Method, device and equipment for evaluating pulse charging performance of power battery system

Abstract

This application discloses a method, apparatus, and device for evaluating the pulse charging performance of a power battery system, relating to the field of power battery technology. The method includes: constructing a corresponding cell pulse charging current optimization model based on the relationship between cell operating voltage and pulse charging current under different characteristic operating conditions; solving the cell pulse charging current optimization model to obtain the maximum cell pulse charging current under different characteristic operating conditions; determining the system maximum pulse charging current of the power battery system under different characteristic operating conditions based on the system's limiting charging current, system peak current, and cell maximum pulse charging current; calculating the pulse charging power of the power battery system under different characteristic operating conditions; and formulating and verifying the pulse charging power MAP. Through the above method, the pulse charging power of the power battery system under different characteristic operating conditions is accurately determined, the pulse charging capability of the power battery system is quickly and accurately evaluated, and the pulse charging power MAP is formulated.

Description

Technical Field

[0001] This application relates to the field of power battery technology, and in particular to methods, apparatus and equipment for evaluating the pulse charging performance of power battery systems. Background Technology

[0002] A single battery cell is the most basic building block of a power battery system, serving as the fundamental unit for absorbing, storing, and supplying electrical energy. To meet the energy supply demands of automobiles, a certain number of cells are typically connected in series and parallel to form modules, and multiple modules are combined to form a battery pack. One or more battery packs, electronic and electrical components, and structural components together constitute a power battery system. The pulse charging power of a cell characterizes its short-term charging capability, and the magnitude of the pulse charging current directly determines the pulse charging power. Therefore, determining the maximum pulse charging current of a cell is crucial for evaluating its pulse charging performance. Thus, the key to evaluating the pulse charging performance of a power battery system lies in determining the maximum pulse charging current of the cell, assessing its pulse charging performance, ensuring safety throughout its entire lifespan, and comprehensively considering the safety of the electronic and electrical systems, high-voltage systems, and structural components within the system, thereby achieving safe and efficient operation of the power battery system.

[0003] In the existing technology, there are few studies on methods for rapidly evaluating the pulse charging current of power battery systems, and the evaluation of the pulse charging performance of power battery systems has problems such as multiple test conditions, long test cycles, and high test resource and manpower costs.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, and equipment for evaluating the pulse charging performance of a power battery system, aiming to solve the technical problems that the traditional methods for evaluating the pulse charging performance of a power battery system are relatively complex, costly, and difficult to achieve quickly and accurately.

[0006] To achieve the above objectives, this application provides a method for evaluating the pulse charging performance of a power battery system, the method comprising:

[0007] Based on the relationship between cell operating voltage and pulse charging current under different characteristic operating conditions, a corresponding cell pulse charging current optimization model is constructed. The characteristic operating conditions consist of characteristic pulse time, characteristic temperature and characteristic state of charge.

[0008] Initialize the positions of gray wolves in the population and construct a fitness function, where position is used to characterize the pulse charging current;

[0009] Based on the fitness function and the position of the gray wolves in the population, the gray wolves in the population are divided into alpha wolf, second best wolf, third best wolf and the rest of the wolves. The position of the rest of the wolves is updated based on the adaptive scaling factor. The alpha wolf, second best wolf, third best wolf and the rest of the wolves in the population are re-divided. This process is repeated until the iteration termination condition is met and the target alpha wolf is obtained.

[0010] The pulse charging current represented by the position of the target wolf is used as the solution of the corresponding cell pulse charging current optimization model to obtain the maximum pulse charging current of the cell under different characteristic working conditions.

[0011] Based on the system's ultimate charging current, the system's peak current, and the cell's maximum pulse charging current, the maximum pulse charging current of the power battery system under different characteristic operating conditions is determined.

[0012] Based on the maximum pulse charging current of the power battery system under different characteristic operating conditions, the pulse charging power of the power battery system under different characteristic operating conditions is determined, and the pulse charging power MAP is formulated and verified.

[0013] In one embodiment, the cell pulse charging current optimization model includes at least the following constraints:

[0014] The cell's operating voltage is equal to the sum of the product of the cell's dynamic internal resistance and the pulse charging current, and the cell's static voltage.

[0015] The working voltage of the battery cell at the end of the charging process is less than or equal to the charging cut-off voltage.

[0016] The dynamic internal resistance of a battery cell is equal to the sum of its ohmic internal resistance and polarization internal resistance.

[0017] The dynamic internal resistance of the battery cell conforms to the internal resistance function relationship obtained by fitting.

[0018] The negative electrode potential is greater than or equal to the negative electrode potential safety threshold.

[0019] The pulse charging current is greater than zero;

[0020] The temperature rise of the battery cell during a single pulse is less than or equal to the preset temperature rise safety threshold.

[0021] In one embodiment, the step of classifying gray wolves in the population into alpha wolves, sub-alpha wolves, third-best wolves, and the remaining wolves based on the fitness function and the position of the gray wolves in the population includes:

[0022] Based on the fitness function and the position of the gray wolves in the population, calculate the fitness value corresponding to the position of the gray wolf in the population.

[0023] Based on the fitness values ​​corresponding to the positions of the gray wolves in the population, the gray wolves in the population are sorted in descending order to obtain the individual sequence of the population.

[0024] The gray wolf with the highest fitness value in the individual sequence is taken as the alpha wolf. The second best wolf is selected from the individual sequences other than the alpha wolf based on a preset second best ratio. The third best wolf is selected from the individual sequences other than the alpha wolf and the second best wolf based on a preset third best ratio.

[0025] Based on the selected alpha wolf, second-best wolf, and third-best wolf, the remaining wolves in the individual sequence are determined.

[0026] In one embodiment, the method further includes:

[0027] In the first iteration, the adaptive scaling factor is the preset scaling factor;

[0028] In non-first iterations, the maximum and minimum fitness values ​​are determined based on the fitness values ​​corresponding to the positions of gray wolves in the population. Based on the maximum and minimum fitness values, the normalized variance of fitness is determined. Based on the normalized variance of fitness and the initial adaptive scaling factor, the adaptive scaling factor is determined. The initial adaptive scaling factor is the adaptive scaling factor used in the previous iteration.

[0029] In one embodiment, the step of determining the adaptive scaling factor based on the fitness normalized variance and the initial adaptive scaling factor includes:

[0030] Obtain the first correspondence between the fitness normalized variance, the initial adaptive scaling factor, and the adaptive scaling factor;

[0031] The adaptive scaling factor is determined based on the fitness normalized variance, the initial adaptive scaling factor, and the first correspondence.

[0032] In one embodiment, the step of updating the positions of the remaining wolves based on an adaptive scaling factor includes:

[0033] The position of the alpha wolf is taken as the first position, the position of a randomly selected second-best wolf is taken as the second position, the position of a randomly selected third-best wolf is taken as the third position, and the positions of the remaining wolves after the update are taken as the fourth position.

[0034] Obtain the second correspondence between the first position, the second position, the third position, the adaptive scaling factor, and the fourth position;

[0035] Based on the first position, second position, third position, adaptive scaling factor, and second correspondence, the fourth position is determined, and the updated positions of the remaining wolves are obtained.

[0036] In one embodiment, the step of determining the maximum pulse charging current of the power battery system under different characteristic operating conditions based on the system's limiting charging current, the system's peak current, and the cell's maximum pulse charging current includes:

[0037] Obtain the system limit voltage, system peak current, and system internal resistance of the power battery system;

[0038] Based on the number of cells connected in series and the cell open-circuit voltage under different characteristic operating conditions, the system open-circuit voltage under different characteristic operating conditions is determined;

[0039] Based on the system open-circuit voltage, system limit voltage and system internal resistance of the power battery system, the system limit charging current under different characteristic operating conditions is determined.

[0040] Based on the number of modules connected in parallel and the maximum pulse charging current of the cells under different characteristic operating conditions, the maximum pulse charging current of the cell pack under different characteristic operating conditions is determined.

[0041] Following the principle of safety first, the minimum value among the system limit charging current, system peak current, and cell pack maximum pulse charging current is taken as the system maximum pulse charging current of the power battery system under the corresponding characteristic operating conditions.

[0042] In one embodiment, the step of determining the pulse charging power of the power battery system under different characteristic operating conditions based on the system's maximum pulse charging current under different characteristic operating conditions includes:

[0043] Obtain the third correspondence between the system's maximum pulse charging current, system open-circuit voltage, system internal resistance, internal resistance correction coefficient, and pulse charging power;

[0044] Based on the third correspondence, the maximum pulse charging current of the power battery system under different characteristic operating conditions, the system open circuit voltage, the system internal resistance, and the internal resistance correction coefficient, the pulse charging power of the power battery system under different characteristic operating conditions is determined.

[0045] Set boundary temperatures, and based on these boundary temperatures, divide the operating temperature range of the battery cell into a normal temperature zone and a high temperature zone;

[0046] Following the high-temperature protection strategy, when the characteristic temperature in a characteristic operating condition is within the high-temperature range, the corresponding pulse charging power is adjusted based on the high-temperature safety factor.

[0047] Furthermore, to achieve the above objectives, this application also proposes an evaluation device for the pulse charging performance of a power battery system, the evaluation device comprising:

[0048] The data solving module is used to construct a corresponding cell pulse charging current optimization model based on the relationship between cell operating voltage and pulse charging current under different characteristic operating conditions. The characteristic operating conditions consist of characteristic pulse time, characteristic temperature and characteristic state of charge.

[0049] The data solving module is also used to initialize the positions of gray wolves in the population and construct a fitness function, where the position is used to characterize the pulse charging current.

[0050] The data solving module is also used to divide the gray wolves in the population into alpha wolves, second-best wolves, third-best wolves and the rest of the wolves based on the fitness function and the position of the gray wolves in the population. It updates the position of the rest of the wolves based on the adaptive scaling factor, and redivides the alpha wolves, second-best wolves, third-best wolves and the rest of the wolves in the population. This process is repeated until the iteration termination condition is met, and the target alpha wolf is obtained.

[0051] The data solving module is also used to take the pulse charging current represented by the position of the target wolf as the solution of the corresponding cell pulse charging current optimization model, and obtain the maximum pulse charging current of the cell under different characteristic working conditions.

[0052] The performance evaluation module is used to determine the maximum pulse charging current of the power battery system under different characteristic operating conditions based on the system's limit charging current, the system's peak current, and the cell's maximum pulse charging current.

[0053] The performance evaluation module is also used to determine the pulse charging power of the power battery system under different characteristic operating conditions based on the maximum pulse charging current of the power battery system under different characteristic operating conditions, and to formulate and verify the pulse charging power MAP.

[0054] In addition, to achieve the above objectives, this application also proposes an evaluation device for the pulse charging performance of a power battery system. The evaluation device for the pulse charging performance of a power battery system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the evaluation method for the pulse charging performance of a power battery system as described above.

[0055] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium, and stores a computer program on the storage medium. When the computer program is executed by a processor, it implements the steps of the above-described method for evaluating the pulse charging performance of a power battery system.

[0056] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the method for evaluating the pulse charging performance of a power battery system as described above.

[0057] This application provides a method for evaluating the pulse charging performance of a power battery system. Based on the relationship between cell operating voltage and pulse charging current under different characteristic operating conditions, a corresponding cell pulse charging current optimization model is constructed. The characteristic operating conditions consist of characteristic pulse time, characteristic temperature, and characteristic state of charge. The positions of the gray wolves in the population are initialized, and a fitness function is constructed, with position used to characterize the pulse charging current. Based on the fitness function and the positions of the gray wolves in the population, the gray wolves in the population are divided into alpha wolves, suboptimal wolves, third-best wolves, and the remaining wolves. The positions of the remaining wolves are updated based on an adaptive scaling factor, and the alpha wolves, suboptimal wolves, third-best wolves, and the remaining wolves are reclassified. The three leading wolves and the remaining wolves are iterated continuously until the iteration termination condition is met, and the target leading wolf is obtained. The pulse charging current represented by the position of the target leading wolf is used as the solution of the corresponding cell pulse charging current optimization model to obtain the maximum pulse charging current of the cell under different characteristic operating conditions. Based on the system limit charging current, system peak current and cell maximum pulse charging current, the system maximum pulse charging current of the power battery system under different characteristic operating conditions is determined. Based on the system maximum pulse charging current of the power battery system under different characteristic operating conditions, the pulse charging power of the power battery system under different characteristic operating conditions is determined, and the pulse charging power MAP is formulated and verified. This application utilizes the relationship between cell operating voltage and pulse charging current to construct a cell pulse charging current optimization model. By solving the model, the maximum pulse charging current of the cell under different pulse times, temperatures, and states of charge is found, enabling a rapid and accurate assessment of the cell's pulse charging capability. Based on the cell's pulse charging capability and following the principle of safety priority, the maximum pulse charging current of the power battery system is determined, and the pulse charging power of the power battery system is calculated. This allows for a rapid and accurate assessment of the power battery system's pulse charging capability, and the development of a pulse charging power MAP to ensure the safety of the cell throughout its entire life cycle. This achieves safe and efficient operation of the power battery, solving the technical problems of traditional methods being complex, costly, and difficult to implement quickly and accurately when evaluating the pulse charging performance of power battery systems. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating an embodiment of the method for evaluating the pulse charging performance of the power battery system in this application.

[0061] Figure 2 This is a flowchart illustrating Embodiment 2 of the method for evaluating the pulse charging performance of the power battery system in this application;

[0062] Figure 3 This is a schematic diagram of the module structure of the evaluation device for the pulse charging performance of a power battery system according to an embodiment of this application;

[0063] Figure 4 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the evaluation method of pulse charging performance of the power battery system in the embodiments of this application.

[0064] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0065] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0066] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0067] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, or an evaluation device for the pulse charging performance of a power battery system, etc. This embodiment does not specifically limit it. The following uses an evaluation device for the pulse charging performance of a power battery system as an example to describe this embodiment and the following embodiments.

[0068] This application provides a method for evaluating the pulse charging performance of a power battery system, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for evaluating the pulse charging performance of the power battery system according to this application.

[0069] In this embodiment, the method for evaluating the pulse charging performance of the power battery system includes steps S10 to S60:

[0070] Step S10: Based on the relationship between cell operating voltage and pulse charging current under different characteristic operating conditions, construct the corresponding cell pulse charging current optimization model. The characteristic operating conditions consist of characteristic pulse time, characteristic temperature and characteristic state of charge.

[0071] It should be noted that the characteristic operating condition refers to the pre-set operating condition. In this embodiment, the characteristic operating condition consists of characteristic pulse time, characteristic temperature, and characteristic state of charge (SOC). Different characteristic pulse times, different characteristic temperatures, and different characteristic SOCs can form different characteristic operating conditions. The characteristic temperature refers to the pre-set cell operating temperature, and multiple temperatures can usually be set. The specific value and number can be set according to actual needs and are not specifically limited. For example, the set characteristic temperatures are 45℃, 25℃, 10℃, 0℃, -10℃, and -20℃. Generally, the characteristic temperature needs to be set within the cell's operating temperature range. The cell's operating temperature range is the temperature range within which the cell operates normally, and can be set according to actual conditions without specific limitations. The characteristic SOC refers to the pre-set cell SOC value, usually set within a SOC range. Multiple SOCs can be set, and the SOC range is the range of the cell's SOC, typically 0 to 100%. In practical implementation, the characteristic state of charge (SOC) can be selected by setting a SOC gradient. Based on the SOC gradient, a characteristic SOC with a corresponding value is selected within the range of SOC values. The SOC gradient can be 5%, and there is no specific limitation on it. For example, if the set SOC gradient is ΔSOC, then the SOC... i = i·ΔSOC, where i is the i-th SOC value, SOC1 = 0 is the minimum SOC value of the cell, and SOC j =100% is the maximum SOC value of the cell, 0≤i≤j. The characteristic pulse time refers to the preset pulse time, which is the duration of the pulse current; multiple characteristic pulse times can usually be set. The characteristic pulse time can be a short pulse, a standard pulse, or a long pulse. A short pulse can be 2s or 5s, a standard pulse can be 10s, and a long pulse can be 30s, which can be set according to actual needs and are not specifically limited. In this embodiment, the cell is a cell in a power battery system. Cells are connected in series and parallel to form modules. Multiple modules are combined to form a battery pack. One or more battery packs, electronic and electrical components, and structural components together constitute a power battery system.

[0072] It is understood that this embodiment sets multiple characteristic temperatures, multiple characteristic states of charge, and multiple characteristic pulse times. Among them, a characteristic temperature, a characteristic state of charge, and a characteristic pulse time can form a characteristic operating condition. Under different characteristic operating conditions, the corresponding maximum pulse charging current of the cell is found, the pulse charging capability of the cell is determined, and the pulse charging power of the power battery system is calculated to obtain the pulse charging capability of the power battery system and formulate a pulse charging power strategy.

[0073] It should be noted that, according to the working principle of the battery, after the power is turned on, the cell operating voltage during the pulse charging process satisfies the following relationship:

[0074] V cell =V cell,static +I·DCR cell ≤V Limit

[0075] In the formula, V cell This indicates the cell's operating voltage during pulse charging, V. cell,static Indicates the static voltage of the battery cell, I represents the pulse charging current, and DCR cell V represents the dynamic internal resistance of the battery cell. Limit This indicates the charging cutoff voltage of the battery cell.

[0076] It should be noted that the static voltage of the battery cell refers to the open-circuit voltage of the battery cell measured after a resting time of ≥2 hours under characteristic operating conditions.

[0077] Therefore, based on the relationship between the cell's operating voltage and the pulse charging current, this embodiment constructs an optimization model for the cell's pulse charging current. After solving the optimization model, the corresponding maximum pulse charging current of the cell can be determined.

[0078] It is understandable that pulse time, temperature, and state of charge have a certain impact on the dynamic internal resistance of the battery cell, and temperature and state of charge have a certain impact on the static voltage of the battery cell. Therefore, different relationships between the battery cell operating voltage and pulse charging current can be obtained under different characteristic pulse times, different characteristic temperatures, and different characteristic states of charge. It is necessary to construct corresponding battery cell pulse charging current optimization models under different characteristic operating conditions.

[0079] It should be understood that in determining the maximum pulse charging current, in addition to satisfying the relationship between the cell operating voltage and the pulse charging current, there are usually other requirements that need to be satisfied. All the requirements that need to be satisfied can be set as constraints of the pulse charging current optimization model, so that the maximum pulse charging current of the cell obtained by the solution can be reasonably applied.

[0080] In one feasible implementation, the cell pulse charging current optimization model includes at least the following constraints: the cell operating voltage is equal to the sum of the product of the cell dynamic internal resistance and the pulse charging current and the cell static voltage; the cell operating voltage at the end of charging is less than or equal to the charging cut-off voltage; the cell dynamic internal resistance is equal to the sum of the ohmic internal resistance and the polarization internal resistance; the cell dynamic internal resistance conforms to the fitted internal resistance function relationship; the negative electrode potential is greater than or equal to the negative electrode potential safety threshold; the pulse charging current is greater than zero; and the cell temperature rise during a single pulse is less than or equal to the preset temperature rise safety threshold.

[0081] It should be noted that the relationship between the cell operating voltage and the pulse charging current can be described as: Cell operating voltage V cell Equal to the cell dynamic internal resistance DCR cellThe product of the pulse charging current I and the cell static voltage V cell,static The sum of, i.e., V cell =V cell,static +I·DCR cell During charging, it is generally necessary to ensure that the cell's operating voltage at the end of the charging process is less than or equal to the charging cut-off voltage. This guarantees that the cell's operating voltage at other times during charging is always less than or equal to the charging cut-off voltage. In other words, the cell's operating voltage during charging is less than or equal to the charging cut-off voltage, which can be expressed as: V cell ≤V Limit Since temperature and SOC value can affect the charging cutoff voltage, it is evident that the charging cutoff voltage differs at different characteristic temperatures and under different characteristic states of charge, and needs to be determined based on the characteristic temperature and characteristic state of charge of the current operating condition. Furthermore, the pulse charging current needs to be greater than 0.

[0082] Additionally, it should be noted that after the battery is connected to a load, the dynamic internal resistance (DCR) of the battery cell... cell Including ohmic internal resistance R Ω and polarization internal resistance R f This can be expressed as: DCR cell =R Ω +R f Ohmic internal resistance R Ω It can be assumed to remain unchanged, and the polarization internal resistance R f It is dynamically changing. The pulse charging process can affect the polarization resistance R. f The main factors include: temperature, state of charge, pulse charging current, and charging time (pulse time). Therefore, in this embodiment, the dynamic internal resistance can be expressed as R. f = f(T,SOC,I,t), where f(T,SOC,I,t) is the internal resistance function, the specific form of which can be obtained by fitting test data. For example, using the dynamic current testing method, pulse charging tests are performed on the battery cell. The specific test process is as follows: pulse charging tests are conducted using different pulse charging currents at different temperatures and under different states of charge. The charging time is equal to the set pulse time. The dynamic internal resistance of the battery cell corresponding to different pulse charging currents is obtained. Using the test data, the internal resistance function is fitted. Using the internal resistance function, the dynamic internal resistance of the battery cell under different characteristic pulse times, different characteristic temperatures, and different states of charge can be calculated, i.e., the dynamic internal resistance of the battery cell under different characteristic operating conditions.

[0083] Understandably, based on the battery's working principle, during pulse charging, the negative electrode potential must be higher than the lithium plating potential to prevent lithium plating from occurring in the cell. Therefore, it is necessary to determine the lithium plating potential to establish the negative electrode safety potential threshold. During charging, lithium ions are extracted from the positive electrode and inserted into the negative electrode. When certain abnormal conditions occur, lithium ions extracted from the positive electrode may fail to insert into the negative electrode, causing them to deposit on the negative electrode surface, forming a gray substance—a phenomenon known as lithium plating. As the charging rate increases, the charging current increases, polarization increases, and the lithium ion insertion reaction kinetics and diffusion rate in the solid phase of the negative electrode material decrease, gradually reducing the negative electrode potential. Simultaneously, polarization also leads to a negative shift in the lithium plating potential. Therefore, the standard lithium plating potential is considered to be ≤0V. When the negative electrode potential is lower than the lithium plating potential, lithium plating occurs, especially at low temperatures, easily forming lithium dendrites that can puncture the separator, causing a short circuit and posing risks of battery life degradation and safety hazards. To ensure safety throughout the entire lifespan of the battery cell and to guarantee sufficient redundancy in the negative electrode potential, a negative electrode potential higher than the standard lithium plating potential is set as the safety potential, i.e., the negative electrode potential V. cath It needs to be greater than or equal to the negative electrode potential safety threshold V Saf It can be expressed as: V cath ≥V Saf The negative electrode potential safety threshold can be 10mV, which can be set according to actual needs, and there is no specific limitation on it.

[0084] It should be understood that, to ensure safety, the temperature rise ΔT of a single pulsed cell must be less than or equal to the preset temperature rise safety threshold T. saf That is, ΔT≤T saf The single-pulse cell temperature rise refers to the temperature rise of the cell under a single pulse. The preset temperature rise safety threshold is a set safe value for the temperature rise, for example, 5℃. It is set according to the actual situation and there is no specific limitation on it.

[0085] It should be noted that, according to the law of conservation of energy, under adiabatic conditions, the following condition must be met: 2 ·R·t=c p From ·m·ΔT, we can obtain the calculation formula for the temperature rise of a single pulse cell, that is, the correspondence between the cell specific heat capacity, cell mass, pulse time, cell dynamic internal resistance, pulse charging current and the temperature rise of a single pulse cell, as shown below:

[0086]

[0087] In the formula, ΔT represents the temperature rise of the battery cell in a single pulse, and c p The specific heat capacity of the battery cell is represented by m, the mass of the battery cell is represented by t, and the pulse time is represented by DCT. cell The value represents the dynamic internal resistance of the battery cell, and I represents the pulse charging current.

[0088] It is understandable that the characteristic operating condition (characteristic pulse time t) will be considered. m Characteristic temperature T k and characteristic state of charge (SOC) i Substituting the cell's dynamic internal resistance, pulse charging current, cell specific heat capacity, cell mass, and pulse time into the above formula, we can obtain the single-pulse cell temperature rise under this characteristic operating condition, and thus calculate the single-pulse cell temperature rise corresponding to the pulse charging current under different characteristic operating conditions.

[0089] Ultimately, the cell pulse charging current optimization model can be described as follows:

[0090]

[0091] In the formula, V cell This indicates the cell's operating voltage during pulse charging, V. cell,static Indicates the static voltage of the battery cell, I represents the pulse charging current, and DCR cell V represents the dynamic internal resistance of the battery cell. Limit R represents the charging cutoff voltage of the battery cell. Ω R represents the ohmic internal resistance. f V represents the polarization resistance, f(T,SOC,I,t) represents the internal resistance function relationship, and V represents the polarization resistance. cath V represents the negative electrode potential. Saf T represents the negative electrode potential safety threshold. saf c represents the preset temperature rise safety threshold. p represents the specific heat capacity of the battery cell, m represents the mass of the battery cell, and t represents the pulse time.

[0092] Step S20: Initialize the positions of gray wolves in the population and construct a fitness function, where the position is used to characterize the pulse charging current;

[0093] It should be noted that this embodiment uses an improved gray wolf optimization algorithm to solve the cell pulse charging current optimization model.

[0094] Understandably, during the solution process, relevant parameters need to be set and the population initialized. The parameters that need to be set typically include: population size, maximum number of iterations, etc., which are set according to the actual situation and are not specifically limited. The population usually consists of multiple individuals, each of which is a gray wolf. The position of each gray wolf can represent the corresponding pulse charging current. A set of potential solutions is randomly generated as the initial values ​​for the positions of the gray wolves in the population. Initialization can use the Pendulum chaotic mapping mechanism. The Pendulum mapping is a chaotic system based on a physical pendulum, which can generate a population with high randomness. This helps the optimization algorithm escape local optima, enhances global search capabilities, and improves population diversity. It can be represented as:

[0095] X i=x n +αsin(βx n )

[0096] In the formula, α and β are parameters that control chaotic behavior and are set according to the actual situation, x n This is the baseline value of the decision variable in the initial stage, set according to the actual situation. X i This indicates the location of the gray wolves within the population.

[0097] It should be understood that the fitness function is usually constructed based on the actual situation. The value of the pulse charging current can be used as the fitness function, or other penalty terms or constraints can be added on top of it; there are no restrictions on this. For example, the fitness function can be designed as follows:

[0098]

[0099] In the formula, Fitness represents the fitness function, I represents the pulse charging current, α and β represent scaling factors, Linearity(I) is a metric between 0 and 1, and V represents the cell operating voltage at the end of charging. Limit This indicates the charging cutoff voltage.

[0100] Step S30: Based on the fitness function and the position of the gray wolves in the population, the gray wolves in the population are divided into alpha wolf, second best wolf, third best wolf and the rest of the wolves. The position of the rest of the wolves is updated based on the adaptive scaling factor. The alpha wolf, second best wolf, third best wolf and the rest of the wolves in the population are re-divided. This process is repeated until the iteration termination condition is met, and the target alpha wolf is obtained.

[0101] It should be noted that in this embodiment, individuals in the population are divided into four levels: alpha wolf, second-best wolf, third-best wolf, and the remaining wolves. The levels of the alpha wolf, second-best wolf, third-best wolf, and the remaining wolves decrease sequentially, with the alpha wolf being the highest and the remaining wolves the lowest. This embodiment uses an adaptive differential evolutionary mutation strategy to update the alpha wolf, second-best wolf, third-best wolf, and the remaining wolves in the population until the iteration termination condition is met. The alpha wolf at this point is the final desired alpha wolf, i.e., the target alpha wolf. The pulse charging current represented by the position of the target alpha wolf is the pulse charging current that meets all the constraints of the cell pulse charging current optimization model. The iteration termination condition can be reaching the maximum number of iterations or the fitness no longer significantly improving; no specific limitation is made.

[0102] In one feasible implementation, the step of dividing gray wolves in the population into alpha wolves, suboptimal wolves, third-best wolves, and the remaining wolves based on the fitness function and their positions in the population includes: calculating the fitness value corresponding to the position of each gray wolf in the population based on the fitness function and their positions; sorting the gray wolves in the population in descending order based on their fitness values ​​to obtain an individual sequence of the population; designating the gray wolf with the highest fitness value in the individual sequence as the alpha wolf; selecting suboptimal wolves from the individual sequence excluding the alpha wolf based on a preset suboptimal ratio; and selecting third-best wolves from the individual sequence excluding the alpha wolf and suboptimal wolves based on a preset third-best ratio; and determining the remaining wolves in the individual sequence based on the selected alpha wolf, suboptimal wolves, and third-best wolves.

[0103] It should be noted that the fitness function can be used to calculate the fitness value corresponding to the position of each gray wolf, thus allowing the gray wolves in the population to be sorted according to their fitness values. This embodiment uses descending order sorting, ranking the gray wolves from highest to lowest fitness value; the resulting sequence is the sorted sequence of gray wolves.

[0104] Understandably, the maximum fitness value is the highest fitness value, and the gray wolf corresponding to the maximum fitness value is selected as the alpha wolf. The preset suboptimal ratio is the set proportion of wolves selected as suboptimal, and the preset third-optimal ratio is the set proportion of wolves selected as third-optimal. Generally, during selection, the gray wolf with the maximum fitness value in the individual sequence is usually selected first as the alpha wolf. Then, in the individual sequence excluding the alpha wolf, the gray wolves ranked higher according to the preset suboptimal ratio are selected as suboptimal wolves. Next, in the individual sequence excluding the alpha wolf and suboptimal wolves, the gray wolves ranked higher according to the preset third-optimal ratio are selected as third-optimal wolves. Finally, the remaining gray wolves are the other wolves.

[0105] It should be understood that after identifying the alpha wolf, second-best wolves, third-best wolves, and the remaining wolves, the positions of the remaining wolves need to be updated, thus updating the population and obtaining new alpha wolves, second-best wolves, third-best wolves, and the remaining wolves. Updating the positions of the remaining wolves requires using the position of the alpha wolf, the position of a randomly selected gray wolf from the second-best wolves, the position of a randomly selected gray wolf from the third-best wolves, and an adaptive scaling factor.

[0106] In one feasible implementation, the step of determining the adaptive scaling factor includes: during the first iteration, the adaptive scaling factor is a preset scaling factor; during subsequent iterations, based on the fitness values ​​corresponding to the positions of gray wolves in the population, the maximum fitness value and the minimum fitness value are determined; based on the maximum fitness value and the minimum fitness value, the fitness normalized variance is determined; and based on the fitness normalized variance and the initial adaptive scaling factor, the adaptive scaling factor is determined, wherein the initial adaptive scaling factor is the adaptive scaling factor used in the previous iteration.

[0107] It should be noted that the first iteration refers to the first round of iterations, which is the first time the positions of the remaining wolves are updated. Sub-first iterations refer to the second round and subsequent iterations. This embodiment uses different methods to determine the corresponding adaptive scaling factor in the first and sub-first iterations.

[0108] It is understandable that the preset scaling factor is the default scaling factor, which is a pre-set scaling factor that can be set according to actual needs, without specific limitations. During the first iteration, the preset scaling factor is used directly as the adaptive scaling factor.

[0109] It should be understood that the minimum fitness value is the minimum fitness value. Among all the calculated fitness values ​​in the current round, the minimum and maximum fitness values ​​are found. These minimum and maximum fitness values ​​can then be used to calculate the normalized variance of fitness. The calculation formula is shown below:

[0110]

[0111] In the formula, ΔG represents the fitness normalized variance, and G max G represents the maximum fitness value. min This represents the minimum fitness value. After calculating the normalized variance of fitness, the adaptive scaling factor for the current round is calculated using the normalized variance of fitness and the adaptive scaling factor from the previous round.

[0112] In one feasible implementation, the step of determining the adaptive scaling factor based on the fitness normalized variance and the initial adaptive scaling factor includes: obtaining a first correspondence between the fitness normalized variance, the initial adaptive scaling factor and the adaptive scaling factor; and determining the adaptive scaling factor based on the fitness normalized variance, the initial adaptive scaling factor and the first correspondence.

[0113] It should be noted that the first correspondence between the fitness normalized variance, the initial adaptive scaling factor, and the adaptive scaling factor, i.e., the calculation formula for the adaptive scaling factor, is as follows:

[0114] F t+1 =F t *e -λΔG

[0115] In the formula, F t+1 F represents the adaptive scaling factor for the current round, ΔG represents the fitness normalized variance, and F represents the adaptive scaling factor for the current round. t This represents the initial adaptive scaling factor. Substituting the relevant data, the adaptive scaling factor for the current round is calculated, and then the positions of the remaining wolves are updated using this adaptive scaling factor.

[0116] In one feasible implementation, the step of updating the positions of the remaining wolves based on the adaptive scaling factor includes: taking the position of the alpha wolf as the first position, taking the position of a randomly selected second-best wolf as the second position, taking the position of a randomly selected third-best wolf as the third position, and taking the updated positions of the remaining wolves as the fourth position; obtaining a second correspondence between the first position, the second position, the third position, the adaptive scaling factor, and the fourth position; and determining the fourth position based on the first position, the second position, the third position, the adaptive scaling factor, and the second correspondence to obtain the updated positions of the remaining wolves.

[0117] It should be noted that the position of the alpha wolf is designated as the first position (X1), the position of a randomly selected second-best wolf is designated as the second position (X2), the position of a randomly selected third-best wolf is designated as the third position (X3), and the positions of the remaining wolves after the update are designated as the fourth position (X). t+1 The second correspondence between the first position, second position, third position, adaptive scaling factor, and fourth position, which is the calculation formula for the updated positions of the remaining wolves, is shown below:

[0118] X t+1 =X1+F t+1 *(X2-X3)

[0119] In the formula, X t+1 X1 represents the fourth position, X2 represents the first position, X3 represents the second position, and F represents the third position. t+1 This represents the adaptive scaling factor.

[0120] It is understandable that updating the position of one of the remaining wolves each time requires updating the positions of all the remaining wolves in turn. The second position X2 and the third position X3 selected in each calculation are random, thus ensuring the diversity and randomness of the generated remaining wolf population and avoiding the algorithm from getting stuck in a local optimum.

[0121] Step S40: Use the pulse charging current represented by the position of the target wolf as the solution of the corresponding cell pulse charging current optimization model to obtain the maximum pulse charging current of the cell under different characteristic working conditions.

[0122] It should be noted that the pulse charging current represented by the position of the target wolf is the maximum pulse charging current of the battery cell, which is the solution of the battery cell pulse charging current optimization model. Thus, the maximum pulse charging current under different characteristic pulse times, different characteristic temperatures, and different characteristic states of charge can be determined in sequence, and the maximum pulse charging current of the battery cell under different characteristic operating conditions can be obtained.

[0123] It can be seen that the characteristic pulse time t m and characteristic temperature T kThe maximum pulse charging current of the battery cell corresponding to different characteristic states of charge, namely:

[0124]

[0125] Among them, T k Represents the k-th characteristic temperature, SOC i Represents the i-th characteristic state of charge. Indicates the characteristic temperature T k and characteristic state of charge (SOC) i The maximum pulse charging current of the battery cell.

[0126] Furthermore, the characteristic pulse time t can be obtained. m The maximum pulse charging current of the battery cell corresponding to different characteristic temperatures and different characteristic states of charge, i.e.:

[0127]

[0128] In the formula, T1~T l Characteristic temperature, SOC1~SOC j Indicates the characteristic state of charge. I represents the maximum pulse charging current of the battery cell at different characteristic temperatures and different characteristic states of charge. m (T,SOC) represents the characteristic pulse time t. m Maximum pulse charging current of battery cells at different characteristic temperatures and different characteristic states of charge.

[0129] Understandably, since the characteristic pulse duration can be long pulse, standard pulse, or short pulse, the maximum pulse charging current of the battery cell corresponding to different characteristic temperatures and different characteristic states of charge under long pulse, the maximum pulse charging current of the battery cell corresponding to different characteristic temperatures and different characteristic states of charge under standard pulse, and the maximum pulse charging current of the battery cell corresponding to different characteristic temperatures and different characteristic states of charge under short pulse can be obtained. This allows us to obtain the maximum pulse charging current of the battery cell under different characteristic operating conditions, which can be used to characterize the pulse charging capability of the battery cell.

[0130] Step S50: Based on the system's limit charging current, the system's peak current, and the cell's maximum pulse charging current, determine the system's maximum pulse charging current for the power battery system under different characteristic operating conditions.

[0131] In one feasible implementation, step S50 may include steps S501 to S505:

[0132] Step S501: Obtain the system limit voltage, system peak current, and system internal resistance of the power battery system;

[0133] It should be noted that the system's limiting voltage V sys,LimitThis refers to the highest voltage that ensures the normal operation of components such as the drive motor, generator, air conditioner, and control system in the vehicle; the system peak current I sys,peak This refers to the maximum permissible current that ensures the normal operation of related components in the electronic and electrical systems of the power battery system and the vehicle electrical system; the system internal resistance R sys It refers to the DC resistance in the entire circuit of the battery pack from high voltage positive to high voltage negative. It is determined by factors including bus design, overcurrent capacity, and selection of electrical components, and there are no restrictions on it.

[0134] It is understandable that the system internal resistance is related to the internal resistance of the electrical system, the dynamic internal resistance of the battery cell, the temperature correction factor, the state of charge correction factor, the number of battery cells connected in series, and the number of modules connected in parallel. The calculation relationship is shown below:

[0135]

[0136] In the formula, R sys R represents the internal resistance of the system. sys，ele DCR represents the internal resistance of an electrical system. cell T represents the dynamic internal resistance of the battery cell. f S represents the temperature correction factor. f α represents the state of charge correction factor, α represents the number of cells connected in series, and α represents the number of modules connected in parallel.

[0137] It should be understood that the number of modules connected in parallel and the number of cells connected in series need to be determined based on the configuration of the power battery system. The power battery system configuration is generally expressed as αPβS, where P represents parallel connection, S represents series connection, α is the number of cells connected in series, and β is the number of modules connected in parallel.

[0138] Step S502: Based on the number of cells connected in series and the cell open-circuit voltage under different characteristic operating conditions, determine the system open-circuit voltage under different characteristic operating conditions.

[0139] It is understandable that the system open-circuit voltage is related to the number of cells connected in series and the open-circuit voltage of the cells, and the calculation relationship is shown below:

[0140] V sys,OCV =α·V cell,OcV

[0141] In the formula, V sys,OCV The system open-circuit voltage is represented by V, and α represents the number of cells connected in series. cell,OCV This represents the cell open-circuit voltage. The system open-circuit voltage is calculated using the number of cells connected in series and the cell open-circuit voltages under different operating conditions.

[0142] Step S503: Based on the system open-circuit voltage, system limit voltage and system internal resistance of the power battery system, determine the system limit charging current under different characteristic operating conditions.

[0143] It should be noted that the system's limiting charging current is related to the system's open-circuit voltage, the system's limiting voltage, and the system's internal resistance. The calculation relationship is shown below:

[0144]

[0145] In the formula, I sys,Limit V represents the system's maximum charging current. sys,OCV V represents the system open-circuit voltage. sys,Limit R represents the system's limiting voltage. sys This represents the system's internal resistance. Using the system's open-circuit voltage, system limiting voltage, and system internal resistance, the system's limiting charging current under different characteristic operating conditions can be calculated.

[0146] Step S504: Based on the number of modules connected in parallel and the maximum pulse charging current of the cells under different characteristic operating conditions, determine the maximum pulse charging current of the cell group under different characteristic operating conditions.

[0147] It is understandable that the maximum pulse charging current of the battery cell pack is related to the number of modules connected in parallel and the maximum pulse charging current of the battery cells. The calculation relationship is shown below:

[0148] I cells,max =β·I cell,max

[0149] In the formula, I cells,max I represents the maximum pulse charging current of the battery cell pack, β represents the number of modules connected in parallel, and I cell,max This represents the maximum pulse charging current of the battery cell. Using the number of modules connected in parallel and the maximum pulse charging current of the battery cell, the maximum pulse charging current of the battery cell assembly under different characteristic operating conditions can be calculated.

[0150] Understandably, I cell,max It is the maximum pulse charging current of the battery cell under different characteristic operating conditions, determined by the cell pulse charging current optimization model.

[0151] Step S505: Following the principle of safety first, the minimum value among the system limit charging current, system peak current, and maximum pulse charging current of the cell pack is taken as the maximum pulse charging current of the power battery system under the corresponding characteristic operating condition.

[0152] It should be noted that, following the principle of safety first, the smallest of the three current values—the maximum pulse charging current of the battery pack, the system's limiting charging current, and the system's peak current—is selected as the maximum pulse charging current of the power battery system. The calculation formula is shown below:

[0153] I sys,pulse =Min(I) cells,max I sys,Limit Isys,peak )

[0154] In the formula, I sys,pulse I represents the system's maximum pulse charging current. cells,max I represents the maximum pulse charging current of the battery pack. sys,Limit I represents the system's maximum charging current. sys,peak This represents the system peak current.

[0155] Step S60: Based on the maximum pulse charging current of the power battery system under different characteristic operating conditions, determine the pulse charging power of the power battery system under different characteristic operating conditions, formulate the pulse charging power MAP, and verify it.

[0156] It should be noted that by using the maximum pulse charging current of the power battery system under different characteristic operating conditions, the pulse charging power of the power battery system under different characteristic operating conditions is calculated, and the pulse charging capability of the power battery system under standard pulse, short pulse and long pulse is obtained, thereby determining the pulse charging capability of the power battery system, and then formulating the corresponding pulse charging power MAP based on the pulse charging capability of the power battery system.

[0157] Understandably, pulse charging power MAP is typically in the form of a two-dimensional table, recording the pulse charging power at different pulse times, temperatures, and states of charge. The MAP table transmits data to the battery management system (BMS), which then retrieves the system's maximum pulse charging current for the corresponding operating point based on the collected real-time temperature and state of charge.

[0158] It should be understood that the established charging power MAP usually needs to be verified. Verification typically involves vehicle performance testing to calibrate and adjust the charging power MAP to meet vehicle performance requirements.

[0159] This embodiment provides a method for evaluating the pulse charging performance of a power battery system. It utilizes the relationship between the cell's operating voltage and the pulse charging current to construct an optimization model for the cell's pulse charging current. By solving the model, the maximum pulse charging current of the cell under different pulse times, temperatures, and states of charge is identified, allowing for a rapid and accurate evaluation of the cell's pulse charging capability. Based on the cell's pulse charging capability and following the principle of safety priority, the maximum pulse charging current of the power battery system is determined, and the pulse charging power of the power battery system is calculated. This allows for a rapid and accurate evaluation of the power battery system's pulse charging capability, the establishment of a pulse charging power MAP, and ensures the safety of the cell throughout its entire lifespan, enabling the power battery to operate safely and efficiently.

[0160] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S60 may include steps S601 to S602:

[0161] Step S601: Obtain the third correspondence between the system's maximum pulse charging current, system open-circuit voltage, system internal resistance, internal resistance correction coefficient, and pulse charging power. Based on the third correspondence, the system's maximum pulse charging current, system open-circuit voltage, system internal resistance, and internal resistance correction coefficient under different characteristic operating conditions, determine the pulse charging power of the power battery system under different characteristic operating conditions.

[0162] It should be noted that the third correspondence between the system's maximum pulse charging current, system open-circuit voltage, system internal resistance, internal resistance correction coefficient, and pulse charging power—that is, the calculation formula for the pulse charging power of the power battery system—is as follows:

[0163] P sys =(V sys,OCV +I sys,pulse ·R sys ·ε)·I sys,pulse

[0164] In the formula, P sys I represents the pulse charging power. sys,pulse V represents the system's maximum pulse charging current. sys,OCV R represents the system open-circuit voltage. sys Let represent the system internal resistance, and ε represent the internal resistance correction coefficient. By substituting relevant data under different characteristic operating conditions, the pulse charging power of the power battery system under different characteristic operating conditions is calculated.

[0165] Step S602: Set the boundary temperature. Based on the boundary temperature, the operating temperature range of the battery cell is divided into a normal temperature zone and a high temperature zone. Following the high temperature protection strategy, when the characteristic temperature in the characteristic operating condition is within the high temperature zone, the corresponding pulse charging power is adjusted based on the high temperature safety factor.

[0166] It should be noted that, in order to protect battery performance, prevent overcharging at high temperatures, and ensure the safety of the entire battery system, this embodiment must follow a high-temperature protection strategy. The boundary temperature is the temperature set to distinguish between the normal temperature zone and the high-temperature zone. Generally, the high-temperature zone has a higher temperature; the specific value can be set according to actual needs and is not specifically limited.

[0167] It is understandable that when the characteristic temperature in a characteristic operating condition is within the high-temperature range, a high-temperature protection strategy is considered necessary. In this case, the high-temperature safety factor is used to adjust the corresponding pulse charging power, and the calculation relationship is shown below:

[0168] P sys，hot =P sys ·η

[0169] In the formula, P sys，hot P represents the adjusted pulse charging power. sys This represents the pulse charging power in the high-temperature zone, and η represents the high-temperature safety factor, the specific value of which is determined based on the actual situation.

[0170] This embodiment provides a method for evaluating the pulse charging performance of a power battery system. It utilizes the relationship between the cell's operating voltage and the pulse charging current to construct an optimization model for the cell's pulse charging current. By solving the model, the maximum pulse charging current of the cell under different pulse times, temperatures, and states of charge is identified, allowing for a rapid and accurate evaluation of the cell's pulse charging capability. Based on the cell's pulse charging capability and following the principle of safety priority, the maximum pulse charging current of the power battery system is determined, and the pulse charging power of the power battery system is calculated. This allows for a rapid and accurate evaluation of the power battery system's pulse charging capability, the establishment of a pulse charging power MAP, and ensures the safety of the cell throughout its entire lifespan, enabling the power battery to operate safely and efficiently.

[0171] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the evaluation method of pulse charging performance of the power battery system of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0172] This application also provides an evaluation device for the pulse charging performance of a power battery system, please refer to... Figure 3 The evaluation device for the pulse charging performance of the power battery system includes:

[0173] Data solving module 10 is used to construct a corresponding cell pulse charging current optimization model based on the relationship between cell operating voltage and pulse charging current under different characteristic operating conditions. The characteristic operating conditions consist of characteristic pulse time, characteristic temperature and characteristic state of charge.

[0174] The data solving module 10 is also used to initialize the position of the gray wolves in the population and construct the fitness function, where the position is used to characterize the pulse charging current;

[0175] The data solving module 10 is also used to divide the gray wolves in the population into alpha wolves, suboptimal wolves, third-best wolves and the remaining wolves based on the fitness function and the position of the gray wolves in the population, and update the position of the remaining wolves based on the adaptive scaling factor, and redivide the alpha wolves, suboptimal wolves, third-best wolves and the remaining wolves in the population, and continue to iterate until the iteration termination condition is met to obtain the target alpha wolf.

[0176] The data solving module 10 is also used to take the pulse charging current represented by the position of the target wolf as the solution of the corresponding cell pulse charging current optimization model, and obtain the maximum pulse charging current of the cell under different characteristic working conditions.

[0177] The performance evaluation module 20 is used to determine the maximum pulse charging current of the power battery system under different characteristic operating conditions based on the system limit charging current, the system peak current, and the maximum pulse charging current of the battery cell.

[0178] The performance evaluation module 20 is also used to determine the pulse charging power of the power battery system under different characteristic operating conditions based on the maximum pulse charging current of the power battery system under different characteristic operating conditions, and to formulate and verify the pulse charging power MAP.

[0179] In one feasible implementation, the cell pulse charging current optimization model includes at least the following constraints:

[0180] The cell's operating voltage is equal to the sum of the product of the cell's dynamic internal resistance and the pulse charging current, and the cell's static voltage.

[0181] The working voltage of the battery cell at the end of the charging process is less than or equal to the charging cut-off voltage.

[0182] The dynamic internal resistance of a battery cell is equal to the sum of its ohmic internal resistance and polarization internal resistance.

[0183] The dynamic internal resistance of the battery cell conforms to the internal resistance function relationship obtained by fitting.

[0184] The negative electrode potential is greater than or equal to the negative electrode potential safety threshold.

[0185] The pulse charging current is greater than zero;

[0186] The temperature rise of the battery cell during a single pulse is less than or equal to the preset temperature rise safety threshold.

[0187] In one feasible implementation, the data solving module 10 is also used to calculate the fitness value corresponding to the position of the gray wolf in the population based on the fitness function and the position of the gray wolf in the population.

[0188] Based on the fitness values ​​corresponding to the positions of the gray wolves in the population, the gray wolves in the population are sorted in descending order to obtain the individual sequence of the population.

[0189] The gray wolf with the highest fitness value in the individual sequence is taken as the alpha wolf. The second best wolf is selected from the individual sequences other than the alpha wolf based on a preset second best ratio. The third best wolf is selected from the individual sequences other than the alpha wolf and the second best wolf based on a preset third best ratio.

[0190] Based on the selected alpha wolf, second-best wolf, and third-best wolf, the remaining wolves in the individual sequence are determined.

[0191] In one feasible implementation, the data solving module 10 is further configured to set the adaptive scaling factor to a preset scaling factor during the first iteration.

[0192] In non-first iterations, the maximum and minimum fitness values ​​are determined based on the fitness values ​​corresponding to the positions of gray wolves in the population. Based on the maximum and minimum fitness values, the normalized variance of fitness is determined. Based on the normalized variance of fitness and the initial adaptive scaling factor, the adaptive scaling factor is determined. The initial adaptive scaling factor is the adaptive scaling factor used in the previous iteration.

[0193] In one feasible implementation, the data solving module 10 is also used to obtain the first correspondence between the fitness normalized variance, the initial adaptive scaling factor, and the adaptive scaling factor;

[0194] The adaptive scaling factor is determined based on the fitness normalized variance, the initial adaptive scaling factor, and the first correspondence.

[0195] In one feasible implementation, the data solving module 10 is further configured to take the position of the alpha wolf as the first position, the position of a randomly selected second-best wolf as the second position, the position of a randomly selected third-best wolf as the third position, and the updated positions of the remaining wolves as the fourth position.

[0196] Obtain the second correspondence between the first position, the second position, the third position, the adaptive scaling factor, and the fourth position;

[0197] Based on the first position, second position, third position, adaptive scaling factor, and second correspondence, the fourth position is determined, and the updated positions of the remaining wolves are obtained.

[0198] In one feasible implementation, the performance evaluation module 20 is also used to obtain the system limit voltage, system peak current and system internal resistance of the power battery system.

[0199] Based on the number of cells connected in series and the cell open-circuit voltage under different characteristic operating conditions, the system open-circuit voltage under different characteristic operating conditions is determined;

[0200] Based on the system open-circuit voltage, system limit voltage and system internal resistance of the power battery system, the system limit charging current under different characteristic operating conditions is determined.

[0201] Based on the number of modules connected in parallel and the maximum pulse charging current of the cells under different characteristic operating conditions, the maximum pulse charging current of the cell pack under different characteristic operating conditions is determined.

[0202] Following the principle of safety first, the minimum value among the system limit charging current, system peak current, and cell pack maximum pulse charging current is taken as the system maximum pulse charging current of the power battery system under the corresponding characteristic operating conditions.

[0203] In one feasible implementation, the performance evaluation module 20 is also used to obtain the third correspondence between the system's maximum pulse charging current, system open-circuit voltage, system internal resistance, internal resistance correction coefficient, and pulse charging power.

[0204] Based on the third correspondence, the maximum pulse charging current of the power battery system under different characteristic operating conditions, the system open circuit voltage, the system internal resistance, and the internal resistance correction coefficient, the pulse charging power of the power battery system under different characteristic operating conditions is determined.

[0205] Set boundary temperatures, and based on these boundary temperatures, divide the operating temperature range of the battery cell into a normal temperature zone and a high temperature zone;

[0206] Following the high-temperature protection strategy, when the characteristic temperature in a characteristic operating condition is within the high-temperature range, the corresponding pulse charging power is adjusted based on the high-temperature safety factor.

[0207] The device for evaluating the pulse charging performance of a power battery system provided in this application employs the evaluation method for the pulse charging performance of a power battery system described in the above embodiments. This solves the technical problems that traditional methods for evaluating the pulse charging performance of a power battery system are complex, costly, and difficult to implement quickly and accurately. Compared with the prior art, the beneficial effects of the device for evaluating the pulse charging performance of a power battery system provided in this application are the same as those of the evaluation method for the pulse charging performance of a power battery system provided in the above embodiments. Furthermore, other technical features of the device for evaluating the pulse charging performance of a power battery system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0208] This application provides an evaluation device for the pulse charging performance of a power battery system. The evaluation device for the pulse charging performance of a power battery system includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the evaluation method for the pulse charging performance of the power battery system in the first embodiment described above.

[0209] The following is for reference. Figure 4This document illustrates a schematic diagram of a structure suitable for evaluating the pulse charging performance of a power battery system according to embodiments of this application. The evaluation device for the pulse charging performance of a power battery system in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The illustrated device for evaluating the pulse charging performance of a power battery system is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0210] like Figure 4 As shown, the power battery system pulse charging performance evaluation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the power battery system pulse charging performance evaluation device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the power battery system pulse charging performance evaluation device to wirelessly or wiredly communicate with other devices to exchange data. Although the figure shows a power battery system pulse charging performance evaluation device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0211] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0212] The pulse charging performance evaluation device for power battery systems provided in this application employs the pulse charging performance evaluation method for power battery systems described in the above embodiments. This solves the technical problems that traditional methods for evaluating the pulse charging performance of power battery systems are complex, costly, and difficult to implement quickly and accurately. Compared with the prior art, the beneficial effects of the pulse charging performance evaluation device for power battery systems provided in this application are the same as those of the pulse charging performance evaluation method for power battery systems provided in the above embodiments. Furthermore, other technical features of this pulse charging performance evaluation device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0213] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0214] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0215] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the method for evaluating the pulse charging performance of the power battery system in the above embodiments.

[0216] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0217] The aforementioned computer-readable storage medium may be included in the evaluation device for the pulse charging performance of the power battery system; or it may exist independently and not be assembled into the evaluation device for the pulse charging performance of the power battery system.

[0218] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the power battery system pulse charging performance evaluation device, the power battery system pulse charging performance evaluation device: constructs a corresponding cell pulse charging current optimization model based on the relationship between cell operating voltage and pulse charging current under different characteristic operating conditions. The characteristic operating conditions consist of characteristic pulse time, characteristic temperature, and characteristic state of charge. It initializes the positions of gray wolves in the population and constructs a fitness function, where position is used to characterize the pulse charging current. Based on the fitness function and the positions of gray wolves in the population, it divides the gray wolves in the population into alpha wolves, suboptimal wolves, third-optimal wolves, and the remaining wolves, and then uses an adaptive scaling factor... The positions of the remaining wolves are updated, and the alpha wolf, second-best wolf, third-best wolf, and other wolves in the population are reclassified. This process is iterated until the iteration termination condition is met, and the target alpha wolf is obtained. The pulse charging current represented by the position of the target alpha wolf is used as the solution of the corresponding cell pulse charging current optimization model to obtain the maximum pulse charging current of the cell under different characteristic operating conditions. Based on the system limit charging current, system peak current, and cell maximum pulse charging current, the maximum pulse charging current of the power battery system under different characteristic operating conditions is determined. Based on the maximum pulse charging current of the power battery system under different characteristic operating conditions, the pulse charging power of the power battery system under different characteristic operating conditions is determined, and a pulse charging power MAP is formulated and verified.

[0219] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0220] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0221] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0222] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for evaluating the pulse charging performance of a power battery system. This solves the technical problems that traditional methods for evaluating the pulse charging performance of a power battery system are complex, costly, and difficult to implement quickly and accurately. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the power battery system pulse charging performance evaluation method provided in the above embodiments, and will not be repeated here.

[0223] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for evaluating the pulse charging performance of a power battery system.

[0224] The computer program product provided in this application can solve the technical problems that traditional methods for evaluating the pulse charging performance of power battery systems are complex, costly, and difficult to implement quickly and accurately. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the power battery system pulse charging performance evaluation method provided in the above embodiments, and will not be repeated here.

[0225] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for evaluating the pulse charging performance of a power battery system, characterized in that, The method includes: Based on the relationship between cell operating voltage and pulse charging current under different characteristic operating conditions, a corresponding cell pulse charging current optimization model is constructed. The characteristic operating conditions consist of characteristic pulse time, characteristic temperature, and characteristic state of charge. Initialize the positions of gray wolves in the population and construct a fitness function, whereby the positions are used to characterize the pulse charging current; Based on the fitness function and the position of the gray wolves in the population, the gray wolves in the population are divided into alpha wolves, second-best wolves, third-best wolves and the remaining wolves. The position of the remaining wolves is updated based on the adaptive scaling factor. The alpha wolves, second-best wolves, third-best wolves and the remaining wolves in the population are re-divided. This process is repeated until the iteration termination condition is met, and the target alpha wolf is obtained. The pulse charging current represented by the position of the target wolf is used as the solution of the corresponding cell pulse charging current optimization model to obtain the maximum pulse charging current of the cell under different characteristic working conditions. Based on the system's ultimate charging current, the system's peak current, and the cell's maximum pulse charging current, the maximum pulse charging current of the power battery system under different characteristic operating conditions is determined. Based on the maximum pulse charging current of the power battery system under different characteristic operating conditions, the pulse charging power of the power battery system under different characteristic operating conditions is determined, and the pulse charging power MAP is formulated and verified.

2. The method as described in claim 1, characterized in that, The cell pulse charging current optimization model includes at least the following constraints: The cell's operating voltage is equal to the sum of the product of the cell's dynamic internal resistance and the pulse charging current, and the cell's static voltage. The working voltage of the battery cell at the end of the charging process is less than or equal to the charging cut-off voltage. The dynamic internal resistance of a battery cell is equal to the sum of its ohmic internal resistance and polarization internal resistance. The dynamic internal resistance of the battery cell conforms to the internal resistance function relationship obtained by fitting. The negative electrode potential is greater than or equal to the negative electrode potential safety threshold. The pulse charging current is greater than zero; The temperature rise of the battery cell during a single pulse is less than or equal to the preset temperature rise safety threshold.

3. The method as described in claim 1, characterized in that, The step of dividing the gray wolves in the population into alpha wolves, second-best wolves, third-best wolves, and the remaining wolves based on the fitness function and the position of the gray wolves in the population includes: Based on the fitness function and the position of the gray wolves in the population, calculate the fitness value corresponding to the position of the gray wolf in the population; Based on the fitness values ​​corresponding to the positions of the gray wolves in the population, the gray wolves in the population are sorted in descending order to obtain the individual sequence of the population; The gray wolf with the highest fitness value in the individual sequence is taken as the alpha wolf. The second best wolf is selected from the individual sequence other than the alpha wolf based on a preset second best ratio. The third best wolf is selected from the individual sequence other than the alpha wolf and the second best wolf based on a preset third best ratio. Based on the selected alpha wolf, second-best wolf, and third-best wolf, the remaining wolves in the individual sequence are determined.

4. The method as described in claim 3, characterized in that, The method further includes: In the first iteration, the adaptive scaling factor is a preset scaling factor; In non-first iterations, based on the fitness values ​​corresponding to the positions of gray wolves in the population, the maximum fitness value and the minimum fitness value are determined. Based on the maximum fitness value and the minimum fitness value, the fitness normalized variance is determined. Based on the fitness normalized variance and the initial adaptive scaling factor, the adaptive scaling factor is determined. The initial adaptive scaling factor is the adaptive scaling factor used in the previous iteration.

5. The method as described in claim 4, characterized in that, The step of determining the adaptive scaling factor based on the fitness normalized variance and the initial adaptive scaling factor includes: Obtain the first correspondence between the fitness normalized variance, the initial adaptive scaling factor, and the adaptive scaling factor; The adaptive scaling factor is determined based on the fitness normalized variance, the initial adaptive scaling factor, and the first correspondence.

6. The method as described in claim 1, characterized in that, The step of updating the positions of the remaining wolves based on the adaptive scaling factor includes: The position of the alpha wolf is taken as the first position, the position of a randomly selected second-best wolf is taken as the second position, the position of a randomly selected third-best wolf is taken as the third position, and the positions of the remaining wolves after the update are taken as the fourth position. Obtain the second correspondence between the first position, the second position, the third position, the adaptive scaling factor, and the fourth position; Based on the first position, the second position, the third position, the adaptive scaling factor, and the second correspondence, the fourth position is determined, and the updated positions of the remaining wolves are obtained.

7. The method as described in claim 1, characterized in that, The steps for determining the maximum pulse charging current of the power battery system under different characteristic operating conditions based on the system's limiting charging current, system peak current, and cell maximum pulse charging current include: Obtain the system limit voltage, system peak current, and system internal resistance of the power battery system; Based on the number of cells connected in series and the cell open-circuit voltage under different characteristic operating conditions, the system open-circuit voltage under different characteristic operating conditions is determined; Based on the system open-circuit voltage, system limit voltage and system internal resistance of the power battery system, the system limit charging current under different characteristic operating conditions is determined. Based on the number of modules connected in parallel and the maximum pulse charging current of the cells under different characteristic operating conditions, the maximum pulse charging current of the cell pack under different characteristic operating conditions is determined. Following the principle of safety first, the minimum value among the system limit charging current, system peak current, and cell pack maximum pulse charging current is taken as the system maximum pulse charging current of the power battery system under the corresponding characteristic operating conditions.

8. The method according to any one of claims 1 to 7, characterized in that, The step of determining the pulse charging power of the power battery system under different characteristic operating conditions based on the maximum pulse charging current of the power battery system under different characteristic operating conditions includes: Obtain the third correspondence between the system's maximum pulse charging current, system open-circuit voltage, system internal resistance, internal resistance correction coefficient, and pulse charging power; Based on the third correspondence, the maximum pulse charging current of the power battery system under different characteristic operating conditions, the system open circuit voltage, the system internal resistance, and the internal resistance correction coefficient, the pulse charging power of the power battery system under different characteristic operating conditions is determined. Set a boundary temperature, and based on the boundary temperature, divide the operating temperature range of the battery cell into a normal temperature zone and a high temperature zone; Following the high-temperature protection strategy, when the characteristic temperature in a characteristic operating condition is within the high-temperature range, the corresponding pulse charging power is adjusted based on the high-temperature safety factor.

9. A device for evaluating the pulse charging performance of a power battery system, characterized in that, The device includes: The data solving module is used to construct a corresponding cell pulse charging current optimization model based on the relationship between cell operating voltage and pulse charging current under different characteristic operating conditions. The characteristic operating conditions consist of characteristic pulse time, characteristic temperature and characteristic state of charge. The data solving module is also used to initialize the position of gray wolves in the population and construct a fitness function, wherein the position is used to characterize the pulse charging current; The data solving module is also used to divide the gray wolves in the population into alpha wolves, suboptimal wolves, third-best wolves and the remaining wolves based on the fitness function and the position of the gray wolves in the population, and update the position of the remaining wolves based on the adaptive scaling factor, and redivide the alpha wolves, suboptimal wolves, third-best wolves and the remaining wolves in the population, and continuously iterate until the iteration termination condition is met to obtain the target alpha wolf. The data solving module is also used to take the pulse charging current represented by the position of the target wolf as the solution of the corresponding cell pulse charging current optimization model, and obtain the maximum pulse charging current of the cell under different characteristic working conditions. The performance evaluation module is used to determine the maximum pulse charging current of the power battery system under different characteristic operating conditions based on the system's limit charging current, the system's peak current, and the cell's maximum pulse charging current. The performance evaluation module is also used to determine the pulse charging power of the power battery system under different characteristic operating conditions based on the maximum pulse charging current of the power battery system under different characteristic operating conditions, formulate the pulse charging power MAP, and verify it.

10. An evaluation device for the pulse charging performance of a power battery system, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for evaluating the pulse charging performance of a power battery system as described in any one of claims 1 to 8.

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