New energy automobile battery pack charging and discharging intelligent control method and system

Abstract

The invention discloses a new energy automobile battery pack charging and discharging intelligent control method and system, and relates to the technical field of battery management, and the method comprises the steps: inputting a quantum state data packet into a variable sub-circuit, carrying out the operation of a multi-layer cascaded quantum gate through a quantum processor, recognizing an optimal parameterized quantum gate sequence of a minimum Hamiltonian expected value, and carrying out the calculation of the optimal parameterized quantum gate sequence; a charging and discharging control instruction is generated; in the charging and discharging process according to the charging and discharging control instruction, state data of the battery pack are monitored in real time, preliminary adjustment is carried out, and the charging and discharging state of the adjusted battery is evaluated; and dynamically triggering a magnetofluid equalization mechanism according to an evaluation result, and optimizing the charging and discharging process of the battery pack through a rotating magnetic field and an alternating magnetic field. According to the invention, the quantum state data packet is input into the variable sub-circuit, and the quantum processor is used for identifying the optimal parameterized quantum gate sequence to dynamically optimize the charging and discharging strategy, so that the charging and discharging efficiency is improved, and the energy loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a method and system for intelligently controlling charging and discharging of a battery pack of a new energy vehicle. Background Art

[0002] With the rapid development of new energy vehicles, battery packs are the core power source, and their charge and discharge control technology is directly related to the performance, safety and service life of the vehicle. In recent years, battery management system (BMS) technology has made significant progress, especially in battery state estimation, balance control and thermal management. Traditional BMS collects data such as voltage, current and temperature, uses methods such as ampere-hour integration method and Kalman filtering to estimate the battery state, and realizes charge and discharge control based on threshold judgment. However, existing technologies still have shortcomings in multi-source data fusion, real-time performance and global optimization capabilities, especially in complex working conditions. It is difficult to achieve efficient and stable control of battery packs.

[0003] The main shortcoming of the existing technology is that it fails to fully combine quantum computing and battery management technology, resulting in limited optimization efficiency of charging and discharging strategies, and difficulty in dealing with the complex correlation and high-dimensional optimization problems of multi-source data in battery packs. In addition, the existing technology has weak quantification and dynamic adjustment capabilities for the state differences of each cell in the battery pack, and cannot achieve accurate power balancing and thermal management. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent control method for charging and discharging a battery pack of a new energy vehicle to solve the problems of complex correlation and high-dimensional optimization of multi-source data.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides an intelligent control method for charging and discharging of a battery pack for a new energy vehicle, which includes collecting multi-source data of each monomer in the battery pack, converting the multi-source data into battery status data, mapping the battery status data into a superposition state of quantum bits through a quantum coding algorithm, and generating a quantum state coding data packet; inputting the quantum state data packet into a variable molecular circuit, running multi-layer cascaded quantum gate operations through a quantum processor, identifying an optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian, and generating a charge and discharge control instruction; during the charge and discharge process according to the charge and discharge control instructions, real-time monitoring of the battery pack status data and performing preliminary adjustments, and evaluating the charge and discharge status of the battery after the adjustment; according to the evaluation result, dynamically triggering a magnetic fluid balance mechanism, and optimizing the charge and discharge process of the battery pack through a rotating magnetic field and an alternating magnetic field.

[0007] As a preferred solution of the intelligent control method for charging and discharging of a new energy vehicle battery pack of the present invention, the multi-source data includes the voltage, temperature distribution and vibration spectrum data of each cell in the battery pack.

[0008] As a preferred solution of the intelligent control method for charging and discharging of a new energy vehicle battery pack of the present invention, the multi-source data is converted into battery state data, and the battery state data is mapped into a superposition state of quantum bits through a quantum coding algorithm to generate a quantum state coding data packet. The specific steps are as follows: The voltage data is converted into the remaining power of each cell in the battery pack by the ampere-hour integration method, and the power difference in the battery pack is identified by comparing the power values ​​of each cell; The temperature distribution data is converted into a temperature gradient by a temperature gradient analysis method; The internal resistance of each monomer is calculated in real time through electrochemical impedance spectroscopy analysis, and the rate of change of internal resistance is derived; The power difference, temperature gradient and internal resistance change data within the battery pack are normalized and mapped to the superposition state of quantum bits through quantum state encoding to generate a quantum state data packet.

[0009] As a preferred solution of the intelligent control method for charging and discharging of a new energy vehicle battery pack of the present invention, the specific steps are as follows: the quantum state data packet is input into the variable molecular circuit, and multi-layer cascade quantum gate operations are run through the quantum processor to dynamically optimize the charging and discharging strategy and generate charging and discharging control instructions. Adjust the rotation angle of the quantum bit through the single quantum bit rotation gate; Through the double-qubit entanglement gate, the entanglement relationship between qubits is constructed; By parameterizing the quantum gate sequence, the rotation angle and entanglement relationship of the quantum bit are converted into a parameterized quantum gate sequence; Combine parameterized quantum gate sequences in time order to construct multi-layer cascade quantum circuits, and combine them with the gradient descent algorithm to form variable molecular circuits; The quantum state data packet is input into the variable molecular circuit, and the multi-layer cascade quantum gate operation is run through the quantum processor to identify the optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian. The expression is: ; in, is a parameterized quantum gate sequence, is a parameterized variational quantum circuit, It is a variational quantum circuit The conjugate transpose of is the Hamiltonian, is the initial quantum state in the quantum state data packet, is the optimal parameterized quantum gate sequence that minimizes the expectation value of the Hamiltonian; According to the optimal parameterized quantum gate sequence , quantum decryption is performed through quantum measurement method to generate charging and discharging control instructions.

[0010] As a preferred solution of the intelligent control method for charging and discharging of a new energy vehicle battery pack according to the present invention, the Hamiltonian It is obtained based on the quantum state encoding data packet, and the expression is: ; in, It is The difference in the power of each monomer, It is The temperature gradient of each monomer, It is The quantum state mapping matrix of the charge difference of each monomer, It is The quantum state mapping matrix of the temperature gradient of each monomer, It is The quantum state mapping matrix of the internal resistance change rate of each monomer, is the total number of cells in the battery pack, is the weight coefficient of the power difference, is the weight coefficient of the temperature gradient, is the weight coefficient of the internal resistance change rate, It is The internal resistance of a single cell, represents the change in time, Indicates Temperature gradient of a monomer.

[0011] As a preferred solution of the intelligent control method for charging and discharging of a new energy vehicle battery pack of the present invention, the state data of the battery pack includes the remaining power, temperature distribution and internal resistance change rate; The preliminary adjustment is performed to evaluate the charge and discharge status of the battery after adjustment. The specific steps are as follows: Based on the statistical analysis of historical charge and discharge data and historical battery pack status data, the power difference threshold A1, temperature threshold A2, internal resistance change rate threshold A3 and comprehensive status threshold A are defined; When it is detected that any data in the real-time status data of the battery pack exceeds the threshold, the charge and discharge status is preliminarily adjusted; Through the probability amplitude mapping and phase interference effect of the quantum state, combined with the dynamic threshold normalization and quantum interference mechanism, the charge and discharge state score of the adjusted battery pack is calculated, and the expression is: ; in, is the charge and discharge status score of the battery pack after adjustment, is the total number of status data of the battery pack, is the index of the battery pack status data, The battery pack The quantum probability amplitude of each state data is is the phase change factor of the quantum state, is the phase factor, The regulated battery pack Status data, Indicates the battery pack The threshold corresponding to the state data, Indicates the battery pack The phase factor of each state data; When S ≥ A, the charge and discharge state of the adjusted battery pack does not need to be further optimized; When S<A, the charge and discharge state of the adjusted battery pack needs to be further optimized.

[0012] As a preferred solution of the intelligent control method for charging and discharging of a new energy vehicle battery pack of the present invention, wherein: according to the evaluation results, the magnetic fluid balancing mechanism is dynamically triggered to optimize the charging and discharging process of the battery pack through the rotating magnetic field and the alternating magnetic field. The specific steps are as follows: Arrange an electromagnetic array around the battery pack to obtain a rotating gradient magnetic field and uniformly inject magnetic nanoparticles into the electrolyte; By rotating the gradient magnetic field, the magnetic nanoparticles absorb lithium ions in the high-charge area and migrate to the low-charge area under the action of the magnetic field, gradually reducing the charge difference until it is below the charge difference threshold A1; Switch the electromagnetic array to an alternating magnetic field, and use the mechanical force generated by the alternating magnetic field to break up the deposits on the electrode surface, thereby optimizing the internal resistance and electrode activity of the battery pack.

[0013] In the second aspect, the present invention provides an intelligent control system for charging and discharging a battery pack of a new energy vehicle, including a quantum mapping module, an instruction generation module, a state evaluation module and an optimization module; the quantum mapping module is used to collect multi-source data of each monomer in the battery pack, convert the multi-source data into battery state data, map the battery state data into a superposition state of quantum bits through a quantum coding algorithm, and generate a quantum state coding data packet; the instruction generation module is used to input the quantum state data packet into a variable molecular circuit, run multi-layer cascaded quantum gate operations through a quantum processor, identify the optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian, and generate a charge and discharge control instruction; the state evaluation module is used to monitor the state data of the battery pack in real time and perform preliminary adjustments during the charge and discharge process according to the charge and discharge control instructions, and evaluate the charge and discharge state of the battery after adjustment; the optimization module is used to dynamically trigger the magnetic fluid balance mechanism according to the evaluation results, and optimize the charge and discharge process of the battery pack through a rotating magnetic field and an alternating magnetic field.

[0014] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the intelligent control method for charging and discharging of a new energy vehicle battery pack as described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the intelligent control method for charging and discharging of a new energy vehicle battery pack as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: by collecting and converting the multi-source data of each cell in the battery pack into state data, and then using the quantum coding algorithm to generate a quantum state coding data packet, the complex state inside the battery pack is accurately captured and efficiently processed, and the accuracy of charge and discharge management and the safety and service life of the battery pack are improved. Then, by inputting the quantum state data packet into the variable subcircuit and using the quantum processor to identify the optimal parameterized quantum gate sequence to dynamically optimize the charge and discharge strategy, not only the charge and discharge efficiency is improved, but also the energy loss is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 only 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 creative work.

[0018] Figure 1 This is a flow chart of the intelligent control method for charging and discharging a new energy vehicle battery pack in Example 1.

[0019] Figure 2 This is a schematic diagram of the intelligent control system for charging and discharging a battery pack of a new energy vehicle in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a new energy vehicle battery pack charging and discharging intelligent control method, comprising the following steps: S1. Collect multi-source data of each cell in the battery pack, convert the multi-source data into battery status data, map the battery status data into a superposition state of quantum bits through a quantum coding algorithm, and generate a quantum state coding data packet.

[0024] The multi-source data includes the voltage, temperature distribution and vibration spectrum data of each cell in the battery pack.

[0025] Furthermore, this is achieved by collecting voltage data, temperature distribution data and vibration spectrum data of each cell in the battery pack, wherein the voltage data reflects the voltage condition of each cell in the battery pack, the temperature distribution data captures the temperature change of each cell in the battery pack, and the vibration spectrum data records the vibration characteristics of each cell in the battery pack.

[0026] The voltage data is converted into the remaining power of each cell in the battery pack through the ampere-hour integration method, and the power difference in the battery pack is identified by comparing the power values ​​of each cell.

[0027] It should be noted that based on the collected voltage data of each cell in the battery pack, the ampere-hour integration method is used to accumulate the charge of each cell from the initial state to the current state, so as to obtain the remaining power of each cell; for example, if the voltage data of a cell A in the battery pack shows that after a period of charge and discharge, the remaining power calculated by the ampere-hour integration method is 50Ah, while the remaining power of another cell B after the same time period is 45Ah, then by comparing the remaining power values ​​of the two cells, it is identified that there is a power difference in the battery pack, that is, the power imbalance between cells A and B.

[0028] The temperature distribution data is converted into temperature gradient by temperature gradient analysis method.

[0029] It should be noted that based on the collected temperature distribution data of each cell in the battery pack, the temperature gradient analysis method is used to calculate the temperature change rate within and between each cell, so as to determine the temperature gradient; for example, for the first cell in the battery pack, the temperature distribution data at different positions show that at a certain measurement moment, there is a change from 35 degrees Celsius at one end to 30 degrees Celsius at the other end. The temperature gradient analysis method is used to calculate this temperature change rate, and it is found that the temperature gradient of the cell is 0.5 degrees Celsius per centimeter, thereby achieving accurate quantification of the temperature changes of each cell in the battery pack.

[0030] The power difference, temperature gradient and internal resistance change data within the battery pack are normalized and mapped to the superposition state of quantum bits through quantum state encoding to generate a quantum state data packet.

[0031] It should be noted that based on the collected and calculated data of the power difference, temperature gradient and internal resistance change of each cell in the battery pack, these data are first normalized to make them in the same comparison scale; for example, the power difference, temperature gradient and internal resistance change of the first cell are normalized to between 0 and 1. Subsequently, the normalized power difference, temperature gradient and internal resistance change data are mapped to the superposition state of quantum bits using the quantum state encoding algorithm, thereby forming a quantum state data packet that can represent the overall state of the battery pack.

[0032] S2. Input the quantum state data packet into the variable molecular circuit, run multi-layer cascade quantum gate operations through the quantum processor, identify the optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian, and generate charge and discharge control instructions.

[0033] The rotation angle of the quantum bit is adjusted through a single quantum bit rotation gate.

[0034] It should be noted that based on the quantum state data of each cell in the battery pack, a single-qubit rotation gate operation is applied to adjust the rotation angle of the qubit. For example, for the qubit representing the state of the first cell, its position on the Bloch sphere can be rotated from the initial state to the target angle by applying a single-qubit rotation gate with specific parameters, such as rotating the original position at The quantum bit of the state is rotated to The superposition state can precisely control the battery state information represented by the quantum bit, where represents the lowest energy state of the quantum bit, Represents the excited energy state of the quantum bit.

[0035] Through the double quantum bit entanglement gate, the entanglement relationship between quantum bits is constructed.

[0036] It should be noted that the two-qubit entanglement gate operation is used to establish an entanglement relationship between the qubits corresponding to the two cells in the battery pack. For example, a two-qubit entanglement gate is applied to the two qubits representing the states of the first cell and the second cell, so that they enter a quantum state in which the measurement result of any qubit will immediately affect the state of the other qubit, no matter how far apart the two are.

[0037] By parameterizing the quantum gate sequence, the rotation angle and entanglement relationship of the quantum bit are converted into a parameterized quantum gate sequence.

[0038] It should be noted that according to the state requirements of each cell in the battery pack, a parameterized quantum gate sequence including specific rotation angles and entanglement relationships is designed. For example, for the states of the first and second cells, a series of parameterized quantum gate operations are formulated, including their respective rotation angles and the entanglement relationship between them, and these operations are sequentially encoded into a parameterized quantum gate sequence.

[0039] The parameterized quantum gate sequences are combined in time order to construct multi-layer cascaded quantum circuits, and combined with the gradient descent algorithm to form variable molecular circuits.

[0040] It should be noted that based on the above-mentioned parameterized quantum gate sequence, they are combined in a predetermined time order to form a multi-layer cascade quantum circuit structure. For example, a series of quantum gate operations for the first monomer are first applied, followed by operations related to the second monomer, and so on, until all monomers of the entire battery pack are covered. On this basis, the parameter settings in the entire quantum circuit are optimized in combination with the gradient descent algorithm to ensure that the variable molecular circuit formed in the end can minimize the expected value of the Hamiltonian, thereby generating the most optimized charge and discharge control instructions.

[0041] The quantum state data packet is input into the variable molecular circuit, and the multi-layer cascade quantum gate operation is run through the quantum processor to identify the optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian. The expression is: ; in, is a parameterized quantum gate sequence, is a parameterized variational quantum circuit, It is a variational quantum circuit The conjugate transpose of is the Hamiltonian, is the initial quantum state in the quantum state data packet, is the optimal parameterized sequence of quantum gates that minimizes the expectation value of the Hamiltonian.

[0042] It should be noted that the optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian is identified by running multi-layer cascaded quantum gate operations through a quantum processor. The specific process is: a quantum state data packet formed based on the state information of each monomer in the battery pack is passed as input to a variable molecular circuit, in which the quantum processor performs a series of predetermined quantum gate operations; for example, for a quantum state data packet containing multiple monomer state information, the quantum processor explores different quantum state transformation paths by adjusting the rotation angle and entanglement relationship between quantum bits to find a quantum gate sequence that can minimize the expected value of the Hamiltonian; the optimal parameterized quantum gate sequence finally determined corresponds to the generation of optimized charge and discharge control instructions.

[0043] According to the optimal parameterized quantum gate sequence , quantum decryption is performed through quantum measurement method to generate charging and discharging control instructions.

[0044] Hamiltonian It is obtained based on the quantum state encoding data packet, and the expression is: ; in, It is The difference in the power of each monomer, It is The temperature gradient of each monomer, It is The quantum state mapping matrix of the charge difference of each monomer, It is The quantum state mapping matrix of the temperature gradient of each monomer, It is The quantum state mapping matrix of the internal resistance change rate of each monomer, is the total number of cells in the battery pack, is the weight coefficient of the power difference, is the weight coefficient of the temperature gradient, is the weight coefficient of the internal resistance change rate, It is The internal resistance of a single cell, represents the change in time, Indicates Temperature gradient of a monomer.

[0045] It should be noted that, first, starting from the charge difference, temperature gradient and internal resistance change rate of each cell in the battery pack, these physical quantities are mapped into their respective quantum state mapping matrices through corresponding conversion methods; for example, the charge difference of the first cell is converted into its corresponding quantum state mapping matrix, and similarly, the temperature gradient and internal resistance change rate of the cell are also converted into corresponding quantum state mapping matrices. Then, for each cell, its charge difference, temperature gradient and internal resistance change rate are multiplied by the corresponding quantum state mapping matrix, and the same operation is performed on all cells in the entire battery pack and then accumulated. Finally, these accumulated results are combined according to a specific structure to form a complete Hamiltonian.

[0046] S3. During the charging and discharging process according to the charging and discharging control instructions, the status data of the battery pack is monitored in real time and preliminary adjustments are made, and the charging and discharging status of the battery after the adjustment is evaluated.

[0047] The status data of the battery pack includes the remaining power, temperature distribution and internal resistance change rate.

[0048] Perform preliminary adjustments and evaluate the charge and discharge status of the battery after adjustment. The specific steps are as follows: Based on the statistical analysis of historical charge and discharge data and historical battery pack status data, the power difference threshold A1, temperature threshold A2, internal resistance change rate threshold A3 and comprehensive status threshold A are defined.

[0049] It should be noted that, first, a large number of battery pack charge and discharge data and their status data under different conditions are collected and analyzed. These data cover the power difference, temperature distribution and internal resistance change rate of each monomer; for example, by sorting out the data of each charge and discharge cycle in the past year, the average power difference, typical temperature range and common internal resistance change rate of each monomer under normal operating conditions are identified. Then, according to the distribution characteristics of these data, reasonable threshold limits are determined, that is, the power difference threshold A1 is set to distinguish between normal and abnormal power deviations, the temperature threshold A2 is set to monitor the safe operating temperature range, the internal resistance change rate threshold A3 is set to evaluate the battery health status, and a comprehensive status threshold A is set in combination with the above three dimensions.

[0050] When it is detected that any data in the real-time status data of the battery pack exceeds a threshold, a preliminary adjustment is made to the charge and discharge status.

[0051] It should be noted that if it is found that the charge difference of a certain cell exceeds the defined charge difference threshold A1, measures are taken immediately to reduce the charge imbalance between the cell and other cells. Preliminary adjustments may include slowing down or suspending the charging rate of the cell, while increasing the charging speed of other cells with lower charge, in order to reduce the charge difference in a short time. Similarly, if the operating temperature of a cell exceeds the temperature threshold A2, the power of the cooling system may be adjusted to reduce the temperature of the cell to ensure that it returns to the safe operating range. If the internal resistance change rate of a cell exceeds the internal resistance change rate threshold A3, the internal pressure of the battery can be reduced by optimizing the charging and discharging strategy, such as adjusting the charging current intensity or frequency, so as to control the growth rate of the internal resistance.

[0052] Through the probability amplitude mapping and phase interference effect of the quantum state, combined with the dynamic threshold normalization and quantum interference mechanism, the charge and discharge state score of the adjusted battery pack is calculated, and the expression is: ; in, is the charge and discharge status score of the battery pack after adjustment, is the total number of status data of the battery pack, is the index of the battery pack status data, The battery pack The quantum probability amplitude of each state data is is the phase change factor of the quantum state, is the phase factor, The regulated battery pack Status data, Indicates the battery pack The threshold corresponding to the state data, Indicates the battery pack The phase factor of each state data.

[0053] It should be noted that, first, based on the state data of each monomer, including the charge difference, temperature gradient and internal resistance change rate, the corresponding quantum probability amplitude is determined. and the phase change factor ,in, reflects the amplitude of the state data in the quantum state, and Then, these state data are compared with their respective thresholds. For comparison, the relative proportion is obtained by dynamic threshold normalization. This process quantifies the degree of deviation of the actual state data from the set threshold. Then, the quantum interference mechanism is applied to the state data of all monomers, that is, by accumulating the product of the quantum state amplitude and phase information of each monomer and taking into account its proportional relationship relative to the threshold, a comprehensive index is formed. Finally, the final charge and discharge state score is obtained by squaring this comprehensive index. The score comprehensively reflects the overall health and working status of the battery pack, ensuring accurate assessment and guiding subsequent optimization measures.

[0054] When S ≥ A, the charge and discharge state of the adjusted battery pack does not need to be further optimized.

[0055] When S<A, the charge and discharge state of the adjusted battery pack needs to be further optimized.

[0056] S4. Based on the evaluation results, the magnetic fluid balancing mechanism is dynamically triggered to optimize the charging and discharging process of the battery pack through rotating magnetic field and alternating magnetic field.

[0057] An electromagnetic array is arranged around the battery pack to obtain a rotating gradient magnetic field and uniformly inject magnetic nanoparticles into the electrolyte.

[0058] It should be noted that, first, an electromagnetic array capable of generating a rotating gradient magnetic field is set up around the battery pack to ensure that the magnetic field can cover the entire battery pack. Then, the pre-prepared magnetic nanoparticles are evenly dispersed into the electrolyte of the battery pack through a precision injection device. These nanoparticles have good magnetic response characteristics and can move under the action of the magnetic field.

[0059] By rotating the gradient magnetic field, the magnetic nanoparticles adsorb lithium ions in the high-charge area and migrate to the low-charge area under the action of the magnetic field, gradually reducing the charge difference until it is below the charge difference threshold A1.

[0060] It should be noted that when the rotating gradient magnetic field generated by the electromagnetic array is activated, the magnetic nanoparticles evenly dispersed in the electrolyte begin to move under the action of the magnetic field and preferentially gather in the area with higher power in the battery pack. These particles can absorb the excess lithium ions in this area. Subsequently, under the guidance of the rotating magnetic field, the magnetic nanoparticles carrying lithium ions migrate to the areas with lower power, releasing lithium ions to supplement the insufficient power in these areas. This process continues, constantly balancing the power distribution between the monomers until the power difference between all monomers drops below the set power difference threshold A1, achieving a balanced distribution of power inside the battery pack.

[0061] Switch the electromagnetic array to an alternating magnetic field, and use the mechanical force generated by the alternating magnetic field to break up the deposits on the electrode surface, thereby optimizing the internal resistance and electrode activity of the battery pack.

[0062] It should be noted that after completing the battery balancing, the electromagnetic array adjusts its working mode to the alternating magnetic field. In this mode, the electromagnetic array generates a periodically changing magnetic field, and this alternating magnetic field applies repeated mechanical forces to the electrode surface. These mechanical forces act on the electrode surface and can effectively break up the deposits accumulated during long-term use. As the deposits are removed, the electrode surface is exposed again, improving the electron conduction path, thereby reducing the internal resistance of the battery pack and increasing the electrode activity. This process helps to restore the performance of the battery, extend its service life, and improve overall working efficiency.

[0063] The present embodiment also provides an intelligent control system for charging and discharging a battery pack of a new energy vehicle, comprising: a quantum mapping module, an instruction generation module, a state evaluation module and an optimization module; the quantum mapping module is used to collect multi-source data of each monomer in the battery pack, convert the multi-source data into battery state data, map the battery state data into a superposition state of quantum bits through a quantum coding algorithm, and generate a quantum state coding data packet; the instruction generation module is used to input the quantum state data packet into a variable molecular circuit, run multi-layer cascaded quantum gate operations through a quantum processor, identify the optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian, and generate a charge and discharge control instruction; the state evaluation module is used to monitor the state data of the battery pack in real time and perform preliminary adjustments during the charge and discharge process according to the charge and discharge control instructions, and evaluate the charge and discharge state of the battery after the adjustment; the optimization module is used to dynamically trigger the magnetic fluid balance mechanism according to the evaluation result, and optimize the charge and discharge process of the battery pack through a rotating magnetic field and an alternating magnetic field.

[0064] This embodiment also provides a computer device, which is suitable for the case of an intelligent control method for charging and discharging a battery pack of a new energy vehicle, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the intelligent control method for charging and discharging a battery pack of a new energy vehicle as proposed in the above embodiment.

[0065] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0066] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent control method for charging and discharging of a new energy vehicle battery pack proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0067] In summary, the present invention collects and converts the multi-source data of each cell in the battery pack into state data, and then uses the quantum coding algorithm to generate a quantum state coding data packet, thereby achieving accurate capture and efficient processing of the complex state inside the battery pack, improving the accuracy of charge and discharge management and the safety and service life of the battery pack. Then, by inputting the quantum state data packet into the variable subcircuit, and using the quantum processor to identify the optimal parameterized quantum gate sequence to dynamically optimize the charge and discharge strategy, not only the charge and discharge efficiency is improved, but also the energy loss is reduced.

[0068] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the intelligent control method for charging and discharging of a new energy vehicle battery pack are provided.

[0069] In order to verify the effectiveness of the intelligent control method for charging and discharging of new energy vehicle battery packs, an experiment was designed with six independent battery packs: three experimental groups (E1, E2, E3) using the technology of the present invention, and three control groups (C1, C2, C3) using traditional technology. Each battery pack consists of ten cells to ensure the consistency of experimental conditions and the comparability of results.

[0070] Before the experiment began, all battery packs were fully charged, and the voltage, temperature distribution, and vibration spectrum data in the initial state were recorded. For the experimental group, the remaining power was calculated using the ampere-hour integration method, the temperature change rate was determined by the temperature gradient analysis method, and the internal resistance change rate was monitored by electrochemical impedance spectroscopy. Then, after normalization, these data were converted into a quantum bit superposition state through a quantum state coding algorithm to form a quantum state coding data packet. Based on this data packet, the variable molecular circuit and quantum processor identify the optimal parameterized quantum gate sequence and generate charge and discharge control instructions. During the charging and discharging process, the battery status is monitored in real time. When an abnormality is detected, the magnetic fluid balance mechanism is triggered for optimization.

[0071] The control group adopted the traditional charge and discharge management strategy without introducing advanced methods such as quantum state encoding or variable molecular circuits. The entire experiment lasted 24 hours, during which key parameter values ​​were recorded every half hour, such as the difference in remaining power, average temperature fluctuation, internal resistance growth rate, and charge and discharge efficiency.

[0072] The details are shown in Table 1: Table 1 Comparison of charging and discharging performance of new energy vehicle battery packs It can be seen from the data in Table 1 that the experimental group (E1, E2, E3) using the present invention has shown significant advantages over the control group (C1, C2, C3) in multiple performance indicators. Specifically, in terms of the difference in remaining power, the numerical range of the experimental group is -0.4 to -0.6Ah, while the control group is as high as -1.8 to -2.1Ah, which shows that the present invention can better maintain the balance of power between the monomers inside the battery pack and reduce problems caused by imbalance. In terms of average temperature fluctuation, the experimental group remained between ±1.3 and ±1.5℃, which is much lower than the ±2.8 to ±3.1℃ of the control group, indicating that the present invention has higher accuracy in temperature control and helps to extend battery life. In addition, the internal resistance growth rate of the experimental group is between 0.25 and 0.30mΩ / h, which is significantly lower than the 1.0 to 1.1mΩ / h of the control group, showing a stronger ability to resist the increase in internal resistance caused by long-term use. Finally, the charge and discharge efficiency of the experimental group reached 98.5% to 98.7%, which was higher than the 95.5% to 96.0% of the control group, proving the effectiveness of the present invention in improving the overall performance.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A new energy vehicle battery pack charging and discharging intelligent control method, characterized in that: include, Collect multi-source data of each cell in the battery pack, convert the multi-source data into battery status data, map the battery status data into a superposition state of quantum bits through a quantum coding algorithm, and generate a quantum state coding data packet; Input the quantum state data packet into the variable molecular circuit, run multi-layer cascade quantum gate operations through the quantum processor, identify the optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian, and generate charge and discharge control instructions; During the charging and discharging process according to the charging and discharging control instructions, the status data of the battery pack is monitored in real time and preliminary adjustments are made, and the charging and discharging status of the adjusted battery is evaluated; According to the evaluation results, the magnetofluid balancing mechanism is dynamically triggered to optimize the charging and discharging process of the battery pack through rotating magnetic fields and alternating magnetic fields.

2. The intelligent control method for charging and discharging a battery pack of a new energy vehicle according to claim 1, characterized in that: The multi-source data includes voltage, temperature distribution and vibration spectrum data of each cell in the battery pack.

3. The intelligent control method for charging and discharging a new energy vehicle battery pack according to claim 2, characterized in that: The multi-source data is converted into battery status data, and the battery status data is mapped into a superposition state of quantum bits through a quantum coding algorithm to generate a quantum state coding data packet. The specific steps are as follows: The voltage data is converted into the remaining power of each cell in the battery pack by the ampere-hour integration method, and the power difference in the battery pack is identified by comparing the power values ​​of each cell; The temperature distribution data is converted into a temperature gradient by a temperature gradient analysis method; The internal resistance of each monomer is calculated in real time through electrochemical impedance spectroscopy analysis, and the rate of change of internal resistance is derived; The power difference, temperature gradient and internal resistance change data within the battery pack are normalized and mapped to the superposition state of quantum bits through quantum state encoding to generate a quantum state data packet.

4. The intelligent control method for charging and discharging a new energy vehicle battery pack according to claim 3, characterized in that: The quantum state data packet is input into the variable subcircuit, and the multi-layer cascade quantum gate operation is performed by the quantum processor to dynamically optimize the charge and discharge strategy and generate the charge and discharge control instructions. The specific steps are as follows: Adjust the rotation angle of the quantum bit through the single quantum bit rotation gate; Through the double-qubit entanglement gate, the entanglement relationship between qubits is constructed; By parameterizing the quantum gate sequence, the rotation angle and entanglement relationship of the quantum bit are converted into a parameterized quantum gate sequence; Combine parameterized quantum gate sequences in time order to construct multi-layer cascade quantum circuits, and combine them with the gradient descent algorithm to form variable molecular circuits; The quantum state data packet is input into the variable molecular circuit, and the multi-layer cascade quantum gate operation is run through the quantum processor to identify the optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian. The expression is: ； in, is a parameterized quantum gate sequence, is a parameterized variational quantum circuit, It is a variational quantum circuit The conjugate transpose of is the Hamiltonian, is the initial quantum state in the quantum state data packet, is the optimal parameterized quantum gate sequence that minimizes the expectation value of the Hamiltonian; According to the optimal parameterized quantum gate sequence , quantum decryption is performed through quantum measurement method to generate charging and discharging control instructions.

5. The intelligent control method for charging and discharging a new energy vehicle battery pack according to claim 4, characterized in that: The Hamiltonian It is obtained based on the quantum state encoding data packet, and the expression is: ； in, It is The difference in the power of each monomer, It is The temperature gradient of each monomer, It is The quantum state mapping matrix of the charge difference of each monomer, It is The quantum state mapping matrix of the temperature gradient of each monomer, It is The quantum state mapping matrix of the internal resistance change rate of each monomer, is the total number of cells in the battery pack, is the weight coefficient of the power difference, is the weight coefficient of the temperature gradient, is the weight coefficient of the internal resistance change rate, It is The internal resistance of a single cell, represents the change in time, Indicates Temperature gradient of a monomer.

6. The intelligent control method for charging and discharging a new energy vehicle battery pack according to claim 5, characterized in that: The status data of the battery pack includes remaining power, temperature distribution and internal resistance change rate; The preliminary adjustment is performed to evaluate the charge and discharge status of the battery after adjustment. The specific steps are as follows: Based on the statistical analysis of historical charge and discharge data and historical battery pack status data, the power difference threshold A1, temperature threshold A2, internal resistance change rate threshold A3 and comprehensive status threshold A are defined; When it is detected that any data in the real-time status data of the battery pack exceeds the threshold, the charge and discharge status is preliminarily adjusted; Through the probability amplitude mapping and phase interference effect of the quantum state, combined with the dynamic threshold normalization and quantum interference mechanism, the charge and discharge state score of the adjusted battery pack is calculated, and the expression is: ； in, is the charge and discharge status score of the battery pack after adjustment, is the total number of status data of the battery pack, is the index of the battery pack status data, The battery pack The quantum probability amplitude of each state data is is the phase change factor of the quantum state, is the phase factor, The regulated battery pack Status data, Indicates the battery pack The threshold corresponding to the state data, Indicates the battery pack The phase factor of each state data; When S ≥ A, the charge and discharge state of the adjusted battery pack does not need to be further optimized; When S<A, the charge and discharge state of the adjusted battery pack needs to be further optimized.

7. The intelligent control method for charging and discharging a battery pack of a new energy vehicle as claimed in claim 6, characterized in that: According to the evaluation results, the magnetic fluid balancing mechanism is dynamically triggered to optimize the charging and discharging process of the battery pack through the rotating magnetic field and the alternating magnetic field. The specific steps are as follows: Arrange an electromagnetic array around the battery pack to obtain a rotating gradient magnetic field and uniformly inject magnetic nanoparticles into the electrolyte; By rotating the gradient magnetic field, the magnetic nanoparticles absorb lithium ions in the high-charge area and migrate to the low-charge area under the action of the magnetic field, gradually reducing the charge difference until it is below the charge difference threshold A1; Switch the electromagnetic array to an alternating magnetic field, and use the mechanical force generated by the alternating magnetic field to break up the deposits on the electrode surface, thereby optimizing the internal resistance and electrode activity of the battery pack.

8. An intelligent control system for charging and discharging a battery pack of a new energy vehicle, based on the intelligent control method for charging and discharging a battery pack of a new energy vehicle according to any one of claims 1 to 7, characterized in that: Including quantum mapping module, instruction generation module, state evaluation module and optimization module; The quantum mapping module is used to collect multi-source data of each cell in the battery pack, convert the multi-source data into battery status data, map the battery status data into a superposition state of quantum bits through a quantum coding algorithm, and generate a quantum state coding data packet; An instruction generation module is used to input quantum state data packets into the variable subcircuit, run multi-layer cascade quantum gate operations through the quantum processor, identify the optimal parameterized quantum gate sequence that minimizes the expected value of the Hamiltonian, and generate charge and discharge control instructions; The state evaluation module is used to monitor the state data of the battery pack in real time and make preliminary adjustments during the charging and discharging process according to the charging and discharging control instructions, and evaluate the charging and discharging state of the battery after the adjustment; The optimization module is used to dynamically trigger the magnetic fluid balancing mechanism according to the evaluation results, and optimize the charging and discharging process of the battery pack through rotating magnetic field and alternating magnetic field.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent control method for charging and discharging of a new energy vehicle battery pack as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent control method for charging and discharging a battery pack of a new energy vehicle according to any one of claims 1 to 7 are implemented.

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