Battery fast charging strategy determination method and apparatus, and electronic device

Abstract

The invention provides a battery fast charging strategy determination method and device and electronic equipment, and relates to the technical field of batteries, and the method comprises the steps: carrying out the three-electrode test of a to-be-tested battery, so as to determine the charging lithium precipitation boundary of the to-be-tested battery; traversing and generating a plurality of candidate current sequences in the charging lithium precipitation boundary of the battery to be tested; according to each candidate current sequence, the heat generation amount of the target battery cell, the heat management parameters and the environment temperature, the total temperature increment of each candidate current sequence is determined by using the battery reduced-order model group which completes parameter identification; according to the total temperature increment and the initial parameter of each candidate current sequence, determining the total charging duration and the highest temperature value of each candidate current sequence; and according to the total charging duration and the highest temperature value of each candidate current sequence, determining a target fast charging strategy corresponding to the target battery from the candidate current sequences. Therefore, the determination time of the fast charging strategy is shortened, and the safety and reliability of battery charging are guaranteed.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, electronic device, and computer-readable storage medium for determining a fast charging strategy for a battery. Background Technology

[0002] The electric vehicle market continues to demand higher charging efficiency, and the energy storage sector also urgently needs high-safety, long-life power batteries. Therefore, improving the fast-charging capability of power batteries has become a necessity for the industry.

[0003] In related technologies, tools such as COMSOL and StarCCM+ are typically used in conjunction with electrochemical models to conduct 3D modeling and simulation to determine fast charging strategies. However, this method of simulation calculation is time-consuming, and parameter inputs require manual screening, resulting in high resource requirements, high manpower and material costs, and increased expenses. Therefore, improving the efficiency of determining fast charging strategies is crucial. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and computer-readable storage medium for determining a battery fast charging strategy.

[0005] According to a first aspect of this application, a method for determining a battery fast charging strategy is provided, the method comprising:

[0006] A three-electrode test is performed on the battery under test to determine the lithium plating boundary of the battery under test, wherein the battery under test is the same as the target battery;

[0007] Within the lithium plating boundary of the battery under test, multiple candidate current sequences are generated.

[0008] Based on each candidate current sequence, the target cell heat generation, thermal management parameters, and ambient temperature, the total temperature increment of each candidate current sequence is determined using a battery reduction model set with completed parameter identification.

[0009] Based on the total temperature increment and initial parameters of each candidate current sequence, determine the total charging time and maximum temperature value of each candidate current sequence.

[0010] Based on the total charging time and the highest temperature value of each candidate current sequence, the target fast charging strategy corresponding to the target battery is determined from the candidate current sequences.

[0011] Optionally, the step of generating multiple candidate current sequences within the charging lithium plating boundary of the battery under test includes:

[0012] Using the initial state of charge as the starting point and the target state of charge as the ending point, the area between the starting point and the ending point is divided into multiple consecutive state of charge intervals.

[0013] Based on the lithium plating boundary of the battery under test, determine the maximum allowable current rate within each state of charge interval;

[0014] A candidate current sequence for each state of charge interval is generated based on the current multiplier increment and the maximum allowable current multiplier for each state of charge interval.

[0015] Optionally, the step of determining the total temperature increment of each candidate current sequence based on each candidate current sequence, the heat generated by the target cell, thermal management parameters, and ambient temperature, using a battery reduction model set with completed parameter identification, includes:

[0016] The square of the candidate current in each candidate current sequence is input into the first battery reduced-order model to determine the first temperature increment of each candidate current sequence;

[0017] The heat generated by the target battery cell is input into the second battery reduction model to determine the corresponding second temperature increment;

[0018] The thermal management parameters are input into the third battery reduction model to determine the corresponding third temperature increment;

[0019] The ambient temperature is input into the fourth battery reduction model to determine the corresponding fourth temperature increment;

[0020] Each of the first temperature increments is fused with the second, third, and fourth temperature increments to determine the total temperature increment for each of the candidate current sequences.

[0021] Optionally, determining the total charging time and maximum temperature value of each candidate current sequence based on the total temperature increment and initial parameters of each candidate current sequence includes:

[0022] Based on the initial temperature value in the initial parameters, the total temperature increment of each candidate current sequence is fused to determine the highest temperature value of each candidate current sequence.

[0023] The charging time corresponding to each state of charge interval in each candidate current sequence is fused to determine the total charging time of each candidate current sequence.

[0024] Optionally, determining the target fast charging strategy corresponding to the target battery from the candidate current sequences based on the total charging time and the highest temperature value of each candidate current sequence includes:

[0025] Candidate current sequences whose highest temperature values ​​exceed preset highest temperature values ​​are eliminated.

[0026] From the candidate current sequences whose highest temperature value is less than or equal to the preset highest temperature value, the candidate current sequence with the shortest total charging time is determined as the target current sequence.

[0027] The target current sequence and the corresponding state of charge interval are determined as the target fast charging strategy for the target battery.

[0028] Optionally, determining the candidate current sequence with the shortest total charging time as the target current sequence includes:

[0029] When there are multiple candidate current sequences with the shortest total charging time, determine the current decrease range of adjacent state of charge intervals in each candidate current sequence.

[0030] Determine the maximum current decrease for each candidate current sequence;

[0031] The candidate current sequence corresponding to the minimum value of the maximum current decrease is determined as the target current sequence.

[0032] Optionally, before determining the total temperature increment for each candidate current sequence using the battery reduction model set with completed parameter identification, the method further includes:

[0033] Multiple sets of training data are obtained through simulation or full battery pack testing. These multiple sets of training data include current training data and the corresponding first training temperature increment, cell training heat generation and the corresponding second training temperature increment, thermal management training parameters and the corresponding third training temperature increment, and training ambient temperature and the corresponding fourth training temperature increment.

[0034] Parameter identification is performed on each set of training data to generate the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model.

[0035] Optionally, the step of performing parameter identification on each set of training data to generate a first battery order reduction model, a second battery order reduction model, a third battery order reduction model, and a fourth battery order reduction model includes:

[0036] The squared value of the current training data is input into the first initial reduced-order model to determine the corresponding first predicted temperature increment;

[0037] The heat generated during the cell training is input into the second initial reduced-order model to determine the corresponding second predicted temperature increment;

[0038] The thermal management training parameters are input into the third initial reduced-order model to determine the corresponding third predicted temperature increment;

[0039] The training environment temperature is input into the fourth initial degradation model to determine the corresponding fourth predicted temperature increment;

[0040] Based on the difference between each predicted temperature increment and the corresponding training temperature increment, the state matrix, input matrix, and output matrix in each initial reduced-order model are iteratively updated according to time steps until a battery reduced-order model set is generated.

[0041] Optionally, after performing parameter identification on each set of training data to generate the first battery order reduction model, the second battery order reduction model, the third battery order reduction model, and the fourth battery order reduction model, the method further includes:

[0042] Acquire multiple sets of test data, including current test data and the corresponding first test temperature increment, cell test heat generation and the corresponding second test temperature increment, thermal management test parameters and the corresponding third test temperature increment, and test ambient temperature and the corresponding fourth test temperature increment.

[0043] The current test data, the cell test heat generation, the thermal management test parameters, and the test environment temperature are respectively input into the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model to determine the corresponding fifth, sixth, seventh, and eighth predicted temperature increments.

[0044] Based on the fifth predicted temperature increment and the first test temperature increment, the sixth predicted temperature increment and the second test temperature increment, the seventh predicted temperature increment and the third test temperature increment, and the eighth predicted temperature increment and the fourth test temperature increment, the evaluation indicators of the first battery reduction model, the second battery reduction model, the third battery reduction model and the fourth battery reduction model are determined respectively.

[0045] Based on the evaluation metrics of the first, second, third, and fourth battery reduction models, the evaluation accuracy of the first, second, third, and fourth battery reduction models is determined.

[0046] According to a second aspect of this application, a device for determining a battery fast charging strategy is provided, comprising:

[0047] The test module is used to perform three-electrode testing on the battery under test to determine the lithium plating boundary of the battery under test, wherein the battery under test is the same as the target battery;

[0048] The generation module is used to generate multiple candidate current sequences by traversing within the charging lithium plating boundary of the battery under test.

[0049] The first determining module is used to determine the total temperature increment of each candidate current sequence based on each candidate current sequence, the heat generated by the target cell, the thermal management parameters, and the ambient temperature, using a battery down-order model group that has completed parameter identification.

[0050] The second determining module is used to determine the total charging time and the highest temperature value of each candidate current sequence based on the total temperature increment and initial parameters of each candidate current sequence.

[0051] The third determining module is used to determine the target fast charging strategy corresponding to the target battery from the candidate current sequences based on the total charging time and the highest temperature value of each candidate current sequence.

[0052] Optionally, the generation module is specifically used for:

[0053] Using the initial state of charge as the starting point and the target state of charge as the ending point, the area between the starting point and the ending point is divided into multiple consecutive state of charge intervals.

[0054] Based on the lithium plating boundary of the battery under test, determine the maximum allowable current rate within each state of charge interval;

[0055] A candidate current sequence for each state of charge interval is generated based on the current multiplier increment and the maximum allowable current multiplier for each state of charge interval.

[0056] Optionally, the first determining module is specifically used for:

[0057] The square of the candidate current in each candidate current sequence is input into the first battery reduced-order model to determine the first temperature increment of each candidate current sequence;

[0058] The heat generated by the target battery cell is input into the second battery reduction model to determine the corresponding second temperature increment;

[0059] The thermal management parameters are input into the third battery reduction model to determine the corresponding third temperature increment;

[0060] The ambient temperature is input into the fourth battery reduction model to determine the corresponding fourth temperature increment;

[0061] Each of the first temperature increments is fused with the second, third, and fourth temperature increments to determine the total temperature increment for each of the candidate current sequences.

[0062] Optionally, the second determining module is specifically used for:

[0063] Based on the initial temperature value in the initial parameters, the total temperature increment of each candidate current sequence is fused to determine the highest temperature value of each candidate current sequence.

[0064] The charging time corresponding to each state of charge interval in each candidate current sequence is fused to determine the total charging time of each candidate current sequence.

[0065] Optionally, the third determining module includes:

[0066] The elimination unit is used to eliminate candidate current sequences whose highest temperature values ​​exceed preset highest temperature values.

[0067] The first determining unit is used to determine the candidate current sequence with the shortest total charging time from the candidate current sequences whose highest temperature value is less than or equal to the preset highest temperature value as the target current sequence.

[0068] The second determining unit is used to determine the target current sequence and the corresponding state of charge interval as the target fast charging strategy corresponding to the target battery.

[0069] Optionally, the first determining unit is specifically used for:

[0070] When there are multiple candidate current sequences with the shortest total charging time, determine the current decrease range of adjacent state of charge intervals in each candidate current sequence.

[0071] Determine the maximum current decrease for each candidate current sequence;

[0072] The candidate current sequence corresponding to the minimum value of the maximum current decrease is determined as the target current sequence.

[0073] Optionally, the first determining module further includes:

[0074] The acquisition unit is used to acquire multiple sets of training data through simulation or full battery pack testing. The multiple sets of training data include current training data and the corresponding first training temperature increment, cell training heat generation and the corresponding second training temperature increment, thermal management training parameters and the corresponding third training temperature increment, and training ambient temperature and the corresponding fourth training temperature increment.

[0075] The generation unit is used to identify parameters for each set of training data to generate the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model.

[0076] Optionally, the generation unit is specifically used for:

[0077] The squared value of the current training data is input into the first initial reduced-order model to determine the corresponding first predicted temperature increment;

[0078] The heat generated during the cell training is input into the second initial reduced-order model to determine the corresponding second predicted temperature increment;

[0079] The thermal management training parameters are input into the third initial reduced-order model to determine the corresponding third predicted temperature increment;

[0080] The training environment temperature is input into the fourth initial degradation model to determine the corresponding fourth predicted temperature increment;

[0081] Based on the difference between each predicted temperature increment and the corresponding training temperature increment, the state matrix, input matrix, and output matrix in each initial reduced-order model are iteratively updated according to time steps until a battery reduced-order model set is generated.

[0082] Optionally, the first determining module is further configured to:

[0083] Acquire multiple sets of test data, including current test data and the corresponding first test temperature increment, cell test heat generation and the corresponding second test temperature increment, thermal management test parameters and the corresponding third test temperature increment, and test ambient temperature and the corresponding fourth test temperature increment.

[0084] The current test data, the cell test heat generation, the thermal management test parameters, and the test environment temperature are respectively input into the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model to determine the corresponding fifth, sixth, seventh, and eighth predicted temperature increments.

[0085] Based on the fifth predicted temperature increment and the first test temperature increment, the sixth predicted temperature increment and the second test temperature increment, the seventh predicted temperature increment and the third test temperature increment, and the eighth predicted temperature increment and the fourth test temperature increment, the evaluation indicators of the first battery reduction model, the second battery reduction model, the third battery reduction model and the fourth battery reduction model are determined respectively.

[0086] Based on the evaluation metrics of the first, second, third, and fourth battery reduction models, the evaluation accuracy of the first, second, third, and fourth battery reduction models is determined.

[0087] According to a third aspect of this application, an electronic device is provided, comprising: a processor and a memory storing computer program instructions; and a method for determining any of the above-described battery fast charging strategies when the processor executes the computer program instructions.

[0088] According to a fourth aspect of this application, a computer-readable storage medium is provided, on which computer program instructions are stored, wherein when the computer program instructions are executed by a processor, the method for determining any of the above-described battery fast charging strategies is implemented.

[0089] In summary, the method and apparatus for determining the fast charging strategy of the battery provided in this application have at least the following beneficial effects: Three-electrode testing can be performed on the battery under test to determine the lithium plating boundary of the battery under test. The battery under test is identical to the target battery. Within the lithium plating boundary of the battery under test, multiple candidate current sequences are generated. Then, based on each candidate current sequence, the heat generated by the target cell, thermal management parameters, and ambient temperature, a battery reduction model set with completed parameter identification is used to determine the total temperature increment of each candidate current sequence. Next, based on the total temperature increment of each candidate current sequence and the initial parameters, the total charging time and maximum temperature value of each candidate current sequence are determined. Finally, based on the total charging time and maximum temperature value of each candidate current sequence, the target fast charging strategy corresponding to the target battery is determined from the candidate current sequences. Therefore, candidate current sequences can be generated within the lithium plating boundary of charging. Combined with the battery reduction model set, the total temperature increment can be quickly determined. Then, based on the total charging time and the highest temperature value of each candidate current sequence, the target fast charging strategy can be selected. By traversing and generating candidate current sequences, all potentially feasible fast charging strategies can be covered, effectively avoiding the omission of the optimal fast charging strategy and improving the accuracy and reliability of the fast charging strategy. At the same time, the battery reduction model set is not only highly accurate, accurately capturing the current jump moment to ensure the accuracy of temperature increment calculation and fast charging strategy selection, but also has a short calculation time, which can effectively shorten the overall determination time of the target fast charging strategy. Thus, while improving efficiency, it also ensures that the fast charging strategy meets the lithium plating boundary constraint and the minimum charging time requirement, effectively ensuring the safety and reliability of battery charging. Attached Figure Description

[0090] To more clearly illustrate the specific embodiments of this application or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0091] Figure 1 A flowchart illustrating a method for determining a battery fast charging strategy provided in an embodiment of this application;

[0092] Figure 2 A schematic diagram comparing the results of a reduced-order battery model (ROM model) under low-temperature conditions with the results of computational fluid dynamics (CFD) under specific conditions, provided for embodiments of this application.

[0093] Figure 3 A schematic diagram showing the comparison between ROM model results and CFD results under high-temperature conditions, provided for an embodiment of this application;

[0094] Figure 4 A schematic diagram showing the comparison between ROM model results and CFD results under normal operating conditions provided for an embodiment of this application;

[0095] Figure 5 A schematic diagram comparing the current, SOC, and temperature of a ROM model and a CFD model under normal temperature fast charging conditions, provided for embodiments of this application;

[0096] Figure 6 A schematic diagram illustrating a battery fast charging strategy and the lithium plating boundary during battery charging, provided as an embodiment of this application;

[0097] Figure 7 A structural diagram of a battery fast charging strategy determination device provided for an embodiment of this application;

[0098] Figure 8 This is a structural diagram of an electronic device provided as an embodiment of the present application. Detailed Implementation

[0099] To make the above and other features and advantages of this application clearer, the application is further described below with reference to the accompanying drawings. It should be understood that the specific embodiments given herein are for the purpose of explanation to those skilled in the art, and are exemplary only, not restrictive.

[0100] In the following description, numerous specific details are set forth to provide a thorough understanding of this application. However, it will be apparent to those skilled in the art that the specific details are not required to practice this application. In other instances, well-known steps or operations have not been described in detail to avoid obscuring this application.

[0101] The method for determining the fast charging strategy provided in this application embodiment can be executed by the device for determining the fast charging strategy provided in this application embodiment, which can be configured in an electronic device.

[0102] refer to Figure 1 This application provides a method for determining a battery fast charging strategy, the method comprising:

[0103] Step 101: Perform a three-electrode test on the battery under test to determine the lithium plating boundary of the battery under test, wherein the battery under test is the same as the target battery.

[0104] The phrase "the battery to be tested is the same as the target battery" can be understood as meaning that the two are identical in terms of chemical system, capacity, structure, model, etc. The batches may be the same or different, and this application does not limit this.

[0105] It is understood that a battery can include individual cells, battery modules, battery packs, battery clusters, battery stacks, battery systems, etc. Among these, an individual cell is the most basic battery unit; a battery module can be a module composed of multiple individual cells connected in series or parallel; a battery pack can be an overall packaged structure containing one or more battery modules and necessary additional components such as protection circuits and cooling systems; a battery cluster can be a larger battery unit composed of a group of battery packs or battery modules; a battery stack can be a large-scale energy storage solution formed by stacking multiple battery clusters together; and a battery system can include all levels from individual cells to the entire energy storage solution. Therefore, the method for determining the battery fast charging strategy provided in this application can be applied to individual cells, battery modules, battery packs, battery clusters, battery stacks, battery systems, etc., and this application does not limit it in this regard.

[0106] Furthermore, in electrochemistry, the point at which the negative electrode potential drops to 0V is typically defined as the critical point for lithium plating. Once the potential falls below 0V, the thermodynamic lithium plating reaction will occur. Therefore, in this embodiment, the battery under test can be fabricated as a three-electrode battery including a reference electrode. Then, an electrochemical workstation is used to connect the reference electrode and the negative electrode of the battery under test. The reference electrode is typically used as a 0V reference. Since the electrochemical workstation measures the potential difference between the negative electrode potential and the reference electrode potential, this difference can characterize the true potential of the negative electrode. That is, when the difference is positive, the negative electrode potential can be considered to be above 0V, with no lithium plating. When the difference is negative, the negative electrode potential can be considered to be below 0V, with lithium plating already occurring. Therefore, the charging current, state of charge (SOC), and temperature parameters when the difference is 0 can be determined as the critical parameters for lithium plating under that operating condition. By testing the critical parameters for lithium plating under different operating conditions, the charging-to-lithiation boundary can be established.

[0107] Step 102: Within the lithium plating boundary of the battery under test, generate multiple candidate current sequences.

[0108] Each candidate current sequence contains a complete set of candidate current values, which may correspond to the SOC, etc., but this application does not limit this.

[0109] Optionally, the initial state of charge can be used as the starting point and the target state of charge as the ending point, dividing the area between the starting point and the ending point into multiple continuous state of charge intervals. Then, based on the lithium plating boundary of the battery under test, the maximum allowable current rate in each state of charge interval is determined. Finally, based on the current rate increment and the maximum allowable current rate in each state of charge interval, a candidate current sequence for each state of charge interval is generated.

[0110] The initial state of charge can be 0, or it can be 10%, 20%, etc., and the target state of charge can be 80%, 85%, 100%, etc., which can be adjusted as needed. This application does not limit this.

[0111] In addition, the number of charge state intervals and the current multiplier increment can be preset values ​​or adjusted as needed, and this application does not limit these.

[0112] For example, when the initial state of charge is 0 and the target state of charge is 80%, it can be divided into three consecutive state of charge intervals, such as 0-20%, 20%-50%, and 50%-80%. If the maximum allowable current multiplier in each of these three state of charge intervals is determined to be 2.5C, 2C, and 1C respectively based on the lithium plating boundary, then in each state of charge interval, all candidate current values ​​not exceeding the maximum current multiplier of that state of charge interval can be traversed in increments of 0.1C. The candidate current values ​​of each state of charge interval are then combined according to the state of charge order to generate a candidate current sequence for each state of charge interval. This application does not limit this.

[0113] Step 103: Based on each candidate current sequence, target cell heat generation, thermal management parameters, and ambient temperature, use the battery reduction model group with completed parameter identification to determine the total temperature increment of each candidate current sequence.

[0114] The candidate current sequence is a current sequence generated by traversing the lithium plating boundary of the battery under test. The heat generated by the target cell is the heat generated by the internal resistance of the cell contained in the target battery. The thermal management parameters may include parameters such as liquid cooling flow rate and water temperature. The ambient temperature is the temperature of the environment in which the battery is currently located. This application does not limit these parameters.

[0115] It is understandable that the temperature changes during battery charging are typically related to the heat generated by the current flowing through electrical components such as the copper busbar and tabs, the heat generated by the internal resistance of the battery cell, heat exchange from thermal management, and heat exchange from the environment. Therefore, in this embodiment, the determination of the total temperature increment for each candidate current sequence is comprehensively considered from four dimensions: heat generated by the current, heat generated by the internal resistance of the battery cell, heat exchange from thermal management, and heat exchange from the environment. This makes the determination of the total temperature increment more comprehensive and reliable.

[0116] In addition, the battery reduction model group may include multiple battery reduction models, such as a first battery reduction model, a second battery reduction model, a third battery reduction model, and a fourth battery reduction model, etc., and this application does not limit this.

[0117] Among them, the first, second, third, and fourth battery reduced-order models are all battery reduced-order models (ROMs) with completed parameter identification. They can directly process the input data to output the temperature increment generated by the input data, or the temperature change. Since the number of parameters in this battery reduced-order model is small, it can quickly obtain a relatively accurate temperature increment without complex parameter tuning and lengthy training. This application does not limit this.

[0118] It is understandable that the temperature changes generated during battery charging are typically related to heat generated by the resistance within the battery cell, heat generated by the current from electrical components such as the copper busbar and tabs, heat exchange from thermal management, and heat exchange from the environment. Based on the linear superposition characteristic of the temperature increments corresponding to these four factors, a linear relationship can be used to characterize the relationship between each factor and its corresponding temperature increment, i.e., a reduced-order battery model. This eliminates the need to rely on complex electrochemical models or lumped-parameter thermal models, allowing for a clearer, more intuitive, and accurate characterization of the temperature increments corresponding to each factor, while simplifying the overall process.

[0119] Optionally, the square of the candidate current in each candidate current sequence can be input into the first battery reduction model to determine the first temperature increment of each candidate current sequence. The heat generated by the target cell can be input into the second battery reduction model to determine the corresponding second temperature increment. The thermal management parameters can be input into the third battery reduction model to determine the corresponding third temperature increment. The ambient temperature can be input into the fourth battery reduction model to determine the corresponding fourth temperature increment. Then, each first temperature increment is fused with the second, third, and fourth temperature increments to determine the total temperature increment of each candidate current sequence.

[0120] Among them, the current-generated heat can include the heat generated by the current flowing through components such as copper busbars, tabs, and electrical components. The first battery reduced-order model can be used to characterize the relationship between current and temperature increment. It can process the square value of each candidate current in the input to determine the unit increment of each candidate current, and then accumulate the unit increments to obtain the first temperature increment of the corresponding candidate current sequence.

[0121] In addition, the heat generated by the target cell can be understood as the heat generated by the internal resistance contained in the target battery. The second battery reduced-order model can be used to characterize the relationship between the heat generated by the cell and the temperature increment. It can process the heat generated by the target cell input and determine the corresponding second temperature increment.

[0122] In addition, thermal management parameters can include parameters such as liquid cooling flow rate and water temperature. The third battery reduction model can be used to characterize the relationship between thermal management parameters and temperature increments. It can process the input thermal management parameters to determine the corresponding third temperature increment.

[0123] In addition, the fourth battery reduced-order model can be used to characterize the relationship between ambient temperature and temperature increment. It can process the input environmental parameters to determine the corresponding fourth temperature increment.

[0124] Then, each first temperature increment can be superimposed with the second, third, and fourth temperature increments according to time steps to determine the total temperature increment corresponding to each candidate current sequence.

[0125] The following is a brief explanation of the battery reduction models.

[0126] First, multiple sets of training data can be obtained through simulation or full battery pack testing. These multiple sets of training data can include the following four sets of input-output data pairs: current training data and the corresponding first training temperature increment, cell training heat generation and the corresponding second training temperature increment, thermal management training parameters and the corresponding third training temperature increment, and training ambient temperature and the corresponding fourth training temperature increment.

[0127] For example, training data can be obtained by testing the entire battery pack. For instance, a test battery identical to the target battery can be selected and placed in an environmental chamber for charge-discharge testing. Real-time data can be collected using current sensors, temperature sensors, flow meters, etc. By fixing other variables and changing only a single input factor, the training temperature increment corresponding to each input factor can be obtained, which are the four sets of training data mentioned above.

[0128] Alternatively, training data can be obtained through simulation. For example, based on high-precision electrochemical thermocoupled models such as COMSOL and GT-AutoLion, different input parameters such as current, heat generation, thermal management parameters, and ambient temperature can be preset, and the training temperature increments corresponding to each type of input factor can be obtained through simulation calculations, which are the four sets of training data mentioned above.

[0129] Therefore, in this embodiment of the application, by conducting complete battery pack tests or simulation tests, the generated training data not only ensures the authenticity of the data but also covers all test scenarios, including all key factors that affect the temperature increment, thereby providing a solid and comprehensive data foundation for subsequent parameter identification and model training.

[0130] Then, parameter identification is performed on each set of training data to generate the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model.

[0131] Optionally, the squared value of the current training data can be input into the first initial reduction model to determine the corresponding first predicted temperature increment; the heat generated during cell training can be input into the second initial reduction model to determine the corresponding second predicted temperature increment; the thermal management training parameters can be input into the third initial reduction model to determine the corresponding third predicted temperature increment; and the training ambient temperature can be input into the fourth initial reduction model to determine the corresponding fourth predicted temperature increment. Based on the difference between each predicted temperature increment and the corresponding training temperature increment, the state matrix, input matrix, and output matrix in each initial reduction model are iteratively updated according to time steps until a battery reduction model group is generated.

[0132] The four initial reduced-order models can all adopt simplified mathematical structures, such as linear state equations, and can include a state matrix, an input matrix, and an output matrix. The state matrix can be used to describe the change of the model's internal state over time, and its initial value can be a zero matrix. The input matrix can be used to describe the influence of the input parameters on the internal state. The output matrix can be used to describe the temperature increment of the output determined by the internal state and the input parameters.

[0133] Optionally, the initial values ​​of the above matrices can be set based on experience, such as setting them as identity matrices, or they can be adjusted as needed and gradually corrected through iterative optimization.

[0134] Understandably, each set of training data can be input into each initial reduced-order model according to time steps to obtain the corresponding predicted temperature increment. Then, for each initial reduced-order model, the error value between the predicted temperature increment at the current time step and the training temperature increment can be calculated, and the state matrix, input matrix, and output matrix can be updated based on this error value. If the predicted temperature increment is greater than the training temperature increment, the matrix parameters can be adjusted to reduce the influence of the input parameters on the output; conversely, if the predicted temperature increment is less than the training temperature increment, the matrix parameters can be adjusted to increase the influence of the input parameters.

[0135] Typically, the adjustment of each matrix parameter can fully consider the influence of historical states. The entire iterative process proceeds sequentially step by step, with each parameter adjustment based on the error result of the previous step, enabling real-time feedback and dynamic optimization. Iteration continues until the convergence condition is met. At this point, the state matrix, input matrix, and output matrix of the four initial reduced-order models have been optimized, forming the final reduced-order models for the first, second, third, and fourth batteries, constituting a model group capable of directly calculating temperature increments.

[0136] Therefore, in this embodiment, the various factors affecting temperature changes during battery charging are decomposed into four categories. A reduced-order model is constructed for each category of factors, and the mapping relationship between input parameters and corresponding temperature increments under each category of factors is fitted by the reduced-order model. The coefficient matrix in the model is solved by parameter identification. This method requires less training data, is easy to obtain, and has high model accuracy. It can accurately capture the current jump moment. In the subsequent temperature increment calculation process, the required time is short, which effectively saves calculation time and improves efficiency, providing a foundation for improving the efficiency of fast charging strategy formulation in the future.

[0137] It is understandable that after performing parameter identification on each set of training data to generate the first, second, third, and fourth battery reduction models, the accuracy of the above four battery reduction models can be verified separately.

[0138] Optionally, multiple sets of test data that were not involved in model training can be obtained first. These multiple sets of test data include current test data and the corresponding first test temperature increment, cell test heat generation and the corresponding second test temperature increment, thermal management test parameters and the corresponding third test temperature increment, and test ambient temperature and the corresponding fourth test temperature increment.

[0139] This application does not limit the acquisition of multiple sets of test data through simulation or full battery pack testing.

[0140] Subsequently, the current test data, cell test heat generation, thermal management test parameters, and test ambient temperature can be input into the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model, respectively. After processing by the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model, respectively, the corresponding fifth predicted temperature increment, the sixth predicted temperature increment, the seventh predicted temperature increment, and the eighth predicted temperature increment are output.

[0141] Then, based on the fifth predicted temperature increment and the first test temperature increment, the sixth predicted temperature increment and the second test temperature increment, the seventh predicted temperature increment and the third test temperature increment, and the eighth predicted temperature increment and the fourth test temperature increment, the evaluation indicators for the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model are determined respectively.

[0142] The evaluation metrics can be varied, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). Lower values ​​for MAE, MSE, and R² indicate higher model accuracy. A R² closer to 1 indicates a better fit and stronger explanatory power. Conversely, a R² less than 0 indicates poorer predictive performance.

[0143] Then, based on the evaluation indicators of the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model, the evaluation accuracy of these models can be determined.

[0144] Among them, the corresponding thresholds can be set in advance for each evaluation index. Then, the evaluation accuracy of the first to fourth battery downgrade models can be determined based on the comparison results between the evaluation index of each battery downgrade model and the preset accuracy threshold.

[0145] Optionally, for any battery downgrade model that does not meet the accuracy threshold, the parameter identification strategy can be re-optimized or training data can be supplemented until the accuracy of the battery downgrade model meets the standard. This application does not limit this.

[0146] Step 104: Determine the total charging time and maximum temperature value of each candidate current sequence based on the total temperature increment and initial parameters of each candidate current sequence.

[0147] The initial parameters may include the initial temperature value or other initial values, and this application does not limit them.

[0148] Optionally, based on the initial temperature value in the initial parameters, the total temperature increment of each candidate current sequence can be fused to determine the highest temperature value of each candidate current sequence. Then, the charging time corresponding to each state of charge interval in each candidate current sequence can be fused to determine the total charging time of each candidate current sequence.

[0149] The calculation can begin by using the initial temperature value from the initial parameters as the starting point. The temperature increment is accumulated step-by-step, meaning the total temperature increment for each candidate current sequence is calculated for each time step. This total temperature increment is then summed with the initial temperature value to obtain the total temperature value for each time step. The maximum total temperature value across all time steps in the candidate current sequence is then determined as the highest temperature value for that candidate current sequence. Finally, for each candidate current sequence, the charging times for each corresponding state-of-charge interval are summed to obtain the total charging time for that candidate current sequence.

[0150] Step 105: Determine the target fast charging strategy corresponding to the target battery from the candidate current sequences based on the total charging time and the highest temperature value of each candidate current sequence.

[0151] Understandably, after determining the highest temperature value and total charging time for each candidate current sequence, the target fast charging strategy can be further determined based on the highest temperature value and total charging time.

[0152] Optionally, candidate current sequences whose maximum temperature value exceeds the preset maximum temperature value can be eliminated. From the candidate current sequences whose maximum temperature value is less than or equal to the boundary temperature value, the candidate current sequence with the shortest total charging time can be determined as the target current sequence. The target current sequence and the corresponding state of charge interval can be determined as the target fast charging strategy for the target battery.

[0153] The preset maximum temperature value reflects the safe temperature threshold during battery charging. If the maximum temperature value of a candidate current sequence exceeds the preset maximum temperature value, the charging process may result in excessively high temperatures, reduced charging efficiency, accelerated battery aging, and adverse effects on battery life. Therefore, in this embodiment, candidate current sequences with maximum temperatures exceeding the preset maximum temperature value can be eliminated. Then, from the remaining candidate current sequences with maximum temperatures less than or equal to the preset maximum temperature value, the candidate current sequence with the shortest total charging time is selected and determined as the target current sequence. This ensures both the safety of the battery charging process and achieves the shortest overall charging time and the highest fast charging efficiency.

[0154] Optionally, if there are multiple candidate current sequences with the shortest total charging time, the current decrease amplitude of adjacent state of charge intervals in each candidate current sequence is determined, and then the maximum current decrease amplitude corresponding to each candidate current sequence is determined, and the candidate current sequence corresponding to the minimum maximum current decrease amplitude is determined as the target current sequence.

[0155] Generally, the smoother the current change and the smaller the abrupt change, the smaller the instantaneous impact on the battery, which is more conducive to extending battery life. Therefore, in this embodiment of the application, when there are multiple candidate current sequences corresponding to the shortest total charging time, the current decrease amplitude between the later and previous states of charge in two adjacent state-of-charge intervals in each candidate current sequence can be further determined, and the maximum current decrease amplitude is determined as the maximum current decrease amplitude of the candidate current sequence.

[0156] Then, the maximum current decrease amplitude corresponding to each candidate current sequence is compared again, and the candidate current sequence corresponding to the minimum maximum current decrease amplitude is determined as the target current sequence. This ensures that while maintaining the shortest charging time and fast charging efficiency, the current change is also the most stable throughout the charging process, with the maximum fluctuation at a low level. This can minimize the damage to the battery caused by sudden current changes and achieve a balance between fast charging speed and battery safety and durability.

[0157] The performance of the battery reduction model group obtained in this application is verified below.

[0158] Figure 2 , Figure 3 , Figure 4 This section compares the results of the ROM model with those of Computational Fluid Dynamics (CFD) at different temperatures. The solid line represents the temperature values ​​under CFD, and the dashed line represents the temperature values ​​under the ROM model. The temperature value is the sum of the total temperature increment and the initial temperature value. Figure 2 This is a schematic diagram illustrating temperature verification under low-temperature operating conditions. Figure 3 This is a schematic diagram for temperature verification under high-temperature operating conditions. Figure 4 This is a schematic diagram for temperature verification under normal operating conditions.

[0159] like Figure 5 The image shows the verification of current, SOC, and temperature under normal temperature fast charging conditions using the ROM model and CFD model. Figure 5 In line a), the solid lines in lines A1 and A2 represent the current value corresponding to the CFD, and the dashed lines represent the current value corresponding to the ROM. Similarly, the solid lines in lines B1 and B2 represent the SOC corresponding to the CFD, and the dashed lines represent the SOC corresponding to the ROM. Figure 5In b), the solid line in C1 and C2 represents the maximum temperature under CFD, and the dashed line represents the maximum temperature under ROM. The solid line in D1 and D2 represents the minimum temperature under CFD, and the dashed line represents the minimum temperature under ROM.

[0160] Depend on Figures 2 to 5 It can be seen that the ROM model provided in this application has good performance and high accuracy, and can accurately capture the current jump moment.

[0161] like Figure 6 The diagram shows the battery fast charging strategy and lithium plating boundary provided in this application. The horizontal axis represents time, and the vertical axes represent SOC, temperature, and current rate, respectively. The blue line represents the lithium plating boundary, and the orange line represents the battery fast charging strategy. As can be seen from the diagram, the battery fast charging strategy provided in this application operates within the lithium plating boundary, effectively ensuring the safety and reliability of battery charging.

[0162] Therefore, in this embodiment of the application, the battery fast charging strategy determined by the battery reduction model group requires less time, effectively saves the calculation time of the operating condition, and improves efficiency. At the same time, all results can be obtained by traversal, avoiding the omission of the optimal solution, thereby improving the accuracy and reliability of the fast charging strategy determination.

[0163] In this embodiment, a three-electrode test can be performed on the battery under test to determine the lithium plating boundary of the battery under test. The battery under test is the same as the target battery. Within the lithium plating boundary of the battery under test, multiple candidate current sequences are generated. Then, based on each candidate current sequence, the heat generated by the target cell, thermal management parameters, and ambient temperature, the total temperature increment of each candidate current sequence is determined using a battery reduction model group with completed parameter identification. Then, based on the total temperature increment of each candidate current sequence and the initial parameters, the total charging time and the highest temperature value of each candidate current sequence are determined. Finally, based on the total charging time and the highest temperature value of each candidate current sequence, the target fast charging strategy corresponding to the target battery is determined from the candidate current sequences. Therefore, candidate current sequences can be generated within the lithium plating boundary of the charging process. Combined with the battery reduction model set, the total temperature increment can be quickly determined. Then, based on the total charging time and the highest temperature value of each candidate current sequence, the target fast charging strategy can be selected. By traversing and generating candidate current sequences, all potentially feasible fast charging strategies can be covered, effectively avoiding the omission of the optimal fast charging strategy and improving the accuracy and reliability of the fast charging strategy. At the same time, the battery reduction model set is not only highly accurate, accurately capturing the current jump moment to ensure the accuracy of temperature increment calculation and fast charging strategy selection, but also has a short calculation time, which can effectively shorten the overall determination time of the target fast charging strategy. Thus, while improving efficiency, it also ensures that the fast charging strategy meets the cell charging lithium plating boundary constraints and the minimum charging time requirements, effectively ensuring the safety and reliability of battery charging.

[0164] According to this application, a battery fast charging strategy determination device 700 is provided, such as... Figure 7 As shown, the device includes a test module 710, a generation module 720, a first determination module 730, a second determination module 740, and a third determination module 750.

[0165] The test module 710 is used to perform a three-electrode test on the battery under test to determine the lithium plating boundary of the battery under test, wherein the battery under test is the same as the target battery.

[0166] The generation module 720 is used to generate multiple candidate current sequences by traversing within the charging lithium plating boundary of the battery under test.

[0167] The first determining module 730 is used to determine the total temperature increment of each candidate current sequence based on each candidate current sequence, the heat generated by the target cell, the thermal management parameters, and the ambient temperature, using a battery downgrade model group that has completed parameter identification.

[0168] The second determining module 740 is used to determine the total charging time and the highest temperature value of each candidate current sequence based on the total temperature increment and initial parameters of each candidate current sequence.

[0169] The third determining module 750 is used to determine the target fast charging strategy corresponding to the target battery from the candidate current sequences based on the total charging time and the highest temperature value of each candidate current sequence.

[0170] Optionally, the generation module 720 is specifically used for:

[0171] Using the initial state of charge as the starting point and the target state of charge as the ending point, the area between the starting point and the ending point is divided into multiple consecutive state of charge intervals.

[0172] Based on the lithium plating boundary of the battery under test, determine the maximum allowable current rate within each state of charge interval;

[0173] A candidate current sequence for each state of charge interval is generated based on the current multiplier increment and the maximum allowable current multiplier for each state of charge interval.

[0174] Optionally, the first determining module 730 is specifically used for:

[0175] The square of the candidate current in each candidate current sequence is input into the first battery reduced-order model to determine the first temperature increment of each candidate current sequence;

[0176] The heat generated by the target battery cell is input into the second battery reduction model to determine the corresponding second temperature increment;

[0177] The thermal management parameters are input into the third battery reduction model to determine the corresponding third temperature increment;

[0178] The ambient temperature is input into the fourth battery reduction model to determine the corresponding fourth temperature increment;

[0179] Each of the first temperature increments is fused with the second, third, and fourth temperature increments to determine the total temperature increment for each of the candidate current sequences.

[0180] Optionally, the second determining module 740 is specifically used for:

[0181] Based on the initial temperature value in the initial parameters, the total temperature increment of each candidate current sequence is fused to determine the highest temperature value of each candidate current sequence.

[0182] The charging time corresponding to each state of charge interval in each candidate current sequence is fused to determine the total charging time of each candidate current sequence.

[0183] Optionally, the third determining module 750 includes:

[0184] The elimination unit is used to eliminate candidate current sequences whose highest temperature values ​​exceed preset highest temperature values.

[0185] The first determining unit is used to determine the candidate current sequence with the shortest total charging time from the candidate current sequences whose highest temperature value is less than or equal to the preset highest temperature value as the target current sequence.

[0186] The second determining unit is used to determine the target current sequence and the corresponding state of charge interval as the target fast charging strategy corresponding to the target battery.

[0187] Optionally, the first determining unit is specifically used for:

[0188] When there are multiple candidate current sequences with the shortest total charging time, determine the current decrease range of adjacent state of charge intervals in each candidate current sequence.

[0189] Determine the maximum current decrease for each candidate current sequence;

[0190] The candidate current sequence corresponding to the minimum value of the maximum current decrease is determined as the target current sequence.

[0191] Optionally, the first determining module 730 further includes:

[0192] The acquisition unit is used to acquire multiple sets of training data through simulation or full battery pack testing. The multiple sets of training data include current training data and the corresponding first training temperature increment, cell training heat generation and the corresponding second training temperature increment, thermal management training parameters and the corresponding third training temperature increment, and training ambient temperature and the corresponding fourth training temperature increment.

[0193] The generation unit is used to identify parameters for each set of training data to generate the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model.

[0194] Optionally, the generation unit is specifically used for:

[0195] The squared value of the current training data is input into the first initial reduced-order model to determine the corresponding first predicted temperature increment;

[0196] The heat generated during the cell training is input into the second initial reduced-order model to determine the corresponding second predicted temperature increment;

[0197] The thermal management training parameters are input into the third initial reduced-order model to determine the corresponding third predicted temperature increment;

[0198] The training environment temperature is input into the fourth initial degradation model to determine the corresponding fourth predicted temperature increment;

[0199] Based on the difference between each predicted temperature increment and the corresponding training temperature increment, the state matrix, input matrix, and output matrix in each initial reduced-order model are iteratively updated according to time steps until a battery reduced-order model set is generated.

[0200] Optionally, the first determining module 730 is further configured to:

[0201] Acquire multiple sets of test data, including current test data and the corresponding first test temperature increment, cell test heat generation and the corresponding second test temperature increment, thermal management test parameters and the corresponding third test temperature increment, and test ambient temperature and the corresponding fourth test temperature increment.

[0202] The current test data, the cell test heat generation, the thermal management test parameters, and the test environment temperature are respectively input into the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model to determine the corresponding fifth, sixth, seventh, and eighth predicted temperature increments.

[0203] Based on the fifth predicted temperature increment and the first test temperature increment, the sixth predicted temperature increment and the second test temperature increment, the seventh predicted temperature increment and the third test temperature increment, and the eighth predicted temperature increment and the fourth test temperature increment, the evaluation indicators of the first battery reduction model, the second battery reduction model, the third battery reduction model and the fourth battery reduction model are determined respectively.

[0204] Based on the evaluation metrics of the first, second, third, and fourth battery reduction models, the evaluation accuracy of the first, second, third, and fourth battery reduction models is determined.

[0205] The battery fast charging strategy determination device provided in this application can perform three-electrode testing on the battery under test to determine the charging lithium plating boundary of the battery under test. Within the charging lithium plating boundary of the battery under test, multiple candidate current sequences are generated. Then, based on each candidate current sequence, the heat generated by the target cell, thermal management parameters, and ambient temperature, the total temperature increment of each candidate current sequence is determined using a battery reduction model group with completed parameter identification. Then, based on the total temperature increment of each candidate current sequence and the initial parameters, the total charging time and the maximum temperature value of each candidate current sequence are determined. Finally, based on the total charging time and the maximum temperature value of each candidate current sequence, the target fast charging strategy corresponding to the target battery is determined from the candidate current sequences. Therefore, candidate current sequences can be generated within the lithium plating boundary of the charging process. Combined with the battery reduction model set, the total temperature increment can be quickly determined. Then, based on the total charging time and the highest temperature value of each candidate current sequence, the target fast charging strategy can be selected. By traversing and generating candidate current sequences, all potentially feasible fast charging strategies can be covered, effectively avoiding the omission of the optimal fast charging strategy and improving the accuracy and reliability of the fast charging strategy. At the same time, the battery reduction model set is not only highly accurate, accurately capturing the current jump moment to ensure the accuracy of temperature increment calculation and fast charging strategy selection, but also has a short calculation time, which can effectively shorten the overall determination time of the target fast charging strategy. Thus, while improving efficiency, it also ensures that the fast charging strategy meets the cell charging lithium plating boundary constraints and the minimum charging time requirements, effectively ensuring the safety and reliability of battery charging.

[0206] like Figure 8 As shown, this application provides an electronic device 800, which includes a processor 801 and a memory 802 storing computer program instructions. The processor 801 executes the computer program instructions to implement the steps of the aforementioned method for determining a battery fast charging strategy. This electronic device 800 can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities.

[0207] This application provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the method for determining the above-mentioned battery fast charging strategy.

[0208] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for determining a battery fast charging strategy, characterized in that, include: A three-electrode test is performed on the battery under test to determine the lithium plating boundary of the battery under test, wherein the battery under test is the same as the target battery; Within the lithium plating boundary of the battery under test, multiple candidate current sequences are generated. Based on each candidate current sequence, the target cell heat generation, thermal management parameters, and ambient temperature, the total temperature increment of each candidate current sequence is determined using a battery reduction model set with completed parameter identification. Based on the total temperature increment and initial parameters of each candidate current sequence, determine the total charging time and maximum temperature value of each candidate current sequence. Based on the total charging time and the highest temperature value of each candidate current sequence, the target fast charging strategy corresponding to the target battery is determined from the candidate current sequences.

2. The method as described in claim 1, characterized in that, Within the lithium plating boundary of the battery under test, multiple candidate current sequences are generated, including: Using the initial state of charge as the starting point and the target state of charge as the ending point, the area between the starting point and the ending point is divided into multiple consecutive state of charge intervals. Based on the lithium plating boundary of the battery under test, determine the maximum allowable current rate within each state of charge interval; A candidate current sequence for each state of charge interval is generated based on the current multiplier increment and the maximum allowable current multiplier for each state of charge interval.

3. The method as described in claim 1, characterized in that, The step of determining the total temperature increment for each candidate current sequence based on each candidate current sequence, the heat generated by the target cell, thermal management parameters, and ambient temperature, using a battery reduction model set with completed parameter identification, includes: The square of the candidate current in each candidate current sequence is input into the first battery reduced-order model to determine the first temperature increment of each candidate current sequence; The heat generated by the target battery cell is input into the second battery reduction model to determine the corresponding second temperature increment; The thermal management parameters are input into the third battery reduction model to determine the corresponding third temperature increment; The ambient temperature is input into the fourth battery reduction model to determine the corresponding fourth temperature increment; Each of the first temperature increments is fused with the second, third, and fourth temperature increments to determine the total temperature increment for each of the candidate current sequences.

4. The method as described in claim 1, characterized in that, The step of determining the total charging time and maximum temperature value of each candidate current sequence based on the total temperature increment and initial parameters of each candidate current sequence includes: Based on the initial temperature value in the initial parameters, the total temperature increment of each candidate current sequence is fused to determine the highest temperature value of each candidate current sequence. The charging time corresponding to each state of charge interval in each candidate current sequence is fused to determine the total charging time of each candidate current sequence.

5. The method as described in claim 1, characterized in that, The step of determining the target fast charging strategy corresponding to the target battery from the candidate current sequences based on the total charging time and the highest temperature value of each candidate current sequence includes: Candidate current sequences whose highest temperature values ​​exceed preset highest temperature values ​​are eliminated. From the candidate current sequences whose highest temperature value is less than or equal to the preset highest temperature value, the candidate current sequence with the shortest total charging time is determined as the target current sequence. The target current sequence and the corresponding state of charge interval are determined as the target fast charging strategy for the target battery.

6. The method as described in claim 5, characterized in that, The step of determining the candidate current sequence with the shortest total charging time as the target current sequence includes: When there are multiple candidate current sequences with the shortest total charging time, determine the current decrease range of adjacent state of charge intervals in each candidate current sequence. Determine the maximum current decrease for each candidate current sequence; The candidate current sequence corresponding to the minimum value of the maximum current decrease is determined as the target current sequence.

7. The method as described in claim 1, characterized in that, Before determining the total temperature increment for each candidate current sequence using the battery reduction model set with completed parameter identification, the method further includes: Multiple sets of training data are obtained through simulation or full battery pack testing. These multiple sets of training data include current training data and the corresponding first training temperature increment, cell training heat generation and the corresponding second training temperature increment, thermal management training parameters and the corresponding third training temperature increment, and training ambient temperature and the corresponding fourth training temperature increment. Parameter identification is performed on each set of training data to generate the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model.

8. The method as described in claim 7, characterized in that, The step of performing parameter identification on each set of training data to generate a first battery order reduction model, a second battery order reduction model, a third battery order reduction model, and a fourth battery order reduction model includes: The squared value of the current training data is input into the first initial reduced-order model to determine the corresponding first predicted temperature increment; The heat generated during the cell training is input into the second initial reduced-order model to determine the corresponding second predicted temperature increment; The thermal management training parameters are input into the third initial reduced-order model to determine the corresponding third predicted temperature increment; The training environment temperature is input into the fourth initial degradation model to determine the corresponding fourth predicted temperature increment; Based on the difference between each predicted temperature increment and the corresponding training temperature increment, the state matrix, input matrix, and output matrix in each initial reduced-order model are iteratively updated according to time steps until a battery reduced-order model set is generated.

9. The method as described in claim 7, characterized in that, After performing parameter identification on each set of training data to generate the first battery order reduction model, the second battery order reduction model, the third battery order reduction model, and the fourth battery order reduction model, the method further includes: Acquire multiple sets of test data, including current test data and the corresponding first test temperature increment, cell test heat generation and the corresponding second test temperature increment, thermal management test parameters and the corresponding third test temperature increment, and test ambient temperature and the corresponding fourth test temperature increment. The current test data, the cell test heat generation, the thermal management test parameters, and the test environment temperature are respectively input into the first battery reduction model, the second battery reduction model, the third battery reduction model, and the fourth battery reduction model to determine the corresponding fifth, sixth, seventh, and eighth predicted temperature increments. Based on the fifth predicted temperature increment and the first test temperature increment, the sixth predicted temperature increment and the second test temperature increment, the seventh predicted temperature increment and the third test temperature increment, and the eighth predicted temperature increment and the fourth test temperature increment, the evaluation indicators of the first battery reduction model, the second battery reduction model, the third battery reduction model and the fourth battery reduction model are determined respectively. Based on the evaluation metrics of the first, second, third, and fourth battery reduction models, the evaluation accuracy of the first, second, third, and fourth battery reduction models is determined.

10. A device for determining a battery fast charging strategy, characterized in that, include: The test module is used to perform three-electrode testing on the battery under test to determine the lithium plating boundary of the battery under test, wherein the battery under test is the same as the target battery; The generation module is used to generate multiple candidate current sequences by traversing within the charging lithium plating boundary of the battery under test. The first determining module is used to determine the total temperature increment of each candidate current sequence based on each candidate current sequence, the heat generated by the target cell, the thermal management parameters, and the ambient temperature, using a battery down-order model group that has completed parameter identification. The second determining module is used to determine the total charging time and the highest temperature value of each candidate current sequence based on the total temperature increment and initial parameters of each candidate current sequence. The third determining module is used to determine the target fast charging strategy corresponding to the target battery from the candidate current sequences based on the total charging time and the highest temperature value of each candidate current sequence.

11. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the method for determining the battery fast charging strategy as described in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the method for determining a battery fast charging strategy as described in any one of claims 1-9.

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