Current constraint table optimization method and electronic equipment

Abstract

The invention discloses a current constraint table optimization method and electronic equipment. The electronic equipment can obtain a charging current constraint table corresponding to a battery; simulating the charging process of the battery based on a preset charging simulation model and the charging current constraint table to obtain fitness; wherein the charging process represents the process of charging the battery to reach the target charge state; based on a preset optimization algorithm and the fitness, optimizing the charging current constraint table to obtain an optimized current constraint table; simulating the charging process of the battery by using a preset charging simulation model and the optimized current constraint table to obtain updated fitness; and the optimized current constraint table is optimized based on a preset optimization algorithm and the updated fitness until a termination condition is reached, a target charging current constraint table is obtained, the battery is charged based on the target charging current constraint table, and the charging rate can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a current constraint table optimization method and electronic equipment. Background Art

[0002] New energy batteries are being used more and more widely in life and industry. For example, new energy vehicles equipped with batteries have been widely used. In addition, batteries are also being increasingly used in areas such as energy storage.

[0003] At present, in the process of fast charging the batteries of new energy vehicles, they are usually charged with the maximum current in the original charging lithium table and the thermal balance table, which will cause the battery temperature to rise too quickly, causing the subsequent charging current to drop rapidly; at the same time, due to the gradual increase in the battery state of charge (SOC), the current value will also drop rapidly, and a good fast charging rate and effect cannot be achieved; therefore, how to optimize the charging current constraint table to obtain a faster charging rate is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The embodiments of the present application provide a current constraint table optimization method and an electronic device, which can obtain a more optimal charging current constraint table, thereby effectively improving the charging rate.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a current constraint table optimization method, the method comprising:

[0007] Get the charging current constraint table corresponding to the battery;

[0008] The charging process of the battery is simulated based on a preset charging simulation model and a charging current constraint table to obtain fitness; wherein the charging process represents the process of charging the battery to reach a target state of charge;

[0009] Based on the preset optimization algorithm and fitness, the charging current constraint table is optimized to obtain an optimized current constraint table;

[0010] The battery charging process is simulated using a preset charging simulation model and an optimized current constraint table to obtain an updated fitness;

[0011] The optimized current constraint table is optimized based on a preset optimization algorithm and an updated fitness until a termination condition is reached, and a target charging current constraint table is obtained to charge the battery based on the target charging current constraint table.

[0012] In this embodiment, by constructing a preset charging simulation model and a preset optimization algorithm, the process of charging the battery from the current state of charge to the target state of charge can be simulated using the preset charging simulation model and the charging current constraint table to obtain a fitness value, which can be used to evaluate the charging duration of the battery based on the charging current constraint table. Then, the charging current constraint table is optimized using the fitness value obtained from the simulation and the preset optimization algorithm. Subsequently, the optimized current constraint table is used to simulate the charging process using the preset charging simulation model to obtain an updated fitness value, which can verify the charging duration and effect based on the optimized current constraint table. Furthermore, the optimized current constraint table can be continuously optimized based on the updated fitness value and the preset optimization algorithm until the termination condition is reached, at which point the optimization stops and the output target charging current constraint table is the optimal charging current constraint table. Using this target charging current constraint table to charge the battery can effectively improve the charging rate and effect of the battery.

[0013] In some embodiments of the present application, the simulation of the charging process of the battery based on the preset charging simulation model and the charging current constraint table includes:

[0014] Based on the preset charging simulation model, the charging current constraint table, and the initial operating condition parameters of the battery at the current moment, determine the initial charging current value; wherein, the initial operating condition parameters include at least the initial temperature and the initial state of charge.

[0015] Based on the preset charging simulation model, the initial operating condition parameters, and the initial charging current value, simulate the first operating condition parameters at the first moment; wherein, the first moment represents the next moment corresponding to the current moment.

[0016] In the case where the first state of charge in the first operating condition parameters reaches the target state of charge, determine that the simulation of the charging process is completed; otherwise, based on the preset charging simulation model and the first operating condition parameters, perform a simulation to obtain the second operating condition parameters at the next moment corresponding to the first moment, until the second state of charge in the second operating condition parameters reaches the target state of charge, and determine that the simulation of the charging process is completed.

[0017] In this embodiment, when simulating the charging process using a preset charging simulation model, after obtaining the initial operating condition parameters of the battery at the current moment, the initial charging current value of the battery at the current moment can be determined by using the preset charging simulation model, the charging current constraint table, and the initial temperature and initial state of charge of the battery at the current moment; then, based on the preset charging simulation model, the initial operating condition parameters, and the initial charging current value, the first operating condition parameters at the next moment, i.e., the first moment, are simulated; if the first state of charge reaches the target state of charge, it is determined that the simulation of the charging process is completed, and if the first state of charge does not reach the target state of charge, the simulation continues to determine the second operating condition parameters at the next moment of the first moment, so as to determine whether the second state of charge in the second operating condition parameters reaches the target state of charge until the target state of charge is reached and the simulation ends; thus, an effective simulation and prediction of the battery charging process can be realized.

[0018] In some embodiments of the present application, the preset charging simulation model includes an ampere-hour integration module, and the method further includes:

[0019] Based on the initial state of charge, the ampere-hour integration module integrates the initial charging current value and the first moment to obtain the first state of charge.

[0020] In this embodiment, the ampere-hour integration module in the preset charging simulation model can be used to estimate the state of charge of the battery at each moment during the battery charging process; for example, based on the initial state of charge, the initial charging current value and the first moment can be integrated to obtain the first state of charge at the first moment, which can effectively predict the state of charge of the battery.

[0021] In some embodiments of the present application, the charging current constraint table is characterized in that it is generated based on a charging lithium deposition table and a thermal balance table, and includes a table of current values under different temperature and battery state of charge constraints;

[0022] Determining the initial charging current value based on the preset charging simulation model, the charging current constraint table, and the initial operating condition parameters of the battery at the current moment includes:

[0023] Determine the target temperature adjacent to the initial temperature in the charging current constraint table based on the preset charging simulation model;

[0024] Determine the target state of charge adjacent to the initial state of charge in the charging current constraint table based on the initial state of charge;

[0025] Determine the initial charging current value based on the target temperature and the target state of charge.

[0026] In this embodiment, a preset charging simulation model can be used to predict the charging current at each moment. For example, when determining the initial charging current value at the current moment according to the current initial working condition parameters of the battery, the preset charging simulation model can be used to find the target temperature adjacent to the initial temperature and the target state of charge adjacent to the initial state of charge in the charging current constraint table, so as to determine the initial charging current value by using the target temperature and the target state of charge, thereby enabling effective prediction of the charging current during the charging process.

[0027] In some embodiments of the present application, the adjacent target temperatures include a first temperature and a second temperature, and the adjacent target states of charge include a first state of charge and a second state of charge.

[0028] Determining the initial charging current value based on the target temperature and the target state of charge includes:

[0029] In the dimension of the first temperature, linear interpolation is performed based on the current value corresponding to the first state of charge and the current value corresponding to the second state of charge to obtain the first constraint current value at the initial temperature.

[0030] In the dimension of the second temperature, linear interpolation is performed based on the current value corresponding to the first state of charge and the current value corresponding to the second state of charge to obtain the second constraint current value at the initial temperature.

[0031] In the dimension of the initial temperature, linear interpolation is performed based on the first constraint current and the second constraint current to obtain the initial charging current value.

[0032] In this embodiment, the charging current value can be determined by means of linear interpolation. For example, when determining the initial charging current value, the first temperature and the second temperature adjacent to the initial temperature, and the first state of charge and the second state of charge adjacent to the initial state of charge can be determined; then, in the dimension of the first temperature, a first linear interpolation can be performed along the current value corresponding to the first state of charge and the current value corresponding to the second state of charge, and the obtained current value is the first constraint current value at the initial temperature. At the same time, in the dimension of the second temperature, a first linear interpolation can be performed along the current value corresponding to the first state of charge and the current value corresponding to the second state of charge to obtain the second constraint current value at the initial temperature; thus, the initial temperature can be fixed, and a first linear interpolation can be performed along the first constraint current value and the second constraint current at the initial temperature to obtain the initial charging current value, thereby enabling accurate estimation of the charging current value.

[0033] In some embodiments of the present application, the method further includes:

[0034] Determining the hyperparameter search space based on the charging current constraint table;

[0035] Search in the hyperparameter search space using a hyperparameter optimization library to obtain a hyperparameter combination;

[0036] Apply the hyperparameter combination to the target algorithm in the optimization algorithm library and train the target algorithm to obtain a preset optimization algorithm.

[0037] In this embodiment, a preset optimization algorithm can be constructed based on a hyperparameter optimization library and an optimization algorithm library; the hyperparameter search space can be determined first based on a charging current constraint table; then search in the hyperparameter search space using the hyperparameter optimization library to obtain a hyperparameter combination, and obtain the target algorithm from the optimization algorithm library, so as to apply the hyperparameter combination to the target algorithm and train it to obtain a preset optimization algorithm, which can realize the efficient construction of the preset optimization algorithm.

[0038] In some embodiments of the present application, optimize the charging current constraint table based on the preset optimization algorithm and fitness to obtain an optimized current constraint table, including:

[0039] Generate an initial population corresponding to the charging current constraint table based on the preset optimization algorithm;

[0040] Evaluate the fitness of the individuals in the initial population based on fitness to determine the parental individuals;

[0041] Perform crossover or mutation operations based on the parental individuals to generate a first population;

[0042] When the first population reaches the convergence condition, obtain the optimized current constraint table; otherwise, perform fitness evaluation, crossover or mutation operations based on the first population to obtain a second population until the second population reaches the convergence condition and obtain the optimized current constraint table.

[0043] In this embodiment, when constructing a preset optimization algorithm based on an improved genetic algorithm and a particle swarm optimization algorithm, when using the preset optimization algorithm to optimize the charging current constraint table, an initial population corresponding to the charging current constraint table can be generated first based on the preset optimization algorithm, and then the fitness of the initial population is evaluated to determine the parental individuals, and then crossover or mutation operations are performed based on the parental individuals to construct a new population, that is, the first population. If the first population has converged, the optimization result can be output. If not, continue to perform fitness evaluation and crossover or mutation operations on the first population to obtain a new second population until the convergence condition is reached, stop iterative update, and output the optimized current constraint table, effectively realizing the optimization of the charging current constraint table.

[0044] In some embodiments of the present application, after evaluating the fitness of the individuals in the initial population based on fitness to determine the parental individuals, the method further includes:

[0045] When it is determined that the parent individuals satisfy the neighborhood update condition, local neighborhood search is performed on the parent individuals based on a preset perturbation operator to generate a third population, and it is determined whether the third population reaches the convergence condition.

[0046] In this embodiment, when constructing a preset optimization algorithm based on the improved genetic algorithm and the particle swarm optimization algorithm, the particle swarm optimization algorithm can be encapsulated as a preset perturbation operator. Thus, in the process of using the preset optimization algorithm to optimize the charging current constraint table, after determining the parent individuals, it can be determined whether the parent individuals satisfy the neighborhood update condition. When it is determined that the neighborhood update condition is satisfied, the preset perturbation operator is used to perform local neighborhood search on the parent individuals to obtain a third population, so as to perform subsequent processes such as convergence condition judgment based on the third population; thereby, finer-grained operations can be provided for the expansion of the population search in the local neighborhood structure, improving the performance of the preset optimization algorithm, and thus improving the optimization effect on the charging current constraint table.

[0047] In some embodiments of the present application, the method further includes:

[0048] When the first population does not reach the convergence condition, it is determined whether the first population satisfies the population reset condition;

[0049] When the first population satisfies the population reset condition, the first population is reset based on a random generator to obtain a fourth population, and the fitness of the fourth population is evaluated.

[0050] In this embodiment, when constructing a preset optimization algorithm based on the improved genetic algorithm and the particle swarm optimization algorithm, in the process of using the preset optimization algorithm to optimize the charging current constraint table, it can also be determined whether the first population satisfies the population reset condition when it is determined that the first population does not reach the convergence condition. If it is satisfied, the first population can be reset using a random generator to obtain a fourth population, so as to restart operations such as fitness evaluation based on the fourth population, which is beneficial to improving the performance of the preset optimization algorithm.

[0051] In a second aspect, an embodiment of the present application provides an electronic device, including an acquisition unit, a simulation unit, and an optimization unit;

[0052] The acquisition unit is configured to acquire a charging current constraint table corresponding to a battery;

[0053] The simulation unit is configured to simulate the charging process of the battery based on a preset charging simulation model and the charging current constraint table to obtain a fitness; wherein, the charging process represents the process of charging the battery until it reaches the target state of charge;

[0054] The optimization unit is configured to optimize the charging current constraint table based on a preset optimization algorithm and the fitness to obtain an optimized current constraint table;

[0055] The simulation unit is further configured to simulate the charging process of the battery by using a preset charging simulation model and an optimized current constraint table, and obtain an updated fitness value.

[0056] The optimization unit is further configured to optimize the optimized current constraint table based on a preset optimization algorithm and the updated fitness value until a termination condition is reached, so as to obtain a target charging current constraint table, and charge the battery based on the target charging current constraint table.

[0057] In a third aspect, an embodiment of the present application provides an electronic device, which further includes a processor and a memory storing processor-executable instructions. When the executable instructions are executed by the processor, the above-mentioned current constraint table optimization method is implemented.

[0058] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program is stored. The program is applied to an electronic device, and when the program is executed by the processor, the above-mentioned current constraint table optimization method is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of illustrating the preferred embodiments and are not considered to be a limitation of the present application. Moreover, in all the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0060] Figure 1 is a schematic flowchart of the implementation of the current constraint table optimization method proposed by the embodiment of the present application Figure 1 ;

[0061] Figure 2 is a schematic diagram of the intelligent optimization algorithm library framework proposed by the embodiment of the present application;

[0062] Figure 3 is a schematic flowchart of the implementation of the current constraint table optimization method proposed by the embodiment of the present application Figure 2 ;

[0063] Figure 4 is a schematic flowchart of the implementation of the current constraint table optimization method proposed by the embodiment of the present application Figure 3 ;

[0064] Figure 5 is a schematic flowchart of the implementation of the current constraint table optimization method proposed by the embodiment of the present application Figure 4 ;

[0065] Figure 6 is a schematic diagram of the composition structure of the electronic device proposed by the embodiment of the present application Figure 1 ;

[0066] Figure 7 Structural schematic diagram of the electronic device proposed in the embodiment of the present application Figure 2 。 Detailed implementation manners

[0067] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. It can be understood that the specific embodiments described herein are only used to explain the related application, rather than limiting the application. Additionally, it should be noted that for the convenience of description, only the parts related to the related application are shown in the drawings.

[0068] Currently, new energy batteries are increasingly widely used in life and industries. New energy batteries are not only applied to energy storage power systems such as hydraulic, thermal, wind, and solar power stations, but also widely used in electric transportation tools such as electric bicycles, electric motorcycles, and electric vehicles, as well as multiple fields such as aerospace. With the continuous expansion of the application fields of power batteries, the market demand is also continuously increasing.

[0069] Currently, the charging speed of electric vehicles has always been one of the biggest obstacles to improving the convenience of electric vehicles; the prior art can complete the charging process relatively quickly based on certain test data. This method usually keeps the charging current at the upper limit of the feasible value all the time. This operation is also likely to cause the battery to overheat, and subject to the gradual increase of the battery SOC, the current value will rapidly decrease; it has been proven by experiments that the original current curve still has room for optimization to further shorten the charging time.

[0070] Among them, SOC is the ratio of the remaining capacity of the battery after being used for a period of time or left unused for a long time to the capacity in its fully charged state, usually expressed as a percentage, and its value range is from 0 to 1. When SOC = 0, it means the battery is completely discharged, and when SOC = 1, it means the battery is fully charged. The current during the battery charging process is often affected by SOC and temperature. Conducting three-dimensional thermal simulation on the battery pack charging process can simulate the temperature distribution and changes of each cell inside the battery, obtain a two-dimensional table (lithium plating table) in which the charging current is constrained by temperature and SOC, and a one-dimensional table (thermal balance table) in which the charging current is constrained by temperature; however, charging with the maximum current in the original current constraint table (including the lithium plating table and the thermal balance table) causes the temperature to rise too fast, resulting in a rapid decrease in the subsequent charging current, which will reduce the overall charging efficiency. Therefore, the charging strategy based on the original current constraint table is not applicable to the fast charging process; at the same time, the current related optimization methods are prone to make the optimization results fall into local optima, that is, the solution with the shortest charging completion time is optimal within its local solution space, but not the optimal solution in the overall solution space; in addition, the charging current curve with respect to time output by the current related optimization methods cannot reflect in dimensions such as temperature and SOC, which brings a lot of inconvenience to simulation and even application.

[0071] To solve the problems existing in the current current constraint table optimization methods, the embodiments of the present application provide a current constraint table optimization method and an electronic device. The electronic device can obtain the charging current constraint table corresponding to the battery; based on a preset charging simulation model and the charging current constraint table, simulate the charging process of the battery to obtain a fitness; where the charging process represents the process of charging the battery until it reaches the target state of charge; based on a preset optimization algorithm and the fitness, optimize the charging current constraint table to obtain an optimized current constraint table; use the preset charging simulation model and the optimized current constraint table to simulate the charging process of the battery to obtain an updated fitness; based on the preset optimization algorithm and the updated fitness, optimize the optimized current constraint table until the termination condition is reached to obtain the target charging current constraint table, which can effectively optimize the current constraint table; thus, charging the battery based on the target charging current constraint table can effectively improve the fast charging rate and effect.

[0072] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application.

[0073] In the embodiments of the present application, Figure 1 is a schematic implementation process diagram of the current constraint table optimization method proposed in the embodiments of the present application Figure 1 , as Figure 1 shown, the method for optimizing the current constraint table may include the following steps:

[0074] Step 101: Obtain the charging current constraint table corresponding to the battery.

[0075] In an embodiment of the present application, the electronic device may first obtain the charging current constraint table corresponding to the battery.

[0076] In an embodiment of the present application, the battery may be a battery cell. A battery cell refers to a basic unit that can realize the mutual conversion between chemical energy and electrical energy, and can be used to make a battery module or a battery pack, so as to supply power to an electrical device. The battery cell may be a secondary battery, and a secondary battery refers to a battery cell that can be activated by charging after discharging. The battery cell may be a lithium-ion battery, a sodium-ion battery, a sodium-lithium-ion battery, a lithium-metal battery, a sodium-metal battery, a lithium-sulfur battery, a magnesium-ion battery, a nickel-metal hydride battery, a nickel-cadmium battery, a lead-acid battery, etc., and the embodiments of the present application are not limited thereto.

[0077] In an embodiment of the present application, the battery may also be a single physical module including one or more battery cells to provide a higher voltage and capacity. When there are multiple battery cells, the multiple battery cells are connected in series, parallel or in a hybrid connection through a busbar component.

[0078] It should be noted that in an embodiment of the present application, the charging current constraint table may be input into a preset charging simulation model in the form of an operation window (OW) table in which the charging current is constrained by temperature and SOC.

[0079] In some embodiments of the present application, the charging current constraint table may be characterized as being generated based on a lithium plating table and a thermal balance table, and includes a table of current values under different temperature and state of charge (SOC) constraints of the battery.

[0080] In an embodiment of the present application, after obtaining the lithium plating table and the thermal balance table, the charging current constraint table can be directly generated according to the lithium plating table and the thermal balance table; or operations such as modifying or supplementing the lithium plating table and the thermal balance table can be performed to generate the charging current constraint table.

[0081] In some embodiments of the present application, when generating the charging current constraint table based on the lithium plating table and the thermal balance table, the constraint current in the charging current constraint table may be the minimum value of the constraint currents at the corresponding temperature and state of charge of the battery in the lithium plating table and the thermal balance table.

[0082] In some embodiments of the present application, it is also possible to directly generate the charging current constraint table in the form of an OW table in which the charging current is constrained by temperature and SOC without relying on the original data of the lithium plating table and the thermal balance table.

[0083] Exemplarily, the content of the lithium deposition table is shown in Table 1 and Table 2. It can be seen that the lithium deposition table may include current values under two-dimensional constraints of temperature and SOC. Among them, the unit of the current value is ampere.

[0084] Table 1

[0085]

[0086]

[0087] Table 2

[0088]

[0089]

[0090] Exemplarily, the content of the thermal balance table is shown in Table 3 and Table 4. It can be seen that the thermal balance table may include current values under temperature constraints; among them, the unit of the current value is ampere.

[0091] Table 3

[0092]

[0093] Table 4

[0094]

[0095] Step 102: Simulate the charging process of the battery based on a preset charging simulation model and a charging current constraint table to obtain a fitness value; where the charging process represents the process of charging the battery until it reaches the target state of charge.

[0096] In the embodiments of the present application, after obtaining the charging current constraint table corresponding to the battery, the electronic device can simulate the charging process of the battery based on the preset charging simulation model and the charging current constraint table to obtain a fitness value; where the charging process represents the process of charging the battery until it reaches the target state of charge.

[0097] In some embodiments of the present application, a preset charging simulation model can be constructed based on an ampere-hour integration module, that is, the preset charging simulation model includes an ampere-hour integration module.

[0098] In the embodiments of the present application, the fitness value can be used to measure the duration of the charging process.

[0099] In the embodiments of the present application, by simulating the charging process of the battery, a battery charging current curve can be obtained.

[0100] It can be understood that, in the embodiments of the present application, the target state of charge is the target SOC, which represents the target power that the battery needs to charge to. For example, if the target state of charge is 80% and the current state of charge of the battery is 10%, the process of charging the battery from 10% to 80% can be simulated based on a preset charging simulation model and a charging current constraint table.

[0101] In some embodiments of the present application, when the electronic device simulates the charging process of the battery based on a preset charging simulation model and a charging current constraint table, it can determine the initial charging current value based on the preset charging simulation model, the charging current constraint table, and the initial operating condition parameters of the battery at the current moment; wherein, the initial operating condition parameters at least include the initial temperature and the initial state of charge; simulate the first operating condition parameters at the first moment based on the preset charging simulation model, the initial operating condition parameters, and the initial charging current value; wherein, the first moment represents the next moment corresponding to the current moment; when the first state of charge in the first operating condition parameters reaches the target state of charge, it is determined that the simulation of the charging process is completed; otherwise, simulate based on the preset charging simulation model and the first operating condition parameters to obtain the second operating condition parameters at the next moment corresponding to the first moment, until the second state of charge in the second operating condition parameters reaches the target state of charge, and it is determined that the simulation of the charging process is completed.

[0102] It should be noted that, in the embodiments of the present application, the initial operating condition parameters may further include cooling parameters, etc., where the cooling parameter may be a parameter representing the flow rate of the coolant per unit cycle.

[0103] It should be noted that, in the embodiments of the present application, the first operating condition parameters may include the battery temperature, SOC, cooling parameters, etc. at the first moment; the second operating condition parameters may include the battery temperature, SOC, cooling parameters, etc. at the next moment corresponding to the first moment.

[0104] It can be understood that, in the embodiments of the present application, the initial temperature is the temperature of the battery at the current moment, and the initial state of charge is the state of charge of the battery at the current moment.

[0105] In some embodiments of the present application, based on the initial state of charge, the ampere-hour integration module can integrate the initial charging current value and the first moment to obtain the first state of charge.

[0106] Exemplarily, the method for determining the state of charge using the ampere-hour integration module can be expressed by the following formula:

[0107]

[0108] Among them, Current represents the charging current value at the current moment, captime represents the battery capacity, SOC(t - 1) represents the state of charge of the battery at the current moment, and SOC(t) represents the predicted state of charge of the battery at the next moment corresponding to the current moment; it can be seen that based on the principle of the ampere-hour integration module to determine the state of charge of the battery at the next moment, it can be understood as the integration of the charging current over time; among them, the charging simulation model runs in a loop in seconds.

[0109] In some embodiments of the present application, when the electronic device determines the initial charging current value based on the preset charging simulation model, the charging current constraint table, and the initial operating condition parameters of the battery at the current moment, it can determine the target temperature adjacent to the initial temperature in the charging current constraint table based on the preset charging simulation model; determine the target state of charge adjacent to the initial state of charge in the charging current constraint table based on the initial state of charge; and determine the initial charging current value based on the target temperature and the target state of charge.

[0110] In the embodiments of the present application, it can be seen from the above Table 1, Table 2, Table 3, and Table 4 that the lithium plating table and the thermal balance table use discrete data points to represent the constraint relationship between the charging current and temperature, SOC, and the corresponding charging current constraint table also uses discrete data points to represent the constraint relationship between the charging current and temperature, SOC; however, in the actual optimization process, both the temperature and SOC indicators are taken from a continuous numerical space. When the required temperature or SOC is not available in the charging current constraint table, a data method is needed to estimate the charging current under the constraint of this temperature or SOC. The present application uses linear difference interpolation to achieve the estimation of the charging current under the constraint of temperature or SOC.

[0111] In some embodiments of the present application, when the electronic device determines the initial charging current value based on the target temperature and the target state of charge, it can predict the initial charging current value under the constraints of the initial state of charge and the initial temperature based on the method of quadratic linear interpolation.

[0112] In some embodiments of the present application, the adjacent target temperature may include a first temperature and a second temperature, and the adjacent target state of charge may include a first state of charge and a second state of charge.

[0113] Exemplarily, the initial temperature is A and the initial state of charge is B, but there is no corresponding current value for A and B in the charging current constraint table. Then, based on the linear difference interpolation method, the temperatures adjacent to A in the charging current constraint table can be determined as C and D, and the states of charge adjacent to B are respectively E and F; thus, the current value under the constraints of A and B can be obtained by linear interpolation based on the current values corresponding to C, D, E, and F.

[0114] In some embodiments of the present application, the electronic device may perform linear interpolation based on the current value corresponding to the first state of charge and the current value corresponding to the second state of charge in the dimension of the first temperature to obtain the first constrained current value at the initial temperature; in the dimension of the second temperature, perform linear interpolation based on the current value corresponding to the first state of charge and the current value corresponding to the second state of charge to obtain the second constrained current value at the initial temperature; in the dimension of the initial temperature, perform linear interpolation based on the first constrained current and the second constrained current to obtain the initial charging current value.

[0115] Exemplarily, Table 5 shows a method for predicting the charging current value through linear interpolation. For example, T1 in Table 5 is the first temperature, T2 is the second temperature, S1 is the first state of charge, and S2 is the second state of charge; in the dimension of T1, the current value corresponding to S1 is P1, and the current value corresponding to S2 is P3; in the dimension of T2, the current value corresponding to S1 is P2, and the current value corresponding to S2 is P4; when predicting and determining the initial charging current value, the SOC can be fixed, and two linear interpolations are performed respectively along the temperature dimensions T1 and T2 to obtain the constrained currents at the initial temperature, including the first constrained current value K1 = P1+(P2 - P2)(t - T1) / (T2 - T1), and the second constrained current value K2 = P3+(P4 - P3)(t - T1) / (T2 - T1). Then, fixing the initial temperature, perform a linear interpolation along the constrained current, and the initial charging current value under the constraints of the initial temperature and the initial state of charge K1+(K2 - K1)(s - S1) / (S2 - S1) can be obtained.

[0116] Table 5

[0117] Temperature T1 Initial Temperature T2 SOC(%) = S1 P1 K1 = P1 + (P2 - P2)(t - T1) / (T2 - T1) P2 Initial State of Charge K1 + (K2 - K1)(s - S1) / (S2 - S1) SOC(%) = S2 P3 K2 = P3 + (P4 - P3)(t - T1) / (T2 - T1) P4

[0118] Furthermore, in the embodiments of the present application, if the charging current constraint table appears in the form of a separate lithium plating table and a separate thermal equilibrium table, the corresponding current values can be predicted in the lithium plating table and the thermal equilibrium table respectively based on the linear interpolation method, and then the minimum value of the current values is taken as the finally determined charging current value.

[0119] Exemplarily, when determining the initial charging current value, since the thermal equilibrium table is a one-dimensional table, the two temperatures adjacent to the initial temperature can be first determined in the thermal equilibrium table based on the linear interpolation method, and then linear interpolation is performed based on the current values corresponding to these two temperature values to obtain the predicted current value. Then, the predicted current value is compared with the predicted current value for the initial temperature and the initial state of charge obtained from the lithium plating table, and the minimum of the current values is used as the initial charging current value to be input into the preset charging simulation model to simulate the charging current at the next moment.

[0120] It can be seen that in the simulation loop of each time step of the preset charging simulation model, the preset charging simulation model can update the relevant parameters at the next simulation moment according to parameters such as the battery temperature, cooling parameters, charging current, and state of charge of the battery at the current moment; among them, the state of charge of the battery at the next moment is predicted based on the ampere-hour integration module, and the charging current at the next moment can be determined based on linear interpolation; the preset charging simulation model can repeat the above process based on the updated parameters until the target state of charge is reached, and then end the simulation of the charging process.

[0121] Step 103: Optimize the charging current constraint table based on the preset optimization algorithm and fitness to obtain an optimized current constraint table.

[0122] In an embodiment of the present application, after the electronic device simulates the charging process of the battery based on the preset charging simulation model and the charging current constraint table to obtain the fitness, it can optimize the charging current constraint table based on the preset optimization algorithm and the fitness to obtain an optimized current constraint table.

[0123] In an embodiment of the present application, an intelligent optimization algorithm library framework can be deployed in the electronic device. The intelligent optimization algorithm library framework is provided with an intelligent optimization algorithm library and a preset charging simulation model; the intelligent optimization algorithm library can be used to construct a preset optimization algorithm, and can provide optimization methods with multiple different mechanisms to implement large-scale global search in parallel mode to improve the optimization performance of the preset optimization algorithm.

[0124] Exemplarily, as Figure 2 shown, the intelligent optimization algorithm library framework 30 can include a standardized part 301 and a non-standardized part 302. Among them, the standardized part 301 can include a uniformly set input format 3011 and output format 3012, as well as a preset charging simulation model 3013. Among them, both the input format 3011 and the output format 3012 are two-dimensional OW tables in the same form as the charging current constraint table, which is convenient for optimizing the current constraint table; the non-standardized part 302 can include algorithm hyperparameter setting 3021, setting of the overall operation process of the algorithm 3022, and setting of the domain search strategy and perturbation operator 3023.

[0125] In an embodiment of the present application, when optimizing the charging current constraint table, the solution space of the problem to be optimized is the charging current constraint table. On this basis, the electronic device can call different algorithms in the intelligent optimization algorithm library for optimization, and output the optimized current constraint table using a unified output format after the optimization ends.

[0126] It should be noted that in an embodiment of the present application, the intelligent optimization algorithm library framework can standardize the input and output formats and the simulation, and can be compatible with different optimization algorithms that are different in theory, process, etc.

[0127] In the embodiments of the present application, the intelligent optimization algorithm library may include a hyperparameter optimization library and an optimization algorithm library.

[0128] In some embodiments of the present application, the intelligent optimization algorithm library may include hyperparameter optimization methods (such as Bayesian optimization, simulated annealing algorithm, etc.), evolutionary algorithms (genetic algorithm, differential evolution algorithm), and swarm intelligence algorithms (such as ant colony algorithm, particle swarm optimization algorithm, etc.), which are not specifically limited in the present application; the intelligent optimization algorithm library aims to provide optimization methods with multiple different mechanisms to achieve large-scale global search in parallel mode and improve the global optimization performance of the algorithm.

[0129] In some embodiments of the present application, the hyperparameter optimization library and the optimization algorithm library in the intelligent optimization algorithm library can be directly implemented using libraries such as hyperopt, Scikit-opt, Geatpy2, mealpy, etc., and multiple intelligent optimization algorithms can be used for parallel optimization.

[0130] Among them, hyperopt is a hyperparameter optimization library. Different from complex manual parameter tuning, hyperparameter optimization can often obtain final results far better than manual parameter tuning in a relatively short time. By setting the search space defined by the charging current constraint table, taking the shortest charging completion time as the optimization goal, evaluating the fitness with a preset charging simulation model, and adjusting the parameters of the preset optimization algorithm, the charging current constraint table can be optimized.

[0131] Among them, Scikit-opt is an integrated intelligent optimization algorithm library. Taking the genetic algorithm in it as an example, when setting the parameters of the genetic algorithm, since the main parameters of the genetic algorithm include population size, maximum number of iterations, mutation probability, and precision; when relaxing the requirements for optimization efficiency, the number of iterations can be increased to make the population evolve more maturely, and a smaller precision can be set; the setting of the population size should be appropriate. If the population size is too small, the overall gene richness of the population will be significantly reduced, and the congenital lack of effective alleles will occur. Even if a mutation operator with a large probability is set, it is still impossible to make up for the lack of sample size of gene fragments under fine-grained partitioning. At the same time, these potentially advantageous gene fragments are difficult to be retained in the offspring population through screening; however, if the population size is too large, the most direct consequence is that the overall running efficiency of the algorithm becomes slow and it is difficult to converge in a short time. The setting of the crossover and mutation probabilities in this algorithm mainly refers to the following rules: First, both the crossover and mutation operations are essentially neighborhood search (perturbation) operators. As part of the genetic algorithm schema theorem, their roles in the overall process are significantly different: the mutation operation can generate a large number of rich new gene fragments, and the crossover operation can retain the advantageous gene fragments in the offspring population by combining different gene fragments and screening through fitness; therefore, setting a large mutation probability in the early stage of the algorithm operation can help the population generate a large number of rich new gene fragments and individuals with advantages in a short time, and at the same time setting a small crossover probability can avoid screening out potentially advantageous gene fragments; setting a small mutation probability and a large crossover probability in the later stage of the algorithm operation is beneficial to retaining the gene fragments with obvious advantages in the population and accelerating the algorithm convergence.

[0132] In some embodiments of the present application, the electronic device can determine the hyperparameter search space based on the charging current constraint table; use the hyperparameter optimization library to search in the hyperparameter search space to obtain a hyperparameter combination; apply the hyperparameter combination to the target algorithm in the optimization algorithm library and train the target algorithm to obtain a preset optimization algorithm.

[0133] In some embodiments of the present application, the intelligent optimization algorithm library framework can not only provide an integrated basis for different optimization algorithms, but also provide an extended interface for other newly developed optimization algorithms.

[0134] In some embodiments of the present application, a hyperheuristic intelligent optimization algorithm can be constructed based on the improved genetic algorithm (IGA) and the particle swarm optimization (PSO) algorithm, and the hyperheuristic intelligent optimization algorithm can be used as a preset optimization algorithm through the extended interface of the intelligent optimization algorithm library framework.

[0135] In some embodiments of the present application, when the preset optimization algorithm is a hyper-heuristic intelligent optimization algorithm constructed based on an improved genetic algorithm and a particle swarm optimization algorithm, when using the preset optimization algorithm to optimize the charging current constraint table, an initial population corresponding to the charging current constraint table can be generated based on the preset optimization algorithm; the fitness of the individuals in the initial population is evaluated based on the fitness to determine the parent individuals; crossover or mutation operations are performed based on the parent individuals to generate a first population; when the first population reaches the convergence condition, the optimized current constraint table is obtained; otherwise, the fitness of the first population is evaluated, and crossover or mutation operations are performed to obtain a second population until the second population reaches the convergence condition, and the optimized current constraint table is obtained.

[0136] In some embodiments of the present application, when the preset optimization algorithm is a hyper-heuristic intelligent optimization algorithm, during the above optimization process, after evaluating the fitness of the individuals in the initial population based on the fitness to determine the parent individuals, when it is determined that the domain update condition is satisfied based on the parent individuals, a local variable domain search is performed on the parent individuals based on the preset perturbation operator to generate a third population, and it is determined whether the third population reaches the convergence condition.

[0137] Among them, the preset perturbation operator can be a perturbation operator encapsulated by the particle swarm optimization method.

[0138] In some embodiments of the present application, when the preset optimization algorithm is a hyper-heuristic intelligent optimization algorithm, during the above optimization process, when the first population does not reach the convergence condition, it is determined whether the first population satisfies the population reset condition; when the first population satisfies the population reset condition, the first population is reset based on a random generator to obtain a fourth population, and the fitness of the fourth population is evaluated.

[0139] Step 104: Use the preset charging simulation model and the optimized current constraint table to simulate the charging process of the battery to obtain an updated fitness.

[0140] In the embodiments of the present application, after the electronic device optimizes the charging current constraint table based on the preset optimization algorithm and the fitness to obtain the optimized current constraint table, the preset charging simulation model and the optimized current constraint table can be used to simulate the charging process of the battery to obtain an updated fitness.

[0141] It can be understood that in the embodiments of the present application, after obtaining the optimized current constraint table, the preset charging simulation model can be continuously used to simulate the charging process of the optimized current constraint table to verify the fast charging effect of charging based on the optimized current constraint table, and an updated fitness is obtained.

[0142] Step 105: Optimize the optimized current constraint table based on a preset optimization algorithm and the updated fitness until a termination condition is reached, obtaining a target charging current constraint table, and charging the battery based on the target charging current constraint table.

[0143] In an embodiment of the present application, after the electronic device simulates the charging process of the battery using a preset charging simulation model and the optimized current constraint table and obtains the updated fitness, it can optimize the optimized current constraint table based on the preset optimization algorithm and the updated fitness until a termination condition is reached, obtaining a target charging current constraint table, and charging the battery based on the target charging current constraint table.

[0144] It can be understood that in an embodiment of the present application, during the process of obtaining the optimized current constraint table, if the termination condition of the preset optimization algorithm is not reached, the optimized current constraint table can continue to be optimized until the termination condition is reached.

[0145] It should be noted that in an embodiment of the present application, the termination condition can be that the preset optimization algorithm converges or the number of iterations is reached; for example, if the updated fitness no longer changes compared to the previous fitness, that is, it tends to converge, it can be determined that the optimal target charging current constraint table has been obtained, otherwise, the optimized current constraint table can continue to be optimized and input into the preset charging simulation model for verification.

[0146] Exemplarily, the fitness value corresponding to the optimized current constraint table is 1800, that is, it can be regarded that the charging duration based on the optimized current constraint table is 1800 seconds. If the new fitness obtained after optimizing the optimized current constraint table again and inputting it into the preset charging simulation model is still 1800, it can be determined that the algorithm converges, and the target charging current constraint table is output.

[0147] In an embodiment of the present application, considering that the three-dimensional thermal simulation model has low efficiency and is difficult to be truly used in the optimization process, a one-dimensional thermal simulation model, that is, a preset charging simulation model, is established based on the heat conduction equation to simulate the temperature distribution and change inside the battery, so as to quickly calculate key parameters such as temperature during the charging process; and based on the preset charging simulation model, an intelligent optimization algorithm library is established to optimize the charging strategy during the fast charging process with the shortest charging completion time to reach the target state of charge as the optimization goal. At the same time, based on the intelligent optimization algorithm library, a variety of intelligent optimization algorithms can be used to reduce the risk of the algorithm falling into a local optimal solution; and a framework of the intelligent optimization algorithm library is proposed to ensure the expandability of the algorithm library; and, the present application directly optimizes the current constraint table in which the charging current is affected by temperature and SOC, and the optimized output can directly reflect the constraint relationship between the current affected by temperature and SOC, and is more adaptable to the simulation and application scenarios.

[0148] In this embodiment, by constructing a preset charging simulation model and a preset optimization algorithm, the process of charging the battery from the current state of charge to the target state of charge can be simulated using the preset charging simulation model and the charging current constraint table to obtain a fitness value, which can be used to evaluate the charging duration of the battery based on the charging current constraint table. Then, the charging current constraint table is optimized using the fitness value obtained from the simulation and the preset optimization algorithm. Subsequently, the optimized current constraint table is used to simulate the charging process again using the preset charging simulation model to obtain an updated fitness value, which can verify the charging duration and effect based on the optimized current constraint table. Furthermore, the optimized current constraint table can be continuously optimized based on the updated fitness value and the preset optimization algorithm until the termination condition is reached, at which point the optimization stops. The output target charging current constraint table is the optimal charging current constraint table. Charging the battery using this target charging current constraint table can effectively improve the charging rate and effect of the battery.

[0149] Based on the above embodiment, in another embodiment of the present application, by way of example, as Figure 3 shown, when simulating the charging process of the battery using the preset charging simulation model, the following steps may be included:

[0150] Step 201: Obtain the initial operating condition parameters of the battery.

[0151] Step 202: Perform data processing based on the initial operating condition parameters and the charging current constraint table to obtain an initial charging current value.

[0152] Among them, the data processing may include linear interpolation, and the initial charging current value can be determined based on the method of linear interpolation.

[0153] Step 203: Simulate the operating condition parameters and the charging current value at the next moment based on the initial charging current value and the initial operating condition parameters.

[0154] Among them, the state of charge of the battery in the operating condition parameters at the next moment can be determined by the ampere-hour integration module.

[0155] Step 204: Determine whether the target state of charge has been reached.

[0156] In the embodiment of the present application, whenever the operating condition parameters at the next moment are predicted, it can be determined whether the target state of charge has been reached based on the state of charge in the operating condition parameters. If not, step 205 is executed; if so, step 206 is executed to stop the simulation of the charging process.

[0157] Step 205: Simulate the operating condition parameters and the charging current value at the next moment.

[0158] Step 206: Stop the simulation of the charging process.

[0159] In an embodiment of the present application, a preset charging simulation model is constructed based on an ampere-hour integration module and a one-dimensional thermal simulation plug-in, which can achieve fast simulation of the battery charging process while ensuring a certain accuracy; at the same time, a charging current constraint table is generated in the form of an OW table where the charging current is constrained by temperature and SOC, which can unify the input and output formats, facilitate the use of the preset charging simulation model to simulate the charging current constraint table, and subsequent use of the preset optimization algorithm to optimize the charging current constraint table.

[0160] Exemplarily, as Figure 4 shown, the process of optimizing by the hyper-heuristic intelligent optimization algorithm proposed in the present application may include the following steps:

[0161] Step 401, initialize the population.

[0162] Step 402, evaluate the fitness value.

[0163] Step 403, determine whether the population evolution limit is reached.

[0164] Step 404, select parent individuals.

[0165] Step 405, determine whether the neighborhood update condition is satisfied.

[0166] In this embodiment, if it is determined that the neighborhood update condition is satisfied, step 406 is executed; if it is determined that the neighborhood update condition is not satisfied, step 407 is executed.

[0167] Step 406, generate offspring individuals based on a preset perturbation operator.

[0168] Step 407, perform crossover or mutation operations.

[0169] Step 408, obtain a new population.

[0170] Step 409, output the population evolution result.

[0171] Step 410, determine whether the convergence condition is satisfied.

[0172] In this embodiment, if the convergence condition is satisfied, step 411 is executed; if it is determined that the convergence condition is not satisfied, step 412 is executed.

[0173] Step 411, output the optimization result.

[0174] Step 412, evaluate the population evolution result.

[0175] Step 413, determine whether the population reset condition is satisfied.

[0176] In this embodiment, if it is determined that the population reset condition is satisfied, step 414 is executed; if it is determined that the population reset condition is not satisfied, step 415 is executed.

[0177] Step 414: Reset the population based on a random generator and the particle swarm optimization algorithm.

[0178] Step 415: Reset the population evolution limit.

[0179] It should be noted that in the embodiments of the present application, for the optimization process of the above-mentioned hyper-heuristic intelligent optimization algorithm, it can be mainly divided into high-level operations and low-level operations. Among them, the high-level operations are implemented based on an improved genetic algorithm, which is responsible for large-scale global search strategies and provides the basic operation framework of the algorithm; the low-level operations encapsulate the particle swarm optimization method as a preset perturbation operator for local variable neighborhood search; the high-level and low-level operations of the algorithm do not function independently. They are complementary in the search logic of the algorithm, interact with each other in the operation process, and replace each other in the master-slave relationship; this algorithm can be mainly divided into four modules in the process, namely, adaptive evolution within the population (steps 402 to 408), variable neighborhood search (step 406), population reset (step 414), and evaluation of the evolution results between populations (steps 409, 410, 412, 413, and 415); among them, the adaptive evolution within the population and the evaluation of the evolution results between populations are high-level operations, and they together constitute the multi-population serial and relay search mode of the algorithm; the variable neighborhood search and population reset can be used as the low-level operations of the algorithm, which can provide more fine-grained operations for the expansion of the local neighborhood structure of the algorithm; the parameter settings of this algorithm can refer to the parameter setting rules in the aforementioned genetic algorithm.

[0180] In the embodiments of the present application, as Figure 5 shown, the method for optimizing the current constraint table in the embodiments of the present application may include the following steps:

[0181] Step 501: Obtain the charging current constraint table.

[0182] In this embodiment, the charging current constraint table can be generated based on the lithium deposition table and the thermal balance table, and this charging current constraint table is input into the preset charging simulation model in the form of an OW table in which the charging current is constrained by temperature and SOC.

[0183] Step 502: Input the charging current constraint table into the preset charging simulation model.

[0184] In this embodiment, the preset charging simulation model can simulate the temperature distribution inside the battery and quickly calculate and simulate the relevant parameters during the charging process of the battery according to the charging current constraint table.

[0185] Step 503: Using a preset optimization algorithm, optimize the charging current constraint table with the goal of minimizing the time consumption to obtain a target charging current constraint table.

[0186] In this embodiment, by using a preset optimization algorithm to optimize the charging current constraint table, a target charging current constraint table with the shortest charging time can be obtained. Thus, charging the battery based on the target charging current constraint table can improve the charging rate of the battery and achieve a better fast charging effect.

[0187] In summary, the simulation of the charging process based on the preset charging simulation model in this application is a fast and effective solution. On this basis, using a preset optimization algorithm to optimize the charging current constraint table can enable the battery pack to reach the target state of charge in the shortest time when charging based on the target charging current constraint table, which is beneficial to improving the charging efficiency.

[0188] In this embodiment, by constructing a preset charging simulation model and a preset optimization algorithm, the process of charging the battery from the current state of charge to the target state of charge can be simulated using the preset charging simulation model and the charging current constraint table to obtain a fitness value, which can be used to evaluate the charging duration of the battery based on the charging current constraint table. Then, using the fitness value obtained from the simulation and the preset optimization algorithm to optimize the charging current constraint table, and then continuing to use the preset charging simulation model to simulate the charging process of the optimized current constraint table to obtain an updated fitness value, which can verify the charging duration and effect based on the optimized current constraint table. Furthermore, the optimized current constraint table can be continuously optimized based on the updated fitness value and the preset optimization algorithm until the termination condition is reached and no further optimization is performed. The output target charging current constraint table is the optimal charging current constraint table. Using this target charging current constraint table to charge the battery can effectively improve the charging rate and effect of the battery fast charging.

[0189] Based on the above embodiments, in another embodiment of this application, an electronic device is provided, as Figure 6 shown. The electronic device 10 includes an acquisition unit 101, a simulation unit 102, an optimization unit 103, and a construction unit 104.

[0190] The acquisition unit 101 is configured to acquire the charging current constraint table corresponding to the battery;

[0191] The simulation unit 102 is configured to simulate the charging process of the battery based on the preset charging simulation model and the charging current constraint table to obtain a fitness value; wherein, the charging process represents the process of charging the battery until it reaches the target state of charge;

[0192] The optimization unit 103 is configured to optimize the charging current constraint table based on the preset optimization algorithm and the fitness value to obtain an optimized current constraint table;

[0193] The simulation unit 102 is further configured to use a preset charging simulation model and an optimized current constraint table to simulate the charging process of the battery, and obtain an updated fitness value.

[0194] The optimization unit 103 is further configured to optimize the optimized current constraint table based on a preset optimization algorithm and the updated fitness value until a termination condition is reached, and obtain a target charging current constraint table, so as to charge the battery based on the target charging current constraint table.

[0195] In some embodiments, the simulation unit 102 is further configured to determine an initial charging current value based on a preset charging simulation model, a charging current constraint table, and initial operating condition parameters of the battery at the current moment; wherein the initial operating condition parameters at least include an initial temperature and an initial state of charge; and simulate first operating condition parameters at a first moment based on the preset charging simulation model, the initial operating condition parameters, and the initial charging current value; wherein the first moment represents the next moment corresponding to the current moment; and when the first state of charge in the first operating condition parameters reaches a target state of charge, determine that the simulation of the charging process is completed; otherwise, simulate second operating condition parameters at the next moment corresponding to the first moment based on the preset charging simulation model and the first operating condition parameters until the second state of charge in the second operating condition parameters reaches the target state of charge, and determine that the simulation of the charging process is completed.

[0196] In some embodiments, the preset charging simulation model includes an ampere-hour integration module; the simulation unit 102 is further configured to, based on the initial state of charge, use the ampere-hour integration module to integrate the initial charging current value and the first moment to obtain the first state of charge.

[0197] In some embodiments, the charging current constraint table is characterized by being generated based on a charging lithium deposition table and a thermal balance table, and includes a table of current values under different temperature and battery state of charge constraints; the simulation unit 102 is further configured to determine a target temperature adjacent to the initial temperature in the charging current constraint table based on the preset charging simulation model; and determine a target state of charge adjacent to the initial state of charge in the charging current constraint table based on the initial state of charge; and determine the initial charging current value based on the target temperature and the target state of charge.

[0198] In some embodiments, the adjacent target temperatures include a first temperature and a second temperature, and the adjacent target state of charge includes a first state of charge and a second state of charge; the simulation unit 102 is further configured to perform linear interpolation based on the current value corresponding to the first state of charge and the current value corresponding to the second state of charge in the dimension of the first temperature to obtain a first constrained current value at the initial temperature; and perform linear interpolation based on the current value corresponding to the first state of charge and the current value corresponding to the second state of charge in the dimension of the second temperature to obtain a second constrained current value at the initial temperature; and perform linear interpolation based on the first constrained current and the second constrained current in the dimension of the initial temperature to obtain an initial charging current value.

[0199] The construction unit 104 is configured to determine a hyperparameter search space based on the charging current constraint table; and search in the hyperparameter search space using a hyperparameter optimization library to obtain a hyperparameter combination; and apply the hyperparameter combination to a target algorithm in the optimization algorithm library and train the target algorithm to obtain a preset optimization algorithm.

[0200] The optimization unit 103 is further configured to generate an initial population corresponding to the charging current constraint table based on the preset optimization algorithm; and perform fitness evaluation on the individuals in the initial population based on fitness to determine parent individuals; and perform crossover or mutation operations based on the parent individuals to generate a first population; and in the case where the first population reaches the convergence condition, obtain an optimized current constraint table; otherwise, perform fitness evaluation, crossover or mutation operations on the first population to obtain a second population until the second population reaches the convergence condition and obtain an optimized current constraint table.

[0201] The optimization unit 103 is further configured to, after performing fitness evaluation on the individuals in the initial population based on fitness to determine parent individuals, perform local variable neighborhood search on the parent individuals based on a preset perturbation operator in the case where it is determined that the domain update condition is satisfied based on the parent individuals, generate a third population, and determine whether the third population reaches the convergence condition.

[0202] The optimization unit 103 is further configured to, in the case where the first population does not reach the convergence condition, determine whether the first population satisfies the population reset condition; and in the case where the first population satisfies the population reset condition, reset the first population based on a random generator to obtain a fourth population, and perform fitness evaluation on the fourth population.

[0203] As Figure 7 shown, the electronic device 10 proposed in the embodiment of the present application may further include a processor 105 and a memory 106 storing instructions executable by the processor 105. Further, the terminal 10 may further include a communication interface 107 and a bus 108 for connecting the processor 105, the memory 106, and the communication interface 107.

[0204] In an embodiment of the present application, the above-mentioned processor 105 may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that for different devices, the electronic devices for implementing the functions of the above-mentioned processor may also be others, and the embodiments of the present application do not make specific limitations. The processor 105 may further include a memory 106, and the memory 106 may be connected to the processor 105. Among them, the memory 106 is used to store executable program codes, and the program codes include computer operation instructions. The memory 106 may include a high-speed RAM memory and may also include a non-volatile memory, for example, at least two disk memories.

[0205] In an embodiment of the present application, the bus 108 is used to connect the communication interface 107, the processor 105, and the memory 106 and for the mutual communication between these devices.

[0206] In an embodiment of the present application, the memory 106 is used to store instructions and data.

[0207] Further, in an embodiment of the present application, the above-mentioned processor 105 is used to obtain a charging current constraint table corresponding to the battery; perform a simulation of the charging process of the battery based on a preset charging simulation model and the charging current constraint table to obtain a fitness; where the charging process represents the process of charging the battery until it reaches the target state of charge; optimize the charging current constraint table based on a preset optimization algorithm and the fitness to obtain an optimized current constraint table; perform a simulation of the charging process of the battery using the preset charging simulation model and the optimized current constraint table to obtain an updated fitness; optimize the optimized current constraint table based on the preset optimization algorithm and the updated fitness until a termination condition is reached to obtain a target charging current constraint table, so as to charge the battery based on the target charging current constraint table.

[0208] In practical applications, the above-mentioned memory 106 can be a volatile memory, such as a Random-Access Memory (RAM); or a non-volatile memory, such as a Read-Only Memory (ROM), a flash memory, a Hard Disk Drive (HDD), or a Solid-State Drive (SSD); or a combination of the above types of memories, and provide instructions and data to the processor 105.

[0209] In addition, in this embodiment, each functional module can be integrated into an analysis unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional module.

[0210] If the integrated unit is implemented in the form of a software functional module and is not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method of this embodiment. The foregoing storage medium includes: USB flash drives, mobile hard disks, Read Only Memories (ROMs), Random Access Memories (RAMs), magnetic disks, or optical discs and other various media that can store program codes.

[0211] An embodiment of the present application provides an electronic device. By constructing a preset charging simulation model and a preset optimization algorithm, the electronic device can use the preset charging simulation model and the charging current constraint table to simulate the process of charging the battery from the current state of charge to the target state of charge, and obtain a fitness value. The fitness value can be used to evaluate the charging duration of the battery based on the charging current constraint table. Then, the charging current constraint table is optimized using the fitness value obtained from the simulation and the preset optimization algorithm. Next, the optimized current constraint table is used to simulate the charging process of the battery using the preset charging simulation model, and an updated fitness value is obtained, which can verify the charging duration and effect based on the optimized current constraint table. Furthermore, the optimized current constraint table can be continuously optimized based on the updated fitness value and the preset optimization algorithm until the termination condition is reached. At this point, the optimization stops, and the output target charging current constraint table is the optimal charging current constraint table. Using this target charging current constraint table to charge the battery can effectively improve the charging rate and effect of the battery.

[0212] Specifically, the program instructions corresponding to a data processing method in this embodiment can be stored on storage media such as optical discs, hard disks, and USB flash drives. When the program instructions corresponding to a data processing method in the storage medium are read or executed by an electronic device, the following steps are included:

[0213] Obtain the charging current constraint table corresponding to the battery;

[0214] Based on the preset charging simulation model and the charging current constraint table, simulate the charging process of the battery to obtain a fitness value. Among them, the charging process represents the process of charging the battery until it reaches the target state of charge;

[0215] Based on the preset optimization algorithm and the fitness value, optimize the charging current constraint table to obtain an optimized current constraint table;

[0216] Use the preset charging simulation model and the optimized current constraint table to simulate the charging process of the battery to obtain an updated fitness value;

[0217] Based on the preset optimization algorithm and the updated fitness value, optimize the optimized current constraint table until the termination condition is reached, and obtain the target charging current constraint table to charge the battery based on the target charging current constraint table.

[0218] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects.

[0219] This application is described with reference to the schematic flow diagrams and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the schematic flow diagrams and / or block diagrams, and the combinations of flows and / or blocks in the schematic flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the Figure 1 flows or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks or multiple blocks.

[0220] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one or more of the Figure 1 flows or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks or multiple blocks.

[0221] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operating steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the Figure 1 flows or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks or multiple blocks.

[0222] As described above, the above is only a preferred embodiment of the present application and is not used to limit the protection scope of the present application.

Claims

1. A method for optimizing a current constraint table, characterized in that, The method includes: Obtaining a charging current constraint table corresponding to the battery; Simulating the charging process of the battery based on a preset charging simulation model and the charging current constraint table to obtain a fitness value; wherein, the charging process represents the process of charging the battery until it reaches the target state of charge; Optimizing the charging current constraint table based on a preset optimization algorithm and the fitness value to obtain an optimized current constraint table; Simulating the charging process of the battery using the preset charging simulation model and the optimized current constraint table to obtain an updated fitness value; Optimizing the optimized current constraint table based on the preset optimization algorithm and the updated fitness value until a termination condition is reached to obtain a target charging current constraint table, and charging the battery based on the target charging current constraint table.

2. The method for optimizing a current constraint table according to claim 1, characterized in that, The simulating the charging process of the battery based on a preset charging simulation model and the charging current constraint table includes: Determining an initial charging current value based on a preset charging simulation model, the charging current constraint table, and the initial operating condition parameters of the battery at the current moment; wherein, the initial operating condition parameters at least include the initial temperature and the initial state of charge; Simulating to obtain the first operating condition parameters at the first moment based on the preset charging simulation model, the initial operating condition parameters, and the initial charging current value; wherein, the first moment represents the next moment corresponding to the current moment; In the case where the first state of charge in the first operating condition parameters reaches the target state of charge, determining that the simulation of the charging process is completed; otherwise, performing simulation based on the preset charging simulation model and the first operating condition parameters to obtain the second operating condition parameters at the next moment corresponding to the first moment until the second state of charge in the second operating condition parameters reaches the target state of charge, and determining that the simulation of the charging process is completed.

3. The method for optimizing a current constraint table according to claim 2, characterized in that, The preset charging simulation model includes an ampere-hour integration module, and the method further includes: Based on the initial state of charge, using the ampere-hour integration module to integrate the initial charging current value and the first moment to obtain the first state of charge.

4. The method for optimizing a current constraint table according to claim 2 or 3, characterized in that, The charging current constraint table is characterized by being generated based on a charging lithium plating table and a thermal balance table, and includes a table of current values under different temperature and battery state of charge constraints; The determining the initial charging current value based on a preset charging simulation model, the charging current constraint table, and the initial operating condition parameters of the battery at the current moment includes: Determining a target temperature adjacent to the initial temperature in the charging current constraint table based on the preset charging simulation model; Determining a target state of charge adjacent to the initial state of charge in the charging current constraint table based on the initial state of charge; Determining the initial charging current value based on the target temperature and the target state of charge.

5. The method for optimizing a current constraint table according to claim 4, characterized in that, The adjacent target temperature includes a first temperature and a second temperature, and the adjacent target state of charge includes a first state of charge and a second state of charge. The determining the initial charging current value based on the target temperature and the target state of charge includes: Under the dimension of the first temperature, linear interpolation is performed based on the current value corresponding to the first state of charge and the current value corresponding to the second state of charge to obtain the first constrained current value at the initial temperature; Under the dimension of the second temperature, linear interpolation is performed based on the current value corresponding to the first state of charge and the current value corresponding to the second state of charge to obtain the second constrained current value at the initial temperature; Under the dimension of the initial temperature, linear interpolation is performed based on the first constrained current and the second constrained current to obtain the initial charging current value.

6. The method for optimizing a current constraint table according to any one of claims 1 to 3, characterized in that, The method further includes: Determining a hyperparameter search space based on the charging current constraint table; Searching in the hyperparameter search space using a hyperparameter optimization library to obtain a hyperparameter combination; Applying the hyperparameter combination to a target algorithm in an optimization algorithm library and training the target algorithm to obtain the preset optimization algorithm.

7. The method for optimizing a current constraint table according to claim 6, characterized in that, Optimizing the charging current constraint table based on the preset optimization algorithm and the fitness, to obtain an optimized current constraint table, includes: Generating an initial population corresponding to the charging current constraint table based on the preset optimization algorithm; Performing fitness evaluation on the individuals in the initial population based on the fitness to determine parent individuals; Performing crossover or mutation operations based on the parent individuals to generate a first population; In the case where the first population reaches the convergence condition, obtaining the optimized current constraint table; otherwise, performing fitness evaluation, crossover or mutation operations on the first population to obtain a second population until the second population reaches the convergence condition, and obtaining the optimized current constraint table.

8. The method for optimizing the current constraint table according to claim 7, wherein After performing fitness evaluation on the individuals in the initial population based on the fitness to determine parent individuals, the method further includes: In the case where it is determined that the domain update condition is satisfied based on the parent individuals, performing local variable domain search on the parent individuals based on a preset perturbation operator to generate a third population, and determining whether the third population reaches the convergence condition.

9. The method for optimizing the current constraint table according to claim 7 or 8, wherein The method further includes: In the case where the first population does not reach the convergence condition, determining whether the first population satisfies the population reset condition; In the case where the first population satisfies the population reset condition, resetting the first population based on a random generator to obtain a fourth population, and performing the fitness evaluation on the fourth population.

10. An electronic device, wherein Including an acquisition unit, a simulation unit, and an optimization unit; The acquisition unit is configured to acquire a charging current constraint table corresponding to a battery; The simulation unit is configured to simulate the charging process of the battery based on a preset charging simulation model and the charging current constraint table to obtain a fitness; wherein, the charging process represents the process of charging the battery until it reaches the target state of charge; The optimization unit is configured to optimize the charging current constraint table based on a preset optimization algorithm and the fitness to obtain an optimized current constraint table; The simulation unit is further configured to simulate the charging process of the battery using the preset charging simulation model and the optimized current constraint table to obtain an updated fitness; The optimization unit is further configured to optimize the optimized current constraint table based on the preset optimization algorithm and the updated fitness until a termination condition is reached, so as to obtain a target charging current constraint table, and charge the battery based on the target charging current constraint table.

11. An electronic device, wherein The electronic device further includes a processor and a memory storing instructions executable by the processor. When the executable instructions are executed by the processor, the method according to any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium, on which a program is stored and applied to an electronic device. When the program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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