New energy automobile discharging method, device and system

Abstract

The invention relates to the technical field of batteries, in particular to a new energy automobile discharging method, device and system. The method comprises the steps of responding to charging connection between a new energy automobile and target equipment, and obtaining initial resistance information of the target equipment in a preset time period; performing filtering processing on the initial resistance information based on a Kalman filtering algorithm to obtain a target resistance value; performing matching verification on the target equipment based on the target resistance value; if the matching verification is passed, acquiring first battery parameter information of the power battery and second battery parameter information of the target equipment, and judging whether the power battery can charge the target equipment or not based on the first battery parameter information and the second battery parameter information; if yes, generating a target discharge parameter based on a preset particle swarm optimization algorithm; and controlling the power battery to charge the target equipment based on the target discharge parameter. The method can reduce the energy loss in the discharge process.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a new energy vehicle discharge method, device and system. Background Art

[0002] In recent years, with the enhancement of environmental awareness and the advancement of energy technology, new energy vehicles, especially pure electric vehicles, have developed rapidly. Electric vehicles, with their advantages such as zero emissions and low noise, have gradually become an important part of urban transportation. The power of electric vehicles comes from power batteries. While meeting the driving needs of electric vehicles, power batteries also have the ability to discharge to external devices. However, due to the lack of optimized setting of discharge parameters, the existing discharge methods for new energy vehicles may not be able to maintain the discharge process within the optimal efficiency range, resulting in increased energy loss. Summary of the invention

[0003] The present application provides a new energy vehicle discharging method, device and system to solve the problems raised by the above background technology.

[0004] In a first aspect, the present application provides a new energy vehicle discharging method, comprising: In response to a charging connection between a new energy vehicle and a target device, initial resistance information of the target device within a preset time period is obtained; wherein the power battery of the new energy vehicle charges the target device, and the initial resistance information includes a plurality of initial resistance values; Performing filtering processing on the initial resistance information based on a Kalman filter algorithm to obtain a target resistance value; Performing matching verification on the target device based on the target resistance value; If the matching verification is passed, obtaining first battery parameter information of the power battery and second battery parameter information of the target device, and judging whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information; If so, the target discharge parameters are generated based on a preset particle swarm optimization algorithm; The power battery is controlled to charge the target device based on the target discharge parameter.

[0005] In a possible implementation, filtering the initial resistance information based on a Kalman filter algorithm to obtain a target resistance value includes: Calculating a first average value and a median value of each of the initial resistance values; Determine a second average of the first average value and the median value as an initial state estimate; Determining the variance of each of the initial resistance values ​​as an initial error variance; Obtaining a preset process noise variance and a measurement noise variance in a database; Kalman filtering is performed on each initial resistance value in the initial resistance information in sequence based on the initial state estimation value, the initial error variance, the process noise variance, and the measurement noise variance to obtain the target resistance value.

[0006] In a possible implementation, performing Kalman filtering on each initial resistance value in the initial resistance information in sequence based on the initial state estimation value, the initial error variance, the process noise variance, and the measurement noise variance to obtain the target resistance value includes: Determine a resistance update value and an error variance update value at a first measurement moment based on the initial state estimate, the initial error variance, the process noise variance, and the measurement noise variance; For the The measurement time is determined The resistance update value at the measurement moment is The a priori estimate of the measurement time, and based on the The error variance update value at the measurement moment and the process noise variance determine the first The error variance prediction value at the measurement moment, and the error variance prediction value based on the first The error variance prediction value at the measurement moment and the measurement noise variance determine the first The Kalman gain at each measurement moment; Based on the The a priori estimate of the measurement time, the The initial resistance value at the measurement time, the The Kalman gain at the measurement moment determines the The resistance update value at each measurement moment; Based on the The error variance prediction value at the measurement moment and the The Kalman gain at the measurement moment determines the The error variance update value at each measurement moment; The resistance update value at the last measurement moment is determined to be the target resistance value.

[0007] In one possible implementation, by Determine the The predicted value of the error variance at the measurement moment; among them, For the said The predicted value of the error variance at each measurement moment, For the said The error variance update value at each measurement moment is: is the process noise variance; pass Determine the The Kalman gain at the measurement moment; where, For the said The Kalman gain at the measurement moment, is the measurement noise variance; pass Determine the The resistance update value at the measurement moment; among them, For the said The resistance update value at each measurement moment, For the said The a priori estimate of the measurement time, For the said The initial resistance value at the measurement moment; pass Determine the The error variance update value at the measurement moment; among them, For the said The error variance update value at each measurement moment.

[0008] In a possible implementation, the first battery parameter information includes a first battery total capacity and a first battery remaining power, the second battery parameter information includes a second battery total capacity and a second battery remaining power, and judging whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information includes: Calculating a first ratio of the remaining power of the first battery to the total capacity of the first battery, and determining whether the first ratio is greater than a preset ratio; If so, calculating a first difference between the first ratio and the preset ratio, and multiplying the first battery total capacity by the first difference to obtain a maximum discharge capacity of the power battery; Calculating a second ratio of the remaining power of the second battery to the total capacity of the second battery, and determining whether the second ratio is greater than the preset ratio; If not, calculating a second difference between the preset ratio and the second ratio, and multiplying the second battery total capacity by the second difference to obtain a minimum charging capacity of the target device; comparing the maximum discharge capacity with the minimum charge capacity; If the maximum discharge power is greater than the minimum charge power, it is determined that the power battery is capable of charging the target device.

[0009] In a possible implementation, the generating target discharge parameters based on a preset particle swarm optimization algorithm includes: Determining a target discharge capacity of the power battery based on the first battery parameter information and the second battery parameter information; Acquire a charging current threshold range and a charging voltage threshold range of the target device; Randomly generate an initial particle population based on the charging current threshold range and the charging voltage threshold range; the initial particle population includes a plurality of initial particles, each of which includes a discharge voltage and a discharge current; Obtaining a thermal internal resistance value of the power battery when charging the target device; For each of the initial particles, determining the effective discharge power corresponding to the initial particle based on the discharge voltage and discharge current of the initial particle and the heating internal resistance value, determining the discharge time corresponding to the initial particle based on the effective discharge power and the target charge amount, and determining the calorific value corresponding to the initial particle based on the discharge time and the heating internal resistance value; For each of the initial particles, determining the initial fitness of the initial particle based on the effective discharge power and the calorific value corresponding to the initial particle; Determining whether the maximum initial fitness among the initial fitness is greater than a preset fitness; If not, the initial particle with the largest initial fitness is determined as the first global optimal particle; Based on a preset particle update algorithm and the first global optimal particle, each of the initial particles is updated respectively to obtain a first intermediate particle population; respectively calculating the first fitness of each first intermediate particle in the first intermediate particle population; Determine whether the maximum first fitness among the first fitnesses is greater than the preset fitness; If not, determine the first intermediate particle with the largest first fitness as the second global optimal particle; Based on a preset particle update algorithm and the second global optimal particle, each of the first intermediate particles is updated respectively to obtain a second intermediate particle population; respectively calculating the second fitness of each second intermediate particle in the second intermediate particle population; Determining whether the maximum second fitness among the second fitnesses is greater than the preset fitness; If not, the second intermediate particle with the largest second fitness is determined to be the third global optimal particle, and the steps after determining the first intermediate particle with the largest first fitness to be the second global optimal particle are iterated until a target optimal particle is obtained, and the target optimal particle is the target discharge parameter; wherein the fitness corresponding to the target optimal particle is greater than the preset fitness.

[0010] In a possible implementation, determining the target discharge capacity of the power battery based on the first battery parameter information and the second battery parameter information includes: The maximum charge capacity of the target device is obtained by subtracting the remaining capacity of the second battery from the total capacity of the second battery; comparing the maximum discharge capacity with the maximum charge capacity; If the maximum discharge capacity is greater than the maximum charge capacity, determining the maximum charge capacity as the target discharge capacity; If the maximum discharge capacity is not greater than the maximum charge capacity, the maximum discharge capacity is determined to be the target discharge capacity.

[0011] In a second aspect, the present application provides a new energy vehicle discharge device, comprising: An acquisition module, configured to respond to a charging connection between a new energy vehicle and a target device and acquire initial resistance information of the target device within a preset time period; wherein the power battery of the new energy vehicle charges the target device, and the initial resistance information includes a plurality of initial resistance values; A processing module, configured to filter the initial resistance information based on a Kalman filter algorithm to obtain a target resistance value; A verification module, configured to perform matching verification on the target device based on the target resistance value; a judgment module, configured to obtain first battery parameter information of the power battery and second battery parameter information of the target device if the matching verification is passed, and judge whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information; A generating module, configured to generate a target discharge parameter based on a preset particle swarm optimization algorithm if the power battery is capable of charging the target device; A control module is used to control the power battery to charge the target device based on the target discharge parameter.

[0012] In a third aspect, the present application provides a new energy vehicle discharging system, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the new energy vehicle discharging method as described in any one of the above items is implemented.

[0013] The present application provides a new energy vehicle discharge method, device and system, the method comprising: responding to the charging connection between the new energy vehicle and the target device, obtaining the initial resistance information of the target device within a preset time period; wherein the power battery of the new energy vehicle charges the target device, and the initial resistance information includes multiple initial resistance values; filtering the initial resistance information based on the Kalman filter algorithm to obtain the target resistance value; matching verification of the target device based on the target resistance value; if the matching verification passes, obtaining the first battery parameter information of the power battery and the second battery parameter information of the target device, and judging whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information; if so, generating the target discharge parameter based on the preset particle swarm optimization algorithm; and controlling the power battery to charge the target device based on the target discharge parameter. During the discharge process of the new energy vehicle, the method can reduce energy loss while ensuring the safety of the discharge process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0015] Figure 1 A schematic diagram of a new energy vehicle discharging method provided in an embodiment of the present application; Figure 2 A schematic block diagram of the structure of a new energy vehicle discharge device provided in an embodiment of the present application; Figure 3 A schematic block diagram of the structure of a new energy vehicle discharge system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0018] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0019] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0020] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0021] See also Figure 1 , Figure 1 A schematic diagram of a new energy vehicle discharge method provided in an embodiment of the present application, such as Figure 1 As shown, the new energy vehicle discharging method provided in the embodiment of the present application includes steps S1 to S6.

[0022] Step S1, in response to a charging connection between a new energy vehicle and a target device, obtaining initial resistance information of the target device within a preset time period; wherein the power battery of the new energy vehicle charges the target device, and the initial resistance information includes a plurality of initial resistance values.

[0023] Specifically, when the target device is connected to the charging interface of the new energy vehicle, initial resistance information of the target device within a preset time period is obtained.

[0024] Step S2: filtering the initial resistance information based on a Kalman filter algorithm to obtain a target resistance value.

[0025] Specifically, step S2 includes the following steps: Calculating a first average value and a median value of each of the initial resistance values; Determine the first average value and the second average value of the median value as the initial state estimated value; it can be understood that taking the first average value and the second average value of the median value as the initial state estimated value realizes determining the initial state estimated value from multiple dimensions, which helps to improve the accuracy of the initial state estimated value, and further improves the accuracy of the target resistance value; Determining the variance of each of the initial resistance values ​​as an initial error variance; Obtaining a preset process noise variance and a measurement noise variance in a database; Kalman filtering is performed on each initial resistance value in the initial resistance information in sequence based on the initial state estimation value, the initial error variance, the process noise variance, and the measurement noise variance to obtain the target resistance value.

[0026] The step of performing Kalman filtering on each initial resistance value in the initial resistance information in sequence based on the initial state estimation value, the initial error variance, the process noise variance, and the measurement noise variance to obtain the target resistance value comprises the following steps: Determine the resistance update value and the error variance update value at the first measurement moment based on the initial state estimate, the initial error variance, the process noise variance and the measurement noise variance; specifically, by Determine the error variance prediction value of the first measurement moment; wherein, is the predicted value of the error variance at the first measurement moment, is the initial error variance, is the process noise variance; Determine the Kalman gain at the first measurement moment; wherein, is the Kalman gain at the first measurement moment, is the measurement noise variance; Determine the resistance update value at the first measurement moment; wherein, is the resistance update value at the first measurement moment, is the a priori estimate of the first measurement moment, is the initial resistance value at the first measurement moment; wherein the a priori estimated value at the first measurement moment is the initial state estimated value; by Determine the error variance update value of the first measurement moment; wherein, is the error variance update value at the first measurement moment; For the The measurement time is determined The resistance update value at the measurement moment is The a priori estimate of the measurement time, and based on the The error variance update value at the measurement moment and the process noise variance determine the first The error variance prediction value at the measurement moment, and the error variance prediction value based on the first The error variance prediction value at the measurement moment and the measurement noise variance determine the first The Kalman gain at the measurement moment; specifically, through Determine the The predicted value of the error variance at the measurement moment; among them, For the said The predicted value of the error variance at each measurement moment, For the said The error variance update value at each measurement moment is: is the process noise variance; Determine the The Kalman gain at the measurement moment; where, For the said The Kalman gain at the measurement moment, is the measurement noise variance; Based on the The a priori estimate of the measurement time, the The initial resistance value at the measurement time, the The Kalman gain at the measurement moment determines the The resistance value at each measurement moment is updated; specifically, by Determine the The resistance update value at the measurement moment; among them, For the said The resistance update value at each measurement moment, For the said The a priori estimate of the measurement time, For the said The initial resistance value at the measurement moment; Based on the The error variance prediction value at the measurement moment and the The Kalman gain at the measurement moment determines the The error variance update value at each measurement moment; specifically, by Determine the The error variance update value at the measurement moment; among them, For the said The error variance update value at each measurement moment; The resistance update value at the last measurement moment is determined to be the target resistance value.

[0027] It can be understood that accurately measuring the resistance value of the target device is a key factor in ensuring discharge safety. Step S2 improves the accuracy of the target resistance value by performing Kalman filtering on the initial resistance information, which helps to ensure the accuracy of subsequent matching verification results and improve the safety of the discharge process.

[0028] Step S3: performing matching verification on the target device based on the target resistance value.

[0029] Specifically, it is determined whether the target resistance value is within a preset resistance value range; if so, it is determined that the matching verification is passed.

[0030] Step S4: If the matching verification is passed, obtain the first battery parameter information of the power battery and the second battery parameter information of the target device, and determine whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information.

[0031] Specifically, the first battery parameter information includes a first battery total capacity and a first battery remaining power, the second battery parameter information includes a second battery total capacity and a second battery remaining power, and judging whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information includes the following steps: Calculating a first ratio of the remaining power of the first battery to the total capacity of the first battery, and determining whether the first ratio is greater than a preset ratio; If not, it is determined that the power battery cannot charge the target device; it can be understood that when the first ratio is not greater than the preset ratio, controlling the power battery to charge the target device will, on the one hand, affect the life of the power battery, and on the other hand, if the vehicle is moving, it will affect the driving ability of the power battery; If so, calculating a first difference between the first ratio and the preset ratio, and multiplying the first battery total capacity by the first difference to obtain a maximum discharge capacity of the power battery; Calculating a second ratio of the remaining power of the second battery to the total capacity of the second battery, and determining whether the second ratio is greater than the preset ratio; If not, calculating a second difference between the preset ratio and the second ratio, and multiplying the second battery total capacity by the second difference to obtain a minimum charging capacity of the target device; comparing the maximum discharge capacity with the minimum charge capacity; If the maximum discharge power is greater than the minimum charge power, it is determined that the power battery can charge the target device. It is understandable that if the target device is used when the battery power is too low, the battery life of the target device will be affected. In order not to affect the battery life of the target device, it should be ensured that the power battery can provide the target device with the minimum charge power. If the maximum discharge power is not greater than the minimum charge power, it is determined that the power battery cannot charge the target device.

[0032] Step S5: If yes, generate target discharge parameters based on a preset particle swarm optimization algorithm; Specifically, step S5 includes the following steps: Determining a target discharge capacity of the power battery based on the first battery parameter information and the second battery parameter information; Acquire a charging current threshold range and a charging voltage threshold range of the target device; An initial particle population is randomly generated based on the charging current threshold range and the charging voltage threshold range; the initial particle population includes a plurality of initial particles, each of which includes a discharge voltage and a discharge current; the discharge voltage corresponding to each of the initial particles is within the charging voltage threshold range, and the discharge current corresponding to each of the initial particles is within the charging current threshold range; Acquiring a thermal internal resistance value when the power battery is charging the target device; the thermal internal resistance value includes an internal resistance value of the power battery, a target resistance value of the target device, and a resistance value of a charging circuit; For each of the initial particles, the effective discharge power corresponding to the initial particle is determined based on the discharge voltage and discharge current of the initial particle and the heating internal resistance value, the discharge time corresponding to the initial particle is determined based on the effective discharge power and the target charge amount, and the calorific value corresponding to the initial particle is determined based on the discharge time and the heating internal resistance value; specifically, the discharge voltage is multiplied by the discharge current to obtain the discharge power; the product of the square value of the discharge current and the heating internal resistance value is used as the heating power; the heating power is subtracted from the discharge power to obtain the effective discharge power; the target charge amount is divided by the effective discharge power to obtain the discharge time corresponding to the initial particle; the heating power is multiplied by the discharge time to obtain the calorific value; For each of the initial particles, determining the initial fitness of the initial particle based on the effective discharge power and the calorific value corresponding to the initial particle; specifically, adding the effective discharge power to the inverse of the calorific value to obtain the initial fitness; Determining whether the maximum initial fitness among the initial fitness is greater than a preset fitness; If not, the initial particle with the largest initial fitness is determined as the first global optimal particle; Based on a preset particle update algorithm and the first global optimal particle, each of the initial particles is updated respectively to obtain a first intermediate particle population; Calculating the first fitness of each first intermediate particle in the first intermediate particle population respectively; it should be noted that the method for calculating the first fitness refers to the method for calculating the initial fitness mentioned above, which will not be repeated here; Determine whether the maximum first fitness among the first fitnesses is greater than the preset fitness; If not, determine the first intermediate particle with the largest first fitness as the second global optimal particle; Based on a preset particle update algorithm and the second global optimal particle, each of the first intermediate particles is updated respectively to obtain a second intermediate particle population; respectively calculating the second fitness of each second intermediate particle in the second intermediate particle population; Determining whether the maximum second fitness among the second fitnesses is greater than the preset fitness; If not, the second intermediate particle with the largest second fitness is determined to be the third global optimal particle, and the steps after determining the first intermediate particle with the largest first fitness to be the second global optimal particle are iterated until a target optimal particle is obtained, and the target optimal particle is the target discharge parameter; wherein the fitness corresponding to the target optimal particle is greater than the preset fitness.

[0033] It can be understood that the above method generates target discharge parameters based on the particle swarm optimization algorithm, takes into account both discharge efficiency and discharge safety, and realizes intelligent optimization control of the discharge process. While improving energy utilization, it reduces system heating and ensures that the discharge parameters are dynamically adjusted within a safe range, which helps to improve the overall performance and user satisfaction of new energy vehicle discharge technology.

[0034] Wherein, determining the target discharge capacity of the power battery based on the first battery parameter information and the second battery parameter information comprises the following steps: The maximum charge capacity of the target device is obtained by subtracting the remaining capacity of the second battery from the total capacity of the second battery; comparing the maximum discharge capacity with the maximum charge capacity; If the maximum discharge capacity is greater than the maximum charge capacity, determining the maximum charge capacity as the target discharge capacity; If the maximum discharge capacity is not greater than the maximum charge capacity, the maximum discharge capacity is determined to be the target discharge capacity.

[0035] It can be understood that the method of determining the target discharge power through the above method, on the one hand, helps to prevent the power battery from over-discharging, reduce the adverse effects on the life of the power battery, and extend the service life of the power battery; on the other hand, it can prevent the target device battery from overcharging, reduce safety risks, and ensure the safety of the charging process.

[0036] Step S6: controlling the power battery to charge the target device based on the target discharge parameter.

[0037] The method provided in this embodiment can reduce energy loss and ensure the safety of the discharge process during the discharge process of a new energy vehicle.

[0038] See also Figure 2 , Figure 2 A schematic block diagram of the structure of the new energy vehicle discharge device 100 provided in the embodiment of the present application, as shown in FIG. Figure 2 As shown, the new energy vehicle discharge device 100 provided in the embodiment of the present application includes: The acquisition module 110 is used to respond to the charging connection between the new energy vehicle and the target device and acquire the initial resistance information of the target device within a preset time period; wherein, the power battery of the new energy vehicle charges the target device, and the initial resistance information includes multiple initial resistance values.

[0039] The processing module 120 is used to filter the initial resistance information based on a Kalman filter algorithm to obtain a target resistance value.

[0040] The verification module 130 is configured to perform matching verification on the target device based on the target resistance value.

[0041] The judgment module 140 is used to obtain the first battery parameter information of the power battery and the second battery parameter information of the target device if the matching verification is passed, and judge whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information.

[0042] The generating module 150 is configured to generate a target discharge parameter based on a preset particle swarm optimization algorithm if the power battery is capable of charging the target device.

[0043] The control module 160 is configured to control the power battery to charge the target device based on the target discharge parameter.

[0044] It should be noted that those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the above-described device and each module can refer to the process in the aforementioned new energy vehicle discharge method embodiment, and will not be repeated here.

[0045] The new energy vehicle discharge device 100 provided in the above embodiment can be implemented in the form of a computer program. The computer program can be used in Figure 3 The new energy vehicle is shown to operate on a discharge system 200 .

[0046] See also Figure 3 , Figure 3The present invention provides a schematic block diagram of the structure of a new energy vehicle discharge system 200, which includes a processor 201 and a memory 202. The processor 201 and the memory 202 are connected via a device bus 203, wherein the memory 202 may include a non-volatile storage medium and an internal memory.

[0047] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 201, the processor 201 can execute any of the above-mentioned new energy vehicle discharging methods.

[0048] The processor 201 is used to provide computing and control capabilities to support the operation of the entire new energy vehicle discharge system 200 .

[0049] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 201, the processor 201 can execute any of the above-mentioned new energy vehicle discharging methods.

[0050] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation on the new energy vehicle discharge system 200 involved in the present application scheme. The specific new energy vehicle discharge system 200 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0051] It should be understood that the processor 201 may be a central processing unit (CPU), and the processor 201 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0052] In some embodiments, the processor 201 is used to run a computer program stored in the memory to implement the following steps: In response to a charging connection between a new energy vehicle and a target device, initial resistance information of the target device within a preset time period is obtained; wherein the power battery of the new energy vehicle charges the target device, and the initial resistance information includes a plurality of initial resistance values; Performing filtering processing on the initial resistance information based on a Kalman filter algorithm to obtain a target resistance value; Performing matching verification on the target device based on the target resistance value; If the matching verification is passed, obtaining first battery parameter information of the power battery and second battery parameter information of the target device, and judging whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information; If so, the target discharge parameters are generated based on a preset particle swarm optimization algorithm; The power battery is controlled to charge the target device based on the target discharge parameter.

[0053] It should be noted that those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the new energy vehicle discharge system 200 described above can refer to the process of the aforementioned new energy vehicle discharge method, and will not be repeated here.

[0054] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement the new energy vehicle discharging method provided in the embodiment of the present application.

[0055] The computer-readable storage medium may be an internal storage unit of the new energy vehicle discharge system 200 of the aforementioned embodiment, such as a hard disk or memory of the new energy vehicle discharge system 200. The computer-readable storage medium may also be an external storage device of the new energy vehicle discharge system 200, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped with the new energy vehicle discharge system 200.

[0056] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A new energy vehicle discharging method, characterized in that: include: In response to a charging connection between a new energy vehicle and a target device, initial resistance information of the target device within a preset time period is obtained; wherein the power battery of the new energy vehicle charges the target device, and the initial resistance information includes a plurality of initial resistance values; Performing filtering processing on the initial resistance information based on a Kalman filter algorithm to obtain a target resistance value; Performing matching verification on the target device based on the target resistance value; If the matching verification is passed, obtaining first battery parameter information of the power battery and second battery parameter information of the target device, and judging whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information; If so, the target discharge parameters are generated based on a preset particle swarm optimization algorithm; The power battery is controlled to charge the target device based on the target discharge parameter.

2. The new energy vehicle discharging method according to claim 1, characterized in that: The filtering process based on the Kalman filter algorithm on the initial resistance information to obtain the target resistance value includes: Calculating a first average value and a median value of each of the initial resistance values; Determine a second average of the first average value and the median value as an initial state estimate; Determining the variance of each of the initial resistance values ​​as an initial error variance; Obtaining a preset process noise variance and a measurement noise variance in a database; Kalman filtering is performed on each initial resistance value in the initial resistance information in sequence based on the initial state estimation value, the initial error variance, the process noise variance, and the measurement noise variance to obtain the target resistance value.

3. The new energy vehicle discharging method according to claim 2, characterized in that: The step of performing Kalman filtering on each initial resistance value in the initial resistance information in sequence based on the initial state estimation value, the initial error variance, the process noise variance, and the measurement noise variance to obtain the target resistance value includes: Determine a resistance update value and an error variance update value at a first measurement moment based on the initial state estimate, the initial error variance, the process noise variance, and the measurement noise variance; For the The measurement time is determined The resistance update value at the measurement moment is The a priori estimate of the measurement time, and based on the The error variance update value at the measurement moment and the process noise variance determine the first The error variance prediction value at the measurement moment, and the error variance prediction value based on the first The error variance prediction value at the measurement moment and the measurement noise variance determine the first The Kalman gain at each measurement moment; Based on the The a priori estimate of the measurement time, the The initial resistance value at the measurement time, the The Kalman gain at the measurement moment determines the The resistance update value at each measurement moment; Based on the The error variance prediction value at the measurement moment and the The Kalman gain at the measurement moment determines the The error variance update value at each measurement moment; The resistance update value at the last measurement moment is determined to be the target resistance value.

4. The new energy vehicle discharging method according to claim 3, characterized in that: include: pass Determine the The predicted value of the error variance at the measurement moment; among them, For the said The predicted value of the error variance at the measurement moment, For the said The error variance update value at each measurement moment is: is the process noise variance; pass Determine the The Kalman gain at the measurement moment; where For the said The Kalman gain at the measurement moment, is the measurement noise variance; pass Determine the The resistance update value at the measurement moment; among them, For the said The resistance update value at each measurement moment, For the said The a priori estimate of the measurement time, For the said The initial resistance value at the measurement moment; pass Determine the The error variance update value at the measurement moment; among them, For the said The error variance update value at each measurement moment.

5. The new energy vehicle discharging method according to claim 1, characterized in that: The first battery parameter information includes a first battery total capacity and a first battery remaining power, the second battery parameter information includes a second battery total capacity and a second battery remaining power, and judging whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information includes: Calculating a first ratio of the remaining power of the first battery to the total capacity of the first battery, and determining whether the first ratio is greater than a preset ratio; If so, calculating a first difference between the first ratio and the preset ratio, and multiplying the first battery total capacity by the first difference to obtain a maximum discharge capacity of the power battery; Calculating a second ratio of the remaining power of the second battery to the total capacity of the second battery, and determining whether the second ratio is greater than the preset ratio; If not, calculating a second difference between the preset ratio and the second ratio, and multiplying the second battery total capacity by the second difference to obtain a minimum charging capacity of the target device; comparing the maximum discharge capacity with the minimum charge capacity; If the maximum discharge power is greater than the minimum charge power, it is determined that the power battery is capable of charging the target device.

6. The new energy vehicle discharging method according to claim 5, characterized in that: The target discharge parameters are generated based on a preset particle swarm optimization algorithm, including: Determining a target discharge capacity of the power battery based on the first battery parameter information and the second battery parameter information; Acquire a charging current threshold range and a charging voltage threshold range of the target device; Randomly generate an initial particle population based on the charging current threshold range and the charging voltage threshold range; the initial particle population includes a plurality of initial particles, each of which includes a discharge voltage and a discharge current; Obtaining a thermal internal resistance value of the power battery when charging the target device; For each of the initial particles, determining the effective discharge power corresponding to the initial particle based on the discharge voltage and discharge current of the initial particle and the heating internal resistance value, determining the discharge time corresponding to the initial particle based on the effective discharge power and the target charge amount, and determining the calorific value corresponding to the initial particle based on the discharge time and the heating internal resistance value; For each of the initial particles, determining the initial fitness of the initial particle based on the effective discharge power and the calorific value corresponding to the initial particle; Determining whether the maximum initial fitness among the initial fitness is greater than a preset fitness; If not, the initial particle with the largest initial fitness is determined as the first global optimal particle; Based on a preset particle update algorithm and the first global optimal particle, each of the initial particles is updated respectively to obtain a first intermediate particle population; respectively calculating the first fitness of each first intermediate particle in the first intermediate particle population; Determine whether the maximum first fitness among the first fitnesses is greater than the preset fitness; If not, determine the first intermediate particle with the largest first fitness as the second global optimal particle; Based on a preset particle update algorithm and the second global optimal particle, each of the first intermediate particles is updated respectively to obtain a second intermediate particle population; respectively calculating the second fitness of each second intermediate particle in the second intermediate particle population; Determining whether the maximum second fitness among the second fitnesses is greater than the preset fitness; If not, the second intermediate particle with the largest second fitness is determined to be the third global optimal particle, and the steps after determining the first intermediate particle with the largest first fitness to be the second global optimal particle are iterated until a target optimal particle is obtained, and the target optimal particle is the target discharge parameter; wherein the fitness corresponding to the target optimal particle is greater than the preset fitness.

7. The new energy vehicle discharging method according to claim 6, characterized in that: The determining the target discharge capacity of the power battery based on the first battery parameter information and the second battery parameter information includes: The maximum charge capacity of the target device is obtained by subtracting the remaining capacity of the second battery from the total capacity of the second battery; comparing the maximum discharge capacity with the maximum charge capacity; If the maximum discharge capacity is greater than the maximum charge capacity, determining the maximum charge capacity as the target discharge capacity; If the maximum discharge capacity is not greater than the maximum charge capacity, the maximum discharge capacity is determined to be the target discharge capacity.

8. A new energy vehicle discharge device, characterized in that: include: An acquisition module, configured to respond to a charging connection between a new energy vehicle and a target device, and acquire initial resistance information of the target device within a preset time period; wherein the power battery of the new energy vehicle charges the target device, and the initial resistance information includes a plurality of initial resistance values; A processing module, configured to filter the initial resistance information based on a Kalman filter algorithm to obtain a target resistance value; A verification module, configured to perform matching verification on the target device based on the target resistance value; A judgment module, configured to obtain first battery parameter information of the power battery and second battery parameter information of the target device if the matching verification is passed, and judge whether the power battery can charge the target device based on the first battery parameter information and the second battery parameter information; A generating module, configured to generate a target discharge parameter based on a preset particle swarm optimization algorithm if the power battery is capable of charging the target device; A control module is used to control the power battery to charge the target device based on the target discharge parameter.

9. A new energy vehicle discharge system, characterized in that: The system includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the new energy vehicle discharging method according to any one of claims 1 to 7 is implemented.

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