Optimization method and device of charge-discharge strategy, electronic equipment and readable storage medium

Abstract

This application discloses a method, apparatus, electronic device, and readable storage medium for optimizing charging and discharging strategies, relating to the field of V2G technology. The method includes: acquiring historical operating data of a battery under test (BUT), and predicting the BUT's loss data based on this historical operating data; determining that an energy storage device carrying the BUT can participate in V2G applications when the loss data meets preset conditions; and determining a corresponding optimization mode for each application based on the needs of various V2G applications and the charging and discharging constraints of the BUT, thereby optimizing the charging and discharging strategy of the energy storage device when participating in V2G applications. Compared to methods that are limited to optimizing charging and discharging strategies based solely on the needs of the energy storage device, this method can construct diverse optimization modes, thus enabling flexible optimization of the energy storage device's charging and discharging strategy based on these diverse modes. This method offers high flexibility and strong applicability, ensuring that the optimized charging and discharging strategy meets the differentiated needs of different applications.

Description

Technical Field

[0001] This application relates to the field of vehicle-to-everything (V2X) interaction technology, and in particular to a method, apparatus, electronic device, and readable storage medium for optimizing a charging and discharging strategy. Background Technology

[0002] The rapid increase in the number of electric vehicles has put significant pressure on the power grid. Vehicle-to-grid (V2G) technology, by controlling the charging and discharging of electric vehicles, enables bidirectional energy transfer between electric vehicles and the power grid, thereby achieving peak shaving and valley filling of the power grid load and alleviating the pressure on the power grid.

[0003] Currently, electric vehicle (EV) charging and discharging strategies are typically optimized based on the vehicle's needs (such as battery range). However, in scenarios using V2G (Vehicle-to-Grid) technology for regulation, there is a multi-tiered structure involving the power grid, energy aggregators, and EVs, with different needs at each level. Therefore, current optimization methods that only consider the needs of EVs to optimize their charging and discharging strategies offer limited flexibility. Summary of the Invention

[0004] In view of this, this application provides a method, apparatus, electronic device, and readable storage medium for optimizing a charging and discharging strategy, which can improve the flexibility of the optimization method for the charging and discharging strategy.

[0005] In a first aspect, a method for determining the charging and discharging strategy of an energy storage device is provided, the method comprising:

[0006] Acquire historical operating data of the battery under test, and predict the loss data of the battery under test based on the historical operating data. The loss data includes: battery loss value and / or loss rate.

[0007] If the loss data meets the preset conditions, it is determined that the energy storage device carrying the battery under test can participate in V2G applications.

[0008] Based on the needs of each application object participating in the V2G application and the charging and discharging constraints of the battery under test, a corresponding optimization mode is determined for each application object to optimize the charging and discharging strategy of the energy storage device when participating in the V2G application.

[0009] This method can predict the loss data of the battery under test based on its historical operating data, and determine whether the energy storage device carrying the battery under test can participate in V2G applications when the loss data meets preset conditions. Furthermore, based on the needs of each application participating in V2G applications and the charging and discharging constraints of the battery under test, this method can individually determine corresponding optimization modes for each application, thereby optimizing the charging and discharging strategy of the energy storage device when participating in V2G applications. Compared to current methods that only optimize charging and discharging strategies based on the needs of the energy storage device, the method provided in this application can construct diverse optimization modes, thus flexibly optimizing the charging and discharging strategy of the energy storage device based on these diverse modes, offering high flexibility. Moreover, this method ensures that the optimized charging and discharging strategy can meet the differentiated needs of different applications, demonstrating strong applicability. In addition, since this method only allows the energy storage device to participate in V2G applications when the loss data of the battery under test meets preset conditions, it can protect the battery's health and effectively extend its lifespan.

[0010] Optionally, when the application object is a power grid, the optimization mode includes a load balancing mode;

[0011] In this mode, a power demand command is obtained from the power grid. In response to the power demand command, the charging and discharging strategy of the energy storage device is optimized based on the balance between the demand power in the power demand command and the supply power provided by the energy storage device to the power grid, so that the energy storage device can participate in V2G applications based on the optimized charging and discharging strategy.

[0012] Optionally, when the application target is an energy aggregator, the optimization mode includes a revenue-first mode;

[0013] In this mode, a power demand command is obtained from the power grid. In response to the power demand command, the charging and discharging strategy of the energy storage device is optimized based on the maximum power supplied by the energy storage device to the power grid, so that the energy storage device can participate in V2G applications based on the optimized charging and discharging strategy.

[0014] Optionally, when the application is an energy storage device, the optimization mode includes a globally optimal mode;

[0015] In this mode, a power demand command is obtained from the power grid. In response to the power demand command, the charging and discharging strategy of the energy storage device is optimized based on maximizing the feasibility of the energy storage device participating in V2G applications and maximizing the power supplied by the energy storage device to the power grid, so that the energy storage device can participate in V2G applications based on the optimized charging and discharging strategy.

[0016] Optionally, when determining the optimization mode based on the needs of each application participating in the V2G application and the charging and discharging constraints of the battery under test, the loss limitation requirements of the battery under test are also taken into account. This can increase the willingness of energy storage device users to participate in V2G applications.

[0017] Optionally, when the application target is the power grid, based on the needs of the power grid participating in the V2G application and the charging and discharging constraints of the battery under test, a corresponding optimization mode is determined for the power grid, including:

[0018] The objective function of the optimization mode is constructed by balancing the power demand of the power grid with the power supply provided by the energy storage device to the power grid, and minimizing the loss value of the battery under test. The constraint conditions of the optimization mode are constructed by using the charging and discharging constraints of the battery under test, thereby obtaining the optimization mode corresponding to the power grid.

[0019] Optionally, when the application target is an energy aggregator, based on the needs of the energy aggregator participating in the V2G application and the charge / discharge constraints of the battery under test, a corresponding optimization mode is determined for the energy aggregator, including:

[0020] The objective function of the optimization mode is constructed with the maximum power supplied to the power grid by the energy storage device as the optimization objective. The constraints of the optimization mode are constructed with the charging and discharging constraints of the battery under test and the loss limit requirement of the battery under test as the loss value not exceeding a first preset threshold, so as to obtain the optimization mode corresponding to the energy aggregator.

[0021] Optionally, when the application is an energy storage device, based on the requirements of the energy storage device participating in the V2G application and the charging and discharging constraints of the battery under test, a corresponding optimization mode is determined for the energy storage device, including:

[0022] The objective function of the optimization mode is constructed with the goal of maximizing the power supplied to the grid by the energy storage device and maximizing the feasibility of the energy storage device participating in V2G applications. The constraints of the optimization mode are constructed with the charging and discharging constraints of the battery under test and the loss limit requirement of the battery under test as the loss value not exceeding a second preset threshold, so as to obtain the optimization mode corresponding to the energy storage device.

[0023] Optionally, when the application object is the energy storage device, before determining the corresponding optimization mode for each application object based on the needs of the V2G application and the charge / discharge constraints of the battery under test, the method further includes:

[0024] Obtain the ownership attributes of the battery under test;

[0025] The requirements for the energy storage device are determined based on the ownership of the battery under test.

[0026] Optionally, determining the energy storage device requirements based on the ownership attributes of the battery under test includes:

[0027] If the ownership of the battery under test is determined to be private, the requirements for the energy storage device are determined as follows: the energy storage device provides the maximum power to the grid, the energy storage device has the maximum feasibility of participating in V2G applications, and the battery under test has the minimum loss value.

[0028] Given that the ownership of the battery under test is public, the requirements for the energy storage device are determined to be: the energy storage device provides the maximum power to the power grid, and the energy storage device has the maximum feasibility of participating in V2G.

[0029] Optionally, when the loss data includes battery loss value and loss rate, the prediction of the loss data of the battery under test based on the historical operating data includes:

[0030] The operating characteristics of the battery under test are predicted based on the historical operating data, and the operating characteristics are used to characterize the operating status of the battery under test.

[0031] The operational features are input into a pre-trained lifetime model to predict the battery loss value in the loss data of the battery under test.

[0032] Based on the battery loss value, the loss rate in the loss data is determined.

[0033] Optionally, after determining the corresponding optimization mode for each application based on the needs of each application participating in the V2G application and the charging and discharging constraints of the battery under test, the method further includes:

[0034] Based on the optimization mode corresponding to at least one of the application objects, the charging and discharging strategy of the energy storage device when participating in V2G applications is optimized.

[0035] Optionally, at least one of the application objects may be multiple. Based on the optimization modes corresponding to the multiple application objects, the charging and discharging strategy of the energy storage device when participating in V2G applications is optimized, including:

[0036] Based on the optimization modes corresponding to the multiple application objects, the charging and discharging strategies of the energy storage devices participating in V2G applications are optimized respectively, resulting in multiple optimized charging and discharging strategies.

[0037] The method further includes:

[0038] Multiple optimized charging and discharging strategies are pushed to the control terminal of the energy storage device, so that the control terminal can select the target charging and discharging strategy adopted by the energy storage device when participating in V2G applications from the multiple optimized charging and discharging strategies.

[0039] Optionally, the method further includes:

[0040] Obtain selection reference information for each optimized charge-discharge strategy, the selection reference information including: a reference value for the benefits of participating in V2G applications using the optimized charge-discharge strategy and / or a reference value for battery loss.

[0041] The optimized charging and discharging strategy selection reference information is pushed to the control terminal of the energy storage device.

[0042] By acquiring and pushing the selection reference information of each optimized charging and discharging strategy to the control terminal, the control terminal can be assisted in selecting the target charging and discharging strategy from multiple optimized charging and discharging strategies. Therefore, the determination efficiency of the target charging and discharging strategy can be improved to a certain extent, thereby improving the control efficiency of the energy storage device.

[0043] Optionally, at least one of the application objects is defined as one. Based on the optimization mode corresponding to one of the application objects, the charging and discharging strategy of the energy storage device when participating in V2G applications is optimized, including:

[0044] Obtain the optimization mode corresponding to the target object from the optimization modes corresponding to the multiple application objects;

[0045] Based on the optimization mode corresponding to the target object, the charging and discharging strategy of the energy storage device when participating in V2G applications is optimized.

[0046] By obtaining the optimization mode corresponding to the target object from the optimization modes corresponding to multiple application objects, and optimizing the charging and discharging strategy based on the optimization mode corresponding to the target object, the computational load of electronic devices can be reduced and the optimization efficiency of the charging and discharging strategy can be improved.

[0047] Secondly, an optimization device for a charging and discharging strategy is provided, the device comprising:

[0048] The prediction module is used to acquire historical operating data of the battery under test and predict the loss data of the battery under test based on the historical operating data. The loss data includes: battery loss value and / or loss rate.

[0049] The determination module is used to determine, under the condition that the loss data meets the preset conditions, whether the energy storage device carrying the battery under test can participate in V2G applications;

[0050] The optimization module is used to determine a corresponding optimization mode for each application object based on the needs of each application object participating in the V2G application and the charging and discharging constraints of the battery under test, so as to optimize the charging and discharging strategy of the energy storage device when participating in the V2G application.

[0051] Thirdly, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method described in the first aspect above.

[0052] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0053] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the implementation environment involved in the method for optimizing a charging and discharging strategy provided in an embodiment of this application;

[0055] Figure 2 This is a schematic diagram of the implementation environment involved in another method for optimizing a charging and discharging strategy provided in this application embodiment;

[0056] Figure 3 This is a flowchart of an optimization method for a charging and discharging strategy provided in an embodiment of this application;

[0057] Figure 4 This is a flowchart of another method for optimizing a charging and discharging strategy provided in an embodiment of this application;

[0058] Figure 5 This is a schematic diagram of the structure of an optimization device for a charging and discharging strategy provided in an embodiment of this application;

[0059] Figure 6 This is a schematic diagram of the structure of an optimization device for another charging and discharging strategy provided in an embodiment of this application;

[0060] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0061] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0063] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0064] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0065] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0066] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0067] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0068] Unless otherwise specified, all embodiments and optional embodiments of this application can be combined to form new technical solutions. Unless otherwise specified, all technical features and optional technical features of this application can be combined to form new technical solutions. Unless otherwise specified, all steps of this application can be performed sequentially or randomly, preferably sequentially. For example, if the method includes steps (a) and (b), it means that the method may include steps (a) and (b) performed sequentially, or it may include steps (b) and (a) performed sequentially. For example, if the method may also include step (c), it means that step (c) can be added to the method in any order. For example, the method may include steps (a), (b), and (c), or it may include steps (a), (c), and (b), or it may include steps (c), (a), and (b), etc.

[0069] In recent years, with the rapid development of electric vehicle technology, the number of electric vehicles on the road has also increased rapidly. This rapid increase in the number of electric vehicles has put considerable pressure on the power grid load. Vehicle-to-grid (V2G) technology, by controlling the charging and discharging of electric vehicles, can achieve bidirectional energy transfer between electric vehicles and the power grid, thereby enabling peak shaving and valley filling of the power grid load and significantly alleviating the pressure on the power grid load.

[0070] Currently, electric vehicle (EV) charging and discharging strategies are typically optimized based on the vehicle's needs (such as battery range). However, in scenarios using V2G (Vehicle-to-Grid) technology for regulation, there is a multi-tiered structure involving the power grid, energy aggregators, and EVs, with different needs at each level. Therefore, optimizing charging and discharging strategies based solely on EV needs currently lacks flexibility.

[0071] In view of this, embodiments of this application provide a method for optimizing charging and discharging strategies. This method can predict the loss data of the battery under test based on its historical operating data, and determine that the energy storage device carrying the battery under test can participate in V2G applications when the loss data meets preset conditions. This method can also determine corresponding optimization modes for each application object participating in V2G applications based on their needs and the charging and discharging constraints of the battery under test, thereby optimizing the charging and discharging strategy of the energy storage device when participating in V2G applications. Compared to current methods that are limited to optimizing charging and discharging strategies based solely on the needs of the energy storage device, the method provided in this application can construct diverse optimization modes, thereby flexibly optimizing the charging and discharging strategy of the energy storage device based on these diverse optimization modes, offering higher flexibility.

[0072] Figure 1 This is a schematic diagram of the implementation environment involved in the method for optimizing a charging and discharging strategy provided in an embodiment of this application. For example... Figure 1 As shown, the implementation environment includes: a power grid control center 100, a V2G charging station 200, and an energy storage device 300. The energy storage device 300 can be connected to the power grid via the V2G charging station 200 and can achieve bidirectional energy flow with the power grid through the V2G charging station 200. This allows energy to be released back to the power grid from the battery carried by the energy storage device 300 during peak electricity demand periods, and to absorb excess energy from the power grid during off-peak electricity demand periods, thus achieving peak shaving and valley filling effects.

[0073] In one alternative implementation, the energy storage device can directly participate in V2G applications. See also... Figure 1 The power grid control center 100 can establish a communication connection with the energy storage device 300, and the control center 100 can directly send the predicted power demand to the energy storage device 300 so that the energy storage device 300 can charge and discharge based on the power demand.

[0074] It is understandable that, in this implementation, the energy storage device 300 can optimize its charging and discharging strategy when participating in V2G applications based on the required power, so as to adopt the optimized charging and discharging strategy. Alternatively, the V2G charging pile 200 has data computing capabilities. The V2G charging pile 200 can obtain the required power by interacting with the energy storage device 300, and then optimize the charging and discharging strategy of the energy storage device 300 when participating in V2G applications based on the required power. Or, as... Figure 1As shown, the implementation environment also includes a V2G charging pile backend management device 400. The backend management device 400 can interact with the energy storage device 300 through the V2G charging pile 200 to obtain the required power, and then optimize the charging and discharging strategy of the energy storage device 300 when participating in V2G applications based on the required power.

[0075] In another alternative implementation, the energy storage device participates in V2G applications through an energy aggregator, which can, for example, manage the energy storage device. In this case, such as... Figure 2 As shown, the implementation environment may further include: a control center 500 of an energy aggregator. This control center 500 is connected to both the grid control center 100 and the V2G charging station 200. The grid control center 100 can directly send the predicted power demand to the energy aggregator's control center 500. The control center 500 can then organize the energy storage device 300 to participate in V2G applications and optimize the charging and discharging strategy of the energy storage device 300 based on the power demand, so that the V2G charging station 200 can control the energy storage device 300 to charge and discharge according to the optimized strategy.

[0076] As described above, the multiple application objects involved in V2G applications can include at least the power grid and energy storage devices. Specifically, in scenarios where energy storage devices directly participate in V2G applications, these multiple application objects include the power grid and the energy storage devices. In scenarios where energy storage devices participate in V2G applications through energy aggregators, these multiple application objects include the power grid, the energy aggregator, and the energy storage devices.

[0077] Optionally, the number of energy storage devices can be one or more. Each energy storage device can be a power bank carrying a battery, an energy storage cabinet, an energy storage box, a charging / swapping station, or... Figure 1 and Figure 2 The vehicle shown can be a pure electric vehicle or a hybrid vehicle.

[0078] The energy aggregator's control center 500 can be the back-end management device 400 for V2G charging piles. The power grid control center 100, the energy aggregator's control center 500, and the charging pile's back-end management device 400 can all be terminals or servers. The terminal can be a mobile terminal or a fixed terminal; the mobile terminal can be a mobile phone, tablet, or laptop, etc. The fixed terminal can be a desktop computer. The server can be a single server, a server cluster consisting of several servers, or a cloud computing service center.

[0079] This application provides an optimization method for charging and discharging strategies, which is applied to electronic devices. In scenarios where energy storage devices directly participate in V2G applications, the electronic device can be an energy storage device, a V2G charging pile, or a back-end management device for the V2G charging pile. In scenarios where energy storage devices participate in V2G applications through energy aggregators, the electronic device can be the control center of the energy aggregator (such as a back-end management device for a V2G charging pile).

[0080] See Figure 3 The method includes:

[0081] Step 101: Obtain the historical operating data of the battery under test, and predict the loss data of the battery under test based on the historical operating data.

[0082] The loss data includes: battery loss value and / or loss rate. For example, the loss data may include: battery loss value and loss rate.

[0083] In this embodiment, the electronic device can predict the future operating characteristics of the battery under test based on its historical operating data, and input these operating characteristics into a pre-stored, pre-trained lifetime model to obtain the loss data. Alternatively, the electronic device can be connected to a computing device, which can acquire future loss data of the battery under test and send this loss data to the electronic device. Accordingly, the electronic device can then acquire the loss data. The implementation method of the computing device acquiring the loss data can be found in the relevant implementation process of the electronic device acquiring the loss data, and will not be elaborated further in this embodiment.

[0084] Step 102: If the loss data of the battery under test meets the preset conditions, determine that the energy storage device carrying the battery under test can participate in V2G applications.

[0085] When the loss data includes battery loss value, the preset condition may include: the battery loss value is less than a first threshold. When the loss data includes loss rate, the preset condition may include: the loss rate is less than a second threshold.

[0086] Step 103: Based on the requirements of each application object in the V2G application and the charging and discharging constraints of the battery under test, determine the corresponding optimization mode for each application object to optimize the charging and discharging strategy when the energy storage device participates in the V2G application.

[0087] The multiple application objects involved in V2G applications include at least the power grid and energy storage devices, such as the power grid, energy aggregators, and energy storage devices. Since the needs of these multiple application objects differ when participating in V2G applications, electronic devices can determine corresponding optimization modes for each application object based on its needs and the charging and discharging constraints of the battery under test, thereby optimizing the vehicle's charging and discharging strategy.

[0088] The grid's demand includes a balance between its power demand and the power supplied by energy storage devices, i.e., supply-demand balance. The energy aggregator's demand includes maximizing the power supplied by energy storage devices to the grid, i.e., maximizing the revenue generated from the electricity supplied to the grid. The energy storage device's demand includes at least maximizing both the power supplied to the grid and the feasibility of participating in V2G applications, i.e., maximizing both the revenue generated from the electricity supplied to the grid and the feasibility of participation.

[0089] In summary, this application provides a method for optimizing charging and discharging strategies. This method can predict the loss data of the battery under test based on its historical operating data, and determine that the energy storage device carrying the battery under test can participate in V2G applications when the loss data meets preset conditions. This method can also determine corresponding optimization modes for each application based on its needs and the charging and discharging constraints of the battery under test, thereby optimizing the charging and discharging strategy of the energy storage device when participating in V2G applications. Compared to current methods that only optimize charging and discharging strategies based on the needs of the energy storage device, the method provided in this application can construct diverse optimization modes, thus flexibly optimizing the charging and discharging strategy of the energy storage device based on these diverse modes, offering high flexibility. Furthermore, this method ensures that the optimized charging and discharging strategy meets the differentiated needs of different application objects, demonstrating strong applicability. In addition, since this method only allows the energy storage device to participate in V2G applications when the loss data of the battery under test meets preset conditions, it can protect the battery's health and effectively extend its lifespan.

[0090] This application uses a vehicle as an example of an energy storage device, and the battery loss data includes battery loss value and loss rate, to illustrate the optimization method of the charging and discharging strategy provided in this application. See also Figure 4 The method may include:

[0091] Step 201: Obtain the historical operating data of the battery under test, and predict the operating characteristics of the battery under test based on the historical operating data.

[0092] In this embodiment of the application, when an electronic device needs to detect whether a vehicle can participate in V2G, it can acquire the historical operating data of the battery under test and predict the future operating characteristics of the battery under test based on the historical operating data.

[0093] The historical operating data includes multiple sets of data from the battery under test during its historical operation, each set containing parameter values ​​for various battery parameters. The operating characteristics include equivalent values ​​of target operating parameters for the battery under test under various operating conditions. The equivalent value of the target operating parameter under each operating condition characterizes the operating status of the battery under test under that condition. Therefore, these operating characteristics can be used to characterize the future operating status of the battery under test.

[0094] Multiple operating conditions include: resting condition, charging condition, and discharging condition. Target operating parameters for the resting condition may include: temperature and state of charge (SOC). Target operating parameters for the charging (or discharging) condition may include: temperature, current, charge / discharge capacity, and SOC.

[0095] Step 202: Input the predicted operating characteristics of the battery under test into the pre-trained lifetime model to predict the battery loss value in the loss data of the battery under test.

[0096] Electronic devices can store pre-trained lifetime models. After the electronic device predicts the operating characteristics of the battery under test, it can input these operating characteristics into the lifetime model to obtain the battery loss value from the loss data of the battery under test output by the lifetime model.

[0097] Step 203: Determine the loss rate in the loss data of the battery under test based on the battery loss value of the battery under test.

[0098] The electronic device can obtain the current battery wear value of the battery under test, and determine the wear rate in the battery wear data as the difference between the current battery wear value and the future battery wear value predicted in step 202. Thus, the electronic device can obtain the future wear data of the battery under test.

[0099] Step 204: If the loss data of the battery under test meets the preset conditions, determine that the vehicle carrying the battery under test can participate in V2G applications.

[0100] The preset conditions include: the battery degradation value is less than a first threshold, and the degradation rate is less than a second threshold. Both the first and second thresholds can be pre-stored by the electronic device.

[0101] In other words, the electronic device can detect whether the future battery degradation value of the battery under test is less than a first threshold, and whether the future degradation rate of the battery under test is less than a second threshold. If the electronic device determines that the battery degradation value is less than the first threshold and the degradation rate is less than the second threshold, it can determine that the degradation data meets the preset conditions, the future condition of the battery under test is good, and thus it can determine that the vehicle carrying the battery under test can participate in V2G applications. If the electronic device determines that the battery degradation value is greater than or equal to the first threshold, and / or the degradation rate is greater than or equal to the second threshold, it can determine that the vehicle cannot participate in V2G applications.

[0102] Since electronic devices are only allowed to participate in V2G applications when the wear data of the battery under test meets preset conditions, the use of V2G applications can avoid significantly accelerating the degradation of the battery under test's lifespan, thus protecting the battery under test to a certain extent.

[0103] Step 205: Based on the requirements of each application object in the V2G application and the charging and discharging constraints of the battery under test, determine the corresponding optimization mode for each application object individually.

[0104] Multiple application entities involved in V2G applications can include: the power grid, energy aggregators, and vehicles. Since these entities have different needs when participating in V2G applications, electronic devices can determine corresponding optimization modes for each application entity based on their needs and the charging and discharging constraints of the battery under test, thereby optimizing the vehicle's charging and discharging strategy. The optimization mode for each application entity includes: an objective function and constraints. Charging and discharging constraints refer to the limitations imposed on the vehicle during charging and discharging processes, and these constraints can include: constraints on charging and discharging current, constraints on state of charge, constraints on temperature, and constraints on depth of discharge.

[0105] In this embodiment, when the application target is the power grid, the power grid's demand includes supply and demand balance, and the corresponding optimization mode for the power grid includes a load balancing mode. In this mode, electronic devices can obtain power demand commands from the power grid and, in response to the power demand commands, optimize the vehicle's charging and discharging strategy based on the balance between the demanded power in the power demand commands and the power supplied by the vehicle to the power grid, so that the vehicle can participate in V2G applications based on the optimized charging and discharging strategy.

[0106] The power demand command can be issued to electronic devices by the power grid control center when it detects an imbalance between energy supply and demand in the power grid.

[0107] When the application target is an energy aggregator, the energy aggregator's needs include maximizing profits, and the corresponding optimization mode for this energy aggregator includes a profit-first mode. In this mode, electronic devices can obtain power demand commands from the power grid and, in response to the power demand commands, optimize the vehicle's charging and discharging strategy based on maximizing the power supplied by the vehicle to the power grid, so that the vehicle can participate in V2G applications based on the optimized charging and discharging strategy.

[0108] When the application target is a vehicle, the vehicle's requirements include at least maximizing revenue and maximizing participation feasibility. The corresponding optimization mode for this vehicle includes a globally optimal mode. In this mode, the electronic device can obtain power demand commands from the power grid and, in response, optimize the vehicle's charging and discharging strategy based on maximizing the vehicle's participation feasibility in V2G applications and maximizing the power supplied by the vehicle to the power grid. This allows the vehicle to participate in V2G applications based on the optimized charging and discharging strategy.

[0109] Optionally, when determining the corresponding optimization mode for each application based on its needs and the charging and discharging constraints of the battery under test, the electronic device can also consider the loss limitation requirements of the battery under test. That is, when determining the optimization mode, the loss limitation requirements of the battery under test when participating in V2G applications can also be considered. This can increase the willingness of vehicle users to participate in V2G applications.

[0110] Taking the example of an electronic device determining the corresponding optimization mode for each application based on the wear and tear requirements of the battery under test, the process of an electronic device individually determining the corresponding optimization mode for each application is illustrated below:

[0111] When the application target is the power grid, the loss limit requirement for the battery under test is to minimize the loss value when participating in V2G applications. Therefore, when the application target is the power grid, the electronic device can construct the objective function of the optimization mode with the balance between the power demand of the power grid and the power supplied by the vehicle to the power grid, and with the minimum loss value of the battery under test participating in V2G applications as the optimization objective. The constraint conditions of the optimization mode are constructed with the charging and discharging constraints of the battery under test, thereby obtaining the optimization mode corresponding to the power grid.

[0112] It is understandable that the balance between demand and supply power can be defined as minimizing the difference between demand and supply power. For example, the objective function of the power grid optimization model can satisfy:

[0113] min(α1×(p d -p g )+β1×L A )Formula (1)

[0114] In formula (1), pd p represents the power supplied to the grid by vehicles via V2G technology. g Let α1 be the power demand of the power grid. d With p g The weight of the difference, β1 is the loss value L of the battery under test participating in V2G application. A The weight of p. g α1 and β1 are given known quantities, and both are known quantities pre-stored by the electronic device.

[0115] p in formula (1) d It can satisfy:

[0116] p d =I V2G ×V V2G Formula (2)

[0117] In formula (2), I V2G It is the charging and discharging current of the battery when participating in V2G, V V2G It is the charging and discharging voltage of the battery when participating in V2G.

[0118] L in formula (1) A It can satisfy:

[0119]

[0120] In formula (3), Q h Q0 represents the capacity loss caused by the historical usage habits of the battery under test, while Q0 is the nominal capacity of the battery. V2G This represents the capacity loss caused by participating in V2G applications. (Q) h Both Q0 and Q0 are known quantities.

[0121] Q V2G It can satisfy:

[0122] Q V2G =f1(SOC) V2G ,ΔDOD V2G ,I V2G ,T V2G )Formula (4)

[0123] In formula (4), f1 represents Q V2G A first functional relationship exists between the battery and several charge / discharge parameters to be solved, which may be pre-acquired by the electronic device. These multiple charge / discharge parameters include: the battery's state of charge (SOC) during V2G operations. V2G Discharge depth ΔDOD V2G Current I V2G and temperature T V2G .

[0124] Combining formulas (1) to (4) above, we can determine that the objective function can be transformed into:

[0125]

[0126] The constraints of the optimization mode corresponding to the power grid may include:

[0127]

[0128] In formula (5), f2 represents Q. V2G The second functional relationship between SOC and the supplied power and charging / discharging duration t1. min The lower limit of the state of charge when participating in V2G applications, SOC max This represents the upper limit of the state of charge when participating in V2G applications. min As the lower limit of the charging and discharging current when participating in V2G applications, I max This represents the upper limit of the charging and discharging current when participating in V2G applications. △DOD min ΔDOD is the lower limit of the depth of discharge (DOD) for participation in V2G applications. max This represents the upper limit of the depth of discharge when participating in V2G applications. This second functional relationship, as well as the lower and upper limits of each charge and discharge parameter, can be known quantities obtained in advance by the electronic device.

[0129] When the application target is an energy aggregator, the loss limit of the battery under test is that the loss value does not exceed a first preset threshold. Since the number of vehicles managed by the energy aggregator that can participate in V2G applications is not fixed, to maximize revenue, each vehicle participating in the V2G application needs to provide the maximum amount of electricity. Therefore, when the application target is an energy aggregator, the electronic device can construct the objective function of the optimization mode with the goal of maximizing the power supplied by the vehicle to the grid, and construct the constraints of the optimization mode with the loss value of the battery under test not exceeding the first preset threshold and the charging and discharging constraints of the battery under test, thus obtaining the optimization mode corresponding to the energy aggregator. The first preset threshold can be pre-stored by the electronic device.

[0130] For example, the objective function of this optimization mode can satisfy:

[0131] max(k×p d )Formula (6)

[0132] In formula (6), k is the price of energy, and it is a known quantity. Combining formulas (2) and (6), the objective function under this second mode can be transformed into: k×I V2G ×V V2G .

[0133] The constraints of the optimization mode corresponding to the energy aggregator can be satisfied as follows:

[0134]

[0135] In formula (7), L at this time A Q h Both L and Q0 are known quantities. A ×Q h -Q0 is the first preset threshold.

[0136] When the application is a vehicle, the wear limit of the battery under test is that the wear value does not exceed a second preset threshold, which can be greater than a first preset threshold. For example, the second preset threshold can be an acceptable maximum wear value. This maximum wear value can be pre-stored by the electronic device. Furthermore, the vehicle's requirements differ depending on whether the battery under test is private or public. That is, the vehicle's requirements depend on the ownership attribute of the battery under test, whether it is public or private.

[0137] In this context, "private" means that the battery under test belongs to the vehicle, meaning it is tied to that vehicle and can only be installed in that vehicle. "Public" means that the battery under test does not belong to the vehicle, meaning it can be installed not only in that vehicle but also in other vehicles.

[0138] Specifically, when ownership is private (i.e., vehicle and battery are not separated), the electronic equipment can determine the vehicle's needs by maximizing the power supplied to the grid (i.e., maximizing the revenue generated by the electricity supplied to the grid), maximizing the feasibility of the vehicle's participation in V2G applications, and minimizing the battery degradation. When ownership is public (i.e., vehicle and battery are separated), the electronic equipment can determine the vehicle's needs by maximizing the power supplied to the grid and maximizing the feasibility of the vehicle's participation in V2G applications.

[0139] For example, assuming the ownership of the battery under test is private, the objective function of the optimization mode for this vehicle can satisfy:

[0140] min(α2×L A -β2×k×p d -γ×S) formula (8)

[0141] In formula (8), α2, β2, and γ are the loss values ​​L of the battery under test when participating in V2G applications. AThe participation feasibility S is determined by weights α², β², and γ, where α², β², and γ are all greater than 0. This participation feasibility S can be determined based on the distance d from the vehicle to the location participating in the V2G application, the waiting time t² required to participate, and the risk value r representing the remaining mileage risk. That is, the participation feasibility S can satisfy: S = f³(d, t, r). f³ represents the functional relationship between the participation feasibility S and the distance d, the waiting time t², and the risk value r. For example, the objective parameter S can satisfy: S = a × d -1 +b×t -1 +c×r -1 Where a, b, and c are the weights of the distance d, the waiting time t2, and the risk value r, respectively.

[0142] The constraints of the optimization mode corresponding to the vehicle can be satisfied:

[0143]

[0144] In formula (9), The maximum acceptable loss value of the battery under test (i.e., the second preset threshold) is a known quantity. This is the lower limit (i.e., minimum supply power) of the supply power when participating in V2G. This is the upper limit of the power supplied when participating in V2G (i.e., the maximum power supplied).

[0145] It is understandable that V2G technology increases the number of charge-discharge cycles of electric vehicle batteries, thereby accelerating the degradation of battery state of health (SOH) and consequently shortening the remaining useful life (RUL). However, the method provided in this application also considers battery degradation limitations to optimize the vehicle's charge-discharge strategy. This avoids the rapid deterioration of battery SOH caused by V2G applications, which could negatively impact battery performance and lifespan. Therefore, the charge-discharge strategy optimized by the method provided in this application is more reasonable.

[0146] Step 206: Optimize the charging and discharging strategy for vehicles participating in V2G applications based on the optimization mode corresponding to at least one application object.

[0147] The charging and discharging strategy may include multiple charging and discharging parameters when the vehicle participates in V2G applications, including charging and discharging capacity, charging and discharging current, temperature, and depth of discharge.

[0148] In one alternative implementation, the electronic device can obtain the optimization mode corresponding to the target object from among the optimization modes corresponding to multiple application objects. Then, the electronic device can optimize the charging and discharging strategy for the vehicle participating in V2G applications based on the optimization mode corresponding to the target object. Here, the target object can be any one of the multiple application objects.

[0149] In this embodiment, for scenarios where the vehicle directly participates in V2G applications, when the electronic device acts as the vehicle's control terminal, the electronic device can directly respond to the selection operation of the optimization mode corresponding to multiple application target objects and obtain the optimization mode corresponding to the target object. This control terminal can be a terminal bound to the vehicle (such as the terminal of the user to whom the vehicle belongs), the vehicle's central control console, or a V2G charging station.

[0150] When the electronic device is not the vehicle's control unit, the vehicle's control unit can display optimization modes corresponding to multiple application objects. In response to a selection operation for the optimization mode corresponding to a target object among the multiple application objects, the control unit can acquire and send the optimization mode corresponding to that target object to the electronic device. Accordingly, the electronic device can then acquire the optimization mode corresponding to that target object.

[0151] In scenarios where vehicles participate in V2G applications through energy aggregators, the electronic device can display optimization modes corresponding to multiple application objects, and can obtain the optimization mode corresponding to the target object in response to the selection operation of the optimization mode corresponding to the target object among the multiple application objects.

[0152] In another alternative implementation, the electronic device can optimize the charging and discharging strategies for the vehicle's participation in V2G applications based on optimization modes corresponding to multiple application objects, resulting in multiple optimized charging and discharging strategies. Then, if the electronic device acts as the vehicle's control unit, it can directly respond to the selection operation among the multiple optimized charging and discharging strategies to obtain the target charging and discharging strategy. If the electronic device is not the vehicle's control unit, it can push the multiple optimized charging and discharging strategies to the vehicle's control unit, enabling the control unit to select the target charging and discharging strategy for the vehicle's participation in V2G applications from among the optimized strategies.

[0153] Optionally, in this implementation, for each optimized charging / discharging strategy, the electronic device can also obtain selection reference information for each optimized charging / discharging strategy and push this selection reference information to the vehicle's control terminal. This selection reference information includes: a reference value for the benefits of using the optimized charging / discharging strategy in V2G applications and / or a reference value for battery loss. For example, the selection reference information may include: a reference value for benefits and a reference value for battery loss.

[0154] Since electronic devices can also push selection reference information for various optimized charging and discharging strategies to assist the control terminal in selecting the target charging and discharging strategy from multiple optimized charging and discharging strategies, the efficiency of determining the target charging and discharging strategy can be improved to a certain extent, thereby improving the control efficiency of the vehicle.

[0155] In the embodiments of this application, for each optimization mode corresponding to an application object, the electronic device can construct an optimization model based on the objective function and constraints of the optimization mode, and use an optimization algorithm to optimize the optimization model so that the function value of the objective function reaches the extreme value (i.e., the maximum or minimum value), thereby obtaining the optimal solution of the objective function and thus obtaining the optimized charging and discharging strategy under the optimization mode.

[0156] Optionally, the optimization algorithm can be a genetic algorithm, a branch and bound algorithm, or a particle swarm optimization (PSO) algorithm.

[0157] This application uses particle swarm optimization as an example to illustrate the charging and discharging strategy for vehicles participating in V2G applications, based on the optimization mode corresponding to the application object:

[0158] Step S1: Initialize the particle swarm.

[0159] In this embodiment, the electronic device can initialize the particle swarm by randomly setting the position and velocity of each particle based on the constraints corresponding to the optimization mode. The position of each of the N particles in the particle swarm is located within the feasible region defined by the constraints. Each particle includes M charging / discharging parameters of the electric vehicle, including charging / discharging current, capacity, temperature, and depth of discharge. Both N and M are integers greater than 1.

[0160] That is, the particle swarm X can satisfy: X = [x1, x2, ..., x n , ..., x N ], where n is an integer greater than or equal to 1 and less than or equal to N. The nth particle in N particles can satisfy: m is an integer greater than or equal to 1 and less than or equal to M. x n This represents the position of the nth particle. Therefore, the particle swarm can be an N×M matrix.

[0161] Step S2: Determine the individual extreme value and the location of the individual extreme value for each particle.

[0162] In this embodiment, for each particle, the electronic device can input the particle's position into the objective function to obtain the particle's fitness value. Then, based on this fitness value, the electronic device can determine the particle's individual extreme value and define the location of this individual extreme value as the particle's individual extreme value location. Here, the individual extreme value is the particle's maximum fitness value up to the current iteration.

[0163] Step S3: Determine the global extremum and its location.

[0164] The global extremum is the current maximum fitness value of all particles, and the global extremum position is the location of the global extremum.

[0165] Step S4: Determine whether the termination condition is met.

[0166] If the electronic device determines that the termination condition is not met, step S5 can be executed. If the electronic device determines that the termination condition is met, step S6 can be executed. The termination condition may include: the number of iterations reaches the maximum number of iterations, and / or, the global extreme value reaches a threshold.

[0167] Step S5: Update the position and velocity of each particle.

[0168] If the electronic device determines that the termination condition is not met, then for each of the multiple particles, the electronic device can update the position and velocity of that particle based on the global extreme position and the particle's individual extreme position. Wherein, the updated position x of the nth particle... n ′ can satisfy:

[0169] x n ′=x n +α3×v n Formula (10)

[0170] In formula (10), x n Let α be the current position of the nth particle, and α3 be a constraint factor used to constrain the step size of each update. n This represents the current velocity of the nth particle.

[0171] The updated velocity v of the nth particle n ′ can satisfy:

[0172] v n ′=w×v n +c1×r1×(pbest n -x n )+c2×r2×(gbest n -x n )Formula (11)

[0173] In formula (11), w is the inertia weight, and w is a non-negative number. c1 is the individual learning factor, i.e., the weight of the local optimum, and c2 is the group learning factor, i.e., the weight of the global optimum. r1 and r2 are both random numbers greater than or equal to 0 and less than or equal to 1. Pbest n gbest is the optimal solution found by the nth particle after the current iteration. n This represents the optimal solution found by the population search after the current iteration.

[0174] After updating the position and velocity of the particles, the electronic device can continue to execute step S3 until the termination condition is met.

[0175] Step S6: Output the optimal solution.

[0176] If the electronic device determines that the termination condition is met, it can output the optimal solution of the optimization model. This optimal solution is the optimal set of multiple charging and discharging parameters when the objective function of the optimization model reaches its extreme value; this is the optimal control strategy for the vehicle.

[0177] In the embodiments of this application, for scenarios where the electronic device obtains the optimization mode corresponding to the target object from the optimization modes corresponding to multiple application objects, after obtaining the optimized charging and discharging strategy, the electronic device can directly control the vehicle to charge and discharge according to the optimized charging and discharging strategy.

[0178] In scenarios where electronic devices optimize the charging and discharging strategies for vehicles participating in V2G applications based on optimization modes corresponding to multiple application objects, the electronic devices can control the vehicle to charge and discharge according to the target charging and discharging strategy selected by the control terminal.

[0179] It is understood that the order of steps in the optimization method of the charge / discharge control strategy provided in this application embodiment can be appropriately adjusted, and steps can be added or removed as appropriate. For example, step 201 can be deleted as needed, such as when the electronic device can receive loss data sent by the computing device; or, step 205 can be executed before step 201, or it can be executed synchronously with step 201. Any variations that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application, and therefore will not be elaborated further.

[0180] In summary, this application provides a method for optimizing charging and discharging strategies. This method can predict the loss data of the battery under test based on its historical operating data, and determine that the energy storage device carrying the battery under test can participate in V2G applications when the loss data meets preset conditions. This method can also determine corresponding optimization modes for each application based on its needs and the charging and discharging constraints of the battery under test, thereby optimizing the charging and discharging strategy of the energy storage device when participating in V2G applications. Compared to current methods that only optimize charging and discharging strategies based on the needs of the energy storage device, the method provided in this application can construct diverse optimization modes, thus flexibly optimizing the charging and discharging strategy of the energy storage device based on these diverse modes, offering high flexibility. Furthermore, this method ensures that the optimized charging and discharging strategy meets the differentiated needs of different application objects, demonstrating strong applicability. In addition, since this method only allows the energy storage device to participate in V2G applications when the loss data of the battery under test meets preset conditions, it can protect the battery's health and effectively extend its lifespan.

[0181] This application provides an apparatus for optimizing a charge / discharge strategy, which can execute the optimization method for the charge / discharge strategy provided in the above-described method embodiments. See also Figure 5 The device 600 includes:

[0182] The prediction module 601 is used to acquire historical operating data of the battery under test and predict the loss data of the battery under test based on the historical operating data. The loss data includes: battery loss value and / or loss rate.

[0183] The participation determination module 602 is used to determine whether the energy storage device carrying the battery under test can participate in V2G applications if the loss data meets the preset conditions.

[0184] The optimization module 603 is used to determine the corresponding optimization mode for each application based on the needs of each application object participating in the V2G application and the charging and discharging constraints of the battery under test, so as to optimize the charging and discharging strategy of the energy storage device when participating in the V2G application.

[0185] Optionally, when the application target is the power grid, the optimization mode includes a load balancing mode. In this mode, the optimization module 603 can be used to: obtain a power demand command from the power grid, respond to the power demand command from the power grid, and optimize the charging and discharging strategy of the energy storage device based on the balance between the demanded power in the power demand command and the supply power provided by the energy storage device to the power grid, so that the energy storage device can participate in V2G applications based on the optimized charging and discharging strategy.

[0186] Optionally, when the application target is an energy aggregator, the optimization mode includes a revenue-first mode. In this mode, the optimization module 603 can be used to: obtain power demand commands from the grid, respond to the power demand commands from the grid, optimize the charging and discharging strategy of the energy storage device based on maximizing the power supplied by the energy storage device to the grid, so that the energy storage device can participate in V2G applications based on the optimized charging and discharging strategy.

[0187] Optionally, when the application target is an energy storage device, the optimization mode includes a globally optimal mode. In this mode, the optimization module 603 can be used to: obtain a power demand command from the power grid, respond to the power demand command from the power grid, and optimize the charging and discharging strategy of the energy storage device based on maximizing the feasibility of the energy storage device participating in V2G applications and maximizing the power supplied by the energy storage device to the power grid, so that the energy storage device can participate in V2G applications based on the optimized charging and discharging strategy.

[0188] Optionally, when determining the optimization mode based on the needs of each application object participating in the V2G application and the charging and discharging constraints of the battery under test, the loss limit requirements of the battery under test are also taken into account.

[0189] Optionally, when the application target is a power grid, the optimization module 603 can be used for:

[0190] The objective function of the optimization mode is constructed with the balance between the power demand of the power grid and the power supply provided by the energy storage device to the power grid, and the minimum loss value of the battery under test as the optimization objective. The constraint conditions of the optimization mode are constructed with the charging and discharging constraints of the battery under test, and the corresponding optimization mode of the power grid is obtained.

[0191] Optionally, when the application target is an energy aggregator, the optimization module 603 can be used for:

[0192] The objective function of the optimization mode is constructed with the goal of maximizing the power supplied by the energy storage device to the power grid. The constraints of the optimization mode are constructed with the charging and discharging constraints of the battery under test and the loss limit requirement of the battery under test, which are set to ensure that the loss value does not exceed the first preset threshold. The optimization mode corresponding to the energy aggregator is obtained.

[0193] Optionally, when the application is an energy storage device, the optimization module 603 can be used for:

[0194] The objective function of the optimization mode is constructed with the goal of maximizing the power supplied by the energy storage device to the grid and maximizing the feasibility of the energy storage device participating in V2G applications. The constraints of the optimization mode are constructed with the charging and discharging constraints of the battery under test and the loss limit requirement of the battery under test, which are set as the loss value not exceeding the second preset threshold. The optimization mode corresponding to the energy storage device is obtained.

[0195] Optional, see Figure 6 The device may further include a demand determination module 604. The demand determination module 604 is used for:

[0196] When the application object is an energy storage device, obtain the ownership attributes of the battery under test;

[0197] The requirements for energy storage devices are determined based on the ownership of the battery under test.

[0198] Optionally, the requirement determination module 604 can be used for:

[0199] Given that the ownership of the battery under test is private, the requirements for the energy storage device are determined as follows: the energy storage device provides the maximum power to the grid, the energy storage device has the maximum feasibility of participating in V2G applications, and the battery under test has the minimum loss value.

[0200] Given that the ownership of the battery under test is public, the requirements for energy storage devices include: maximizing the power supply that the energy storage device can provide to the grid, and maximizing the feasibility of the energy storage device participating in V2G.

[0201] Optionally, the prediction module 601 can be used for:

[0202] The operating characteristics of the battery under test are predicted based on historical operating data, and these operating characteristics are used to characterize the operating status of the battery under test.

[0203] The running features are input into a pre-trained lifetime model to predict the battery loss value in the loss data of the battery under test.

[0204] Based on the battery loss value, the loss rate in the obtained loss data is determined.

[0205] Optionally, the optimization module 603 can also be used for:

[0206] Based on the optimization mode corresponding to at least one application object, the charging and discharging strategy of energy storage devices participating in V2G applications is optimized.

[0207] Optionally, at least one application object may be multiple, and the optimization module 603 may also be used for:

[0208] Based on the optimization modes corresponding to multiple application objects, the charging and discharging strategies of energy storage devices participating in V2G applications are optimized to obtain multiple optimized charging and discharging strategies.

[0209] See Figure 6 The device 600 may further include:

[0210] The push module 605 is used to push multiple optimized charging and discharging strategies to the control terminal of the energy storage device, so that the control terminal can select the target charging and discharging strategy adopted by the energy storage device when participating in V2G applications from the multiple optimized charging and discharging strategies.

[0211] Optionally, the push module 605 can also be used for:

[0212] Obtain selection reference information for each optimized charge / discharge strategy. The selection reference information includes: reference values ​​for the benefits of using the optimized charge / discharge strategy to participate in V2G applications and / or reference values ​​for battery loss.

[0213] Push the selection reference information for each optimized charging and discharging strategy to the control terminal of the energy storage device.

[0214] By acquiring and pushing the selection reference information of each optimized charging and discharging strategy to the control terminal, the control terminal can be assisted in selecting the target charging and discharging strategy from multiple optimized charging and discharging strategies. Therefore, the determination efficiency of the target charging and discharging strategy can be improved to a certain extent, thereby improving the control efficiency of the energy storage device.

[0215] Optionally, at least one application object is required, and the optimization module 603 can also be used for:

[0216] Obtain the optimization mode corresponding to the target object from the optimization modes corresponding to multiple application objects;

[0217] Based on the optimization mode corresponding to the target object, the charging and discharging strategy of energy storage equipment when participating in V2G applications is optimized.

[0218] In summary, this application provides a charging and discharging strategy optimization device. This device can predict the loss data of the battery under test based on its historical operating data, and determine that the energy storage device carrying the battery under test can participate in V2G applications when the loss data meets preset conditions. The device can also determine corresponding optimization modes for each application based on its needs and the charging and discharging constraints of the battery under test, thereby optimizing the charging and discharging strategy of the energy storage device when participating in V2G applications. Compared to current methods that only optimize charging and discharging strategies based on the needs of the energy storage device, the device provided in this application can construct diverse optimization modes, thus flexibly optimizing the charging and discharging strategy of the energy storage device based on these diverse modes, offering high flexibility. Furthermore, the device ensures that the optimized charging and discharging strategy meets the differentiated needs of different applications, demonstrating strong applicability. In addition, since the device only allows the energy storage device to participate in V2G applications when the loss data of the battery under test meets preset conditions, it can protect the battery's health and effectively extend its lifespan.

[0219] This application provides an electronic device, see [link to relevant documentation] Figure 7 The electronic device 700 includes a memory 701 and a processor 702. The processor 702 and the memory 701 are connected, for example, via a bus 703. Optionally, the electronic device 700 may also include a transceiver 704. It should be noted that in practical applications, the transceiver 704 is not limited to one type, and the structure of the electronic device 700 does not constitute a limitation on the embodiments of this application.

[0220] Processor 702 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 702 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0221] Bus 703 may include a pathway for transmitting information between the aforementioned components. Bus 703 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 703 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0222] The memory 701 stores a computer program corresponding to the optimization method of the charge / discharge strategy in the above embodiments of this application. This computer program is controlled and executed by the processor 702. The processor 702 executes the computer program stored in the memory 701 to implement the content shown in the aforementioned method embodiments.

[0223] This application also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the method for optimizing the charging and discharging strategy as provided in the above-described method embodiments. For example, Figure 3 or Figure 4 The method shown.

[0224] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0225] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0226] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0227] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing a charge-discharge strategy, characterized in that, The method includes: Acquire historical operating data of the battery under test, and predict the loss data of the battery under test based on the historical operating data. The loss data includes: battery loss value and / or loss rate. If the loss data meets the preset conditions, it is determined that the energy storage device carrying the battery under test can participate in V2G applications. Based on the needs of each application object participating in the V2G application and the charging and discharging constraints of the battery under test, a corresponding optimization mode is determined for each application object to optimize the charging and discharging strategy of the energy storage device when participating in the V2G application.

2. The method according to claim 1, characterized in that, Based on the needs of each application participating in the V2G application and the charging and discharging constraints of the battery under test, a corresponding optimization mode is determined individually for each application to optimize the charging and discharging strategy of the energy storage device when participating in the V2G application, including: When the application target is the power grid, the optimization mode includes a load balancing mode; In this mode, a power demand command is obtained from the power grid. In response to the power demand command, the charging and discharging strategy of the energy storage device is optimized based on the balance between the demand power in the power demand command and the supply power provided by the energy storage device to the power grid, so that the energy storage device can participate in V2G applications based on the optimized charging and discharging strategy.

3. The method according to claim 1, characterized in that, Based on the needs of each application participating in the V2G application and the charging and discharging constraints of the battery under test, a corresponding optimization mode is determined individually for each application to optimize the charging and discharging strategy of the energy storage device when participating in the V2G application, including: When the application target is an energy aggregator, the optimization mode includes a profit-first mode; In this mode, a power demand command is obtained from the power grid. In response to the power demand command, the charging and discharging strategy of the energy storage device is optimized based on the maximum power supplied by the energy storage device to the power grid, so that the energy storage device can participate in V2G applications based on the optimized charging and discharging strategy.

4. The method according to claim 1, characterized in that, Based on the needs of each application participating in the V2G application and the charging and discharging constraints of the battery under test, a corresponding optimization mode is determined individually for each application to optimize the charging and discharging strategy of the energy storage device when participating in the V2G application, including: When the application is an energy storage device, the optimization mode includes a globally optimal mode; In this mode, a power demand command is obtained from the power grid. In response to the power demand command, the charging and discharging strategy of the energy storage device is optimized based on maximizing the feasibility of the energy storage device participating in V2G applications and maximizing the power supplied by the energy storage device to the power grid, so that the energy storage device can participate in V2G applications based on the optimized charging and discharging strategy.

5. The method according to any one of claims 1-4, characterized in that, When determining the optimization mode based on the needs of each application object participating in the V2G application and the charging and discharging constraints of the battery under test, the loss limitation requirements of the battery under test are also taken into account.

6. The method according to claim 5, characterized in that, When the application target is the power grid, based on the needs of the power grid participating in the V2G application and the charging and discharging constraints of the battery under test, a corresponding optimization mode is determined for the power grid, including: The objective function of the optimization mode is constructed by balancing the power demand of the power grid with the power supply provided by the energy storage device to the power grid, and minimizing the loss value of the battery under test. The constraint conditions of the optimization mode are constructed by using the charging and discharging constraints of the battery under test, thereby obtaining the optimization mode corresponding to the power grid.

7. The method according to claim 5, characterized in that, When the application target is an energy aggregator, based on the needs of the energy aggregator participating in the V2G application and the charge / discharge constraints of the battery under test, a corresponding optimization mode is determined for the energy aggregator, including: The objective function of the optimization mode is constructed with the maximum power supplied to the power grid by the energy storage device as the optimization objective. The constraints of the optimization mode are constructed with the charging and discharging constraints of the battery under test and the loss limit requirement of the battery under test as the loss value not exceeding a first preset threshold, so as to obtain the optimization mode corresponding to the energy aggregator.

8. The method according to claim 5, characterized in that, When the application target is an energy storage device, based on the requirements of the energy storage device participating in the V2G application and the charging and discharging constraints of the battery under test, a corresponding optimization mode is determined for the energy storage device, including: The objective function of the optimization mode is constructed with the goal of maximizing the power supplied to the grid by the energy storage device and maximizing the feasibility of the energy storage device participating in V2G applications. The constraints of the optimization mode are constructed with the charging and discharging constraints of the battery under test and the loss limit requirement of the battery under test as the loss value not exceeding a second preset threshold, so as to obtain the optimization mode corresponding to the energy storage device.

9. The method according to any one of claims 1-8, characterized in that, When the application object is the energy storage device, before determining the corresponding optimization mode for each application object based on the needs of the V2G application and the charge / discharge constraints of the battery under test, the method further includes: Obtain the ownership attributes of the battery under test; The requirements for the energy storage device are determined based on the ownership of the battery under test.

10. The method according to claim 9, characterized in that, Determining the energy storage device requirements based on the ownership attributes of the battery under test includes: If the ownership of the battery under test is determined to be private, the requirements for the energy storage device are determined as follows: the energy storage device provides the maximum power to the grid, the energy storage device has the maximum feasibility of participating in V2G applications, and the battery under test has the minimum loss value. Given that the ownership of the battery under test is public, the requirements for the energy storage device are determined to be: the energy storage device provides the maximum power to the power grid, and the energy storage device has the maximum feasibility of participating in V2G.

11. The method according to any one of claims 1-10, characterized in that, When the loss data includes battery loss value and loss rate, the prediction of the loss data of the battery under test based on the historical operating data includes: The operating characteristics of the battery under test are predicted based on the historical operating data, and the operating characteristics are used to characterize the operating status of the battery under test. The operational features are input into a pre-trained lifetime model to predict the battery loss value in the loss data of the battery under test. Based on the battery loss value, the loss rate in the loss data is determined.

12. The method according to any one of claims 1-11, characterized in that, After determining the corresponding optimization mode for each application based on the needs of each application participating in the V2G application and the charge / discharge constraints of the battery under test, the method further includes: Based on the optimization mode corresponding to at least one of the application objects, the charging and discharging strategy of the energy storage device when participating in V2G applications is optimized.

13. The method according to claim 12, characterized in that, At least one of the application objects may be multiple. Based on the optimization modes corresponding to the multiple application objects, the charging and discharging strategy of the energy storage device when participating in V2G applications is optimized, including: Based on the optimization modes corresponding to the multiple application objects, the charging and discharging strategies of the energy storage devices participating in V2G applications are optimized respectively, resulting in multiple optimized charging and discharging strategies. The method further includes: Multiple optimized charging and discharging strategies are pushed to the control terminal of the energy storage device, so that the control terminal can select the target charging and discharging strategy adopted by the energy storage device when participating in V2G applications from the multiple optimized charging and discharging strategies.

14. The method according to claim 13, characterized in that, The method further includes: Obtain selection reference information for each optimized charge-discharge strategy, the selection reference information including: a reference value for the benefits of participating in V2G applications using the optimized charge-discharge strategy and / or a reference value for battery loss. The optimized charging and discharging strategy selection reference information is pushed to the control terminal of the energy storage device.

15. The method according to claim 12, characterized in that, At least one of the application objects is defined as one. Based on the optimization mode corresponding to one of the application objects, the charging and discharging strategy of the energy storage device when participating in V2G applications is optimized, including: Obtain the optimization mode corresponding to the target object from the optimization modes corresponding to the multiple application objects; Based on the optimization mode corresponding to the target object, the charging and discharging strategy of the energy storage device when participating in V2G applications is optimized.

16. An optimization device for a charging and discharging strategy, characterized in that, The device includes: The prediction module is used to acquire historical operating data of the battery under test and predict the loss data of the battery under test based on the historical operating data. The loss data includes: battery loss value and / or loss rate. The determination module is used to determine, under the condition that the loss data meets the preset conditions, whether the energy storage device carrying the battery under test can participate in V2G applications; The optimization module is used to determine a corresponding optimization mode for each application object based on the needs of each application object participating in the V2G application and the charging and discharging constraints of the battery under test, so as to optimize the charging and discharging strategy of the energy storage device when participating in the V2G application.

17. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method as described in any one of claims 1-15.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-15.

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