Intelligent battery low-temperature top-speed heating method, system and device and storage medium

Abstract

The invention discloses an intelligent battery low-temperature top-speed heating method, system and device and a storage medium, and the method comprises the steps: obtaining the battery temperature and battery state information of a target battery, and the battery state information comprises an SOC state and a battery open-circuit voltage; when the battery temperature is smaller than a preset target temperature, determining an EIS impedance spectrum according to the battery temperature and the SOC state, and determining a target equivalent circuit model according to the EIS impedance spectrum; performing parameter updating on the target equivalent circuit model through a global optimization algorithm to obtain equivalent circuit parameters; and updating pulse parameters according to the equivalent circuit parameters, the battery open-circuit voltage and a preset boundary condition, and performing variable bidirectional pulse cyclic heating on the target battery according to the pulse parameters. The low-temperature heating efficiency of the lithium ion battery is improved, the energy consumption and the cost are reduced, and the method can be widely applied to the technical field of battery management.

Description

Technical Field

[0001] The present invention relates to the field of battery management technology, and in particular to a method, system, device and storage medium for low-temperature rapid heating of an intelligent battery. Background Art

[0002] Lithium-ion batteries are widely used in energy storage systems, new energy vehicles, and consumer electronic devices due to their high energy density, long cycle life, low self-discharge rate, and environmental friendliness. It is estimated that by 2030, the global total consumption of lithium-ion batteries will reach 1.2TWh, of which the new energy vehicle sector will account for 53%. However, lithium-ion batteries have performance degradation problems such as significantly reduced capacity, increased internal resistance, decreased electrochemical reaction rate, and decreased charge and discharge efficiency under low temperature conditions. At the same time, their low-temperature performance is unstable and prone to the formation of lithium dendrites, which increases the risk of short circuit and thermal runaway, seriously affecting the driving range, charging time, and safety, and hindering the application of lithium-ion batteries in all-weather climate environments.

[0003] To address the low temperature limitations of lithium-ion batteries, researchers and manufacturers have adopted a variety of solutions, mainly focusing on the following four aspects:

[0004] 1) Develop low-temperature resistant electrolytes, additives and electrode materials. To improve the low-temperature performance of batteries from the material perspective, it is necessary to comprehensively consider the physical and chemical properties of the electrolyte, the electrolyte solvation structure, the properties of the interface passivation layer and the synergistic effect between the battery structures. The development and industrialization cycle is too long.

[0005] 2) External preheating technology mainly uses air preheating, liquid preheating, phase-changing material preheating, resistance preheating, electric heating mold preheating, Palma effect preheating, etc. This method is more feasible from the perspective of engineering application, but it has fatal defects, such as too low temperature rise rate, too high energy consumption, and requires large and complex auxiliary mechanisms.

[0006] 3) Internal preheating technology mainly adopts SHLB preheating, CCD preheating, CVD preheating, DC preheating, AC preheating, pulse preheating and other methods. Internal preheating has efficient and uniform preheating effect, low energy consumption, and little damage to the battery, but it requires the modification of the internal structure of the battery.

[0007] 4) Composite preheating technology combines multiple preheating methods to improve the temperature rise rate and uniformity, coupling the advantages of multiple methods, but is limited by the shortcomings of its own coupling method.

[0008] In summary, existing solutions for low temperature limitation of lithium-ion batteries have the disadvantages of low efficiency and high cost. Summary of the invention

[0009] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0010] To this end, an object of an embodiment of the present invention is to provide a method for extremely fast low-temperature heating of an intelligent battery, which improves the efficiency of low-temperature heating of a lithium-ion battery and reduces energy consumption and cost.

[0011] Another object of an embodiment of the present invention is to provide a smart battery low-temperature extremely fast heating system.

[0012] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0013] In a first aspect, an embodiment of the present invention provides a method for extremely fast heating of a smart battery at low temperature, comprising the following steps:

[0014] Acquire battery temperature and battery status information of the target battery, wherein the battery status information includes SOC status and battery open circuit voltage;

[0015] When the battery temperature is lower than a preset target temperature, determining an EIS impedance spectrum according to the battery temperature and the SOC state, and determining a target equivalent circuit model according to the EIS impedance spectrum;

[0016] The target equivalent circuit model is updated with parameters by a global optimization algorithm to obtain equivalent circuit parameters;

[0017] The pulse parameters are updated according to the equivalent circuit parameters, the battery open circuit voltage and preset boundary conditions, and the target battery is subjected to variable bidirectional pulse cycle heating according to the pulse parameters.

[0018] Furthermore, in one embodiment of the present invention, the method for extremely fast low-temperature heating of a smart battery further comprises the following steps:

[0019] When the heating time reaches a preset temperature update time interval, obtaining the current battery temperature of the target battery;

[0020] If the current battery temperature is greater than or equal to the target temperature, the heating is terminated; if the current battery temperature is less than the target temperature, it is determined whether the difference between the current battery temperature and the battery temperature is less than a preset temperature change threshold;

[0021] When the difference between the current battery temperature and the battery temperature is greater than or equal to the temperature change threshold, updating the EIS impedance spectrum, and returning to the step of determining a target equivalent circuit model according to the EIS impedance spectrum;

[0022] When the difference between the current battery temperature and the battery temperature is less than the temperature change threshold, the process returns to the step of performing bidirectional pulse cycle heating on the target battery according to the equivalent circuit parameters and the pulse parameters.

[0023] Further, in one embodiment of the present invention, determining a target equivalent circuit model according to the EIS impedance spectrum specifically includes:

[0024] Performing quality assessment and pre-processing on the EIS impedance spectrum to obtain qualified EIS graphic data;

[0025] Inputting the qualified EIS graphic data into a pre-trained multi-classification machine learning model to perform equivalent circuit type recognition to obtain a target equivalent circuit type;

[0026] The corresponding target equivalent circuit model is determined according to the target equivalent circuit type.

[0027] Further, in one embodiment of the present invention, the quality assessment and pre-processing of the EIS impedance spectrum to obtain qualified EIS graphic data specifically includes:

[0028] Performing linear Kramers-Kronig relationship verification on the EIS impedance spectrum to obtain a quality assessment result;

[0029] When the quality assessment is qualified, the EIS impedance spectrum is subjected to abnormal point detection by the quartile method, and then the abnormal point data is deleted to obtain the qualified EIS graph data.

[0030] Furthermore, in one embodiment of the present invention, the multi-classification machine learning model is trained by the following steps:

[0031] Acquire a plurality of preset sample EIS graph data, perform feature extraction on the sample EIS graph data, and obtain EIS graph feature data, wherein the EIS graph feature data includes an impedance phase shift, a maximum impedance amplitude point, an inflection point of a curve waveform change, and an intersection point of the curve and a real axis;

[0032] Construct multiple binary classification learners based on the OvO decomposition strategy and a preset set of equivalent circuit types;

[0033] The EIS graphic feature data is input into the binary classification learner for training to obtain a strong learner, and the strong learner is used as the trained multi-classification machine learning model.

[0034] Furthermore, in one embodiment of the present invention, the parameter updating of the target equivalent circuit model by using a global optimization algorithm specifically includes:

[0035] Initializing a population, and using the real part and the imaginary part of the EIS data of the EIS impedance spectrum as individual input populations;

[0036] With the goal of minimizing the error between the fitted EIS function and the experimental EIS function, a fitness function is constructed and the population size is set;

[0037] Evaluate the fitness of individuals in the population and retain elite individuals based on the fitness evaluation results;

[0038] Perform crossover and mutation operations on the population, and return to the step of evaluating the fitness of individuals in the population;

[0039] When the fitness evaluation result meets the preset requirements, the parameters of the target equivalent circuit model are updated according to the corresponding individuals to obtain the equivalent circuit parameters.

[0040] Furthermore, in one embodiment of the present invention, the boundary condition is:

[0041] I·|Z ct |≤U e

[0042] U ct ≤|U t -U ocv

[0043] t Discharge +t Charge =T D_C

[0044] I Discharge ·t Discharge =I Charge ·t Charge

[0045] Where I represents the pulse current, Z ct represents the impedance of the constant phase element, U e Indicates the negative electrode potential, U t Indicates the battery terminal voltage, U OCV Indicates the battery open circuit voltage, U ct represents the transfer voltage, T D_C Represents the pulse period, I Discharge represents the pulse discharge current, t Discharge Indicates the pulse discharge time, I Charge represents the pulse charging current, t Charge Indicates the pulse charging time.

[0046] In a second aspect, an embodiment of the present invention provides a smart battery low-temperature rapid heating system, comprising:

[0047] A data acquisition module, used to acquire the battery temperature and battery status information of the target battery, wherein the battery status information includes the SOC status and the battery open circuit voltage;

[0048] an equivalent circuit model determination module, configured to determine an EIS impedance spectrum according to the battery temperature and the SOC state when the battery temperature is lower than a preset target temperature, and determine a target equivalent circuit model according to the EIS impedance spectrum;

[0049] An equivalent circuit parameter updating module is used to update the parameters of the target equivalent circuit model through a global optimization algorithm to obtain equivalent circuit parameters;

[0050] The circulation heating module is used to update the pulse parameters according to the equivalent circuit parameters, the battery open circuit voltage and the preset boundary conditions, and perform bidirectional pulse circulation heating on the target battery according to the pulse parameters.

[0051] In a third aspect, an embodiment of the present invention provides a smart battery low-temperature extremely fast heating device, comprising:

[0052] at least one processor;

[0053] at least one memory for storing at least one program;

[0054] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned intelligent battery low-temperature extremely fast heating method.

[0055] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the above-mentioned method for low-temperature rapid heating of a smart battery.

[0056] The advantages and beneficial effects of the present invention will be partly given in the following description, partly become apparent from the following description, or be understood through the practice of the present invention:

[0057] The embodiment of the present invention obtains the battery temperature and battery status information of the target battery, the battery status information includes the SOC status and the battery open circuit voltage, when the battery temperature is less than the preset target temperature, the EIS impedance spectrum is determined according to the battery temperature and the SOC status, and the target equivalent circuit model is determined according to the EIS impedance spectrum, the target equivalent circuit model is updated with parameters through a global optimization algorithm to obtain equivalent circuit parameters, the pulse parameters are updated according to the equivalent circuit parameters, the battery open circuit voltage and preset boundary conditions, and the target battery is subjected to variable bidirectional pulse cycle heating according to the pulse parameters. The embodiment of the present invention comprehensively considers the influence of factors such as battery temperature, SOC status, and battery open circuit voltage, and considers the boundary condition restrictions at the same time, and adopts a variable bidirectional pulse heating strategy to achieve low-temperature preheating of lithium-ion batteries, thereby improving the efficiency of low-temperature heating of lithium-ion batteries and reducing energy consumption and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 A flowchart of the steps of a method for extremely fast low-temperature heating of a smart battery provided by an embodiment of the present invention;

[0060] Figure 2 A schematic diagram of instructions for a method for extremely fast low-temperature heating of a smart battery provided by an embodiment of the present invention;

[0061] Figure 3 A schematic diagram of a target equivalent circuit model provided by an embodiment of the present invention;

[0062] Figure 4 A schematic diagram of a process for identifying equivalent circuit types based on a multi-classification machine learning model provided in an embodiment of the present invention;

[0063] Figure 5 A structural block diagram of a smart battery low-temperature rapid heating system provided by an embodiment of the present invention;

[0064] Figure 6 This is a structural block diagram of a smart battery low-temperature extremely fast heating device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0066] In the description of the present invention, the meaning of "a plurality" is two or more than two. If there is a description of "a first" or "a second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by those skilled in the art.

[0067] Reference Figure 1 The embodiment of the present invention provides a method for extremely fast heating of a smart battery at low temperature, which specifically includes the following steps:

[0068] S101, obtaining battery temperature and battery status information of a target battery, where the battery status information includes SOC status and battery open circuit voltage;

[0069] S102, when the battery temperature is lower than a preset target temperature, determining an EIS impedance spectrum according to the battery temperature and the SOC state, and determining a target equivalent circuit model according to the EIS impedance spectrum;

[0070] S103, updating the parameters of the target equivalent circuit model by using a global optimization algorithm to obtain equivalent circuit parameters;

[0071] S104, updating pulse parameters according to equivalent circuit parameters, battery open circuit voltage and preset boundary conditions, and performing bidirectional pulse cycle heating on the target battery according to the pulse parameters.

[0072] like Figure 2 FIG. 1 is a schematic diagram of instructions for a method for extremely fast heating a smart battery at low temperature provided by an embodiment of the present invention, wherein T set Indicates the preheating set temperature (it is recommended to select the lower limit of the optimal operating temperature range of the lithium-ion battery), and it is recommended to be set to 15°C, n indicates the number of pulse parameter updates, and k indicates the number of battery temperature updates. The embodiment of the present invention couples the temperature and the EIS parameter identification equivalent circuit under the charge state, and adopts a variable bidirectional pulse heating strategy to perform low-temperature preheating of the lithium-ion battery, which can be applied to the charging pile end and the vehicle end respectively. The specific process is as follows:

[0073] Get the battery temperature T through BMS (battery management system) bat 、Ambient temperature T amb , open circuit voltage U ocv and initial SOC, initialize the battery temperature update times k, its value range is [0,1,2,.....], the initial value of the battery temperature is T bat (0) indicates the number of pulse parameter updates n, and its value range is [1,2,3,.....]. Initialize ΔT. According to experimental experience, the smaller the value, the faster the temperature rises. It is recommended to select the range [2℃-5℃].

[0074] According to the battery temperature T bat (0) and the system set preheating temperature T set , determine whether the battery needs to be preheated by bidirectional pulse. If yes, execute; if no, end.

[0075] Determine the equivalent circuit model form and initialize the model parameters. According to the battery temperature and SOC given by the BMS, use experimental detection technology to obtain the EIS impedance spectrum, and perform quality detection and pre-processing on the impedance spectrum. Subsequently, extract feature data based on qualified EIS graphs, and use a multi-classification machine learning (ML) model to identify the optimal equivalent circuit model (ECM). Different types of EIS impedance spectra correspond to different ECMs, such as Figure 3 FIG. 1 is a schematic diagram of a target equivalent circuit model provided by an embodiment of the present invention. The target circuit model is an ECM-9 model, wherein U t Indicates the battery terminal voltage, U OCV Represents the battery open circuit voltage, Z ct and Z SEI represents the constant phase element, R ct and R SEI Respectively represent and Z ct and Z SEI The corresponding resistor element, Z CPEO , Z CPE2 As shown in Figure 2 and Z ct and Z SEI The corresponding impedance element, R eq1 and R eq2 Respectively represent and Z ct and Z SEI The corresponding equivalent resistance, C eq1 and C eq2 Respectively represent and Z ct and Z SEI The corresponding equivalent capacitance, R0 represents an ohmic resistance. In the subsequent description of the embodiment of the present invention, the ECM-9 model is taken as an example for detailed description.

[0076] The ECM parameters are identified by the global optimization algorithm (GOA). Figure 3 The ECM-9 model shown in the figure is taken as an example, where R ct , R SEI , Z CPEO and Z CPE2 Through genetic algorithm (GA) identification and update, for the convenience of engineering application, R eq1 , R eq2 , C eq1 , C eq2 Update by replacing it with the following equivalent:

[0077]

[0078] Among them, Z CPE It consists of the generalized capacitance Q and the suppression factor n, j is the imaginary unit, and ω is the angular frequency.

[0079] To update the bidirectional pulse parameters, first, the pulse period at different temperature stages and other pulse parameters I are obtained based on experimental experience. Discharge ,t Discharge ,I Charge ,t Charge , calculated and updated by preset boundary conditions, where U t with U e Obtained through sensor detection and experimental detection, U e Indicates the negative electrode potential, U t Represents the battery terminal voltage, I Discharge represents the pulse discharge current, t Discharge Indicates the pulse discharge time, I Charge represents the pulse charging current, t Charge Indicates the pulse charging time.

[0080] The target battery is subjected to variable bidirectional pulse cycle heating according to the equivalent circuit parameters and pulse parameters.

[0081] As an optional implementation, the intelligent battery low-temperature rapid heating method further includes the following steps:

[0082] S105, when the heating time reaches the preset temperature update time interval, obtaining the current battery temperature of the target battery;

[0083] S106, if the current battery temperature is greater than or equal to the target temperature, end the heating; if the current battery temperature is less than the target temperature, determine whether the difference between the current battery temperature and the battery temperature is less than a preset temperature change threshold;

[0084] S107, when the difference between the current battery temperature and the battery temperature is greater than or equal to the temperature change threshold, updating the EIS impedance spectrum, and returning to the step of determining the target equivalent circuit model according to the EIS impedance spectrum;

[0085] S108: When the difference between the current battery temperature and the battery temperature is less than the temperature change threshold, return to the step of performing bidirectional pulse cycle heating on the target battery according to the equivalent circuit parameters and the pulse parameters.

[0086] Specifically, when the current heating time reaches the preset temperature update time interval, it is determined whether the target temperature T is reached. set If yes, end the cycle heating, if no, jump to the next step to determine the current battery temperature T bat (k) and battery temperature T bat(0) is greater than or equal to the preset temperature change threshold n·ΔT. If not, jump to the step of performing bidirectional pulse cycle heating on the target battery according to the equivalent circuit parameters and pulse parameters. If yes, update the EIS impedance spectrum, return to the step of determining the target equivalent circuit model according to the EIS impedance spectrum, and perform subsequent pulse parameter updates and bidirectional pulse cycle heating.

[0087] As an optional implementation, determining a target equivalent circuit model according to the EIS impedance spectrum specifically includes:

[0088] S1021, performing quality assessment and pre-processing on the EIS impedance spectrum to obtain qualified EIS graphic data;

[0089] S1022, inputting the qualified EIS graphic data into a pre-trained multi-classification machine learning model to perform equivalent circuit type recognition to obtain a target equivalent circuit type;

[0090] S1023. Determine a corresponding target equivalent circuit model according to the target equivalent circuit type.

[0091] As an optional implementation, the EIS impedance spectrum is subjected to quality assessment and pre-processing to obtain qualified EIS graph data, which specifically includes:

[0092] S10211. Verify the linear Kramers-Kronig relationship of the EIS impedance spectrum and obtain the quality assessment result;

[0093] S10212. When the quality assessment is qualified, the EIS impedance spectrum is detected for abnormal points using the quartile method, and then the abnormal point data is deleted to obtain qualified EIS graphic data.

[0094] Specifically, the detected EIS impedance spectrum may have problems such as poor quality, which affects the ECM type judgment and parameter identification. The EIS quality assessment of the embodiment of the present invention adopts linear Kramers-Kronig verification, numerically integrates the experimental EIS imaginary and real part data and compares the calculated value with the detected value, and obtains the quality assessment result based on the difference between the two. The numerical integration calculation formula is:

[0095]

[0096] Where P represents the Cauchy principal value integral, Zim(ω) and Zreal(ω) represent the imaginary and real parts of the impedance spectrum data, respectively.

[0097] Outlier detection and deletion, using the quartile method to sort the data, and then judge the outliers. After deleting the abnormal data points, qualified EIS data is formed and provided to the ML model to judge the ECM category.

[0098] As an optional implementation, the multi-classification machine learning model is trained by the following steps:

[0099] S201, obtaining a plurality of preset sample EIS graph data, performing feature extraction on the sample EIS graph data, and obtaining EIS graph feature data, wherein the EIS graph feature data includes an impedance phase shift, a maximum impedance amplitude point, an inflection point of a curve waveform change, and an intersection point of the curve and a real axis;

[0100] S202, constructing multiple binary classification learners according to the OvO decomposition strategy and a preset equivalent circuit type set;

[0101] S203, inputting the EIS graphic feature data into a binary classification learner for training to obtain a strong learner, and using the strong learner as a trained multi-classification machine learning model.

[0102] Specifically, the embodiment of the present invention selects the optimal equivalent circuit part through a multi-classification machine learning (ML) model to automatically determine the ECM category and ensure the accuracy of the ECM category. The multi-classification machine learning model of the embodiment of the present invention can be trained by SVM, decision tree, and AdaBoost algorithm. Taking the AdaBoost algorithm as an example, Figure 4 The figure shows a flow chart of equivalent circuit type identification based on a multi-classification machine learning model provided by an embodiment of the present invention, extracting EIS graph feature values, namely, the intersection point R0 of the EIS curve and the real axis, the impedance phase offset, the maximum impedance amplitude point, the curve waveform change inflection point and other feature points, the integrated learning uses the OvO (One vs One) decomposition strategy, the basic learner uses binary logistic regression (LR) classification, there are 9 ECM categories from ECM-1 to ECM-9, 36 basic learners are constructed, and the strong learner is trained. where a i is the basic learner weight, h i (x i ) is an exponential function, x i The input sample is finally used as the strong learner based on the training to perform ECM type recognition and judgment.

[0103] As an optional implementation, the target equivalent circuit model is updated with parameters by a global optimization algorithm, which specifically includes:

[0104] S1031, initializing the population, and using the real part and imaginary part of the EIS data of the EIS impedance spectrum as individuals to input the population;

[0105] S1032, with the goal of minimizing the error between the fitted EIS function and the experimental EIS function, construct a fitness function and set the population size;

[0106] S1033, evaluate the fitness of individuals in the population, and retain elite individuals based on the fitness evaluation results;

[0107] S1034, performing crossover and mutation operations on the population, and returning to the step of evaluating the fitness of individuals in the population;

[0108] S1035. When the fitness evaluation result reaches the preset requirement, the parameters of the target equivalent circuit model are updated according to the corresponding individuals to obtain the equivalent circuit parameters.

[0109] Specifically, taking the genetic algorithm as an example, the population Zim(w), Zreal(w) and pulse current frequency f are initialized, and qualified EIS data are input; the goal is to minimize the error between the fitted EIS function and the experimental EIS, and a fitness function is constructed; binary coding is used, and the population size is set; the fitness of the individuals in the population is evaluated; according to the rule of survival of the fittest, elite individuals with high fitness are retained; crossover and mutation operations are performed on the population to generate a new population; the fitness evaluation meets the requirements and parameter identification and update are completed.

[0110] As an optional implementation, the boundary condition is:

[0111] I·|Z ct |≤U e

[0112] U ct ≤|U t -U ocv

[0113] t Discharge +t Charge =T D_C

[0114] I Discharge ·t Discharge =I Charge ·t Charge

[0115] Where I represents the pulse current, Z ct represents the impedance of the constant phase element, U e Indicates the negative electrode potential, U t Indicates the battery terminal voltage, U OCV Indicates the battery open circuit voltage, U ct represents the transfer voltage, T D_C Represents the pulse period, I Discharge represents the pulse discharge current, t Discharge Indicates the pulse discharge time, I Charge represents the pulse charging current, t Charge Indicates the pulse charging time.

[0116] The method steps of the embodiment of the present invention are described above. In the field of new energy, thermal heating technology is crucial for all-weather applications of batteries, especially for applications in low-temperature environments. The present invention takes into account the battery, ambient temperature and initial state of charge, updates the equivalent circuit model through EIS parameter identification, completes the preheating process, and solves the problems of low capacity and life of the battery in a low-temperature environment. The present invention has a small amount of calculation and a wide range of usage scenarios, and can be applied to battery application scenarios such as energy storage systems, new energy vehicles, and drones. It can be recognized that the embodiment of the present invention comprehensively considers the influence of factors such as battery temperature, SOC state, and battery open circuit voltage, while considering boundary condition restrictions, and adopts a variable bidirectional pulse heating strategy to achieve low-temperature preheating of lithium-ion batteries, thereby improving the efficiency of low-temperature heating of lithium-ion batteries and reducing energy consumption and costs.

[0117] Compared with the prior art, the embodiments of the present invention also have the following advantages:

[0118] 1) It solves the performance degradation problems of lithium-ion batteries in low temperature environments, such as reduced capacity, increased internal resistance, decreased electrochemical reaction rate, and decreased charging and discharging efficiency, breaks through its environmental constraints, and extends the cycle life.

[0119] 2) It avoids the problems faced by lithium-ion batteries in dealing with low temperature limitations from the aspects of lithium-ion battery materials, external instant heating technology, battery structure modification, composite heating technology, etc., and has obvious advantages.

[0120] 3) It overcomes the limitations of different battery types, initial charge states and temperature heating methods, and is suitable for various battery products and various usage scenarios.

[0121] Reference Figure 5 The embodiment of the present invention provides a smart battery low-temperature rapid heating system, comprising:

[0122] A data acquisition module, used to acquire the battery temperature and battery status information of the target battery, wherein the battery status information includes the SOC status and the battery open circuit voltage;

[0123] An equivalent circuit model determination module, used to determine an EIS impedance spectrum according to the battery temperature and SOC state when the battery temperature is lower than a preset target temperature, and determine a target equivalent circuit model according to the EIS impedance spectrum;

[0124] An equivalent circuit parameter updating module is used to update the parameters of the target equivalent circuit model through a global optimization algorithm to obtain equivalent circuit parameters;

[0125] The circulation heating module is used to update the pulse parameters according to the equivalent circuit parameters, the battery open circuit voltage and the preset boundary conditions, and to perform bidirectional pulse circulation heating on the target battery according to the equivalent circuit parameters and the pulse parameters.

[0126] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0127] Reference Figure 6 The embodiment of the present invention provides a smart battery low-temperature extremely fast heating device, comprising:

[0128] at least one processor;

[0129] at least one memory for storing at least one program;

[0130] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for extremely fast low-temperature heating of a smart battery.

[0131] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0132] An embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the above-mentioned method for low-temperature rapid heating of a smart battery.

[0133] A computer-readable storage medium according to an embodiment of the present invention can execute a method for extremely fast low-temperature heating of a smart battery provided by an embodiment of the method of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0134] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0135] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0136] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0137] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0138] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0139] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0140] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0141] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0142] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0143] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for extremely fast heating of a smart battery at low temperature, characterized in that: The following steps are involved: Acquire battery temperature and battery status information of the target battery, wherein the battery status information includes SOC status and battery open circuit voltage; When the battery temperature is lower than a preset target temperature, determining an EIS impedance spectrum according to the battery temperature and the SOC state, and determining a target equivalent circuit model according to the EIS impedance spectrum; The target equivalent circuit model is updated with parameters by a global optimization algorithm to obtain equivalent circuit parameters; The pulse parameters are updated according to the equivalent circuit parameters, the battery open circuit voltage and preset boundary conditions, and the target battery is subjected to variable bidirectional pulse cycle heating according to the pulse parameters.

2. A method for low-temperature rapid heating of a smart battery according to claim 1, characterized in that: The method for extremely fast heating of a smart battery at low temperature also includes the following steps: When the heating time reaches a preset temperature update time interval, obtaining the current battery temperature of the target battery; If the current battery temperature is greater than or equal to the target temperature, the heating is terminated; if the current battery temperature is less than the target temperature, it is determined whether the difference between the current battery temperature and the battery temperature is less than a preset temperature change threshold; When the difference between the current battery temperature and the battery temperature is greater than or equal to the temperature change threshold, updating the EIS impedance spectrum, and returning to the step of determining a target equivalent circuit model according to the EIS impedance spectrum; When the difference between the current battery temperature and the battery temperature is less than the temperature change threshold, the process returns to the step of performing bidirectional pulse cycle heating on the target battery according to the equivalent circuit parameters and the pulse parameters.

3. A method for low-temperature rapid heating of a smart battery according to claim 1, characterized in that: The step of determining a target equivalent circuit model according to the EIS impedance spectrum specifically includes: Performing quality assessment and pre-processing on the EIS impedance spectrum to obtain qualified EIS graphic data; Inputting the qualified EIS graphic data into a pre-trained multi-classification machine learning model to perform equivalent circuit type recognition to obtain a target equivalent circuit type; The corresponding target equivalent circuit model is determined according to the target equivalent circuit type.

4. A method for low-temperature rapid heating of a smart battery according to claim 3, characterized in that: The quality assessment and pre-processing of the EIS impedance spectrum to obtain qualified EIS graph data specifically includes: Performing a linear Kramers-Kronig relationship verification on the EIS impedance spectrum to obtain a quality assessment result; When the quality assessment is qualified, the EIS impedance spectrum is subjected to abnormal point detection by the quartile method, and then the abnormal point data is deleted to obtain the qualified EIS graph data.

5. The method for low-temperature rapid heating of a smart battery according to claim 3, characterized in that: The multi-classification machine learning model is trained by the following steps: Acquire a plurality of preset sample EIS graph data, perform feature extraction on the sample EIS graph data, and obtain EIS graph feature data, wherein the EIS graph feature data includes an impedance phase shift, a maximum impedance amplitude point, an inflection point of a curve waveform change, and an intersection point of the curve and a real axis; Construct multiple binary classification learners based on the OvO decomposition strategy and a preset set of equivalent circuit types; The EIS graphic feature data is input into the binary classification learner for training to obtain a strong learner, and the strong learner is used as the trained multi-classification machine learning model.

6. The method for low-temperature rapid heating of a smart battery according to claim 1, characterized in that: The updating of parameters of the target equivalent circuit model by a global optimization algorithm specifically includes: Initializing a population, and using the real part and the imaginary part of the EIS data of the EIS impedance spectrum as individual input populations; With the goal of minimizing the error between the fitted EIS function and the experimental EIS function, a fitness function is constructed and the population size is set; Evaluate the fitness of individuals in the population and retain elite individuals based on the fitness evaluation results; Perform crossover and mutation operations on the population, and return to the step of evaluating the fitness of individuals in the population; When the fitness evaluation result meets the preset requirements, the parameters of the target equivalent circuit model are updated according to the corresponding individuals to obtain the equivalent circuit parameters.

7. A method for low-temperature rapid heating of a smart battery according to any one of claims 1 to 6, characterized in that: The boundary conditions are: I·Z ct |≤U e IN ct ≤|U t -IN ocv t Discharge +t Charge =T D_C I Discharge ·t Discharge =I Charge ·t Charge Where I represents the pulse current, Z ct represents the impedance of the constant phase element, U e Indicates the negative electrode potential, U t Indicates the battery terminal voltage, U OCV Indicates the battery open circuit voltage, U ct represents the transfer voltage, T D_C Represents the pulse period, I Discharge represents the pulse discharge current, t Discharge Indicates the pulse discharge time, I Charge represents the pulse charging current, t Charge Indicates the pulse charging time.

8. An intelligent battery low-temperature rapid heating system, characterized in that: include: A data acquisition module, used to acquire the battery temperature and battery status information of the target battery, wherein the battery status information includes the SOC status and the battery open circuit voltage; an equivalent circuit model determination module, configured to determine an EIS impedance spectrum according to the battery temperature and the SOC state when the battery temperature is lower than a preset target temperature, and determine a target equivalent circuit model according to the EIS impedance spectrum; An equivalent circuit parameter updating module is used to update the parameters of the target equivalent circuit model through a global optimization algorithm to obtain equivalent circuit parameters; The circulation heating module is used to update the pulse parameters according to the equivalent circuit parameters, the battery open circuit voltage and the preset boundary conditions, and perform bidirectional pulse circulation heating on the target battery according to the pulse parameters.

9. An intelligent battery low-temperature rapid heating device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the low-temperature extremely fast heating method for an intelligent battery as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to execute a low-temperature extremely fast heating method for a smart battery as claimed in any one of claims 1 to 7 when executed by the processor.

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