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

Abstract

The invention discloses a method, device and equipment for evaluating the pulse charging performance of a power battery system, and relates to the technical field of power batteries, and the method comprises the steps: constructing a corresponding cell pulse charging current optimization model based on the relation between the cell working voltage and the pulse charging current under different characteristic working conditions; solving the battery cell pulse charging current optimization model to obtain the maximum pulse charging current of the battery cell under different characteristic working conditions; based on the system limit charging current, the system peak current and the battery cell maximum pulse charging current, determining the system maximum pulse charging current of the power battery system under different characteristic working conditions, calculating the pulse charging power of the power battery system under different characteristic working conditions, and formulating and verifying the pulse charging power MAP. Through the above mode, the pulse charging power of the power battery system under different characteristic working conditions is accurately determined, the pulse charging capability of the power battery system is rapidly and accurately evaluated, and the pulse charging power MAP is formulated.

Description

Technical Field

[0001] The present application relates to the field of power battery technology, and in particular to a method, device and equipment for evaluating the pulse charging performance of a power battery system. Background Art

[0002] The single cell (battery cell) is the most basic component unit of the power battery system and is also the basic unit for absorbing, storing and supplying electric energy. In order to meet the energy supply needs of the car, a certain number of batteries are usually connected in series and parallel to form a module, and multiple modules are combined to form a battery pack. One or more battery packs, electronic and electrical components, and structural components together constitute the power battery system. The pulse charging power of the battery cell characterizes the short-term charging capability of the battery cell. The magnitude of the pulse charging current directly determines the pulse charging power. It can be seen that determining the maximum pulse charging current of the battery cell is the key to evaluating the pulse charging performance of the battery cell. Therefore, the key to evaluating the pulse charging performance of the power battery system lies in determining the maximum pulse charging current of the battery cell, determining the pulse charging performance of the battery cell, ensuring the safety of the battery cell throughout its life cycle, and at the same time, comprehensively considering the safety of the electronic and electrical system, high-voltage system, and structural parts in the system, so as to achieve safe and efficient operation of the power battery system.

[0003] In the prior art, there are few studies on methods for quickly evaluating the pulse charging current of a power battery system, and the evaluation of the pulse charging performance of a power battery system has problems such as many test conditions, long test cycles, and high test resources and labor costs.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a method, device and equipment for evaluating the pulse charging performance of a power battery system, aiming to solve the technical problems that the traditional method of evaluating the pulse charging performance of a power battery system in the prior art is relatively complex, costly, and difficult to implement quickly and accurately.

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

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

[0008] Initialize the position of the gray wolf in the population and construct a fitness function. The 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 the alpha wolf, the second-best wolf, the third-best wolf and the rest of the wolves, and the positions of the rest of the wolves are updated based on the adaptive scaling factor, and the alpha wolf, the second-best wolf, the third-best wolf and the rest of the wolves in the population are re-divided, and the iteration is continuously performed until the iteration termination condition is met to obtain the target alpha wolf;

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

[0011] Based on the system limit charging current, system peak current and cell maximum pulse charging current, determine the system maximum pulse charging current of the power battery system under different characteristic working conditions;

[0012] Based on the system maximum pulse charging current of the power battery system under different characteristic working conditions, the pulse charging power of the power battery system under different characteristic working 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 working voltage of the battery cell is equal to the sum of the product of the dynamic internal resistance of the battery cell and the pulse charging current and the static voltage of the battery cell;

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

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

[0017] The dynamic internal resistance of the battery cell conforms to the fitted internal resistance function relationship;

[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, based on the fitness function and the position of the gray wolves in the population, the step of dividing the gray wolves in the population into the alpha wolf, the second-best wolf, the third-best wolf and the remaining wolves includes:

[0022] Based on the fitness function and the position of the gray wolf in the population, the fitness value corresponding to the position of the gray wolf in the population is calculated;

[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 corresponding to the maximum fitness value in the individual sequence is selected 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, and 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;

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

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

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

[0028] In non-first iteration, the maximum fitness value and the minimum fitness value are determined based on the fitness value corresponding to the position of the gray wolf in the population, the fitness normalized variance is determined based on the maximum fitness value and the minimum fitness value, and the adaptive scaling factor is determined based on the fitness normalized variance and the initial adaptive scaling factor. 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] Obtaining a first correspondence between a fitness normalized variance, an initial adaptive scaling factor, and an adaptive scaling factor;

[0031] An 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 the adaptive scaling factor comprises:

[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 updated positions of the remaining wolves are taken as the fourth position;

[0034] Acquire a second corresponding relationship between the first position, the second position, the third position, the adaptive scaling factor and the fourth position;

[0035] Based on the first position, the second position, the third position, the adaptive scaling factor and the second corresponding relationship, a fourth position is determined to obtain updated positions of the remaining wolves.

[0036] In one embodiment, based on the system limit charging current, the system peak current and the cell maximum pulse charging current, the step of determining the system maximum pulse charging current of the power battery system under different characteristic working conditions 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 in series and the open circuit voltage of the cells under different characteristic working conditions, determine the system open circuit voltage under different characteristic working conditions;

[0039] 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 working conditions;

[0040] Based on the number of modules connected in parallel and the maximum pulse charging current of the battery cells under different characteristic working conditions, determine the maximum pulse charging current of the battery cell group under different characteristic working conditions;

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

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

[0043] Obtaining a third corresponding relationship between the system maximum pulse charging current, the system open circuit voltage, the system internal resistance, the internal resistance correction coefficient and the pulse charging power;

[0044] Determine the pulse charging power of the power battery system under different characteristic working conditions based on the third corresponding relationship, the system maximum pulse charging current, the system open circuit voltage, the system internal resistance, and the internal resistance correction coefficient of the power battery system under different characteristic working conditions;

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

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

[0047] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for evaluating the pulse charging performance of a power battery system, and the device for evaluating the pulse charging performance of a power battery system includes:

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

[0049] The data solving module is also used to initialize the position of the gray wolf in the population and construct a fitness function. 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 the alpha wolf, the suboptimal wolf, the third-optimal wolf and the remaining wolves based on the fitness function and the position of the gray wolves in the population, and to update the positions of the remaining wolves based on the adaptive scaling factor, and to re-divide the alpha wolf, the suboptimal wolf, the third-optimal wolf and the remaining wolves in the population, and to continuously iterate until the iteration termination condition is met to obtain the target alpha wolf;

[0051] The data solution module is also used to use the pulse charging current represented by the position of the target wolf leader as the solution of the corresponding battery cell pulse charging current optimization model to obtain the maximum pulse charging current of the battery cell under different characteristic working conditions;

[0052] A performance evaluation module is used to determine the maximum pulse charging current of the power battery system under different characteristic working conditions based on the system limit charging current, the system peak current and the maximum pulse charging current of the battery cell;

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

[0054] In addition, to achieve the above-mentioned objectives, the present application also proposes a device for evaluating the pulse charging performance of a power battery system. The device for evaluating 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 method for evaluating the pulse charging performance of a power battery system as described above.

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

[0056] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the method for evaluating the pulse charging performance of a power battery system as described above are implemented.

[0057] The present application provides a method for evaluating the pulse charging performance of a power battery system. Based on the relationship between the working voltage of a battery cell and the pulse charging current under different characteristic working conditions, a corresponding battery cell pulse charging current optimization model is constructed, wherein the characteristic working condition is composed of a characteristic pulse time, a characteristic temperature, and a characteristic state of charge; the position of the gray wolves in the population is initialized, and a fitness function is constructed, wherein the position is 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 the alpha wolf, the suboptimal wolf, the third-optimal wolf, and the remaining wolves, and the positions of the remaining wolves are updated based on an adaptive scaling factor, and the alpha wolf, the suboptimal wolf, the third-optimal wolf, and the remaining wolves in the population are re-divided. The three best wolves and the rest of the wolves continue to iterate until the iteration termination conditions are 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 battery cell pulse charging current optimization model to obtain the maximum pulse charging current of the battery cell under different characteristic working conditions; based on the system limit charging current, the system peak current and the maximum pulse charging current of the battery cell, the system maximum pulse charging current of the power battery system under different characteristic working conditions is determined; based on the system maximum pulse charging current of the power battery system under different characteristic working conditions, the pulse charging power of the power battery system under different characteristic working conditions is determined, and the pulse charging power MAP is formulated and verified. The present application utilizes the relationship between the working voltage of a battery cell and the pulse charging current to construct a battery cell pulse charging current optimization model. By solving the model, the maximum pulse charging current of the battery cell under different pulse times, different temperatures, and different states of charge is found, and the pulse charging capability of the battery cell is quickly and accurately evaluated. Based on the pulse charging capability of the battery cell and in compliance with the principle of safety first, the maximum system pulse charging current of the power battery system is determined, and the pulse charging power of the power battery system is calculated. The pulse charging capability of the power battery system is quickly and accurately evaluated, and a pulse charging power MAP is formulated to ensure the safety of the battery cell throughout its life cycle and to achieve safe and efficient operation of the power battery. The technical problem that the traditional method of evaluating the pulse charging performance of the power battery system is relatively complex, costly, and difficult to implement quickly and accurately is solved. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0060] Figure 1 This is a flow chart of Embodiment 1 of the method for evaluating pulse charging performance of a power battery system of the present application;

[0061] Figure 2 This is a flow chart of Embodiment 2 of the method for evaluating pulse charging performance of a power battery system of the present application;

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

[0063] Figure 4 Schematic diagram of the device structure of the hardware operating environment involved in the method for evaluating the pulse charging performance of a power battery system in an embodiment of the present application.

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

[0065] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0066] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

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

[0068] The present application embodiment provides a method for evaluating the pulse charging performance of a power battery system, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for evaluating pulse charging performance of a power battery system of the present 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, constructing a corresponding cell pulse charging current optimization model based on the relationship between the cell operating voltage and the pulse charging current under different characteristic working conditions, wherein the characteristic working condition is composed of a characteristic pulse time, a characteristic temperature, and a characteristic state of charge;

[0071] It should be noted that the characteristic operating condition is a pre-set operating condition. The characteristic operating condition in this embodiment is composed of a characteristic pulse time, a characteristic temperature and a characteristic state of charge. Different characteristic pulse times, different characteristic temperatures and different characteristic states of charge can constitute different characteristic operating conditions. The characteristic temperature refers to the pre-set operating temperature of the battery cell. Usually, multiple values ​​can be set. The specific value and specific quantity can be set according to actual needs. There is no specific limitation on this. For example, the set characteristic temperature is 45°C, 25°C, 10°C, 0°C, -10°C, and -20°C. Generally speaking, the characteristic temperature needs to be set within the operating temperature range of the battery cell. The operating temperature range of the battery cell is the temperature range in which the battery cell works normally. It can be set according to actual conditions. There is no specific limitation on this. The characteristic state of charge refers to the pre-set SOC value of the battery cell, which is usually set within the state of charge range. Multiple values ​​can be set. The state of charge range is the range of the state of charge (SOC) of the battery cell, which is usually 0 to 100%. In specific implementation, the characteristic state of charge can be selected by setting the SOC gradient. According to the SOC gradient, the characteristic state of charge of the corresponding value is selected within the state of charge range. The SOC gradient can be 5%, which is not specifically limited. For example, if the set SOC gradient is ΔSOC, then SOC i =i·ΔSOC, where i is the i-th SOC value, SOC1=0 is the minimum SOC value of the battery cell, and SOC j =100% is the maximum SOC value of the battery cell, 0≤i≤j. The characteristic pulse time refers to the pre-set pulse time, which is also the duration of the pulse current, and usually multiple times can be set. The characteristic pulse time can be a short pulse, a standard pulse, or a long pulse. The short pulse can be 2s or 5s, the standard pulse can be 10s, and the long pulse can be 30s. It can be set according to actual needs and is not specifically limited. The battery cells in this embodiment are battery cells in a power battery system. The battery 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 can be understood that the present embodiment sets multiple characteristic temperatures, multiple characteristic charge states and multiple characteristic pulse times, wherein a characteristic temperature, a characteristic charge state and a characteristic pulse time can constitute a characteristic operating condition, and the corresponding maximum pulse charging current of the battery cell is found under different characteristic operating conditions, and the pulse charging capacity of the battery cell is determined, thereby calculating the pulse charging power of the power battery system, obtaining the pulse charging capacity of the power battery system, and formulating 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 working voltage of the battery cell during the pulse charging process satisfies the following relationship:

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

[0075] Where V cell Indicates the working voltage of the battery cell during pulse charging, V cell,static Indicates the static voltage of the battery cell, I indicates the pulse charging current, DCR cell Represents the dynamic internal resistance of the battery cell, V Limit Indicates the charging cut-off 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 at a characteristic operating point with a standing time of ≥ 2 hours.

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

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

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

[0080] In a feasible embodiment, the battery cell pulse charging current optimization model includes at least the following constraints: the battery cell operating voltage is equal to the product of the battery cell dynamic internal resistance and the pulse charging current and the battery cell static voltage; the battery cell operating voltage at the end of charging is less than or equal to the charging cut-off voltage; the battery cell dynamic internal resistance is equal to the sum of the ohmic internal resistance and the polarization internal resistance; the battery 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; the single pulse battery cell temperature rise 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 dynamic internal resistance DCR of the battery cell cellThe product of the pulse charging current I and the static voltage V cell,static The sum of V cell =V cell,static +I·DCR cell During the charging process, it is usually necessary to ensure that the working voltage of the battery cell at the end of charging is less than or equal to the charging cut-off voltage. This can ensure that the working voltage of the battery cell at other times during the charging process must be less than or equal to the charging cut-off voltage. In other words, the working voltage of the battery cell during the charging process is less than or equal to the charging cut-off voltage. Therefore, it can be expressed as: V cell ≤V Limit Since temperature and SOC value can affect the charging cut-off voltage, it can be seen that the charging cut-off voltage at different characteristic temperatures and different characteristic states of charge is different, and needs to be determined according to the characteristic temperature and characteristic state of charge of the current characteristic working condition. In addition, the pulse charging current needs to be greater than 0.

[0082] In addition, it should be noted that after the battery is connected to the load, the dynamic internal resistance DCR of the battery cell cell Including ohmic internal resistance R Ω and polarization internal resistance R f , which can be expressed as: DCR cell =R Ω +R f . Ohm internal resistance R Ω It can be considered unchanged, the polarization internal resistance R f It changes dynamically. The pulse charging process can affect the polarization internal resistance R f The factors that affect the dynamic internal resistance are: temperature, state of charge, pulse charging current, charging time (pulse time), etc. It can be seen that the dynamic internal resistance in this embodiment can be expressed as R f =f(T,SOC,I,t), where f(T,SOC,I,t) is the internal resistance function relationship, and the specific function form can be fitted according to the test data. Exemplarily, the dynamic current test method is used to perform a pulse charging test on the battery cell. The specific test process is as follows: at different temperatures and different states of charge, different pulse charging currents are used to perform pulse charging tests, and 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, and the internal resistance function relationship is fitted using the test data. Using the internal resistance function relationship, the dynamic internal resistance of the battery cell under different characteristic pulse times, different characteristic temperatures, and different characteristic states of charge, that is, the dynamic internal resistance of the battery cell under different characteristic working conditions, can be calculated.

[0083] It is understandable that according to the working principle of the battery, during the pulse charging process, the negative electrode potential must be higher than the lithium plating potential to ensure that the battery cell does not plating lithium. Therefore, it is necessary to determine the lithium plating potential to determine the negative electrode safety potential threshold line. During the charging process of the lithium battery, lithium ions will be deintercalated from the positive electrode and embedded in the negative electrode. When some abnormal conditions occur, it is easy to cause the lithium deintercalated from the positive electrode to be unable to be embedded in the negative electrode. At this time, the lithium ions will precipitate on the surface of the negative electrode, thereby forming a layer of gray material. This phenomenon is called lithium plating. As the charging rate increases, the charging current increases, the polarization effect increases, the lithium ion embedding reaction kinetics and the diffusion rate in the solid phase of the negative electrode material decrease, and the negative electrode potential gradually decreases. At the same time, polarization will also cause the lithium plating potential to shift negatively. Therefore, it is considered that the standard lithium plating potential is ≤0V. When the negative electrode potential is lower than the lithium plating potential, lithium plating will occur, especially under low temperature conditions, it is easy to form lithium dendrites, pierce the diaphragm and cause a short circuit, and there is a risk of battery life attenuation and safety risks. In order to ensure the safety of the battery throughout its life cycle and to ensure that the negative electrode potential has sufficient redundancy, a negative electrode potential higher than the standard lithium deposition potential is set as a safety potential, that is, the negative electrode potential V cath Need to be greater than or equal to the negative electrode potential safety threshold V Saf , which can be expressed as: V cath ≥V Saf The negative electrode potential safety threshold may be 10 mV, which may be set according to actual needs and is not specifically limited thereto.

[0084] It should be understood that in order to ensure safety, the temperature rise ΔT of the single pulse cell needs to 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 is the temperature rise of the cell under a single pulse. The preset temperature rise safety threshold is the set safety value of the temperature rise, for example: 5°C, which is set according to the actual situation and is not specifically limited.

[0085] It should be noted that according to the law of conservation of energy, under adiabatic conditions, I 2 ·R·t=c p ·m·ΔT, the calculation relationship of the temperature rise of a single pulse battery cell can be obtained, that is, the corresponding relationship between the specific heat capacity of the battery cell, the mass of the battery cell, the pulse time, the dynamic internal resistance of the battery cell, the pulse charging current and the temperature rise of the single pulse battery cell, as shown below:

[0086]

[0087] In the formula, ΔT represents the temperature rise of the battery cell in a single pulse, c p represents the specific heat capacity of the battery cell, m represents the mass of the battery cell, t represents the pulse time, DCT cell It 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 m , characteristic temperature T k and characteristic state of charge SOC i ) under the characteristic working condition, the dynamic internal resistance of the battery cell, the pulse charging current, the specific heat capacity of the battery cell, the battery cell mass and the pulse time are substituted into the above formula to obtain the single pulse battery cell temperature rise under the characteristic working condition, thereby calculating the single pulse battery cell temperature rise corresponding to the pulse charging current under different characteristic working conditions.

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

[0090]

[0091] Where V cell Indicates the working voltage of the battery cell during pulse charging, V cell,static Indicates the static voltage of the battery cell, I indicates the pulse charging current, DCR cell Represents the dynamic internal resistance of the battery cell, V Limit Indicates the charging cut-off voltage of the battery cell, R Ω Represents the ohmic internal resistance, R f represents the polarization internal resistance, f(T,SOC,I,t) represents the internal resistance function relationship, V cath Represents the negative electrode potential, V Saf Indicates the negative electrode potential safety threshold, T saf Indicates the preset temperature rise safety threshold, c 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, initializing the position of the gray wolf in the population and constructing a fitness function, where the position is used to characterize the pulse charging current;

[0093] It should be noted that this embodiment solves the battery cell pulse charging current optimization model based on the improved Grey Wolf optimization algorithm.

[0094] It is understandable that when solving the problem, it is necessary to set relevant parameters and initialize the population. The parameters that need to be set usually include: population size, maximum number of iterations, etc., which are set according to actual conditions and are not specifically limited. The population is usually composed of multiple individuals, each of which is a gray wolf. The position of each gray wolf can characterize the corresponding pulse charging current, and randomly generate a set of potential solutions as the initial value of the position of the gray wolf in the population. The Pendulum chaotic mapping mechanism can be used for initialization. The Pendulum mapping is a chaotic system based on a physical pendulum that can generate a population with high randomness, which helps the optimization algorithm to jump out of the local optimal solution, enhance the global search capability, and improve the population diversity. It can be expressed as:

[0095] X i=x n +αsin(βx n )

[0096] In the formula, α and β are parameters for controlling chaotic behavior, which are set according to actual conditions. n is the benchmark value of the decision variable in the initial stage, which is set according to the actual situation. i Indicates the position of gray wolves in the population.

[0097] It should be understood that the fitness function is usually constructed according to the actual situation. The value of the pulse charging current can be used as the fitness function, and other penalty terms or constraints can be added on this basis. There is no limitation on this. For example, the fitness function can be designed as:

[0098]

[0099] Where Fitness represents the fitness function, I represents the pulse charging current, α and β represent the scaling factors, Linearity (I) is a metric between 0 and 1, V represents the working voltage of the battery cell at the end of charging, and V Limit Indicates the charging cut-off voltage.

[0100] Step S30, based on the fitness function and the positions of the gray wolves in the population, the gray wolves in the population are divided into the alpha wolf, the suboptimal wolf, the third-optimal wolf and the remaining wolves, and the positions of the remaining wolves are updated based on the adaptive scaling factor, and the alpha wolf, the suboptimal wolf, the third-optimal wolf and the remaining wolves in the population are re-divided, and the iteration is continuously performed until the iteration termination condition is met to obtain the target alpha wolf;

[0101] It should be noted that the present embodiment divides the individuals in the population into four levels, namely, the alpha wolf, the second-best wolf, the third-best wolf and the rest of the wolves, wherein the levels of the alpha wolf, the second-best wolf, the third-best wolf and the rest of the wolves decrease in sequence, with the alpha wolf having the highest level and the rest of the wolves having the lowest level. The present embodiment adopts an adaptive differential evolution mutation strategy to update the alpha wolf, the second-best wolf, the third-best wolf and the rest of the wolves in the population until the iteration termination condition is met. The alpha wolf at this time is the alpha wolf that is ultimately required, that is, the target alpha wolf, and 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 battery cell pulse charging current optimization model. The iteration termination condition may be that the maximum number of iterations is reached or the fitness is no longer significantly improved, and is not specifically limited.

[0102] In a feasible implementation, the step of dividing the gray wolves in a population into an alpha wolf, a second-best wolf, a third-best wolf and the remaining wolves based on the fitness function and the position of the gray wolves in the population includes: calculating 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; sorting the gray wolves in the population in descending order based on the fitness value corresponding to the position of the gray wolf in the population to obtain an individual sequence of the population; taking the gray wolf corresponding to the maximum fitness value in the individual sequence as the alpha wolf, selecting the second-best wolf in the individual sequence other than the alpha wolf based on a preset second-best ratio, and selecting the third-best wolf in the individual sequence other than the alpha wolf and the second-best wolf based on a preset third-best ratio; determining the remaining wolves in the individual sequence based on the selected alpha wolf, the second-best wolf and the third-best wolf.

[0103] It should be noted that, using the fitness function, the fitness value corresponding to the position of each gray wolf can be calculated, so that the gray wolves in the population can be sorted according to the fitness value. This embodiment adopts descending sorting, and sorts the gray wolf individuals from high to low according to the fitness value, and the individual sequence is the sequence after the gray wolf is sorted.

[0104] It can be understood that the maximum fitness value is the maximum value of the 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 ratio of selecting suboptimal wolves, and the preset third-optimal ratio is the set ratio of selecting third-optimal wolves. Generally speaking, when selecting, the gray wolf corresponding to the maximum fitness value in the individual sequence is usually selected as the alpha wolf first, and then the gray wolf with a higher ranking in the individual sequence other than the alpha wolf is selected as the second-optimal wolf according to the preset second-optimal ratio, and then the gray wolf with a higher ranking in the individual sequence other than the alpha wolf and the second-optimal wolf is selected as the third-optimal wolf according to the preset third-optimal ratio, and the remaining gray wolves are the remaining wolves.

[0105] It should be understood that after the alpha wolf, the second-best wolf, the third-best wolf and the rest of the wolves are divided, the positions of the rest of the wolves need to be updated, so as to update the population and obtain new alpha wolves, second-best wolves, third-best wolves and the rest of the wolves. When updating the positions of the rest of the wolves, it is necessary to use the position of the alpha wolf, the position of a gray wolf randomly selected from the second-best wolves, the position of a gray wolf randomly selected from the third-best wolves and the adaptive scaling factor.

[0106] In a feasible implementation, the step of determining the adaptive scaling factor includes: in the first iteration, the adaptive scaling factor is a preset scaling factor; in a non-first iteration, based on the fitness value corresponding to the position of the gray wolf 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, and the initial adaptive scaling factor is the adaptive scaling factor used in the previous round of iteration.

[0107] It should be noted that the first iteration refers to the first round of iteration, which is the first time to update the positions of the remaining wolves, and the non-first iteration refers to the second round and subsequent iterations. In this embodiment, different methods are used to determine the corresponding adaptive scaling factor in the first iteration and the non-first iteration.

[0108] It is understandable that the preset scaling factor is a default scaling factor, that is, a pre-set scaling factor, which can be set according to actual needs and is not specifically limited. In the first iteration, the preset scaling factor is directly used as the adaptive scaling factor.

[0109] It should be understood that the minimum fitness value is the minimum value of the fitness value. The minimum fitness value and the maximum fitness value are found among all the calculated fitness values ​​in the current round, so that the fitness normalized variance can be calculated using the minimum fitness value and the maximum fitness value. The calculation relationship is as follows:

[0110]

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

[0112] In a feasible implementation, based on the fitness normalized variance and the initial adaptive scaling factor, the step of determining the 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 corresponding relationship between the fitness normalized variance, the initial adaptive scaling factor and the adaptive scaling factor, that is, the calculation relationship of the adaptive scaling factor, is as follows:

[0114] F t+1 =F t *e -λΔG

[0115] In the formula, F t+1 represents the adaptive scaling factor of the current round, ΔG represents the normalized variance of fitness, and F t Represents the initial adaptive scaling factor. Substitute the relevant data into it, calculate the adaptive scaling factor of the current round, and then use the adaptive scaling factor to update the positions of the remaining wolves.

[0116] In a feasible implementation, the step of updating the positions of the remaining wolves based on the adaptive scaling factor includes: taking the position of the head 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 corresponding relationship between the first position, the second position, the third position, the adaptive scaling factor and the fourth position; determining the fourth position based on the first position, the second position, the third position, the adaptive scaling factor and the second corresponding relationship to obtain the updated positions of the remaining wolves.

[0117] It should be noted that the position of the alpha wolf is taken as the first position X1, the position of a randomly selected second-best wolf is taken as the second position X2, the position of a randomly selected third-best wolf is taken as the third position X3, and the positions of the remaining wolves after update are taken as the fourth position X t+1 The second corresponding relationship between the first position, the second position, the third position, the adaptive scaling factor and the fourth position, i.e., the calculation relationship of the updated positions of the remaining wolves, is as follows:

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

[0119] Where, X t+1 represents the fourth position, X1 represents the first position, X2 represents the second position, X3 represents the third position X3, F t+1 Represents the adaptive scaling factor.

[0120] It is understandable that each time the position of one of the remaining wolves is updated, the positions of all the remaining wolves need to be updated in sequence. The second position X2 and the third position X3 selected in each calculation process are random, thereby ensuring the diversity and randomness of the generated remaining wolf population and avoiding the algorithm from falling into a local optimal solution.

[0121] Step S40, using the pulse charging current represented by the position of the target wolf leader as the solution of the corresponding battery cell pulse charging current optimization model to obtain the maximum pulse charging current of the battery cell under different characteristic working conditions;

[0122] It should be noted that the pulse charging current represented by the position of the target wolf leader is the maximum pulse charging current of the battery cell, which is also the solution of the corresponding battery cell pulse charging current optimization model. Therefore, the maximum pulse charging current under different characteristic pulse times, different characteristic temperatures, and different characteristic charge states can be determined in turn, and the maximum pulse charging current of the battery cell under different characteristic working 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 the different characteristic charge states is:

[0124]

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

[0126] Then, 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 charge states is:

[0127]

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

[0129] It can be understood that since the characteristic pulse time can be a long pulse, a standard pulse and a short pulse, the maximum pulse charging current of the battery cell corresponding to different characteristic temperatures and different characteristic states of charge under the long pulse, the maximum pulse charging current of the battery cell corresponding to different characteristic temperatures and different characteristic states of charge under the standard pulse, and the maximum pulse charging current of the battery cell corresponding to different characteristic temperatures and different characteristic states of charge under the short pulse can be obtained, and then the maximum pulse charging current of the battery cell under different characteristic working conditions can be obtained, which is used to characterize the pulse charging capability of the battery cell.

[0130] Step S50, determining the system maximum pulse charging current of the power battery system under different characteristic working conditions based on the system limit charging current, the system peak current and the maximum pulse charging current of the battery cell;

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

[0132] Step S501, obtaining a system limit voltage, a system peak current, and a system internal resistance of a power battery system;

[0133] It should be noted that the system limit voltage V sys,LimitRefers to the maximum voltage that ensures the normal operation of the drive motor, generator, air conditioner, control system and other mechanisms in the vehicle; the system peak current I sys,peak Refers to the maximum allowable current to ensure the normal operation of the electronic and electrical systems in the power battery system, the vehicle electrical system and other related components; the system internal resistance R sys It refers to the DC resistance in the entire circuit of the battery pack from the high voltage total positive to the high voltage total negative, which is determined by factors including bus design, current capacity, selection of electrical components, etc., and there is no limitation on this.

[0134] It can be understood 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 in series, and the number of modules in parallel. The calculation relationship is as follows:

[0135]

[0136] In the formula, R sys Represents the system internal resistance, R sys，ele Indicates the internal resistance of the electrical system, DCR cell Indicates the dynamic internal resistance of the battery cell, T f represents the temperature correction factor, S f 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 according to the configuration of the power battery system. The power battery system configuration is generally expressed as αPβS, where P represents parallel connection and 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, determining the system open circuit voltage under different characteristic operating conditions based on the number of battery cells connected in series and the open circuit voltage of the battery cells under different characteristic operating conditions;

[0139] It can be understood that the system open circuit voltage is related to the number of cells in series and the open circuit voltage of the cells. The calculation relationship is as follows:

[0140] V sys,OCV =α·V cell,OcV

[0141] Where V sys,OCV represents the system open circuit voltage, α represents the number of cells in series, V cell,OCV Indicates the open circuit voltage of the battery cell. The system open circuit voltage is calculated using the number of battery cells connected in series and the open circuit voltage of the battery cells under different characteristic working conditions.

[0142] Step S503, determining the system limit charging current under different characteristic working conditions based on the system open circuit voltage, the system limit voltage and the system internal resistance of the power battery system;

[0143] It should be noted that the system limit charging current is related to the system open circuit voltage, system limit voltage and system internal resistance. The calculation relationship is as follows:

[0144]

[0145] In the formula, I sys,Limit Indicates the system limit charging current, V sys,OCV Indicates the system open circuit voltage, V sys,Limit Indicates the system limit voltage, R sys Represents the system internal resistance. The system open circuit voltage, system limit voltage and system internal resistance are used to calculate the system limit charging current under different characteristic working conditions.

[0146] Step S504, determining the maximum pulse charging current of the battery cell group under different characteristic working conditions based on the number of modules connected in parallel and the maximum pulse charging current of the battery cell under different characteristic working conditions;

[0147] It can be understood that the maximum pulse charging current of the battery pack is related to the number of modules in parallel and the maximum pulse charging current of the battery cell. The calculation relationship is as follows:

[0148] I cells,max =β·I cell,max

[0149] In the formula, I cells,max Indicates the maximum pulse charging current of the battery pack, β indicates the number of modules in parallel, I cell,max Indicates the maximum pulse charging current of the battery cell. The maximum pulse charging current of the battery cell group under different characteristic working conditions is calculated using the number of modules in parallel and the maximum pulse charging current of the battery cell.

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

[0151] Step S505 , following the safety priority principle, the minimum value among the system limit charging current, the system peak current and the maximum pulse charging current of the battery cell group is used as the system maximum pulse charging current of the power battery system under the corresponding characteristic working condition.

[0152] It should be noted that, in accordance with the principle of safety first, the smallest current value among the maximum pulse charging current of the battery pack, the system limit charging current and the system peak current is selected as the system maximum pulse charging current of the power battery system. The calculation relationship is as follows:

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

[0154] In the formula, I sys,pulse Indicates the maximum pulse charging current of the system, I cells,max Indicates the maximum pulse charging current of the battery pack, I sys,Limit Indicates the system limit charging current, I sys,peak Indicates the system peak current.

[0155] Step S60, based on the system maximum pulse charging current of the power battery system under different characteristic working conditions, the pulse charging power of the power battery system under different characteristic working conditions is determined, and the pulse charging power MAP is formulated and verified.

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

[0157] It is understandable that the pulse charging power MAP is usually in the form of a two-dimensional table, recording the pulse charging power at different pulse times, different temperatures, and different states of charge. The MAP table transmits data to the power battery management system BMS, and the BMS retrieves the system maximum pulse charging current at the corresponding operating point based on the collected real-time temperature and real-time state of charge.

[0158] It should be understood that the established charging power MAP usually needs to be verified. Verification usually uses the whole vehicle performance test calibration method to check and adjust the charging power MAP table to meet the performance requirements of the whole vehicle.

[0159] The present embodiment provides a method for evaluating the pulse charging performance of a power battery system. The relationship between the working voltage of a battery cell and the pulse charging current is used to construct a battery cell pulse charging current optimization model. By solving the model, the maximum pulse charging current of the battery cell at different pulse times, different temperatures, and different states of charge is found. The pulse charging capability of the battery cell is quickly and accurately evaluated. Based on the pulse charging capability of the battery cell and in accordance with the principle of safety first, the maximum pulse charging current of the power battery system is determined, and the pulse charging power of the power battery system is calculated. The pulse charging capability of the power battery system is quickly and accurately evaluated, and a pulse charging power MAP is formulated to ensure the safety of the battery cell throughout its life cycle and to achieve safe and efficient operation of the power battery.

[0160] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , step S60 may include steps S601 to S602:

[0161] Step S601, obtaining a third corresponding relationship between a system maximum pulse charging current, a system open circuit voltage, a system internal resistance, an internal resistance correction coefficient, and a pulse charging power, and determining the pulse charging power of the power battery system under different characteristic operating conditions based on the third corresponding relationship, the system maximum pulse charging current, the system open circuit voltage, the system internal resistance, and the internal resistance correction coefficient of the power battery system under different characteristic operating conditions;

[0162] It should be noted that the third corresponding relationship between the system maximum pulse charging current, the system open circuit voltage, the system internal resistance, the internal resistance correction coefficient and the pulse charging power is the calculation relationship of the pulse charging power of the power battery system, as shown below:

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

[0164] Where P sys Indicates the pulse charging power, I sys,pulse Indicates the maximum pulse charging current of the system, V sys,OCV Represents the system open circuit voltage, R sys Represents the system internal resistance, and ε represents the internal resistance correction coefficient. Substitute the relevant data under different characteristic working conditions to calculate the pulse charging power of the power battery system under different characteristic working conditions.

[0165] Step S602, setting 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 working 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 the performance of the battery, avoid overcharging at high temperatures, and ensure the safety of the entire battery system, this embodiment needs to follow the high temperature protection strategy. The boundary temperature is the temperature set to distinguish between the normal temperature zone and the high temperature zone. Generally speaking, the temperature in the high temperature zone is higher. 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 the characteristic working condition is within the high temperature range, it is considered necessary to implement the high temperature protection strategy. At this time, the high temperature safety factor is used to adjust the corresponding pulse charging power. The calculation relationship is as follows:

[0168] P sys，hot =P sys ·η

[0169] Where P sys，hot Represents the adjusted pulse charging power, P sys It represents the pulse charging power in the high temperature area, η represents the high temperature safety factor, and the specific value is determined according to the actual situation.

[0170] The present embodiment provides a method for evaluating the pulse charging performance of a power battery system. The relationship between the working voltage of a battery cell and the pulse charging current is used to construct a battery cell pulse charging current optimization model. By solving the model, the maximum pulse charging current of the battery cell at different pulse times, different temperatures, and different states of charge is found. The pulse charging capability of the battery cell is quickly and accurately evaluated. Based on the pulse charging capability of the battery cell and in accordance with the principle of safety first, the maximum pulse charging current of the power battery system is determined, and the pulse charging power of the power battery system is calculated. The pulse charging capability of the power battery system is quickly and accurately evaluated, and a pulse charging power MAP is formulated to ensure the safety of the battery cell throughout its life cycle and to achieve safe and efficient operation of the power battery.

[0171] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the evaluation method of the pulse charging performance of the power battery system of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

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

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

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

[0175] The data solving module 10 is further used to divide the gray wolves in the population into the alpha wolf, the suboptimal wolf, the third-optimal wolf and the remaining wolves based on the fitness function and the positions of the gray wolves in the population, and to update the positions of the remaining wolves based on the adaptive scaling factor, and to re-divide the alpha wolf, the suboptimal wolf, the third-optimal wolf and the remaining wolves in the population, and to continuously iterate until the iteration termination condition is met to obtain the target alpha wolf;

[0176] The data solution module 10 is also used to use the pulse charging current represented by the position of the target wolf leader as the solution of the corresponding battery cell pulse charging current optimization model to obtain the maximum pulse charging current of the battery cell under different characteristic working conditions;

[0177] The performance evaluation module 20 is used to determine the system maximum pulse charging current of the power battery system under different characteristic working 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 working conditions based on the system maximum pulse charging current of the power battery system under different characteristic working conditions, formulate and verify the pulse charging power MAP.

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

[0180] The working voltage of the battery cell is equal to the sum of the product of the dynamic internal resistance of the battery cell and the pulse charging current and the static voltage of the battery cell;

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

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

[0183] The dynamic internal resistance of the battery cell conforms to the fitted internal resistance function relationship;

[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 a feasible implementation, the data solving module 10 is further 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 corresponding to the maximum fitness value in the individual sequence is selected 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, and 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;

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

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

[0192] In non-first iteration, the maximum fitness value and the minimum fitness value are determined based on the fitness value corresponding to the position of the gray wolf in the population, the fitness normalized variance is determined based on the maximum fitness value and the minimum fitness value, and the adaptive scaling factor is determined based on the fitness normalized variance and the initial adaptive scaling factor. The initial adaptive scaling factor is the adaptive scaling factor used in the previous iteration.

[0193] In a feasible implementation manner, the data solving module 10 is further used to obtain a first corresponding relationship between the normalized variance of the fitness, the initial adaptive scaling factor and the adaptive scaling factor;

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

[0195] In a feasible implementation, the data solving module 10 is further used to use 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] Acquire a second corresponding relationship between the first position, the second position, the third position, the adaptive scaling factor and the fourth position;

[0197] Based on the first position, the second position, the third position, the adaptive scaling factor and the second corresponding relationship, a fourth position is determined to obtain updated positions of the remaining wolves.

[0198] In a feasible implementation manner, the performance evaluation module 20 is further 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 in series and the open circuit voltage of the cells under different characteristic working conditions, determine the system open circuit voltage under different characteristic working conditions;

[0200] 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 working conditions;

[0201] Based on the number of modules connected in parallel and the maximum pulse charging current of the battery cells under different characteristic working conditions, determine the maximum pulse charging current of the battery cell group under different characteristic working conditions;

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

[0203] In a feasible implementation manner, the performance evaluation module 20 is further used to obtain a third corresponding relationship between the system maximum pulse charging current, the system open circuit voltage, the system internal resistance, the internal resistance correction coefficient and the pulse charging power;

[0204] Determine the pulse charging power of the power battery system under different characteristic working conditions based on the third corresponding relationship, the system maximum pulse charging current, the system open circuit voltage, the system internal resistance, and the internal resistance correction coefficient of the power battery system under different characteristic working conditions;

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

[0206] Following the high temperature protection strategy, when the characteristic temperature in the 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 the present application adopts the method for evaluating the pulse charging performance of a power battery system in the above-mentioned embodiment, which can solve the technical problem that the traditional method for evaluating the pulse charging performance of a power battery system is relatively complex, costly, and difficult to achieve 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 the present application are the same as the beneficial effects of the method for evaluating the pulse charging performance of a power battery system provided in the above-mentioned embodiment, and the other technical features of the device for evaluating the pulse charging performance of a power battery system are the same as the features disclosed in the above-mentioned embodiment method, which will not be described in detail here.

[0208] The present application provides a device for evaluating the pulse charging performance of a power battery system, and the device for evaluating 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 so that the at least one processor can execute the method for evaluating the pulse charging performance of the power battery system in the above-mentioned embodiment one.

[0209] Reference below Figure 4, which shows a schematic diagram of the structure of a device for evaluating the pulse charging performance of a power battery system suitable for implementing the embodiment of the present application. The device for evaluating the pulse charging performance of a power battery system in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The evaluation device for the pulse charging performance of the power battery system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0210] like Figure 4 As shown, the evaluation device for the pulse charging performance of the power battery system may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the evaluation device for the pulse charging performance of the power battery system are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the evaluation device for the pulse charging performance of a power battery system to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an evaluation device for the pulse charging performance of a power battery system with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0211] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0212] The power battery system pulse charging performance evaluation device provided in the present application adopts the power battery system pulse charging performance evaluation method in the above embodiment, which can solve the technical problems that the traditional method of evaluating the power battery system pulse charging performance is relatively complex, costly, and difficult to achieve quickly and accurately. Compared with the prior art, the beneficial effects of the power battery system pulse charging performance evaluation device provided in the present application are the same as the beneficial effects of the power battery system pulse charging performance evaluation method provided in the above embodiment, and the other technical features of the power battery system pulse charging performance evaluation device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0213] It should be understood that the various parts disclosed in this application can be implemented by 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 one or more embodiments or examples in a suitable manner.

[0214] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0215] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for evaluating the pulse charging performance of a power battery system in the above-mentioned embodiment.

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

[0217] The computer-readable storage medium may be included in the power battery system pulse charging performance evaluation device; or may exist independently without being assembled into the power battery system pulse charging performance evaluation device.

[0218] The computer-readable storage medium carries one or more programs. When the one or more 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 battery cell pulse charging current optimization model based on the relationship between the battery cell operating voltage and the pulse charging current under different characteristic working conditions, wherein the characteristic working condition is composed of a characteristic pulse time, a characteristic temperature, and a characteristic state of charge; initializes the position of the gray wolves in the population, and constructs a fitness function, wherein the position is used to characterize the pulse charging current; based on the fitness function and the position of the gray wolves in the population, divides the gray wolves in the population into the alpha wolf, the suboptimal wolf, the third-optimal wolf, and the remaining wolves, and based on the adaptive scaling factor Update the positions of the remaining wolves, re-divide the population into the alpha wolf, the suboptimal wolf, the third-optimal wolf and the remaining wolves, and continue to iterate until the iteration termination condition is met to obtain the target alpha wolf; use the pulse charging current represented by the position of the target alpha wolf as the solution of the corresponding battery cell pulse charging current optimization model to obtain the maximum pulse charging current of the battery cell under different characteristic working conditions; determine the system maximum pulse charging current of the power battery system under different characteristic working conditions based on the system limit charging current, the system peak current and the maximum pulse charging current of the battery cell; determine the pulse charging power of the power battery system under different characteristic working conditions based on the system maximum pulse charging current of the power battery system under different characteristic working conditions, formulate and verify the pulse charging power MAP.

[0219] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0220] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0221] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0222] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for evaluating the pulse charging performance of the power battery system, and can solve the technical problems that the traditional method for evaluating the pulse charging performance of the power battery system is relatively 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 the beneficial effects of the method for evaluating the pulse charging performance of the power battery system provided in the above-mentioned embodiment, and will not be repeated here.

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

[0224] The computer program product provided by the present application can solve the technical problem that the traditional method of evaluating the pulse charging performance of the power battery system is relatively complex, costly, and difficult to achieve quickly and accurately. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the method for evaluating the pulse charging performance of the power battery system provided by the above embodiment, and will not be repeated here.

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

Claims

1. A method for evaluating pulse charging performance of a power battery system, characterized in that: The method comprises: Based on the relationship between the battery cell operating voltage and the pulse charging current under different characteristic working conditions, a corresponding battery cell pulse charging current optimization model is constructed, wherein the characteristic working condition is composed of characteristic pulse time, characteristic temperature and characteristic state of charge; Initializing the position of the gray wolf in the population and constructing a fitness function, wherein the position is 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 an alpha wolf, a second-best wolf, a third-best wolf, and the remaining wolves, and the positions of the remaining wolves are updated based on an adaptive scaling factor, and the alpha wolf, the second-best wolf, the third-best wolf, and the remaining wolves in the population are re-divided, and iterations are continuously performed until an iteration termination condition is met to obtain a target alpha wolf; The pulse charging current represented by the position of the target wolf leader is used as the solution of the corresponding battery cell pulse charging current optimization model to obtain the maximum pulse charging current of the battery cell under different characteristic working conditions; Based on the system limit charging current, system peak current and cell maximum pulse charging current, determine the system maximum pulse charging current of the power battery system under different characteristic working conditions; Based on the system maximum pulse charging current of the power battery system under different characteristic working conditions, the pulse charging power of the power battery system under different characteristic working conditions is determined, and the pulse charging power MAP is formulated and verified.

2. The method according to claim 1, characterized in that The cell pulse charging current optimization model includes at least the following constraints: The working voltage of the battery cell is equal to the sum of the product of the dynamic internal resistance of the battery cell and the pulse charging current and the static voltage of the battery cell; The working voltage of the battery cell at the end of charging is less than or equal to the charging cut-off voltage; The dynamic internal resistance of the battery cell is equal to the sum of the ohmic internal resistance and the polarization internal resistance; The dynamic internal resistance of the battery cell 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; 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 according to claim 1, characterized in that The step of dividing the gray wolves in the population into alpha wolves, suboptimal wolves, third-optimal wolves and remaining wolves based on the fitness function and the positions of the gray wolves in the population comprises: Based on the fitness function and the position of the gray wolves in the population, calculating the fitness value corresponding to the position of the gray wolves 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 an individual sequence of the population; The gray wolf corresponding to the maximum fitness value in the individual sequence is selected as the alpha wolf, the suboptimal wolves are selected from the individual sequence other than the alpha wolf based on a preset suboptimal ratio, and the third-optimal wolf is selected from the individual sequence other than the alpha wolf and the second-optimal wolf based on a preset third-optimal ratio; Based on the selected alpha wolf, second-best wolf and third-best wolf, the remaining wolves are determined in the individual sequence.

4. The method according to claim 3, characterized in that The method further comprises: In the first iteration, the adaptive scaling factor is a preset scaling factor; In non-first iteration, the maximum fitness value and the minimum fitness value are determined based on the fitness value corresponding to the position of the gray wolf in the population, the fitness normalized variance is determined based on the maximum fitness value and the minimum fitness value, and the adaptive scaling factor is determined based on the fitness normalized variance and the initial adaptive scaling factor, and the initial adaptive scaling factor is the adaptive scaling factor used in the previous round of iteration.

5. The method according to 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 comprises: Obtaining a first correspondence between a fitness normalized variance, an initial adaptive scaling factor, and an adaptive scaling factor; The adaptive scaling factor is determined based on the fitness normalized variance, the initial adaptive scaling factor, and the first corresponding relationship.

6. The method according to claim 1, characterized in that The step of updating the positions of the remaining wolves based on the adaptive scaling factor comprises: 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 updated positions of the remaining wolves are taken as the fourth position; Acquire a second corresponding relationship 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 corresponding relationship, the fourth position is determined to obtain updated positions of the remaining wolves.

7. The method according to claim 1, characterized in that The step of determining the system maximum pulse charging current of the power battery system under different characteristic working conditions based on the system limit charging current, the system peak current and the maximum pulse charging current of the battery cell comprises: Obtain the system limit voltage, system peak current and system internal resistance of the power battery system; Based on the number of cells in series and the open circuit voltage of the cells under different characteristic working conditions, determine the system open circuit voltage under different characteristic working conditions; 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 working conditions; Based on the number of modules connected in parallel and the maximum pulse charging current of the battery cells under different characteristic working conditions, determine the maximum pulse charging current of the battery cell group under different characteristic working conditions; In accordance with the principle of safety first, the minimum value among the system limit charging current, the system peak current and the maximum pulse charging current of the battery pack is taken as the system maximum pulse charging current of the power battery system under the corresponding characteristic working 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 working conditions based on the system maximum pulse charging current of the power battery system under different characteristic working conditions comprises: Obtaining a third corresponding relationship between the system maximum pulse charging current, the system open circuit voltage, the system internal resistance, the internal resistance correction coefficient and the pulse charging power; Determine the pulse charging power of the power battery system under different characteristic conditions based on the third corresponding relationship, the system maximum pulse charging current, the system open circuit voltage, the system internal resistance, and the internal resistance correction coefficient of the power battery system under different characteristic conditions; Setting a boundary temperature, and dividing the operating temperature range of the battery cell into a normal temperature zone and a high temperature zone based on the boundary temperature; Following the high temperature protection strategy, when the characteristic temperature in the 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 pulse charging performance of a power battery system, characterized in that: The device comprises: A data solution module is used to construct a corresponding cell pulse charging current optimization model based on the relationship between the cell operating voltage and the pulse charging current under different characteristic working conditions, wherein the characteristic working conditions are composed of characteristic pulse time, characteristic temperature and characteristic state of charge; The data solving module is also used to initialize the position of the gray wolf in the population and construct a fitness function, wherein the position is used to characterize the pulse charging current; The data solving module is further used to divide the gray wolves in the population into alpha wolves, suboptimal wolves, third-optimal wolves and other wolves based on the fitness function and the positions of the gray wolves in the population, and to update the positions of the other wolves based on an adaptive scaling factor, and to re-divide the alpha wolves, suboptimal wolves, third-optimal wolves and other wolves in the population, and to continuously iterate until an iteration termination condition is met to obtain a target alpha wolf; The data solving module is further used to use the pulse charging current represented by the position of the target wolf leader as the solution of the corresponding battery cell pulse charging current optimization model to obtain the maximum pulse charging current of the battery cell under different characteristic working conditions; A performance evaluation module is used to determine the maximum pulse charging current of the power battery system under different characteristic working conditions based on the system limit charging current, the system peak current and the maximum pulse charging current of the battery cell; The performance evaluation module is also used to determine the pulse charging power of the power battery system under different characteristic working conditions based on the system maximum pulse charging current of the power battery system under different characteristic working conditions, formulate and verify the pulse charging power MAP.

10. A device for evaluating pulse charging performance of a power battery system, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for evaluating the pulse charging performance of a power battery system as claimed in any one of claims 1 to 8.

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