SOC control) and the power control are performed on the charger 140 side. Figure 1 In the embodiment, charging cost optimization and SOC control can be performed on the vehicle controller 110 side, and power control can be performed on the charger 140 side. Figure 3 In the embodiment, only charging cost optimization may be performed on the vehicle controller 110 side, and SOC control and power control may be performed on the charger 140 side. Figure 3 The charger 140 side may be configured to perform charging state control and power control using the optimal charging power map 310 and the optimal SOC map 320 .

[0066] The optimal charging power map 310 is a map showing the relationship between time and power, and the optimal state map 320 is a map showing the relationship between time and charging state (e.g., SOC). As described above, the charging state has been described by SOC in the exemplary embodiment of the present application, but the charging state may also be a state of health (SOH), a depth of discharge (DOD), a functional state (SOF), etc.

[0067] In other words, for example, Figure 1 and Figure 3 is an example of a cooperative control configuration according to an execution object of SOC control. Since the vehicle controller 110 performs SOC control, during actual charging after optimization calculation, Figure 1 The optimal charging power instruction is sent to the charger 140. On the other hand, since the charger 140 performs SOC control, after the optimization calculation, Figure 3 The optimal charging power map and the optimal SOC map for each hour are sent to the charger, and the charger performs SOC control and power control in an actual charging cycle to charge the battery.

[0068] Figure 4A and Figure 4B FIG. 1 is a flowchart showing a minimum cost reservation charging control process according to an exemplary embodiment of the present application. FIG. 4A to FIG. 4B The user of the electric vehicle inputs charging reservation setting information (eg, target charging amount, scheduled charging completion time, charging mode, etc.) to the vehicle through the communication controller 120 for charging. The input information may be transmitted to the vehicle controller 110 (S410).

[0069] The vehicle controller 110 may then be configured to communicate with the management server 20 via the communication controller 120, and input power rate information per unit time per hour from the communication controller 120 to the vehicle. The input information may be sent to the vehicle controller 110 (S420). The power rate (i.e., the electricity fee) is a function of time and load. The vehicle controller 110 may then be configured to confirm whether the charging mode is a minimum cost charging mode (or smart charging mode) (S430). The smart charging mode directly adjusts the charging time and amount of charge in the power source (e.g., charging station).

[0070] The vehicle controller 110 may be configured to confirm whether an optimal charging curve is determined (S440) when the charging mode is the minimum cost charging mode in S430. In other words, the charger 140 may be configured to confirm information (such as power, current, and voltage (maximum, rated)) that can be sent to the vehicle by an external power source (e.g., a charging station), and then the charger 140 may be configured to send the information to the vehicle controller 110. The vehicle controller 110 considers the information of the maximum power / voltage / current that the system can allow, and may be configured to perform an optimization calculation that can achieve a target charge amount (not shown) of the battery within the scheduled charging completion time while minimizing the total power rate, so as to calculate the optimal charging power and charging curve per hour.

[0071] In S440, when the charging curve is not calculated, the optimization execution block 401 may be executed. In the optimization execution block 401, the input information may be converted into a format for performing optimization calculation (e.g., electricity cost information processing, charging constraint condition determination, and search grid generation) (S451), and a charging curve as a result of the optimization calculation may be generated by performing a charging cost optimization calculation based on dynamic programming (S452). The charging curve may include a power curve and a charging state curve (e.g., an SOC curve), which becomes an optimal curve per hour when entering the minimum cost charging mode.

[0072] As a result of executing the optimization execution block 401, the obtained hourly optimal power curve and charging state curve information can be used to perform optimal charging control (S460). At the same time, in S440, when the charging curve is calculated, it can be determined whether the charging curve is consistent with the one obtained from Figure 1 The power rate information (ie, the electricity rate information) received by the management server 20 in the control unit 10 is the same as that in the control unit 10 (S450). As a result of the confirmation, when the power rate information is not the same as the charging curve, the process may proceed to S451.

[0073] On the contrary, in S450, when the charging curve is the same as the power rate information, the charging state control can perform S460. The charging power command P can be calculated by charging power command positive feedforward (FF) term calculation or charging power command positive feedforward (FF) + positive feedback (FB) term calculation. chg_FFcmd and P chg_cmd .

[0074] Herein, an example of f() is proportional integral derivative (PID) control. In addition, the optimal charging power and the optimal SOC are the results of optimization calculations, and an estimate of the actual SOC can be used. In other words, the optimal charging power information per hour can be used as a feedforward control item that follows the optimal SOC. The difference between the target SOC information and the actual SOC can then be used as a feedback control item. Charging power instruction = optimal charging power + f(optimal SOC - actual SOC).

[0075] When the battery is charged using the hourly charging power command thus obtained, the optimal SOC obtained by the optimization calculation process can be followed. In the present application, this is referred to as charging state control (e.g., SOC control). Then, specific control of the voltage / current of the power electronic device can be performed so that Figure 1 The charger 140 in follows the charging power instruction S470. At the same time, in S430, when it is not the minimum cost charging mode, the vehicle controller 110 can be configured to confirm whether the charging mode is the user condition charging mode (S431). According to the result of the confirmation, when the charging mode is the user condition charging mode in S431, the user condition charging control can be performed, and when it is not the user condition charging mode, the general charging control (S433 and S435) can be performed. Then, the vehicle controller 110 can be configured to confirm that the charging is completed (S480). In other words, when the power rate information is partially changed during the battery charging control, S420 to S480 can be executed again.

[0076] Figure 5A To show FIG. 4A to FIG. 4B Flow chart of the optimization process for optimizing the charging cost of the grid-vehicle only in the optimization execution block 401 shown in FIG. Figure 5A The charging cost optimization process for grid → vehicle only is shown. Figure 5A , the vehicle controller 110 may be configured to process the power rate information (S510-1). In other words, the vehicle controller 110 may be configured to generate an hourly power rate mapping table 501-1. The power rate mapping table 501-1 may include the power cost per hour. The power rate of 1 kwh during 6 pm to 7 pm is 140, and the power rate during 8 pm to 9 pm is 110. Then, when 30 kwh is used during 6 pm to 7 pm, the rate is 700.

[0077] The vehicle controller 110 may then be configured to determine charging constraints based on the power rate mapping table 501-1 (S520-1). In other words, table 502-1 may be generated with charging constraints. Table 502-1 may include starting / target charging SOC, charging start / completion time, system maximum charging power, and the like. The starting / target charging SOC, charging start / completion time, and maximum charging power information of the system (e.g., battery, charger, and charging device) may be received and stored in the vehicle controller 110. The maximum charging power of the system reflects the characteristics of the battery and the charger. The vehicle controller 110 may then be configured to generate search grid information that satisfies the charging constraints (S530-1). The search grid information 503-1 may include initial SOC-target SOC, arbitrary interval, start time-end time, arbitrary interval, maximum charging power-0kw, arbitrary interval, and the like.

[0078] The search grid information 503-1 shows a SOC grid formed at arbitrary intervals from the initial SOC to the target SOC, and for each SOC case, a charging power grid is formed by dividing at arbitrary intervals from charging power 0 to the maximum charging power. When the number of SOC intervals is M, the number of time intervals is N+1, and the number of charging power intervals is L, the total number of grids formed is ultimately M×(N+1)×L. The vehicle controller 110 can then be configured to perform charging cost optimization based on dynamic programming (DP) (S540-1). Here, J represents the charging cost, and min represents the minimization function.

[0079] Figure 5B To show Figure 4A and Figure 4B The flowchart of the optimization process including vehicle-grid charging cost optimization in the optimization execution block 401 shown in FIG. Figure 5B is a flowchart for performing charging cost optimization including vehicle→grid and grid→vehicle. Figure 5B , the vehicle controller 110 may be configured to process the power rate information (S510-2). In other words, the vehicle controller 110 may be configured to generate an hourly power rate mapping table 501-2. The power rate mapping table 501-2 may include rates corresponding to the amount of electricity per hour for charging and discharging. When charging, power may be supplied from the grid to the vehicle, and when discharging, power may be supplied from the vehicle to the grid. Therefore, when the power is less than 0 kwh, power may be transferred to the grid to obtain revenue.

[0080] Further, the vehicle controller 110 may be configured to determine the charging / discharging constraint condition based on the power rate mapping table 501-2 (S520-2). In other words, the table 502-2 may be generated with the charging / discharging constraint condition. The table 502-2 may include the starting / target SOC, the charging start / completion time, the maximum charging power, the maximum discharging power, etc. The maximum charging power of the system reflects the characteristics of the battery and the charger.

[0081] In addition, the vehicle controller 110 may be configured to generate search grid information satisfying the charging / discharging constraint condition (S530-2). The search grid information 503-2 may include initial SOC-target SOC, arbitrary interval, start time-end time, arbitrary interval, maximum charging power-maximum discharging power, arbitrary interval, etc. Then, the vehicle controller 110 may be configured to perform charging cost optimization based on dynamic programming (DP) (S540-2).

[0082] Figure 6 To show Figure 5A and Figure 5B The flowchart of the optimization process based on dynamic programming (DP) is shown in S540-1 and S540-2. Figure 6 , a reverse calculation (S610) can be performed first. In this reverse calculation concept 601, a cost-travel map and a maximum charging power map can be obtained, wherein the cost-travel map represents the total consumption cost when traveling from a specific time k and a specific SOC to a target SOC, and the maximum charging power map represents the charging power at a specific time k and a specific SOC. Then, a forward calculation (S620) can be performed. In this forward calculation concept 602, when the initial SOC (k=0) is input into the optimal charging power map, the optimal charging power curve and the optimal SOC curve over the entire time (k=0-N) can be obtained.

[0083] Figure 7 To show Figure 6 The detailed process of S610 is shown in FIG. Figure 7 In step N-1, the minimum cost of the charging cost consumed from each SOC grid to the final target SOC (value or range) (step N) and the charging power at this time are stored in each SOC grid (e.g., SOC 90%, 88%, 86%, etc.) (S710). In the step N-1→step N part, the minimum cost charging power calculation concept 701 is shown.

[0084] In step k (k≤0<N-1): In step N-2 (i.e., step k (k=N-2)), the minimum cost of the cost of adding the charging cost consumed from each SOC grid to step N-1 to the charging cost in the SOC reached at this time (cost-traveling cost), and the charging power at this time can be stored in each SOC grid (S720). In step k (0 to N-2 part), the minimum cost charging power calculation concept 702 is shown. The minimum cost charging power calculation concept 702 is an example of SOC 50%.

[0085] The method for studying all situations to search for the minimum cost is the most basic global optimization method, but when the charging time is set to be long or the electricity rate is subdivided, the calculation amount is too large, requiring server-level computing power. Therefore, in order to overcome this deficiency, a dynamic programming method that calculates the Bellman equation from the last hour in a reverse method is applied to the scheduled charging control.

[0086] When performing the reverse calculation, the charging cost per hour and per SOC can be calculated and plotted. In addition, the optimal charge / SOC curve can be calculated by performing a forward calculation using the map obtained by performing the reverse calculation.

[0087] exist Figure 7 In the example, arg min() is used to obtain the function (P chg (k) etc.) is a function of the smallest k value. Figure 8 To show Figure 6 The detailed process of S620 is shown in FIG. Figure 8 , the minimum cost charging power curve for the step 0 → step N portion can be calculated (S810). The forward calculation execution concept 801 is shown. In step k obtained by performing the reverse calculation, the optimal charging power map stored for each SOC grid can be used to obtain the optimal charging power and SOC curve. Since the SOC at step k = 0 is the charging start SOC, for example, when the charging start SOC is 10%, steps k = 1, 2, 3 can be performed in sequence and the SOC can be increased. In this article, η sys Represents system efficiency, R bat represents the battery impedance, V oc represents the turn-on voltage of the battery, and Qb represents the charge capacity of the battery.

[0088] Fig.9A This is to illustrate an example of battery charging schedule optimization when the power rate is subdivided into hourly intervals according to an embodiment of the present application. The power rate (ie, electricity fee) of the hourly interval is as follows.

[0089] Table 1

[0090]

[0091] The charging start SOC, target SOC, charging start time, charging completion time, input of maximum power, etc. are as follows.

[0092] Table 2

[0093]

[0094] Based on the above results, the electricity rate per hour can be used for billing, and the battery efficiency can be increased (for V ocx I out / V outx I out ) is charged during about 9-10 hours, and 95% charging is completed before 11 hours (which is the termination time). Accordingly, an electricity rate graph 910-1 for each hour interval, a charging power graph 920-1 for each hour, and a battery SOC graph 930-1 for each hour are shown.

[0095] Fig. 9B This is to illustrate an example of battery charging schedule optimization when the electricity rate is subdivided into hourly intervals according to another exemplary embodiment of the present application. The electricity rate (ie, electricity fee) for each hour interval is as follows. Table 3

[0096]

[0097] Inputs of the charging start SOC, target SOC, charging start time, charging completion time, maximum charging power, maximum discharging power, etc. are as follows.

[0098] Table 4

[0099]

[0100] According to the above results, the battery energy discharge (e.g., SOC: 45% → 0%) can be performed in the hour interval with higher initial power cost (e.g., between 0 hours and 4 hours) to supply energy to the grid. That is, electricity is sold. At the same time, the battery can be charged at maximum power in the hour interval with lower cost (e.g., between 5 hours and 8 hours) (e.g., SOC: 0% → 95%). Therefore, the electricity cost graph 910-2 for each hour interval, the charging power graph 920-2 for each hour, and the battery SOC graph 930-2 for each hour are shown. Fig.10 To show the basis Fig.9A Graph of battery efficiency.

[0101] Fig.11To illustrate an example of battery charging schedule optimization when the electricity rate of each hour interval is subdivided according to an exemplary embodiment of the present application. The electricity rate (i.e., electricity fee) of each hour interval is as shown in the above table, and the input of charging start SOC, target SOC and charging completion time is as follows.

[0102] Table 5

[0103]

[0104] According to the above results, the early charging schedule is changed from about 4-10 hours to about 2-7 hours to meet the charging of SOC 95%. Accordingly, the electricity cost diagram 1110 of each hour interval, the charging power diagram 1120 of each hour, and the battery SOC diagram 1130 of each hour are shown.

[0105] Fig.12 To illustrate an example of battery charging schedule optimization when the electricity rate is subdivided into hourly intervals according to an exemplary embodiment of the present application. That is, this is an example explained by the minute. The electricity rate (i.e., electricity fee) for the hourly interval is shown in Table 1, and the input of the charging start SOC, target SOC, and charging completion time is shown in Table 2. According to the above results, the battery is charged with continuous power according to the change in electricity rate between the 4 to 10 hour intervals. Accordingly, an electricity rate graph 1210 for the hourly interval, a charging power graph 1220 for each hour, and a battery SOC graph 1230 for each hour are shown.

[0106] Fig.13 To illustrate an example of battery charging schedule optimization when the electricity rate of each hour interval is subdivided according to an exemplary embodiment of the present application. The electricity rate (i.e., electricity fee) of each hour interval is shown in Table 1, and the charging start SOC, target SOC and charging completion time are as follows.

[0107] Table 6

[0108]

[0109] According to the above results, the charging schedule is changed from about 4-10 hours to about 5-8 hours (which is the lowest electricity rate interval). The maximum power is 40kW, but charging is performed within the range of 40kW or less to improve battery efficiency. Accordingly, an electricity rate graph 1310 per hour interval, a charging power graph 1320 per hour, and a battery SOC graph 1330 per hour are shown. Fig.14 To show the basis Fig.13 Graph of battery efficiency.

[0110] Fig.15To illustrate an example of battery charging schedule optimization when the electricity rate is subdivided into hourly intervals according to an exemplary embodiment of the present application. That is, Fig.15 The case where cumulative pricing of the power rate per hour section is applied is shown. The power rate per hour section (ie, electricity fee) is as follows.

[0111] Table 7

[0112] Hour 1 2 3 4 5 6 7 8 9 10 11 10kwh 140 120 110 100 50 50 50 100 105 120 130 20kwh 420 360 330 300 150 150 150 300 315 360 390 30kwh 700 600 550 500 250 250 250 500 525 600 650

[0113] The input of charging start SOC, target SOC and charging completion time is shown in the table above. According to the above results, the charging schedule can be changed by averaging the power by changing the charging hour interval from about 5-8 hours to about 0-11 hours to avoid electricity accumulation pricing. Accordingly, the electricity rate graph 1510 of each hour interval, the charging power graph 1520 of each hour, and the battery SOC graph 1530 of each hour are shown.

[0114] In addition, the steps of the method or algorithm described in the exemplary embodiments disclosed herein are implemented as a program command format that can be executed by various computer devices to be recorded in a non-transitory computer-readable medium. The non-transitory computer-readable medium may include a program (instruction) code, a data file, a data structure, etc. alone or in combination thereof.

[0115] The program (instruction) code recorded in the medium may be a program (instruction) code specially designed or configured for the present application, or may be a program (instruction) code known and usable by technicians in the field of computer software. Examples of non-transitory computer-readable media may include magnetic media (such as hard disks, floppy disks, and magnetic tapes), optical media (such as CD-ROMs, DVDs, and Blu-ray discs, and semiconductor storage devices (such as ROMs, RAMs, and flash memories) specially configured to store and execute program (instruction) codes.

[0116] Herein, examples of program (instruction) codes include high-level language codes that can be executed by a computer using an interpreter, etc., and machine language codes generated by a compiler, for example. A hardware device can be configured to operate as one or more software modules to perform the operations of the present application, and vice versa.

Claims

1. An electric vehicle reservation charging system, comprising: a communication controller configured to receive charging reservation setting information and discharging reservation setting information and electric power rate information; a vehicle controller configured to determine a preset charging curve based on an input of whether there is a preset minimum cost charging mode using the charging reservation setting information and the discharging reservation setting information and the electric power rate information, and to perform charging state control according to an optimal charging state curve and an optimal charging power curve preset based on the charging curve; and a charger configured to perform power control for charging or discharging to correspond to a target charging and discharging power instruction determined by the charging state control; Wherein, the charging curve is a first charging curve preset based on an input of a preset minimum cost charging mode, or a second charging curve determined using a preset dynamic programming method when there is no first charging curve; An intermediate map is generated by calculating the charging cost per hour and state of charge from the last hour in a backward method, and the second charging curve is calculated by a forward method using the intermediate map.

2. The electric vehicle reservation charging system according to claim 1, wherein: The charging reservation setting information includes a target charging amount, a scheduled charging completion time, and a charging mode.

3. The electric vehicle reservation charging system according to claim 1, wherein: The power control controls the voltage and current to correspond to the optimal state of charge curve.

4. The electric vehicle reservation charging system according to claim 1, wherein: The optimal charging power curve is a curve showing the relationship between time and power, and the optimal charging state curve is a curve showing the relationship between time and charging state.

5. The electric vehicle reservation charging system according to claim 1, wherein: When the minimum cost charging mode is not input, the first charging profile performs user condition charging control set by the user or general charging control.

6. The electric vehicle reservation charging system according to claim 1, wherein: The vehicle controller is configured to re-determine the charging curve when the power rate information changes while performing the power control.

7. The electric vehicle reservation charging system according to claim 1, wherein: The second charging curve is generated using the following information: an electric power rate mapping table including rates of electric power per hourly interval based on electric power rate information; charging constraint information based on the electric power rate mapping table, the charging constraint information including a starting charging state, a target charging state, a charging start time, a charging completion time, and a system maximum charging power; power search grid information, the power search grid information is generated to meet the charging constraint information, and a grid is formed at any interval between the starting charging state and the target charging state, at any interval between the charging start time and the charging completion time, and at any interval between 0 kW and the system maximum charging power.

8. The electric vehicle reservation charging system according to claim 1, wherein: The second charging curve is generated using the following information: an electric power rate mapping table including rates of electric power per hourly interval based on electric power rate information; charging constraint information based on the electric power rate mapping table, the charging constraint information including a starting charging state, a target charging state, a charging start time, a charging completion time, and a system maximum charging power; and power search grid information, the power search grid information is generated to satisfy the charging constraint information, and a grid is formed at any interval between the starting charging state and the target charging state, at any interval between the charging start time and the charging completion time, and at any interval between the system maximum discharge power and the system maximum charging power.

9. The electric vehicle reservation charging system according to claim 8, wherein: The system maximum charging power reflects characteristics of at least one of a battery, a charging device, and a charger.

10. An electric vehicle reservation charging system, comprising: a communication controller configured to receive charging reservation setting information and discharging reservation setting information and electric power rate information; a vehicle controller configured to determine a preset charging curve based on an input of whether there is a preset minimum cost charging mode using the charging reservation setting information and the discharging reservation setting information and the electric power rate information, and to perform charging state control according to an optimal charging state curve and an optimal charging power curve preset based on the charging curve; and a charger configured to perform power control for charging or discharging to correspond to a target charging and discharging power instruction determined by the charging state control; Wherein, the charging curve is a first charging curve preset based on an input of a preset minimum cost charging mode, or a second charging curve determined using a preset dynamic programming method when there is no first charging curve; The second charging curve is obtained using the intermediate maps stored for each grid in each state of charge obtained in a reverse method from the last hour.

11. An electric vehicle reservation charging system, comprising: a communication controller configured to receive charging reservation setting information and discharging reservation setting information and electric power rate information; a vehicle controller configured to determine a preset charging curve based on an input of whether there is a preset minimum cost charging mode using the charging reservation setting information and the discharging reservation setting information and the electric power rate information, and generate the charging curve; as well as a charger configured to perform charging state control according to an optimal charging state curve and an optimal charging power curve preset based on the charging curve, and to perform power control for charging and discharging to correspond to a target charging and discharging power instruction determined by the charging state control; Wherein, the charging curve is a first charging curve preset based on an input of a preset minimum cost charging mode, or a second charging curve determined using a preset dynamic programming method when there is no first charging curve; An intermediate map is generated by calculating the charging cost per hour and state of charge from the last hour in a backward method, and the second charging curve is calculated by a forward method using the intermediate map.

12. A method for charging an electric vehicle, comprising: receiving, by the communication controller, charging reservation setting information, discharging reservation setting information and power rate information; determining, by a vehicle controller, a preset charging curve based on input of whether there is a preset minimum cost charging mode using the charging reservation setting information and the discharging reservation setting information and the power rate information, and performing charging state control according to an optimal charging state curve and an optimal charging power curve preset based on the charging curve; executing, by the charger, power control for charging or discharging to correspond to the target charging and discharging power command determined by the charging state control; Wherein, the charging curve is a first charging curve preset based on an input of a preset minimum cost charging mode, or a second charging curve determined using a preset dynamic programming method when there is no first charging curve; An intermediate map is generated by calculating the charging cost per hour and state of charge from the last hour in a backward method, and the second charging curve is calculated by a forward method using the intermediate map.

13. A method for charging an electric vehicle, comprising: receiving, by the communication controller, charging reservation setting information, discharging reservation setting information and power rate information; determining, by a vehicle controller, a preset charging curve based on input of whether there is a preset minimum cost charging mode using the charging reservation setting information and the discharging reservation setting information and the power rate information, and generating the charging curve; performing, by the charger, a charging state control according to an optimal charging state curve and an optimal charging power curve preset based on the charging curve, and performing power control for charging or discharging to correspond to a target charging and discharging power instruction determined by the charging state control; Wherein, the charging curve is a first charging curve preset based on an input of a preset minimum cost charging mode, or a second charging curve determined using a preset dynamic programming method when there is no first charging curve; An intermediate map is generated by calculating the charging cost per hour and state of charge from the last hour in a backward method, and the second charging curve is calculated by a forward method using the intermediate map.

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