Energy storage battery pack based charging station energy storage control method, device and readable medium

Abstract

This disclosure presents an embodiment of a charging station energy storage control method, apparatus, and readable medium based on an energy storage battery pack. One specific implementation of the method includes: in response to receiving a grid load suppression command, collecting battery information corresponding to each energy storage battery in the energy storage battery pack; for the battery information, performing the following processing steps: generating a battery health level corresponding to the energy storage battery information; generating initial discharge scheduling information; generating a target discharge scheduling information group based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station; controlling the energy storage battery pack to discharge to the power supply network connected to the charging station; in response to the start of discharge, performing real-time battery status monitoring on each energy storage battery in the energy storage battery pack, and discharging control on energy storage batteries with abnormal battery states. This implementation achieves grid supplementation during peak electricity consumption periods, thereby achieving the effect of peak shaving and valley filling of grid load.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the fields of new energy technology and computer technology, specifically to a charging station energy storage control method, apparatus, and readable medium based on energy storage battery packs. Background Technology

[0002] The number of new energy vehicles on the road is increasing year by year. From a technical perspective, the power batteries in these vehicles can be considered distributed energy storage devices. Therefore, by charging and storing energy in the power batteries during off-peak hours and then feeding the stored energy back to the grid during peak hours, peak shaving and valley filling of the grid load can be achieved. However, current mainstream solutions mainly focus on orderly charging combined with time-of-use pricing. Therefore, how to achieve automated discharge control of the power batteries has become a pressing issue. Summary of the Invention

[0003] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0004] Some embodiments of this disclosure propose a charging station energy storage control method, apparatus, and readable medium based on energy storage battery packs to address the technical problems mentioned in the background section above.

[0005] In a first aspect, some embodiments of this disclosure provide a charging station energy storage control method based on an energy storage battery pack. The method includes: in response to receiving a grid load suppression command, collecting battery information corresponding to each energy storage battery in the energy storage battery pack to obtain a battery information group, wherein the energy storage battery is a power battery mounted on a new energy vehicle that has been verified through reverse power transmission and is connected to a charging pile included in the charging station; the battery information includes: first battery attribute information and historical discharge behavior information; for each battery information in the battery information group, performing the following processing steps: generating a battery health level for the energy storage battery corresponding to the battery information based on the first battery attribute information included in the battery information; in response to... If the battery health level is higher than the preset battery health level, initial discharge scheduling information is generated based on the battery health level and the battery information including the first battery attribute information and historical discharge behavior information. The initial discharge scheduling information includes the maximum discharge amount and discharge power. Based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station, a target discharge scheduling information group is generated. Based on the target discharge scheduling information group, the energy storage battery group is controlled to discharge to the power supply network connected to the charging station. In response to the start of discharge, real-time battery status monitoring is performed on each energy storage battery in the energy storage battery group, and discharge control is performed on energy storage batteries with abnormal battery status.

[0006] Secondly, some embodiments of this disclosure provide a charging station energy storage control device based on an energy storage battery pack. The device includes: a data acquisition unit configured to, in response to receiving a grid load suppression command, acquire battery information corresponding to each energy storage battery in the energy storage battery pack to obtain a battery information group, wherein the energy storage battery is a power battery of a new energy vehicle that has been verified through reverse power transmission and is connected to a charging pile included in the charging station, and the battery information includes: first battery attribute information and historical discharge behavior information; and an execution unit configured to, for each battery information in the battery information group, perform the following processing steps: generate a battery health level corresponding to the energy storage battery based on the first battery attribute information included in the battery information; and, in response to the battery health... The battery health level is greater than the preset battery health level. Based on the battery health level and the battery information including the first battery attribute information and historical discharge behavior information, initial discharge scheduling information is generated, wherein the initial discharge scheduling information includes: maximum discharge amount and discharge power; a generation unit is configured to generate a target discharge scheduling information group based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station; a control unit is configured to control the energy storage battery group to discharge to the power supply network connected to the charging station based on the target discharge scheduling information group; and a status monitoring unit is configured to perform real-time battery status monitoring on each energy storage battery in the energy storage battery group in response to the start of discharge, and to perform discharge control on energy storage batteries with abnormal battery status.

[0007] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0008] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0009] The above embodiments of this disclosure have the following beneficial effects: Through the charging station energy storage control method based on energy storage battery packs in some embodiments of this disclosure, the automated discharge control of the power battery is realized, thereby achieving the effect of peak shaving and valley filling of the grid load. Specifically, the current mainstream solutions are still mainly at the level of orderly charging combined with time-of-use pricing. Especially in the charging scenario of new energy vehicles, this is mainly manifested by setting peak and valley pricing to encourage electricity users to charge and store energy for new energy vehicles during the off-peak period, thereby indirectly achieving the effect of reducing the electricity load during the peak period. However, this method fails to effectively utilize the discharge advantages of the power battery. Based on this, the charging station energy storage control method based on energy storage battery packs in some embodiments of this disclosure firstly, in response to receiving the grid load suppression command, collects the battery information corresponding to each energy storage battery in the energy storage battery pack to obtain a battery information group. The energy storage battery is the power battery of the new energy vehicle that has been verified by reverse power transmission and is connected to the charging piles included in the charging station. The battery information includes: first battery attribute information and historical discharge behavior information. By combining grid load suppression commands for control, the system achieves global scheduling of charging stations capable of reverse discharge to the grid. Simultaneously, by collecting battery information sets, the system quantifies the battery status of the power batteries in new energy vehicles that can be discharged. Secondly, for each battery information in the aforementioned battery information set, the following processing steps are performed: First, based on the first battery attribute information included in the battery information, a battery health level is generated for the corresponding energy storage battery. Since the usage and battery status of power batteries in different new energy vehicles vary, a battery health level is generated to quantify the health status of the power batteries to avoid potential damage during subsequent discharge. Second, in response to a battery health level exceeding a preset battery health level, initial discharge scheduling information is generated based on the battery health level, the first battery attribute information included in the battery information, and historical discharge behavior information. This initial discharge scheduling information includes the maximum discharge amount and discharge power. By combining the battery health level, battery attribute information, and historical discharge behavior, corresponding discharge scheduling information is dynamically generated. Compared to simply setting thresholds, this approach achieves effective discharge scheduling while further reducing damage to the power batteries. Next, based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the aforementioned charging station, a target discharge scheduling information group is generated. This avoids damage to the power battery caused by sudden high-power discharge. Furthermore, based on the aforementioned target discharge scheduling information group, the energy storage battery pack is controlled to discharge to the power grid connected to the aforementioned charging station. Finally, in response to the start of discharge, real-time battery status monitoring is performed on each energy storage battery in the aforementioned energy storage battery pack, and discharge control is implemented for energy storage batteries with abnormal battery status.This allows for real-time monitoring of the power battery to detect any abnormalities during the discharge process, ensuring the overall robustness of the solution. In summary, the above solution achieves automated discharge control combined with the power battery. By discharging the power battery into the grid, it replenishes the grid during peak electricity demand periods, thus achieving peak shaving and valley filling of the grid load. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0011] Figure 1 This is a flowchart of some embodiments of the energy storage control method for charging stations based on energy storage battery packs according to the present disclosure; Figure 2 This is a schematic diagram of the communication architecture between charging stations, computing devices, and user databases. Figure 3 This is a schematic diagram of another communication architecture between charging stations, computing devices, and user databases; Figure 4 This is a structural schematic diagram of some embodiments of a charging station energy storage control device based on an energy storage battery pack according to the present disclosure; Figure 5 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0012] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0013] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0014] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0015] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0016] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0017] Before performing any operations related to the collection, storage, or use of vehicle-related data (such as battery information, second battery attribute information, etc.) involved in this disclosure, the relevant organizations or individuals shall fulfill their obligations, including conducting information security impact assessments, informing the information subjects, and obtaining prior authorization and consent from the information subjects.

[0018] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] refer to Figure 1 The diagram illustrates a flow 100 of some embodiments of a charging station energy storage control method based on an energy storage battery pack according to the present disclosure. This charging station energy storage control method based on an energy storage battery pack includes the following steps: Step 101: In response to receiving the grid load suppression command, collect the battery information corresponding to each energy storage battery in the energy storage battery pack to obtain the battery information group.

[0020] In some embodiments, the execution entity (e.g., a computing device) of the charging station energy storage control method based on the energy storage battery pack can, in response to receiving a grid load suppression command, collect battery information corresponding to each energy storage battery in the energy storage battery pack to obtain a battery information group.

[0021] The grid load suppression command can be a control command used to control the charging station to discharge to the connected power grid. Specifically, the command header information corresponding to the grid load suppression command may include, but is not limited to: the sender's identity identifier, the command issuance timestamp, the command type, the effective discharge time period, and the command anti-tampering verification code. The sender's identity identifier represents the unique identity of the sender of the grid load suppression command; the command issuance timestamp represents the timestamp of the sender issuing the grid load suppression command; the command type represents the command type of the grid load suppression command; the effective discharge time period represents the effective discharge time period of the operating charging station; and the command anti-tampering verification code is a verification code used to prevent the grid load suppression command from being tampered with or forged during transmission. For example, the command anti-tampering verification code can be generated using the HMAC-SHA256 algorithm, where HMAC-SHA256 is a message authentication code algorithm.

[0022] The energy storage battery is a power battery installed in a new energy vehicle that has been verified through reverse power transmission and is connected to the charging piles included in the charging station. Specifically, the new energy vehicles in this disclosure include: pure electric vehicles (BEVs, Battery Electric Vehicles) and plug-in hybrid electric vehicles (including range-extended vehicles) (PHEVs). When there are multiple new energy vehicles that have been verified through reverse power transmission and are connected to the charging piles included in the charging station, there are correspondingly multiple energy storage batteries as energy storage battery packs.

[0023] In this system, the battery information in the battery information group corresponds one-to-one with the energy storage batteries in the energy storage battery pack. The battery information represents the battery description corresponding to the energy storage battery. This battery information includes: first battery attribute information and historical discharge behavior information. The first battery attribute information represents the battery state data corresponding to the energy storage battery collected by the charging station after receiving a grid load suppression command. Specifically, the first battery attribute information may include, but is not limited to: vehicle identification, battery type, nominal battery capacity, current SOC (State of Charge) value, current SOH (State of Health) value, battery temperature, individual cell voltage range, maximum discharge power, battery cycle count, and minimum SOC threshold. The vehicle identification represents the unique vehicle identifier of the new energy vehicle corresponding to the energy storage battery; for example, the vehicle identification could be a VIN (Vehicle Identification Number). (Number, Vehicle Identification Code); Battery Type: Indicates the type of energy storage battery, which may include, but is not limited to, lithium iron phosphate batteries and ternary lithium batteries; Nominal Capacity: Indicates the rated capacity of the energy storage battery as specified at the factory; Current SOC: The ratio of the current battery capacity to the nominal battery capacity; Current SOH: Indicates the battery's health status; Battery Temperature: Indicates the current battery temperature, for example, the battery temperature can represent the overall average temperature of the energy storage battery, or it can be represented in matrix form as the cell temperature of each cell within the energy storage battery; Single Cell Voltage Range: Indicates the voltage range of multiple cells within the energy storage battery, for example, the single cell voltage range... The range can be composed of the lowest and highest single-cell voltages; the upper limit of discharge power represents the maximum discharge power of the energy storage battery; the battery cycle count represents the number of times the energy storage battery has undergone a complete charge and discharge cycle; the minimum SOC threshold represents the minimum state of charge threshold (used to control the cessation of discharge or forced charging to avoid battery aging and lifespan degradation caused by deep discharge); historical discharge behavior information represents the discharge behavior data of the energy storage battery within a historical time period; the historical time period can be the most recent 30 days; historical discharge behavior information may include, but is not limited to: vehicle identification, statistical time period, discharge frequency, average discharge amount, average SOC value variation range, number of discharges, and cumulative discharge amount. Among them, the discharge frequency represents the discharge frequency of the energy storage battery within the historical time period; the average discharge amount represents the average discharge amount of the energy storage battery within the historical time period; the average SOC value variation range represents the average variation range of the SOC value of the energy storage battery during the discharge process within the historical time period; the number of discharges represents the total number of discharges of the energy storage battery within the historical time period; and the cumulative discharge amount represents the total discharge amount of the energy storage battery within the historical time period. In particular, depending on actual needs, the attributes included in the first battery attribute information and historical discharge behavior information listed above can be directly used for subsequent steps or indirectly used for subsequent use.

[0024] In practice, when it is necessary to discharge to the power grid, it must be ensured that the new energy vehicle corresponding to the energy storage battery and the charging station, including the charging pile, are in a connected state. Specifically, the connection state disclosed herein refers to: the new energy vehicle corresponding to the energy storage battery and the charging pile are physically connected through charging cables and the corresponding communication connection has also been established. Specifically, the physical connection between the new energy vehicle and the charging pile through charging cables is the physical basis for subsequent discharge; the communication connection between the new energy vehicle and the charging pile is the communication basis for obtaining battery information and other data, as well as for discharge control.

[0025] In practice, due to the large variety of new energy vehicles, some new energy vehicles may not have the function of discharging to the power grid or have not been authorized to discharge. Therefore, it is necessary to conduct reverse power transmission verification. Specifically, firstly, it is necessary to verify whether the on-board charger of the new energy vehicle connected to the charging pile of the charging station has the function of charging and discharging and whether it complies with the corresponding discharge protocol. The purpose of this verification is: (1) the external discharge function (V2L, Vehicle to Load (or Load) primarily refers to high-power discharge achieved through DC fast charging ports or external inverters. Its essence is to supply power to loads (e.g., electrical appliances), not to discharge to the power grid. The on-board charger's charging and discharging capabilities are the hardware foundation for new energy vehicles to discharge to the power grid. Furthermore, the charger must comply with relevant discharge protocols. Therefore, reverse energy transmission verification is performed while the new energy vehicle and charging pile are connected. This determines whether the power battery in the new energy vehicle can be used as an energy storage battery for subsequent discharge operations. Only when the new energy vehicle passes the reverse energy transmission verification and is connected to the charging piles in the charging station can its power battery be used as an energy storage battery for subsequent discharge operations. Specifically, the discharge protocol can be a V2G (Vehicle to Grid) discharge protocol.

[0026] In practice, firstly, the computing device and the new energy vehicle (NEV) connected to the charging piles within the charging station (verified via reverse power transmission) can communicate via a CAN (Controller Area Network) bus. Upon receiving a request from the charging station, the NEV's BMS (Battery Management System) collects battery attribute information as the primary battery attribute information. Secondly, the computing device reads historical discharge behavior records from the user database to obtain historical discharge behavior information. The user database stores these historical discharge behavior records. Specifically, the computing device can be an edge control terminal for a single charging station; alternatively, it can be a cloud control terminal or platform for controlling multiple charging stations.

[0027] Example 1: The battery information corresponding to the energy storage battery may include the following first battery attribute information: {"Vehicle Identifier": "VIN-1233567", "Battery Type": "Ternary Lithium (NCM)", "Nominal Battery Capacity": {"Value": 92.0, "Unit": "kWh"}, "Current SOC": {"Value": 89, "Unit": "%"}, "Current SOH": {"Value": 91, "Unit": "%"}, "Battery Temperature": {"Value": 28. 5, "Unit": "℃", "Single Cell Voltage Range": {"Minimum Single Cell Voltage": {"Value": 3.62, "Unit": "V"}, "Maximum Single Cell Voltage": {"Value": 3.68, "Unit": "V"}}, "Upper Limit Discharge Power": {"Value": 120, "Unit": "kW"}, "Battery Cycle Count": {"Value": 320, "Unit": "times"}, "Minimum SOC Threshold": {"Value": 25, "Unit": "%"}}.

[0028] Example 2: The battery information corresponding to the energy storage battery, including historical discharge behavior information, can be: {"Vehicle Identifier": "VIN-1233567", "Statistical Time Period": {"Start Date": "2025-07-21", "End Date": "2025-08-20", "Statistical Days": {"Value": 30, "Unit": "days"}}, "Number of Discharges": {"Value": 15, "Unit": "times"}}, "Discharge Frequency": {"Average Number of Discharges per Week": {"Value": 3.5, "Unit": "times / week"}}, "Average Discharge Amount": {"Value": 12.5, "Unit": "kWh / time"}, "Average SOC Variation Range": {"Average SOC at the Start of Discharge": {"Value": 76, "Unit": "%"}}, "Cumulative Discharge Amount": {"Value": 187.5, "Unit": "kWh"}}.

[0029] Example 3, see Figure 2 The diagram illustrates the communication architecture between the charging station, computing device, and user database. When the computing device is the edge control terminal corresponding to a single charging station, it can communicate with charging station A via a wired connection to obtain the first battery attribute information, which is part of the battery information group. Simultaneously, the computing device can remotely access the user database via either a wired or wireless connection to obtain historical discharge behavior information, which is also part of the battery information group.

[0030] Example 4, see Figure 3The diagram illustrates another communication architecture between the charging station, computing device, and user database. In this architecture, when the computing device is a cloud-based control terminal or platform for controlling multiple charging stations, it can communicate with the user database via wired or wireless connections. Figure 3 The charging stations A, B, and C shown communicate remotely. Taking a single charging station as an example, the computing device can remotely collect the first battery attribute information, which is part of the battery information group, through wired or wireless connection, and read the user database through wired or wireless connection to obtain the historical discharge behavior information, which is part of the battery information group.

[0031] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (Ultra Wide Band) connections, and other currently known or future wireless connection methods.

[0032] It should be noted that the aforementioned computing devices can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed on the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0033] Step 102: For each battery information in the battery information group, perform the following processing steps: Step 1021: Generate the battery health level of the energy storage battery corresponding to the battery information based on the first battery attribute information included in the battery information.

[0034] In some embodiments, the aforementioned executing entity may generate the battery health level of the energy storage battery corresponding to the battery information based on the first battery attribute information included in the battery information.

[0035] Among them, the battery health level is a classification level that characterizes the discharge capability of the energy storage battery corresponding to the battery information.

[0036] In practice, firstly, the aforementioned execution entity can use a Word2Vec encoder to encode the first battery attribute information included in the battery information to obtain an encoded vector; then, through a battery health level classifier, it generates the battery health level of the energy storage battery corresponding to the battery information. The battery health level classifier can include a downsampling network and a multi-classifier. The downsampling network can be used to reduce the dimensionality of the encoded vector features to a specific feature dimension, and the multi-classifier is used to output the confidence scores of different battery health levels. For example, the downsampling network can include 5 downsampling layers; the multi-classifier can use a Softmax classifier.

[0037] As an example, battery health levels can include: Grade A, Grade B, Grade C, and Grade D. Grade A indicates the corresponding energy storage battery is in excellent condition and can withstand high-power discharge; Grade B indicates the corresponding energy storage battery is in good condition and suitable for medium-power discharge; Grade C indicates the corresponding energy storage battery is in fair condition and suitable for low-power discharge; and Grade D indicates the corresponding energy storage battery is in poor condition and unsuitable for discharge. Grade A > Grade B > Grade C > Grade D. Therefore, when generating battery health levels using a Word2Vec encoder and battery health level classifier, the corresponding battery health level can be directly mapped using a Softmax classifier.

[0038] In some optional implementations of certain embodiments, the execution entity generates a battery health level for the energy storage battery corresponding to the battery information based on the first battery attribute information included in the battery information, including: Step S1021-1: Map the battery health status based on the first battery attribute information included in the above battery information to obtain the battery health status.

[0039] Among them, the battery health status represents the health of the energy storage battery, and the value range of the battery health status is [0,1].

[0040] In practice, the aforementioned executing entity can read the current SOH value, which is included in the first battery attribute information, as the battery health status. Since the current SOH value ranges from [0% to 100%], there is no need to adjust the corresponding units. The battery health status can be obtained simply by removing the unit (%) of the current SOH value.

[0041] Step S1021-2: Extract battery temperature state features from the first battery attribute information included in the above battery information to obtain battery temperature state features.

[0042] Among them, the battery temperature state characteristic is the characteristic expression of the battery temperature of the energy storage battery corresponding to the battery information.

[0043] In practice, the battery temperature included in the first battery attribute information can be mapped to the range of [0, 1] through normalization, and used as a battery temperature state feature.

[0044] Example 1, taking the maximum-minimum normalization algorithm as an example, requires combining the maximum battery temperature and the minimum battery temperature. When the battery temperature included in the first battery attribute information represents the average temperature of the entire energy storage battery, the upper and lower temperature boundaries of the calibrated temperature range of the corresponding energy storage battery can be used as the maximum battery temperature and the minimum battery temperature. In this way, the battery temperature state features included in the first battery attribute information can be extracted.

[0045] Example 2, taking the maximum-minimum normalization algorithm as an example, requires combining the maximum battery temperature and the minimum battery temperature. When the battery temperature included in the first battery attribute information is represented in matrix form as the cell temperature of each cell in the energy storage battery, the upper and lower temperature boundaries of the calibrated temperature range of the corresponding energy storage battery can be used as the maximum battery temperature and the minimum battery temperature to extract the battery temperature state features included in the first battery attribute information.

[0046] Step S1021-3: Generate the battery temperature state based on the above battery temperature state characteristics.

[0047] Among them, the battery temperature state can characterize the temperature state of the energy storage battery corresponding to the battery information, and the value range of the battery temperature state is [0,1].

[0048] Example 1: When the battery temperature included in the first battery attribute information represents the average temperature of the entire energy storage battery, the obtained battery temperature state feature can be directly used as the battery temperature state.

[0049] Example 2: When the battery temperature included in the first battery attribute information is represented by a matrix of the cell temperatures of each cell within the energy storage battery, the resulting battery temperature state feature is expressed in matrix form. Therefore, the battery temperature state can be represented as: Battery Temperature State = min(1, mean(matrix) + std(matrix) × λ), where min() represents the minimum value function, i.e., taking the minimum value from "1" and "mean(matrix) + std(matrix) × λ", mean() represents the mean function, used to extract the mean of the matrix values, and std() represents the standard deviation function, used to extract the standard deviation of the matrix values. Matrix represents the battery temperature state feature expressed in matrix form. λ represents the temperature sensitivity coefficient, with a value range of [2, 3]. The larger the value of λ, the more sensitive it is to temperature differences. For example, taking ternary lithium batteries and lithium iron phosphate batteries as examples, ternary lithium batteries are more sensitive to temperature differences. Therefore, when the battery information corresponds to a lithium iron phosphate battery, the corresponding temperature sensitivity coefficient can be 2.2; when the battery information corresponds to a ternary lithium battery, the corresponding temperature sensitivity coefficient can be 2.8. Of course, the range of the temperature sensitivity coefficient and the value of the temperature sensitivity coefficient corresponding to different battery types can be adjusted according to actual needs.

[0050] Step S1021-4: Extract battery voltage state features from the first battery attribute information included in the above battery information to obtain battery voltage state features.

[0051] Among them, the battery voltage state characteristic is the characteristic expression of the battery voltage of the energy storage battery corresponding to the battery information.

[0052] In practice, firstly, the aforementioned execution entity can normalize the highest single-cell voltage (upper bound) and lowest single-cell voltage (lower bound) within the voltage range of the individual cells included in the battery information, based on the rated voltage range of the cells corresponding to the battery information, to obtain a normalized single-cell voltage range. Next, the execution entity can generate the voltage extreme difference and the voltage median value based on the normalized single-cell voltage range, where the voltage extreme difference = upper bound of the normalized single-cell voltage range - lower bound of the normalized single-cell voltage range; and the voltage median value = (upper bound of the normalized single-cell voltage range + lower bound of the normalized single-cell voltage range) / 2. Furthermore, the execution entity can construct a 1×2 feature vector from the voltage extreme difference and the voltage median value as a battery voltage state feature.

[0053] Step S1021-5: Generate the battery voltage state based on the above battery voltage state characteristics.

[0054] Among them, the battery voltage state can characterize the voltage state of the energy storage battery corresponding to the battery information, and the value range of the battery voltage state is [0,1].

[0055] As an example, a pre-trained linear regression model can be used to obtain the battery voltage state by taking battery voltage state features as input and smoothing them using the Sigmoid() function. The corresponding expression can be: Battery Voltage State = Sigmoid(Voltage Extreme Difference × a1 + Voltage Median × a2 + b1), where a1 is the weight of the voltage extreme difference, a2 is the weight of the voltage median, and b is the bias term. For example, a1 can be -3.2, a2 ​​can be 0.08, and b can be 2.1. The linear regression model can be trained in a supervised manner using labeled training samples. The loss function is the mean squared error loss function, the optimizer is the Adam optimizer, and the learning rate can be set to 0.01. It is important to note that the values ​​in the above example are for illustrative purposes only; the values ​​of a1, a2, and b may vary depending on the training samples and the parameter settings during model training.

[0056] Step S1021-6: Generate battery service life status and battery cycle loss status based on the first battery attribute information included in the above battery information.

[0057] Among them, the battery service life status represents the service age of the energy storage battery corresponding to the battery information, and the battery cycle loss status represents the cycle loss of the energy storage battery corresponding to the battery information. The values ​​of both the battery service life status and the battery cycle loss status are in the range of [0,1].

[0058] In practice, the first battery attribute information included in battery information may also include: battery usage time, battery rated service life, and battery rated cycle count. The battery usage time can be measured in years, representing the usage time of the energy storage battery since its manufacture. The battery rated service life represents the rated service life of the energy storage battery, and the battery rated cycle count represents the rated number of cycles. Battery service life status = Battery usage time / Battery rated service life; Battery cycle loss status = Battery cycle count / Battery rated cycle count.

[0059] Step S1021-7: Perform battery discharge adaptability mapping on the battery type of the energy storage battery corresponding to the above battery information to obtain the battery discharge adaptability state.

[0060] Among them, the battery discharge adaptability state characterizes the battery information corresponding to the energy storage battery's tolerance to discharge. The value range of the battery discharge adaptability state is [0,1]. The energy storage battery's tolerance to discharge is directly proportional to the value of the corresponding battery discharge adaptability state. Among them, the battery health state, the battery temperature state, the battery voltage state, the battery service life state, the battery cycle loss state, and the battery discharge adaptability state all correspond to the same dimension, and the corresponding dimension can all be [0,1].

[0061] In practice, corresponding battery discharge adaptability states can be preset for different battery types. Therefore, the battery discharge adaptability state can be determined through value mapping.

[0062] As an example, taking lithium iron phosphate batteries as energy storage batteries, lithium iron phosphate batteries have a long cycle life and are resistant to deep discharge, and their corresponding state of discharge adaptability can be 0.9. Taking ternary lithium batteries as energy storage batteries as an example, ternary lithium batteries have high energy density but poor discharge tolerance, and their corresponding state of discharge adaptability can be 0.7. In particular, the pre-set state of discharge adaptability corresponding to different battery types can be adjusted according to different scenarios. For example, if it is necessary to distinguish the discharge tolerance of different battery types of energy storage batteries through the state of discharge adaptability, the difference in the state of discharge adaptability of the corresponding batteries can be increased.

[0063] Step S1021-8: Generate a global battery state based on the battery health state, battery temperature state, battery voltage state, battery service life state, battery cycle loss state, and battery discharge adaptability state.

[0064] The global battery state is an overall state representation of the energy storage battery corresponding to the battery information. The value range of the global battery state is [0,1].

[0065] In practice, a weighted summation method can be used to generate the global battery state based on the aforementioned battery health state, battery temperature state, battery voltage state, battery service life state, battery cycle loss state, and battery discharge adaptability state. Specifically, it can be expressed as: Global Battery State = c1 × Battery Health State + c2 × Battery Temperature State + c3 × Battery Voltage State + c4 × Battery Service Life State + c5 × Battery Cycle Loss State + c6 × Battery Discharge Adaptability State, where c1 + c2 + c3 + c4 + c5 + c6 = 1. For example, c1 = c2 = c3 = c4 = c5 = c6 = 1 / 6. Of course, the importance of different state values ​​may differ during the weighted summation process, and the corresponding weight values ​​can be adjusted accordingly. For example, c1 = 0.25, c2 = 0.15, c3 = 0.15, c4 = 0.20, c5 = 0.15, c6 = 0.10.

[0066] Step S1021-9: Map the global battery status to battery health level to obtain the battery health level of the energy storage battery corresponding to the above battery information.

[0067] In practice, since battery health levels include A-level, B-level, C-level, and D-level, it is necessary to map the global battery state to discrete battery health levels. Therefore, for different health levels, the corresponding range of global battery state values ​​can be preset to achieve the mapping of the global battery state to the corresponding battery health level.

[0068] As an example, the global battery status value range corresponding to health level A can be [1, 0.85), the global battery status value range corresponding to health level B can be [0.85, 0.70), the global battery status value range corresponding to health level C can be [0.70, 0.55), and the global battery status value range corresponding to health level D can be [0.55, 0]. In particular, the global battery status value range corresponding to different health levels can be adaptively adjusted according to actual needs.

[0069] Step 1022: In response to the battery health level being greater than the preset battery health level, generate initial discharge scheduling information based on the battery health level and the battery information including the first battery attribute information and historical discharge behavior information.

[0070] In some embodiments, the aforementioned execution entity may, in response to a battery health level greater than a preset battery health level, generate initial discharge scheduling information based on the battery health level and the first battery attribute information and historical discharge behavior information included in the battery information.

[0071] The preset battery health level is used to filter out energy storage batteries that are not suitable for subsequent discharge. For example, the preset battery health level could be level D. The preset battery health level can be adaptively adjusted according to actual needs. The initial discharge scheduling information represents the discharge scheduling plan for the energy storage battery corresponding to the battery information. The initial discharge scheduling information can include: maximum discharge capacity and discharge power. The maximum discharge capacity represents the maximum discharge capacity of the energy storage battery corresponding to the battery information during this discharge, and the unit can be kWh; the discharge power represents the maximum discharge power of the energy storage battery corresponding to the battery information during this discharge, and the unit can be kW.

[0072] In practice, the aforementioned implementing entity can pre-train a decision tree model, using the battery health level, battery information including the first battery attribute information and historical discharge behavior information as input to the decision tree model, and the initial discharge scheduling information including the maximum discharge amount and discharge power as output, to generate initial discharge scheduling information. The decision tree model can be a random forest model.

[0073] In some optional implementations of certain embodiments, the execution entity, in response to the battery health level being greater than a preset battery health level, generates initial discharge scheduling information based on the battery health level and the battery information including first battery attribute information and historical discharge behavior information, including: Step S1022-1: Based on the first battery attribute information included in the above battery information, determine whether there is a default value for the maximum discharge capacity.

[0074] The default maximum discharge capacity represents the maximum discharge capacity preset by the owner of the new energy vehicle corresponding to the energy storage battery. Specifically, when discharging the energy storage battery, in addition to reducing the power load on the power grid, it is also necessary to ensure that the new energy vehicle corresponding to the energy storage battery has sufficient remaining power to meet normal vehicle operation.

[0075] In practice, the aforementioned execution entity can determine whether the minimum SOC threshold in the first battery attribute information is empty. When the minimum SOC threshold is 0, it indicates that there is no default value for the maximum discharge capacity; when the minimum SOC threshold is not 0, it indicates that there is a default value for the maximum discharge capacity. In this case, the aforementioned execution entity can use the product of the minimum SOC threshold and the battery's nominal capacity as the default value for the maximum discharge capacity. In particular, the minimum SOC threshold is generally not equal to 0 because: even if the user does not set a minimum SOC threshold separately, the battery management system will set a minimum SOC threshold to avoid the battery being completely empty.

[0076] Step S1022-2: In response to the existence of a default value for the maximum discharge amount, the default value for the maximum discharge amount is determined as the maximum discharge amount included in the initial discharge scheduling information.

[0077] In practice, when the minimum SOC threshold is not 0, the minimum SOC threshold × nominal battery capacity = maximum discharge capacity. The default value is the maximum discharge capacity included in the initial discharge scheduling information.

[0078] Step S1022-3: In response to the absence of a default maximum discharge value, perform the following maximum discharge value generation steps: Step S1022-31: Extract discharge behavior features from the historical discharge behavior information included in the above battery information to obtain historical discharge behavior features.

[0079] Among them, historical discharge behavior features are the feature representations corresponding to historical discharge behavior information. For example, the feature dimension of historical discharge behavior features can be 128 dimensions. The reason is that a feature dimension of 128 dimensions is sufficient to fully express nonlinear relationships. An excessively high feature dimension will increase the amount of data computation during feature processing and will also introduce noise.

[0080] In practice, the aforementioned implementing entities can use the Word2Vec model to encode the historical discharge behavior information included in the battery information, thereby obtaining historical discharge behavior features. Specifically, during the encoding process, vehicle identification is not very meaningful for representing vehicle behavior features and would result in an extremely large dimensionality for the lookup table; therefore, vehicle identification is not included in the encoding process.

[0081] Steps S1022-32: Based on the above-mentioned historical discharge behavior characteristics, generate candidate maximum discharge quantities.

[0082] Among them, the candidate maximum discharge quantity is the candidate maximum discharge quantity.

[0083] In practice, a regression model can be used to generate candidate maximum discharge amounts by taking historical discharge behavior features as input. For example, the XGBoost regression model can be used, but other models such as the LightGBM regression model can also be used depending on factors such as prediction accuracy or computing power limitations. In particular, taking the XGBoost regression model as an example, N historical discharge behavior information points are collected, and N historical discharge behavior features are generated as training samples (e.g., 70% for the training set and 30% for the test set), and the mean squared error loss function is used.

[0084] Steps S1022-33: Based on the first battery attribute information included in the above battery information, determine the maximum safe discharge value and the upper limit of discharge power.

[0085] The maximum safe discharge capacity is calculated as follows: maximum safe discharge capacity = nominal battery capacity × (current SOC value - minimum SOC threshold) × current SOH value. The unit of the maximum safe discharge capacity can be kWh, the unit of the nominal battery capacity can be kWh, and the unit of the upper limit of discharge power can be kW.

[0086] In practice, firstly, the aforementioned executing entity can read the battery's nominal capacity, current SOC value, minimum SOC threshold, and current SOH value from the first battery attribute information, and generate the maximum safe discharge value using the formula for calculating the maximum safe discharge value. Secondly, the aforementioned executing entity can read the upper limit of discharge power from the first battery attribute information as the upper limit value of discharge power.

[0087] Step S1022-34: In response to the fact that the above-mentioned candidate maximum discharge quantity is less than the above-mentioned maximum discharge quantity safety value, the above-mentioned candidate maximum discharge quantity is determined as the maximum discharge quantity included in the initial discharge scheduling information.

[0088] In practice, the candidate maximum discharge quantity is generated by combining the prediction of the regression model. In order to avoid the problem of the candidate maximum discharge quantity being unusable in extreme cases (such as the prediction result being greater than the safe value), the maximum discharge quantity is determined by comparing it with the safe value of the maximum discharge quantity.

[0089] Step S1022-35: In response to the above candidate maximum discharge quantity being greater than or equal to the above maximum discharge quantity safety value, the above maximum discharge quantity safety value is determined as the maximum discharge quantity included in the initial discharge scheduling information.

[0090] Steps S1022-36: Generate the discharge power included in the initial discharge scheduling information based on the discharge power weight corresponding to the above battery health level and the above discharge power upper limit.

[0091] The discharge power weight is a safety weight set from the battery health level dimension, based on the discharge power dimension, to avoid battery damage caused by over-discharge during subsequent discharge. The value range of the discharge power weight is [0%, 100%].

[0092] In practice, the initial discharge scheduling information includes discharge power = upper limit of discharge power × discharge power weight corresponding to the battery health level.

[0093] As an example, the discharge power weight corresponding to a health level of A can be 95%, the discharge power weight corresponding to a health level of B can be 85%, and the discharge power weight corresponding to a health level of C can be 75%. Since generating initial discharge scheduling information requires the battery health level to be greater than the preset battery health level, i.e., greater than the health level of D, the discharge power weight corresponding to the health level of D is no longer set. In particular, the discharge power weight corresponding to different health levels can be adaptively adjusted according to actual needs.

[0094] Step 103: Generate the target discharge scheduling information group based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station.

[0095] In some embodiments, the aforementioned execution entity may generate a target discharge scheduling information group based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station.

[0096] Among them, the gradient discharge configuration information represents the discharge power limit configuration of the charging station during a continuous discharge period.

[0097] In practice, directly controlling the energy storage battery to discharge to the power grid using the discharge power included in the initial discharge scheduling information still carries the risk of affecting the battery's health. Therefore, discharge stations can set discharge power limits for different discharge time periods according to gradient settings. For example, the gradient discharge configuration information could be [0-30 minutes: 70%; after 30 minutes: 100%]. That is, within the first 30 minutes of discharge, the corresponding energy storage battery is controlled to discharge at 70% of the discharge power included in the initial discharge scheduling information. If the discharge exceeds 30 minutes and the battery discharges without abnormalities, the corresponding energy storage battery is controlled to discharge at 100% of the discharge power included in the initial discharge scheduling information. For each initial discharge scheduling information in the initial discharge scheduling information group, the aforementioned execution entity can calculate the specific discharge power value within the continuous discharge time period based on the gradient discharge configuration information and the discharge power included in the initial discharge scheduling information, and combine it with the maximum discharge amount included in the initial discharge scheduling information to obtain the target discharge scheduling information. For example, the target discharge scheduling information could be [maximum discharge capacity: XX kWh, discharge power from 0 to 30 minutes: discharge power included in the initial discharge scheduling information × 70%; discharge power after 30 minutes: discharge power included in the initial discharge scheduling information × 100%].

[0098] In some optional implementations of certain embodiments, the execution entity generates a target discharge scheduling information group based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station, including: Step S103-1: Determine the power weighting reorganization based on the gradient discharge configuration information described above.

[0099] In the above power weighting reorganization, the power weights correspond to different discharge stages. The gradient discharge configuration information can be stored in JSON (JavaScript Object Notation) format, which includes the time range of each time period and the corresponding power weight. The power weight values ​​range from [0% to 100%].

[0100] In practice, regular expressions can be used to parse the gradient discharge configuration information to determine the power weight reassembly. For example, if the gradient discharge configuration information sets corresponding power weights for K consecutive time periods, then parsing the gradient discharge configuration information will yield K power weights, which can be used as the power weight reassembly, where K ≥ 2.

[0101] Step S103-2: For each initial discharge scheduling information in the above initial discharge scheduling information group, perform the following scheduling information update steps: Step S103-21: Based on the above power weighting reorganization, the discharge power included in the above initial discharge scheduling information is segmented to obtain the updated discharge power group.

[0102] Wherein, the updated discharge power = power weight × discharge power included in the initial discharge scheduling information.

[0103] In practice, assuming there are K power weights in the power weight reorganization, the kth power weight in the power weight reorganization is obtained by traversing the kth power weight and multiplying the kth power weight by the discharge power included in the initial discharge scheduling information. The kth updated discharge power in the updated discharge power group is obtained. The value of k is in the range of [1, K] and k is a positive integer.

[0104] Step S103-22: The maximum discharge amount and the updated discharge power group included in the initial discharge scheduling information are determined as the target discharge scheduling information in the target discharge scheduling information group that corresponds to the initial discharge scheduling information.

[0105] In practice, the aforementioned executing entity can encapsulate the maximum discharge amount and the updated discharge power group included in the initial discharge scheduling information into target discharge scheduling information corresponding to the initial discharge scheduling information in the target discharge scheduling information group, using JSON format.

[0106] Step 104: Based on the target discharge scheduling information group, control the energy storage battery pack to discharge to the power supply network connected to the charging station.

[0107] In some embodiments, the aforementioned executing entity may control the energy storage battery pack to discharge to the power grid connected to the charging station based on the target discharge scheduling information group.

[0108] Among them, the target discharge scheduling information in the target discharge scheduling information group corresponds one-to-one with the energy storage batteries in the energy storage battery group.

[0109] In practice, the aforementioned executing entity can convert target discharge scheduling information into control commands to control the corresponding energy storage battery to discharge to the power grid.

[0110] In some optional implementations of certain embodiments, the execution entity controls the energy storage battery pack to discharge to the power grid connected to the charging station based on the target discharge scheduling information group, including: Step S104-1: For each target discharge scheduling information in the above target discharge scheduling information group, perform the following discharge processing steps: Step S104-11: Perform secondary connection status confirmation on the energy storage battery corresponding to the above target discharge scheduling information.

[0111] In practice, during the time period from when the execution entity obtains the battery information to when it generates the target discharge scheduling information, the connection status between the new energy vehicle corresponding to the energy storage battery and the charging station may change, such as being disconnected. Therefore, after generating the target discharge scheduling information, it is necessary to confirm the communication connection status between the new energy vehicle corresponding to the energy storage battery and the charging pile. Specifically, heartbeat detection can be used to determine whether the communication connection status between the charging pile and the new energy vehicle is in a connected state.

[0112] Step S104-12: In response to the confirmation of the secondary connection status of the energy storage battery corresponding to the above target discharge scheduling information, a discharge control command is generated according to the above target discharge scheduling information.

[0113] Among them, the discharge control command is a control command used to control the new energy vehicle corresponding to the energy storage battery to discharge to the power grid.

[0114] In practice, the aforementioned executing entities can convert the target discharge scheduling information into discharge control commands according to the instruction encapsulation format set by the communication protocol.

[0115] Step S104-13: Send the above discharge control command to the charging pile corresponding to the above target discharge scheduling information, so as to control the energy storage battery corresponding to the target discharge scheduling information to discharge to the power supply network connected to the above charging station.

[0116] In practice, when the computing device can be the edge control terminal corresponding to a single charging station, it can directly send discharge control commands to the charging piles corresponding to the aforementioned target discharge scheduling information via CAN bus communication. When the computing device can also be a cloud control terminal or platform used to control multiple charging stations, the discharge control commands can first be sent to the charging station where the charging piles corresponding to the aforementioned target discharge scheduling information are located. Then, the controller within the charging station can send the discharge control commands to the charging piles corresponding to the aforementioned target discharge scheduling information via CAN bus communication.

[0117] Step 105: In response to the start of discharge, real-time battery status monitoring is performed on each energy storage battery in the energy storage battery pack, and discharge control is performed on energy storage batteries with abnormal battery status.

[0118] In some embodiments, the aforementioned execution entity may, in response to the start of discharge, perform real-time battery status monitoring on each energy storage battery in the energy storage battery pack, and perform discharge control on energy storage batteries with abnormal battery status.

[0119] In practice, once discharge begins, the executing entity can periodically continue to collect battery attribute information and determine whether the energy storage battery is abnormal during discharge by using threshold judgment. For example, warning thresholds can be set for multiple dimensions such as battery voltage and battery temperature. In particular, corresponding warning thresholds can be customized for different battery types and different monitoring sensitivities. When the energy storage battery is abnormal, operations including but not limited to reducing discharge power and disconnecting the connection can be performed on the abnormal battery to achieve discharge control for different risk situations.

[0120] In some optional implementations of certain embodiments, the execution entity, in response to the start of discharge, performs real-time battery status monitoring on each energy storage battery in the energy storage battery pack, and performs discharge control on energy storage batteries with abnormal battery states, including: Step S105-1: Collect the second battery attribute information of the above-mentioned energy storage battery.

[0121] The second battery attribute information is primarily used for battery status monitoring during the discharge process. In addition to the attributes included in the first battery attribute information, the second battery attribute information may also include the battery temperature change rate. The first and second battery attribute information are collected at different times; specifically, the first battery attribute information is collected when a grid load suppression command is received, while the second battery attribute information is collected when the discharge to the power grid begins.

[0122] In practice, the aforementioned implementing entity can periodically collect the second battery attribute information of the energy storage battery at a fixed frequency to monitor the real-time changing battery state of the energy storage battery during the discharge process.

[0123] Step S105-2: Based on the second battery attribute information mentioned above, generate the battery anomaly level corresponding to the energy storage battery.

[0124] The battery anomaly level represents the risk level of the battery. The battery anomaly level can include: a first battery anomaly level, a second battery anomaly level, and a third battery anomaly level, wherein the order is: first battery anomaly level < second battery anomaly level < third battery anomaly level.

[0125] In practice, corresponding early warning rules are set for attributes such as battery temperature and battery temperature change rate. Combined with the attribute information of the second battery, the corresponding battery anomaly level of the energy storage battery is generated through rule triggering.

[0126] Example 1: When the battery anomaly level is Level 1, the warning rule for battery temperature can be that the battery temperature is too high but does not exceed the threshold, or the battery temperature change rate is too fast. The rate of change of battery temperature can be in the range of [1.5℃ / min, 2℃ / min]. For example, taking a ternary lithium battery, the corresponding battery temperature range could be [40℃, 45℃]. Similarly, taking a lithium iron phosphate battery, the corresponding battery temperature range could be [45℃, 50℃].

[0127] Example 2: When the battery anomaly level is the second battery anomaly level, the warning rules corresponding to the battery temperature can be that the battery temperature exceeds the threshold or the battery temperature change rate changes too quickly. The range of the battery temperature change rate can be (2℃ / min, 2.5℃ / min). For example, taking a ternary lithium battery as an example, its corresponding battery temperature range can be (45℃, 55℃). Similarly, taking a lithium iron phosphate battery as an example, its corresponding battery temperature range can be (50℃, 55℃).

[0128] Example 3: When the battery anomaly level is the third battery anomaly level, the warning rules corresponding to the battery temperature can be that the battery temperature exceeds the threshold or the battery temperature change rate changes too quickly. The range of the battery temperature change rate can be (2.5℃ / min, 3℃ / min). For example, taking a ternary lithium battery as an example, its corresponding battery temperature range can be (55℃, 65℃). Similarly, taking a lithium iron phosphate battery as an example, its corresponding battery temperature range can be (55℃, 60℃).

[0129] Step S105-3: In response to the above-mentioned battery abnormality level being the first battery abnormality level, the discharge power of the above-mentioned energy storage battery is reduced.

[0130] In practice, when the battery abnormality level is the first battery abnormality level, the current discharge power of the corresponding energy storage battery can be reduced to 80% and an alarm notification can be issued, but the connection is not disconnected. When the energy storage battery returns to normal, the discharge power is restored. When the energy storage battery continues to be abnormal, the battery abnormality level is abnormally upgraded, for example, upgraded to the second battery abnormality level.

[0131] Step S105-4: In response to the above-mentioned battery abnormality level being the second battery abnormality level, the discharge power of the above-mentioned energy storage battery is reduced to zero.

[0132] In practice, when the battery anomaly level is the second battery anomaly level, the current discharge power of the corresponding energy storage battery can be reduced to 0%, but the connection is not disconnected, and the battery status of the energy storage battery continues to be monitored. When the energy storage battery returns to normal, the battery anomaly level is downgraded, for example, downgraded to the first battery anomaly level.

[0133] Step S105-5: In response to the above battery abnormality level being the third battery abnormality level, a discharge disconnection command is sent to the charging pile corresponding to the above energy storage battery.

[0134] Among them, the discharge disconnection command is used to control the disconnection between the charging pile and the new energy vehicle corresponding to the energy storage battery.

[0135] In practice, when the battery abnormality level is the third battery abnormality level, the aforementioned execution entity can send a discharge disconnection command to the charging pile corresponding to the aforementioned energy storage battery to control the charging pile to directly disconnect the connection between the charging pile and the new energy vehicle corresponding to the energy storage battery, for example, to control the charging pile to trip.

[0136] In some optional implementations of certain embodiments, the above method further includes: Step S1: For each energy storage cell in the above energy storage battery pack, perform the following synchronization steps: Step S11: In response to the end of discharge or the discharge being disconnected, generate the discharge record corresponding to the above-mentioned energy storage battery.

[0137] Among them, the discharge record characterizes the discharge situation of the energy storage battery from the start of self-discharge to the end of discharge.

[0138] In practice, the aforementioned executing entity collects battery attribute information (second battery attribute information) in real time. Therefore, it can extract the attributes from the second battery attribute information to fill in the records and obtain the discharge record.

[0139] Step S12: Store the above discharge records to the discharge record database and synchronize them to the user account corresponding to the above energy storage battery.

[0140] Among them, the discharge record database is a database specifically used to store the discharge records of new energy vehicles corresponding to different user accounts.

[0141] In practice, firstly, the aforementioned executing entity can synchronize the discharge records to the discharge record database (for example, write the discharge records to the discharge record database in real time). Then, after the discharge records are successfully written to the discharge record database, the discharge records are sent to the user account corresponding to the aforementioned energy storage battery via message notification.

[0142] Further reference Figure 4 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a charging station energy storage control device based on an energy storage battery pack. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this energy storage control device for charging stations based on energy storage battery packs can be specifically applied to various electronic devices.

[0143] like Figure 4 As shown, some embodiments of a charging station energy storage control device 400 based on an energy storage battery pack include: a data acquisition unit 401, an execution unit 402, a generation unit 403, a control unit 404, and a status monitoring unit 405. The data acquisition unit 401 is configured to, in response to receiving a grid load suppression command, acquire battery information corresponding to each energy storage battery in the energy storage battery pack to obtain a battery information group. The energy storage battery is a power battery of a new energy vehicle that has been verified through reverse power transmission and is connected to a charging pile included in the charging station. The battery information includes: first battery attribute information and historical discharge behavior information. The execution unit 402 is configured to, for each battery information in the battery information group, perform the following processing steps: based on the first battery attribute information included in the battery information, generate a data storage battery corresponding to the battery information. Battery health level; in response to the battery health level being greater than a preset battery health level, initial discharge scheduling information is generated based on the battery health level and the battery information including the first battery attribute information and historical discharge behavior information, wherein the initial discharge scheduling information includes: maximum discharge amount and discharge power; generation unit 403 is configured to generate a target discharge scheduling information group based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station; control unit 404 is configured to control the energy storage battery group to discharge to the power supply network connected to the charging station based on the target discharge scheduling information group; state monitoring unit 405 is configured to perform real-time battery state monitoring on each energy storage battery in the energy storage battery group in response to the start of discharge, and to perform discharge control on energy storage batteries with abnormal battery states.

[0144] It is understandable that the units described in the energy storage control device 400 for charging stations based on energy storage battery packs are similar to those in the reference device. Figure 1 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to the energy storage control device 400 for charging stations based on energy storage battery packs and the units contained therein, and will not be repeated here.

[0145] The following is for reference. Figure 5 It shows a schematic diagram of the structure of an electronic device (e.g., a computing device) 500 suitable for implementing some embodiments of the present disclosure. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0146] like Figure 5 As shown, the electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory 502 or a program loaded from a storage device 508 into a random access memory 503. The random access memory 503 also stores various programs and data required for the operation of the electronic device 500. The processing unit 501, the read-only memory 502, and the random access memory 503 are interconnected via a bus 504. An input / output interface 505 is also connected to the bus 504.

[0147] Typically, the following devices can be connected to the input / output interface 505: input devices 506 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 5 Each box shown can represent a device or multiple devices as needed.

[0148] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a read-only memory 502. When the computer program is executed by the processing device 501, it performs the functions defined above in the methods of some embodiments of this disclosure.

[0149] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0150] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0151] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: in response to receiving a grid load suppression command, collect battery information corresponding to each energy storage battery in the energy storage battery pack to obtain a battery information group, wherein the energy storage battery is a power battery of a new energy vehicle verified through reverse power transmission and connected to a charging pile included in the charging station, and the battery information includes: first battery attribute information and historical discharge behavior information; for each battery information in the aforementioned battery information group, perform the following processing steps: based on the first battery attribute information included in the aforementioned battery information, generate a battery health level for the energy storage battery corresponding to the aforementioned battery information. In response to the battery health level being greater than a preset battery health level, initial discharge scheduling information is generated based on the battery health level and the battery information including the first battery attribute information and historical discharge behavior information. The initial discharge scheduling information includes the maximum discharge amount and discharge power. Based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station, a target discharge scheduling information group is generated. Based on the target discharge scheduling information group, the energy storage battery pack is controlled to discharge to the power supply network connected to the charging station. In response to the start of discharge, real-time battery status monitoring is performed on each energy storage battery in the energy storage battery pack, and discharge control is applied to energy storage batteries with abnormal battery states.

[0152] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and Python, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0153] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0154] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0155] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A charging station energy storage control method based on an energy storage battery pack, characterized in that, include: In response to receiving a grid load suppression command, the battery information corresponding to each energy storage battery in the energy storage battery pack is collected to obtain a battery information group. The energy storage battery is the power battery of a new energy vehicle that has been verified by reverse power transmission and is connected to the charging piles included in the charging station. The battery information includes: first battery attribute information and historical discharge behavior information. For each battery information in the battery information group, perform the following processing steps: Based on the first battery attribute information included in the battery information, a battery health level corresponding to the energy storage battery is generated; In response to the battery health level being greater than a preset battery health level, initial discharge scheduling information is generated based on the battery health level and the battery information including the first battery attribute information and historical discharge behavior information. The initial discharge scheduling information includes: maximum discharge amount and discharge power. Based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station, a target discharge scheduling information group is generated. According to the target discharge scheduling information group, the energy storage battery pack is controlled to discharge to the power supply network connected to the charging station; In response to the start of discharge, real-time battery status monitoring is performed on each energy storage battery in the energy storage battery pack, and discharge control is performed on energy storage batteries with abnormal battery status.

2. The energy storage control method for charging stations based on energy storage battery packs according to claim 1, characterized in that, The method further includes: For each energy storage cell in the energy storage battery pack, the following synchronization steps are performed: In response to the end of discharge or the discharge being disconnected, a discharge record corresponding to the energy storage battery is generated; The discharge records are stored in the discharge record database and synchronized to the user account corresponding to the energy storage battery.

3. The energy storage control method for charging stations based on energy storage battery packs according to claim 2, characterized in that, The step of generating a target discharge scheduling information group based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station includes: Based on the gradient discharge configuration information, a power weight reorganization is determined, wherein the power weights in the power weight reorganization correspond to different discharge stages; For each initial discharge scheduling information in the initial discharge scheduling information group, the following scheduling information update steps are performed: Based on the power weight reorganization, the discharge power included in the initial discharge scheduling information is segmented to obtain an updated discharge power group, wherein the updated discharge power = power weight × discharge power included in the initial discharge scheduling information; The maximum discharge amount and the updated discharge power group included in the initial discharge scheduling information are determined as the target discharge scheduling information in the target discharge scheduling information group that corresponds to the initial discharge scheduling information.

4. The energy storage control method for charging stations based on energy storage battery packs according to claim 3, characterized in that, The step of controlling the energy storage battery pack to discharge to the power grid connected to the charging station according to the target discharge scheduling information group includes: For each target discharge scheduling information in the target discharge scheduling information group, the following discharge processing steps are performed: A secondary connection status confirmation is performed on the energy storage battery corresponding to the target discharge scheduling information. In response to the confirmation of the secondary connection status of the energy storage battery corresponding to the target discharge scheduling information, a discharge control command is generated according to the target discharge scheduling information; The discharge control command is sent to the charging pile corresponding to the target discharge scheduling information to control the energy storage battery corresponding to the target discharge scheduling information to discharge to the power supply network connected to the charging station.

5. The energy storage control method for charging stations based on energy storage battery packs according to claim 4, characterized in that, The step of real-time battery status monitoring for each energy storage cell in the energy storage battery pack in response to the start of discharge, and discharge control for energy storage cells with abnormal battery status, includes: Collect the second battery attribute information of the energy storage battery; Based on the second battery attribute information, the battery anomaly level corresponding to the energy storage battery is generated; In response to the battery anomaly level being the first battery anomaly level, the discharge power of the energy storage battery is reduced. In response to the battery anomaly level being the second battery anomaly level, the energy storage battery is discharged to zero. In response to the battery anomaly level being the third battery anomaly level, a discharge disconnection command is sent to the charging pile corresponding to the energy storage battery.

6. The energy storage control method for charging stations based on energy storage battery packs according to claim 5, characterized in that, The step of generating the battery health level of the energy storage battery corresponding to the battery information based on the first battery attribute information included in the battery information includes: The battery health status is obtained by mapping the battery health status based on the first battery attribute information included in the battery information. Battery temperature state features are extracted from the first battery attribute information included in the battery information to obtain battery temperature state features; Based on the battery temperature state characteristics, the battery temperature state is generated; Battery voltage state features are extracted from the first battery attribute information included in the battery information to obtain battery voltage state features; Based on the battery voltage state characteristics, the battery voltage state is generated; Based on the first battery attribute information included in the battery information, the battery service life status and battery cycle loss status are generated. Battery discharge adaptability mapping is performed on the battery type of the energy storage battery corresponding to the battery information to obtain the battery discharge adaptability state, wherein the battery health state, the battery temperature state, the battery voltage state, the battery service life state, the battery cycle loss state and the battery discharge adaptability state correspond to the same dimension. A global battery state is generated based on the battery health state, battery temperature state, battery voltage state, battery service life state, battery cycle loss state, and battery discharge adaptability state. The global battery state is mapped to a battery health level to obtain the battery health level of the energy storage battery corresponding to the battery information.

7. The energy storage control method for charging stations based on energy storage battery packs according to claim 6, characterized in that, In response to the battery health level being greater than a preset battery health level, initial discharge scheduling information is generated based on the battery health level and the battery information including first battery attribute information and historical discharge behavior information, including: Based on the first battery attribute information included in the battery information, determine whether there is a default value for the maximum discharge capacity; In response to the existence of a default maximum discharge amount, the default maximum discharge amount is determined to be the maximum discharge amount included in the initial discharge scheduling information; In response to the absence of a default maximum discharge value, the following maximum discharge value generation steps are performed: Discharge behavior features are extracted from the historical discharge behavior information included in the battery information to obtain historical discharge behavior features; Based on the historical discharge behavior characteristics, candidate maximum discharge amounts are generated; Based on the first battery attribute information included in the battery information, determine the maximum safe discharge value and the upper limit of discharge power. In response to the candidate maximum discharge quantity being less than the maximum discharge quantity safety value, the candidate maximum discharge quantity is determined as the maximum discharge quantity included in the initial discharge scheduling information; In response to the candidate maximum discharge quantity being greater than or equal to the maximum discharge quantity safety value, the maximum discharge quantity safety value is determined as the maximum discharge quantity included in the initial discharge scheduling information; Based on the discharge power weight corresponding to the battery health level and the upper limit of discharge power, the initial discharge scheduling information including the discharge power is generated.

8. A charging station energy storage control device based on an energy storage battery pack, characterized in that, include: The acquisition unit is configured to acquire battery information corresponding to each energy storage battery in the energy storage battery pack in response to receiving a grid load suppression command, and obtain a battery information group. The energy storage battery is the power battery of a new energy vehicle that has been verified by reverse power transmission and is connected to the charging piles included in the charging station. The battery information includes: first battery attribute information and historical discharge behavior information. The execution unit is configured to perform the following processing steps for each battery information in the battery information group: generating a battery health level of the energy storage battery corresponding to the battery information based on the first battery attribute information included in the battery information; in response to the battery health level being greater than a preset battery health level, generating initial discharge scheduling information based on the battery health level, the first battery attribute information included in the battery information, and historical discharge behavior information, wherein the initial discharge scheduling information includes: maximum discharge amount and discharge power; The generation unit is configured to generate a target discharge scheduling information group based on the obtained initial discharge scheduling information group and the gradient discharge configuration information corresponding to the charging station. The control unit is configured to control the energy storage battery pack to discharge to the power grid connected to the charging station according to the target discharge scheduling information group; The status monitoring unit is configured to perform real-time battery status monitoring of each energy storage cell in the energy storage battery pack in response to the start of discharge, and to perform discharge control on energy storage cells with abnormal battery status.

9. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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