New Energy Electric Vehicle Energy Storage Real-Time Control Method and Device Based on RISC-V Architecture

Abstract

This application relates to the technical field of new energy vehicles, and provides a real-time control method and device for energy storage of new energy electric vehicles based on the RISC-V architecture. The method includes: obtaining the driving parameter information of the new energy electric vehicle in real time; the driving parameter information includes speed and acceleration; determining whether the new energy electric vehicle is in a deceleration state based on the acceleration; if so, obtaining the battery parameter information and the ambient temperature, and inputting the battery parameter information and the ambient temperature into a preset charging current threshold prediction model to obtain the charging current threshold of the battery; wherein, the battery is used to supply power to the new energy electric vehicle; determining the target reverse rotation speed of the motor based on the charging current threshold; the motor is arranged on the new energy electric vehicle; controlling the motor to reverse based on the target reverse rotation speed. This method can maximize energy recovery during the kinetic energy recovery process while ensuring the safety of the battery.

Description

Technical Field

[0001] This application relates to the technical field of new energy vehicles, and particularly to a real-time control method and device for energy storage of new energy electric vehicles based on the RISC-V architecture. Background Art

[0002] With the increasing global energy crisis and environmental pollution problems, new energy vehicles have received extensive attention and rapid development due to their environmental protection and high efficiency characteristics. In new energy vehicles, the battery, as the main energy storage device, directly affects the vehicle's endurance and use safety. To improve the vehicle's energy utilization efficiency, kinetic energy recovery technology is widely used in new energy vehicles.

[0003] Kinetic energy recovery technology converts the vehicle's kinetic energy into electrical energy and stores it in the battery when the vehicle decelerates, which can not only extend the vehicle's driving range but also reduce energy waste. However, in practical applications, the kinetic energy recovery process faces the problem of how to ensure the safety of the battery while maximizing energy recovery. Summary of the Invention

[0004] This application provides a real-time control method and device for energy storage of new energy electric vehicles based on the RISC-V architecture to solve the problems raised in the above background art.

[0005] In a first aspect, this application provides a real-time control method for energy storage of new energy electric vehicles based on the RISC-V architecture, which is used for the RISC-V processor on the new energy electric vehicle. The method includes:

[0006] Obtaining the driving parameter information of the new energy electric vehicle in real time; the driving parameter information includes speed and acceleration;

[0007] Judging whether the new energy electric vehicle is in a deceleration state based on the acceleration;

[0008] If so, obtaining the battery parameter information and the environmental temperature, and inputting the battery parameter information and the environmental temperature into a preset charging current threshold prediction model to obtain the charging current threshold of the battery; wherein the battery is used to supply power to the new energy electric vehicle;

[0009] Determining the target reverse rotation speed of the motor based on the charging current threshold; the motor is arranged on the new energy electric vehicle;

[0010] Controlling the reverse rotation of the motor based on the target reverse rotation speed.

[0011] In a possible implementation manner, the judging whether the new energy electric vehicle is in a deceleration state based on the acceleration includes:

[0012] Judging whether the acceleration is less than 0;

[0013] If the acceleration is less than 0, it is determined that the new energy electric vehicle is in a deceleration state.

[0014] In a possible implementation manner, the determining the target reverse rotation speed of the motor based on the charging current threshold includes:

[0015] By obtaining the initial reverse speed of the motor; where, is the initial reverse speed of the motor, is the speed, is the th transmission ratio corresponding to the th wheel of the new energy electric vehicle, is the radius corresponding to the th wheel of the new energy electric vehicle, represents that the new energy electric vehicle has

[0016] By obtaining the charging current value corresponding to the battery at the initial reverse speed; where, is the charging current, is the voltage conversion coefficient corresponding to the motor, is the current voltage of the battery, is the internal resistance value of the battery;

[0017] comparing the charging current value with the charging current threshold;

[0018] If the charging current value is not greater than the charging current threshold, determining the initial reverse speed as the target reverse speed;

[0019] If the charging current value is greater than the charging current threshold, by determining the target reverse speed, where, is the target reverse speed, is the charging current threshold.

[0020] In a possible implementation manner, the training method of the charging current threshold prediction model includes:

[0021] obtaining a training data set; the training data set includes the matching relationships of multiple input parameter information and output parameter information, the input parameter information is battery parameter information and environmental temperature, and the output parameter information is the charging current threshold;

[0022] dividing the training data set into a training set, a validation set and a calibration set;

[0023] For each matching relationship in the training set, use the input parameter information of the matching relationship as the input of a preset neural network, and use the output parameter information of the matching relationship as the output of the preset neural network to train the preset neural network, obtaining an initial charging current threshold prediction model;

[0024] Based on the validation set, determine the prediction accuracy rate of the initial charging current threshold prediction model, and determine whether the prediction accuracy rate is greater than a preset prediction accuracy rate;

[0025] If not, optimize the model parameters of the initial charging current threshold prediction model based on a preset gradient descent algorithm and the calibration set until the prediction accuracy rate of the initial charging current threshold prediction model is greater than the preset prediction accuracy rate, obtaining the charging current threshold prediction model.

[0026] In a possible implementation manner, the determining the prediction accuracy rate of the initial charging current threshold prediction model based on the validation set includes:

[0027] For each matching relationship in the validation set, input the input parameter information corresponding to the matching relationship into the initial charging current threshold prediction model to obtain a predicted value of the charging current threshold, and obtain the absolute value of the difference between the predicted value of the charging current threshold and the charging current threshold corresponding to the matching relationship, and when the absolute value is less than a preset absolute value, determine the matching relationship as a target matching relationship;

[0028] Use the ratio between the number of the target matching relationships and the total number of the matching relationships in the validation set as the prediction accuracy rate.

[0029] In a possible implementation manner, the optimizing the model parameters of the initial charging current threshold prediction model based on a preset gradient descent algorithm and the calibration set includes:

[0030] For each matching relationship in the calibration set, input the input parameter information corresponding to the matching relationship into the initial charging current threshold prediction model to obtain a predicted value of the charging current threshold, and determine a prediction loss value based on the predicted value of the charging current threshold and the charging current threshold corresponding to the matching relationship, and perform backpropagation of the prediction loss value in the initial charging current threshold prediction model to update the model parameters of the initial charging current threshold prediction model.

[0031] In a possible implementation manner, the method further includes:

[0032] Bind the driving parameter information, the battery parameter information, the ambient temperature, and the target reverse rotation speed to obtain a binding result;

[0033] Generate an encryption password based on the identification image of the new energy electric vehicle;

[0034] Perform an encryption process on the binding result based on the encryption password to obtain an encrypted binding result;

[0035] Store the encrypted binding result in a database.

[0036] In a possible implementation manner, the generating of the encryption password based on the identification image of the new energy electric vehicle includes:

[0037] For each pixel of the identification image, obtain the RGB space information corresponding to the pixel; the RGB space information includes a red value, a blue value, and a green value;

[0038] Sequentially extract the maximum red value, maximum blue value, maximum green value, minimum red value, minimum blue value, and minimum green value in all the RGB space information to obtain a color value sequence;

[0039] Subtract the minimum red value from the maximum red value to obtain a first color difference, and subtract the minimum blue value from the maximum blue value to obtain a second color difference;

[0040] Obtain a coding algorithm matrix; the coding algorithm matrix includes multiple rows and multiple columns, and a coding algorithm is provided at each position of the coding algorithm matrix;

[0041] Determine a target coding algorithm in the coding algorithm matrix; the number corresponding to the row where the target coding algorithm is located is the same as the number corresponding to the first color difference, and the number corresponding to the column where the target coding algorithm is located is the same as the number corresponding to the second color difference;

[0042] Perform a coding process on the color value sequence based on the target coding algorithm to obtain the encryption password.

[0043] In a second aspect, the present application provides a new energy electric vehicle energy storage real-time control device based on the RISC-V architecture, which is used for the RISC-V processor on the new energy electric vehicle. The device includes:

[0044] An acquisition module, configured to acquire the driving parameter information of the new energy electric vehicle in real time; the driving parameter information includes speed and acceleration;

[0045] A judgment module, configured to judge whether the new energy electric vehicle is in a deceleration state based on the acceleration;

[0046] An input module, configured to obtain battery parameter information and ambient temperature if the new energy electric vehicle is in a deceleration state, and input the battery parameter information and the ambient temperature into a preset charging current threshold prediction model to obtain the charging current threshold of the battery; wherein, the battery is used to supply power to the new energy electric vehicle;

[0047] A determination module, configured to determine a target reverse rotation speed of the motor based on the charging current threshold; the motor is disposed on the new energy electric vehicle;

[0048] A control module, configured to control the motor to reverse based on the target reverse rotation speed.

[0049] This application provides a real-time energy storage control method and device for a new energy electric vehicle based on the RISC-V architecture, which is used for the RISC-V processor on the new energy electric vehicle. The method includes: obtaining the driving parameter information of the new energy electric vehicle in real time; the driving parameter information includes speed and acceleration; determining whether the new energy electric vehicle is in a deceleration state based on the acceleration; if so, obtaining battery parameter information and ambient temperature, and inputting the battery parameter information and the ambient temperature into a preset charging current threshold prediction model to obtain the charging current threshold of the battery; wherein, the battery is used to supply power to the new energy electric vehicle; determining the target reverse rotation speed of the motor based on the charging current threshold; the motor is disposed on the new energy electric vehicle; controlling the motor to reverse based on the target reverse rotation speed. On the one hand, this method uses the RISC-V processor as the core control unit, which has high instruction execution ability and low-latency response characteristics, and can obtain and process the driving parameters of the new energy electric vehicle, as well as battery parameter information and ambient temperature in real time, ensuring that at the moment when the vehicle decelerates, the system can quickly judge and start the kinetic energy recovery process, improving the response speed and real-time performance of the control system. On the other hand, based on the predicted charging current threshold, the target reverse rotation speed of the motor is accurately determined, and the motor is controlled to reverse through the RISC-V processor to achieve efficient conversion of kinetic energy into electrical energy. This method maximizes the kinetic energy recovery amount on the premise of ensuring the safe charging of the battery, significantly improves the overall energy utilization efficiency, and helps to extend the cruising range of the vehicle. Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a schematic flowchart of the real-time energy storage control method for a new energy electric vehicle based on the RISC-V architecture provided by the embodiment of this application;

[0052] Figure 2 It is a schematic block diagram of the structure of the real-time control device for new energy electric vehicle energy storage based on the RISC-V architecture provided by the embodiments of the present application. Specific embodiments

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] The flowchart shown in the accompanying drawings is only an example, and does not necessarily include all contents and operations / steps, nor does it necessarily execute in the described order. For example, some operations / steps can be decomposed, combined, or partially merged, so the actual execution order may be changed according to the actual situation.

[0055] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0056] It should be further understood that the term " / and" as used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0057] Next, some embodiments of the present application will be described in detail in conjunction with the accompanying drawings. Without conflict, the features in the following embodiments and the embodiments can be combined with each other.

[0058] Please refer to Figure 1 , Figure 1 It is a schematic flowchart of the real-time control method for new energy electric vehicle energy storage based on the RISC-V architecture provided by the embodiments of the present application. This method is used for the RISC-V processor on a new energy electric vehicle. As Figure 1 shown, the real-time control method for new energy electric vehicle energy storage based on the RISC-V architecture provided by the embodiments of the present application includes steps S1 to S5.

[0059] Step S1: Real-time obtain the driving parameter information of the new energy electric vehicle; the driving parameter information includes speed and acceleration.

[0060] Specifically, the speed is obtained by a speed sensor provided in the new energy electric vehicle, and the acceleration is obtained by an acceleration sensor provided in the new energy electric vehicle.

[0061] Step S2: Determine whether the new energy electric vehicle is in a decelerating state based on the acceleration.

[0062] Specifically, step S2 includes the following steps:

[0063] Judge whether the acceleration is less than 0;

[0064] If the acceleration is less than 0, it is determined that the new energy electric vehicle is in a decelerating state.

[0065] Step S3: If so, obtain battery parameter information and ambient temperature, and input the battery parameter information and the ambient temperature into a preset charging current threshold prediction model to obtain the charging current threshold of the battery; wherein, the battery is used to supply power to the new energy electric vehicle.

[0066] Specifically, the battery parameter information is obtained by various sensors provided in the battery. The battery parameter information includes the remaining charge amount of the battery, the battery temperature, and the remaining battery life. The charging current threshold is the maximum current value to ensure the safety of the battery.

[0067] Step S4: Determine the target reverse rotation speed of the motor based on the charging current threshold; the motor is provided in the new energy electric vehicle.

[0068] Specifically, step S4 includes the following steps:

[0069] By obtain the initial reverse speed of the motor; wherein, is the initial reverse speed of the motor, is the speed, is the th gear ratio corresponding to the th wheel of the new energy electric vehicle, is the radius corresponding to the th wheel of the new energy electric vehicle, represents that the new energy electric vehicle has wheels;

[0070] By obtain the charging current value corresponding to the battery at the initial reverse speed; wherein, is the charging current, is the voltage conversion coefficient corresponding to the motor, is the current voltage of the battery, is the internal resistance value of the battery; in this embodiment, a motor with a voltage conversion coefficient of is installed in the new energy electric vehicle, and is obtained by referring to the technical specifications of the motor;

[0071] Compare the charging current value with the charging current threshold;

[0072] If the charging current value is not greater than the charging current threshold, determine the initial reverse speed as the target reverse speed;

[0073] If the charging current value is greater than the charging current threshold, determine the target reverse speed through wherein, is the target reverse speed, is the charging current threshold.

[0074] It can be understood that the method in step S4 can maximize energy recovery during kinetic energy recovery while ensuring the safety of the battery.

[0075] Step S5, control the motor to reverse based on the target reverse rotation speed.

[0076] The method provided in this embodiment, on the one hand, uses the RISC-V processor as the core control unit, which has high-efficient instruction execution ability and low-latency response characteristics, and can obtain and process the driving parameters, battery parameter information and ambient temperature of the new energy electric vehicle in real time, ensuring that at the moment of vehicle deceleration, the system can quickly judge and start the kinetic energy recovery process, improving the response speed and real-time performance of the control system. On the other hand, based on the predicted charging current threshold, accurately determine the target reverse rotation speed of the motor, and control the motor to reverse through the RISC-V processor to achieve efficient conversion of kinetic energy into electrical energy. This method maximizes the kinetic energy recovery amount on the premise of ensuring the safe charging of the battery, significantly improves the overall energy utilization efficiency, and helps to extend the vehicle's cruising range.

[0077] In some embodiments, the training method of the charging current threshold prediction model includes the following steps:

[0078] Obtain a training data set; the training data set includes the matching relationships between multiple input parameter information and output parameter information, where the input parameter information is battery parameter information and environmental temperature, and the output parameter information is the charging current threshold; it should be noted that for each of the matching relationships, the charging current threshold corresponding to the matching relationship is the maximum safe current obtained through experimental methods that enables the battery to be in the charging environment corresponding to the input parameter information corresponding to the matching relationship;

[0079] Divide the training data set into a training set, a validation set, and a calibration set;

[0080] For each matching relationship in the training set, use the input parameter information of the matching relationship as the input of a preset neural network, and use the output parameter information of the matching relationship as the output of the preset neural network to train the preset neural network to obtain an initial charging current threshold prediction model;

[0081] Based on the validation set, determine the prediction accuracy rate of the initial charging current threshold prediction model, and determine whether the prediction accuracy rate is greater than a preset prediction accuracy rate;

[0082] If not, optimize the model parameters of the initial charging current threshold prediction model based on a preset gradient descent algorithm and the calibration set until the prediction accuracy rate of the initial charging current threshold prediction model is greater than the preset prediction accuracy rate to obtain the charging current threshold prediction model.

[0083] Among them, the determining the prediction accuracy rate of the initial charging current threshold prediction model based on the validation set includes the following steps:

[0084] For each matching relationship in the validation set, input the input parameter information corresponding to the matching relationship into the initial charging current threshold prediction model to obtain a predicted value of the charging current threshold, and obtain the absolute value of the difference between the predicted value of the charging current threshold and the charging current threshold corresponding to the matching relationship, and when the absolute value is less than a preset absolute value, determine the matching relationship as the target matching relationship;

[0085] Take the ratio between the number of the target matching relationships and the total number of the matching relationships in the validation set as the prediction accuracy rate.

[0086] Among them, the optimizing the model parameters of the initial charging current threshold prediction model based on a preset gradient descent algorithm and the calibration set includes the following steps:

[0087] For each matching relationship in the calibration set, input the input parameter information corresponding to the matching relationship into the initial charging current threshold prediction model to obtain a predicted value of the charging current threshold, determine a prediction loss value based on the predicted value of the charging current threshold and the charging current threshold corresponding to the matching relationship, and perform backpropagation of the prediction loss value in the initial charging current threshold prediction model to update the model parameters of the initial charging current threshold prediction model.

[0088] In some embodiments, the method further includes the following steps:

[0089] Bind the driving parameter information, the battery parameter information, the ambient temperature, and the target reverse rotation speed to obtain a binding result;

[0090] Generate an encryption password based on the identification image of the new energy electric vehicle;

[0091] Perform encryption processing on the binding result based on the encryption password to obtain an encrypted binding result;

[0092] Store the encrypted binding result in a database.

[0093] Among them, generating the encryption password based on the identification image of the new energy electric vehicle includes the following steps:

[0094] For each pixel of the identification image, obtain the RGB space information corresponding to the pixel; the RGB space information includes a red value, a blue value, and a green value;

[0095] Sequentially extract the maximum red value, the maximum blue value, the maximum green value, the minimum red value, the minimum blue value, and the minimum green value in all the RGB space information to obtain a color value sequence;

[0096] Obtain a first color difference by subtracting the minimum red value from the maximum red value, and obtain a second color difference by subtracting the minimum blue value from the maximum blue value;

[0097] Obtain an encoding algorithm matrix; the encoding algorithm matrix includes multiple rows and multiple columns, and an encoding algorithm is provided at each position of the encoding algorithm matrix;

[0098] Determine a target encoding algorithm in the encoding algorithm matrix; the number corresponding to the row where the target encoding algorithm is located is the same as the number corresponding to the first color difference, and the number corresponding to the column where the target encoding algorithm is located is the same as the number corresponding to the second color difference;

[0099] Perform encoding processing on the color value sequence based on the target encoding algorithm to obtain the encryption password.

[0100] On the one hand, the method provided in this embodiment realizes the structured storage of the driving parameter information, the battery parameter information, the ambient temperature, and the target reverse rotation speed, providing a reliable data basis for further improving the intelligence of the new energy electric vehicle energy storage real-time control method. On the other hand, by encrypting the binding result, it can prevent unauthorized personnel from tampering with the binding result, ensuring the security of the binding result. On the further hand, by generating an encryption password through the identification image of the new energy electric vehicle, the difficulty of cracking the encryption password is increased, further improving the security of the binding result.

[0101] Please refer to Figure 2 , Figure 2 which is a schematic block diagram of the structure of the new energy electric vehicle energy storage real-time control device 100 based on the RISC-V architecture provided by the embodiment of the present application. The new energy electric vehicle energy storage real-time control device 100 based on the RISC-V architecture is used for the RISC-V processor on the new energy electric vehicle, such as Figure 2 shown in the figure. The new energy electric vehicle energy storage real-time control device 100 provided by the embodiment of the present application includes:

[0102] An acquisition module 110, configured to acquire the driving parameter information of the new energy electric vehicle in real time; the driving parameter information includes speed and acceleration.

[0103] A judgment module 120, configured to judge whether the new energy electric vehicle is in a deceleration state based on the acceleration.

[0104] An input module 130, configured to, if the new energy electric vehicle is in a deceleration state, acquire the battery parameter information and the ambient temperature, and input the battery parameter information and the ambient temperature into a preset charging current threshold prediction model to obtain the charging current threshold of the battery; wherein, the battery is used to supply power to the new energy electric vehicle.

[0105] A determination module 140, configured to determine the target reverse rotation speed of the motor based on the charging current threshold; the motor is provided on the new energy electric vehicle.

[0106] A control module 150, configured to control the motor to reverse based on the target reverse rotation speed.

[0107] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described device and each module can refer to the processes in the embodiment of the new energy electric vehicle energy storage real-time control method based on the RISC-V architecture described above, and will not be elaborated here.

[0108] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A real-time control method for energy storage of new energy electric vehicles based on RISC-V architecture, characterized in that: A RISC-V processor for a new energy electric vehicle, the method comprising: Acquire driving parameter information of the new energy electric vehicle in real time; the driving parameter information includes speed and acceleration; Determining whether the new energy electric vehicle is in a deceleration state based on the acceleration; If yes, obtain battery parameter information and ambient temperature, and input the battery parameter information and the ambient temperature into a preset charging current threshold prediction model to obtain the charging current threshold of the battery; wherein the battery is used to power the new energy electric vehicle; Determining a target reverse speed of the motor based on the charging current threshold; the motor is provided in the new energy electric vehicle; controlling the reverse rotation of the motor based on the target reverse rotation speed; Binding the driving parameter information, the battery parameter information, the ambient temperature and the target reverse speed to obtain a binding result; generating an encrypted password based on the identification image of the new energy electric vehicle; Encrypting the binding result based on the encryption password to obtain an encrypted binding result; The encrypted binding result is stored in a database.

2. The real-time control method for energy storage of new energy electric vehicles based on RISC-V architecture according to claim 1 is characterized in that: The determining whether the new energy electric vehicle is in a deceleration state based on the acceleration includes: Determine whether the acceleration is less than 0; If the acceleration is less than 0, it is determined that the new energy electric vehicle is in a deceleration state.

3. The real-time control method for energy storage of new energy electric vehicles based on RISC-V architecture according to claim 1 is characterized in that: The step of determining a target reverse rotation speed of the motor based on the charging current threshold comprises: pass Obtaining the initial reverse speed of the motor; wherein, is the initial reversal speed of the motor, For the speed, The new energy electric vehicle The gear ratio corresponding to each wheel is The new energy electric vehicle The radius of the wheel is Representatives of the new energy electric vehicles include wheels; pass Obtaining the charging current value corresponding to the battery at the initial reversal speed; wherein, is the charging current, is the voltage conversion coefficient corresponding to the motor, is the current voltage of the battery, is the internal resistance of the battery; comparing the charging current value with the charging current threshold; If the charging current value is not greater than the charging current threshold, determining the initial reversal speed as a target reversal speed; If the charging current value is greater than the charging current threshold, Determine the target reversal speed, wherein, is the target reversal speed, is the charging current threshold.

4. The real-time control method for energy storage of new energy electric vehicles based on RISC-V architecture according to claim 1 is characterized in that: The training method of the charging current threshold prediction model comprises: Acquire a training data set; the training data set includes a matching relationship between multiple input parameter information and output parameter information, the input parameter information is battery parameter information and ambient temperature, and the output parameter information is a charging current threshold; Dividing the training data set into a training set, a validation set and a calibration set; For each matching relationship in the training set, the input parameter information of the matching relationship is used as the input of the preset neural network, and the output parameter information of the matching relationship is used as the output of the preset neural network to train the preset neural network to obtain an initial charging current threshold prediction model; Determining a prediction accuracy of the initial charging current threshold prediction model based on the validation set, and judging whether the prediction accuracy is greater than a preset prediction accuracy; If not, the model parameters of the initial charging current threshold prediction model are optimized based on the preset gradient descent algorithm and the correction set until the prediction accuracy of the initial charging current threshold prediction model is greater than the preset prediction accuracy, thereby obtaining the charging current threshold prediction model.

5. The real-time control method for energy storage of new energy electric vehicles based on RISC-V architecture according to claim 4 is characterized in that: The determining the prediction accuracy of the initial charging current threshold prediction model based on the verification set includes: For each matching relationship in the verification set, inputting the input parameter information corresponding to the matching relationship into the initial charging current threshold prediction model to obtain a charging current threshold prediction value, and obtaining an absolute value of a difference between the charging current threshold prediction value and the charging current threshold corresponding to the matching relationship, and determining that the matching relationship is a target matching relationship when the absolute value is less than a preset absolute value; The ratio between the number of the target matching relationships and the total number of matching relationships in the validation set is taken as the prediction accuracy.

6. The real-time control method for energy storage of new energy electric vehicles based on RISC-V architecture according to claim 4 is characterized in that: The optimizing the model parameters of the initial charging current threshold prediction model based on the preset gradient descent algorithm and the correction set includes: For each matching relationship in the correction set, the input parameter information corresponding to the matching relationship is input into the initial charging current threshold prediction model to obtain a charging current threshold prediction value, and a predicted loss value is determined based on the charging current threshold prediction value and the charging current threshold corresponding to the matching relationship, and the predicted loss value is back-propagated in the initial charging current threshold prediction model to update the model parameters of the initial charging current threshold prediction model.

7. The real-time control method for energy storage of new energy electric vehicles based on RISC-V architecture according to claim 1 is characterized in that: The step of generating an encrypted password based on the identification image of the new energy electric vehicle comprises: For each pixel of the identification image, obtaining RGB space information corresponding to the pixel; the RGB space information includes a red value, a blue value and a green value; Sequentially extract the maximum red value, the maximum blue value, the maximum green value, the minimum red value, the minimum blue value and the minimum green value from all the RGB space information to obtain a color value sequence; Subtracting the minimum red value from the maximum red value to obtain a first color difference, and subtracting the minimum blue value from the maximum blue value to obtain a second color difference; Obtaining a coding algorithm matrix; the coding algorithm matrix includes multiple rows and columns, and each position of the coding algorithm matrix is ​​provided with a coding algorithm; Determine a target coding algorithm in the coding algorithm matrix; the number corresponding to the row where the target coding algorithm is located is consistent with the number corresponding to the first color difference value, and the number corresponding to the column where the target coding algorithm is located is consistent with the number corresponding to the second color difference value; The color value sequence is encoded based on the target encoding algorithm to obtain the encrypted password.

8. A new energy electric vehicle energy storage real-time control device based on RISC-V architecture, characterized in that: A RISC-V processor for a new energy electric vehicle, the device comprising: An acquisition module is used to acquire the driving parameter information of the new energy electric vehicle in real time; the driving parameter information includes speed and acceleration; A judgment module, used for judging whether the new energy electric vehicle is in a deceleration state based on the acceleration; An input module, used for obtaining battery parameter information and ambient temperature when the new energy electric vehicle is in a deceleration state, and inputting the battery parameter information and the ambient temperature into a preset charging current threshold prediction model to obtain the charging current threshold of the battery; wherein the battery is used to power the new energy electric vehicle; A determination module, configured to determine a target reverse rotation speed of the motor based on the charging current threshold; the motor is provided in the new energy electric vehicle; A control module is used to control the reversal of the electric motor based on the target reversal speed, and to bind the driving parameter information, the battery parameter information, the ambient temperature and the target reversal speed to obtain a binding result, and to generate an encryption password based on the identification image of the new energy electric vehicle, to encrypt the binding result based on the encryption password to obtain an encrypted binding result, and to store the encrypted binding result in a database.

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