Battery charging optimization method and device, equipment and storage medium

Abstract

The invention discloses a battery charging optimization method, device and equipment and a storage medium, and relates to the technical field of power electronics, and the method comprises the steps: building a dual-objective optimization function according to the relation between the charging current of a battery and the charging time and the charging temperature rise; determining a plurality of charging currents of the battery; taking each charging current as a habitat, carrying out migration and variation on each habitat, and determining a migration fitness index corresponding to each migration habitat and a variation fitness index corresponding to each variation habitat according to a dual-objective optimization function; and returning to execute migration and variation on each habitat, and determining the target charging current according to the migration fitness index and the variation fitness index after a preset stop condition is met. According to the technical scheme, the target charging current is determined, when the battery is charged based on the target charging current, the charging time and the charging temperature rise of the battery are balanced, the charging time and the charging temperature rise are both small, and optimization of battery charging is achieved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of power electronics technology, and in particular to a battery charging optimization method, device, equipment and storage medium. Background Art

[0002] Lithium batteries have the characteristics of high power and energy density, wide operating temperature range, long service life, no memory effect, and low self-discharge rate, and have become the main energy storage system for electric vehicles. Fast charging electric vehicles shortens the charging time and improves the charging efficiency, which is of great significance to the promotion and application of electric vehicles. However, the safety hazards caused by the capacity decay of power batteries and the extreme heat generated by fast charging have become the bottleneck problem restricting the fast charging technology of electric vehicles.

[0003] Therefore, a battery charging optimization method is urgently needed to reduce the charging time and charging temperature rise. Summary of the invention

[0004] The present invention provides a battery charging optimization method, device, equipment and storage medium to reduce charging time and charging temperature rise.

[0005] In a first aspect, an embodiment of the present invention provides a battery charging optimization method, comprising:

[0006] A dual-objective optimization function is established based on the relationship between the battery's charging current, charging time, and charging temperature rise;

[0007] determining multiple charging currents for the battery;

[0008] Taking each of the charging currents as a habitat, migrating and mutating each of the habitats, and determining a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function;

[0009] Return to execute migration and mutation of each habitat, and determine the target charging current according to the migration fitness index and the mutation fitness index after the preset stop condition is met.

[0010] The technical solution of the embodiment of the present invention provides a battery charging optimization method, including: establishing a dual-objective optimization function according to the relationship between the battery's charging current, charging time, and charging temperature rise; determining multiple charging currents of the battery; taking each of the charging currents as a habitat, migrating and mutating each of the habitats, and determining a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function; returning to execute the migration and mutation of each of the habitats, and determining a target charging current according to the migration fitness index and the mutation fitness index after a preset stop condition is met. The above technical scheme can first establish a dual-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise, and secondly determine multiple charging currents of the battery, and then optimize the dual-objective optimization function based on the biogeography-based optimization (BBO) algorithm. Specifically, each charging current is used as a habitat, and the migration and mutation of each habitat are repeatedly performed. The migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat are determined according to the dual-objective optimization function until the preset stop condition is met. The current corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat determined when the preset stop condition is met is determined as the target charging current, so as to determine the target charging current. When the battery is charged based on the target charging current, the charging time of the battery and the charging temperature rise are balanced, and the charging time and the charging temperature rise are both small, so as to optimize the battery charging.

[0011] Furthermore, a dual-objective optimization function is established based on the relationship between the battery charging current, charging time and charging temperature rise, including:

[0012] Establishing a time optimization function according to the relationship between the charging current and the charging time;

[0013] Establishing a temperature rise optimization function according to the relationship between the charging current and the charging temperature rise;

[0014] The dual-objective optimization function is established according to the first weight time corresponding to the charging time and the time optimization function, and the second weight corresponding to the charging temperature rise and the temperature rise optimization function.

[0015] Further, multiple charging currents of the battery are determined, including:

[0016] The plurality of charging currents are determined based on a current constraint of the battery.

[0017] Furthermore, the habitats are relocated, including:

[0018] Determining an initial fitness index of each of the habitats according to the dual-objective optimization function;

[0019] For each of the habitats, the migration source habitat is determined based on the initial fitness index of the current habitat, and the migration habitat is determined based on the current habitat and the migration source habitat;

[0020] For each of the habitats, the current habitat is mutated based on the selected mutation method to determine a variant habitat.

[0021] Furthermore, the migration source habitat is determined according to the initial fitness index of the current habitat, and the migration habitat is determined according to the current habitat and the migration source habitat, including:

[0022] Determine any habitat whose initial fitness index is greater than the initial fitness index of the current habitat as the migration source habitat;

[0023] The migration habitat is determined according to the current habitat, the migration source habitat and the migration rate corresponding to the current habitat.

[0024] Further, determining the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat according to the dual-objective optimization function includes:

[0025] Substituting the migration charging current corresponding to each of the migration habitats into the dual-objective optimization function to determine the migration fitness index corresponding to each of the migration habitats;

[0026] The variable charging current corresponding to each of the variable habitats is substituted into the dual-objective optimization function to determine the migration fitness index corresponding to each of the variable habitats.

[0027] Further, after a preset stop condition is met, determining a target charging current according to the migration fitness index and the variation fitness index includes:

[0028] After determining that the preset stop condition is met, the target charging current is determined as the current corresponding to the migration habitat or the variation habitat corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat.

[0029] In a second aspect, an embodiment of the present invention further provides a battery charging optimization device, comprising:

[0030] Establishing a module for establishing a dual-objective optimization function according to the relationship between the charging current, charging time and charging temperature rise of the battery;

[0031] A determination module, used for determining a plurality of charging currents of the battery;

[0032] A first execution module is used to use each charging current as a habitat, migrate and mutate each habitat, and determine a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function;

[0033] The second execution module is used to return to execute the migration and mutation of each habitat, and determine the target charging current according to the migration fitness index and the mutation fitness index after the preset stop condition is met.

[0034] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0035] at least one processor; and a memory communicatively coupled to the at least one processor;

[0036] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the battery charging optimization method as described in any one of the first aspects.

[0037] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute a battery charging optimization method as described in any one of the first aspects.

[0038] In a fifth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are executed on a computer, the computer executes the battery charging optimization method provided in the first aspect.

[0039] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the battery charging optimization device, or may be packaged separately from the processor of the battery charging optimization device, and this application does not limit this.

[0040] The description of the second, third, fourth and fifth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third, fourth and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0041] In this application, the name of the battery charging optimization device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalent technologies.

[0042] These and other aspects of the present application will become more apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A flowchart of a battery charging optimization method provided by an embodiment of the present invention;

[0045] Figure 2 A flowchart of another battery charging optimization method provided by an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the structure of a battery charging optimization device provided by an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0049] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0050] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0051] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0052] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the embodiments in the present invention and the features in the embodiments can be combined with each other without conflict.

[0053] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0054] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0055] Figure 1 A flowchart of a battery charging optimization method provided by an embodiment of the present invention. This embodiment is applicable to the situation where both the charging time and the charging temperature rise need to be reduced. The method can be executed by a battery charging optimization device, such as Figure 1 As shown, the specific steps include:

[0056] Step 110: Establish a dual-objective optimization function according to the relationship between the charging current, charging time and charging temperature rise of the battery.

[0057] Specifically, the purpose of optimizing battery charging is to determine the target charging current while reducing the charging time and charging temperature rise. First, the relationship between the charging current and the charging time can be determined based on the charging properties of the battery, and the relationship between the charging current and the charging temperature rise can be determined based on the temperature rise properties of the battery. Then, a dual-objective optimization function can be constructed based on the pre-set weights corresponding to the charging time, the weights corresponding to the charging temperature rise, the relationship between the charging current and the charging time, and the relationship between the charging current and the charging temperature rise.

[0058] In the embodiment of the present invention, a dual-objective optimization function is established according to the relationship between the charging current, charging time and charging temperature rise of the battery.

[0059] Step 120: Determine multiple charging currents of the battery.

[0060] There are multiple constraints in the battery charging process, such as current constraint, voltage constraint, remaining power (State of Charge, SOC) range constraint, temperature constraint, and charging time constraint. Therefore, the current constraint needs to be considered when determining multiple charging currents of the battery.

[0061] Specifically, multiple charging currents may be determined according to the current constraint of the battery and the preset current interval. For example, the current constraint is I min ≤I≤I max , I min Indicates the minimum charging current of the battery, I max Indicates the maximum charging current of the battery. When the preset current interval is ΔI, multiple charging currents can be determined to be I min ,I min +ΔI,I min +2ΔI,…,I max .

[0062] In the embodiment of the present invention, multiple charging currents of the battery are determined by referring to the current constraint of the battery.

[0063] Step 130: taking each charging current as a habitat, migrating and mutating each habitat, and determining a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function.

[0064] Since it is necessary to determine the target charging current and reduce the charging time and charging temperature rise at the same time, the dual-objective optimization function can be optimized based on the BBO algorithm to determine the target charging current. Therefore, the multiple charging currents of the battery determined above can be used as the habitat in the BBO algorithm, and the dual-objective optimization function is used to determine the fitness index of the habitat. By migrating and mutating the habitat, the dual-objective optimization function can be optimized.

[0065] Migrating a habitat can be understood as migrating some species from a habitat with high fitness to a habitat with low fitness, thereby renewing the migrated habitat. Mutating a habitat can be understood as mutating the habitat based on a preset mutation method.

[0066] Specifically, since the migration of habitats needs to refer to the fitness of habitats, it is necessary to determine the initial fitness of each habitat. Specifically, each charging current can be substituted into the dual-objective optimization function to determine the function value of each charging current, and the function value of each charging current is determined as the initial fitness of the habitat corresponding to each charging current. Furthermore, for each habitat, any habitat with an initial fitness greater than the initial fitness of the current habitat is used as the migration source habitat, and the migration habitat corresponding to the current habitat is determined based on the current habitat and the migration source habitat. For each habitat, the target variation method can be determined in the preset variation method, and the current habitat can be mutated based on the target variation method to obtain the variation habitat corresponding to the current habitat.

[0067] Of course, after determining the migration habitat and variant habitat corresponding to the current habitat, the migration charging current corresponding to the migration habitat and the variant charging current corresponding to the variant habitat can be determined. Substituting the migration charging current into the dual-objective optimization function, the migration fitness index corresponding to the migration habitat can be determined, and substituting the variant charging current into the objective optimization function can determine the variant fitness index corresponding to the variant habitat.

[0068] In an embodiment of the present invention, after determining the migration habitat and the variant habitat by migrating and mutating each habitat, the migration charging current corresponding to each migration habitat and the variant charging current corresponding to each variant habitat are determined, and the migration fitness index corresponding to each migration habitat and the variant fitness index corresponding to each variant habitat are determined according to each migration charging current and each variant charging current and a dual-objective optimization function, thereby achieving optimization of the dual-objective optimization function.

[0069] Step 140 , returning to execute migration and mutation of each habitat, and determining a target charging current according to the migration fitness index and the mutation fitness index after a preset stop condition is met.

[0070] The preset stop condition may be that the number of migrations and mutations is greater than a threshold number, or that the migration fitness index and / or mutation fitness index converge.

[0071] Specifically, the migration and mutation of each habitat are repeatedly performed, and the migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat are determined according to the dual-objective optimization function, and after the preset stop condition is met, the final migration habitat and mutation habitat are obtained. The migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat are sorted, and the current corresponding to the minimum fitness index is determined as the target charging current.

[0072] In an embodiment of the present invention, the current corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat determined when the preset stop condition is met is determined as the target charging current, so as to achieve the determination of the target charging current. When the battery is charged based on the target charging current, the charging time and the charging temperature rise of the battery are balanced, and both the charging time and the charging temperature rise are small, thereby achieving optimization of battery charging.

[0073] The battery charging optimization method provided by an embodiment of the present invention includes: establishing a dual-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise; determining multiple charging currents of the battery; taking each of the charging currents as a habitat, migrating and mutating each of the habitats, and determining a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function; returning to execute the migration and mutation of each of the habitats, and determining the target charging current according to the migration fitness index and the mutation fitness index after a preset stop condition is met. The above technical scheme can first establish a dual-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise, and secondly determine multiple charging currents of the battery, and then optimize the dual-objective optimization function based on the BBO algorithm. Specifically, each charging current is used as a habitat, and the migration and mutation of each habitat are repeatedly performed. The migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat are determined according to the dual-objective optimization function until the preset stop condition is met. The current corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat determined when the preset stop condition is met is determined as the target charging current, so as to determine the target charging current. When the battery is charged based on the target charging current, the charging time of the battery and the charging temperature rise are balanced, and the charging time and the charging temperature rise are both small, thereby optimizing the battery charging.

[0074] Figure 2 This is a flow chart of another battery charging optimization method provided by an embodiment of the present invention. This embodiment is specific based on the above embodiment. Figure 2 As shown, in this embodiment, the method may further include:

[0075] Step 210: Establish a dual-objective optimization function according to the relationship between the charging current, charging time and charging temperature rise of the battery.

[0076] In one implementation, step 210 may specifically include:

[0077] A time optimization function is established according to the relationship between the charging current and the charging time; a temperature rise optimization function is established according to the relationship between the charging current and the charging temperature rise; the dual-objective optimization function is established according to the first weight time corresponding to the charging time and the time optimization function and the second weight corresponding to the charging temperature rise and the temperature rise optimization function.

[0078] Specifically, the charging time is related to factors such as the charging current and the battery capacity. The battery charging process follows a constant current-constant voltage charging model, and the charging time can be determined based on the relationship between the charging current and the charging time. Where C represents the battery capacity, I represents the charging current, and t(I) represents the charging time required to charge the battery at I. The charging temperature rise is related to factors such as the charging current, the internal resistance of the battery, and the heat dissipation conditions. According to Joule's law, the heat generation power during the charging process P(I) = IR, where R represents the internal resistance of the battery, and P(I) represents the heat generation power of the battery when the battery is charged at I. The battery charging process follows a first-order thermal model, and the charging temperature rise can be determined based on the relationship between the charging current and the charging temperature rise. Among them, h represents the heat dissipation coefficient of the battery, A represents the heat dissipation area of ​​the battery, t represents the charging time, and ΔT0 represents the initial temperature rise of the battery.

[0079] Then, the dual-objective optimization function is determined based on the first weight time and time optimization function corresponding to the charging time and the second weight and temperature rise optimization function corresponding to the charging temperature rise. That is, the dual-objective optimization function is determined as Among them, α+β=1, α represents the weight corresponding to the charging time, β represents the weight corresponding to the charging temperature rise, t0 represents the initial charging time of the battery, t max (I) represents the charging time when the battery is charged with an acceptable current, ΔT max (I) represents the charging temperature rise when the battery is charged at an acceptable current.

[0080] It should be noted that when α=1, the dual-objective optimization function will only target the charging time and ignore the charging temperature rise. At this time, the battery can be charged with the maximum charging current corresponding to the current constraint. When the current conversion condition of multi-stage charging is reached, the battery switches to the next stage, and the battery is charged with the maximum charging current of each stage in each stage. When β=1, the dual-objective optimization function will only target the charging temperature rise and ignore the charging time. At this time, the battery can be charged with the minimum charging current corresponding to the current constraint. When the current conversion condition of multi-stage charging is reached, the battery switches to the next stage, and the battery is charged with the minimum charging current of each stage in each stage.

[0081] In the embodiment of the present invention, a dual-objective optimization function is established according to the relationship between the charging current, charging time and charging temperature rise of the battery.

[0082] Step 220: Determine multiple charging currents of the battery.

[0083] In one implementation, step 220 may specifically include:

[0084] The plurality of charging currents are determined based on a current constraint of the battery.

[0085] As mentioned before, the current constraint is I min ≤I≤I max When the preset current interval is ΔI, multiple charging currents can be determined to be I min ,I min +ΔI,I min +2ΔI,…,I max .

[0086] In the embodiment of the present invention, multiple charging currents of the battery are determined by referring to the current constraint of the battery.

[0087] Step 230: taking each of the charging currents as a habitat, migrating and mutating each of the habitats, and determining a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function.

[0088] In one implementation, step 230 may specifically include:

[0089] Taking each of the charging currents as a habitat, the initial fitness index of each of the habitats is determined according to the dual-objective optimization function; for each of the habitats, the migration source habitat is determined according to the initial fitness index of the current habitat, and the migration habitat is determined according to the current habitat and the migration source habitat; for each of the habitats, the current habitat is mutated based on the selected mutation method to determine the variant habitat; the migration charging current corresponding to each of the migration habitats is substituted into the dual-objective optimization function to determine the migration fitness index corresponding to each of the migration habitats; the variant charging current corresponding to each of the variant habitats is substituted into the dual-objective optimization function to determine the migration fitness index corresponding to each of the variant habitats.

[0090] Furthermore, the migration source habitat is determined according to the initial fitness index of the current habitat, and the migration habitat is determined according to the current habitat and the migration source habitat, including:

[0091] Any habitat whose initial fitness index is greater than the initial fitness index of the current habitat is determined as the migration source habitat; and the migration habitat is determined according to the current habitat, the migration source habitat and the migration rate corresponding to the current habitat.

[0092] Specifically, first, each charging current is substituted into the dual-objective optimization function to determine the function value of each charging current, and the function value of each charging current is determined as the initial fitness of the habitat corresponding to each charging current.

[0093] Secondly, for each habitat, any habitat with an initial fitness greater than that of the current habitat is taken as the migration source habitat, and the migration habitat corresponding to the current habitat is determined based on the current habitat and the migration source habitat. Specifically, the migration habitat corresponding to the current habitat can be obtained by migrating some characteristics of the migration source habitat to the current habitat. The habitat is the charging current, and the current charging current I corresponding to the current habitat can be used to determine the migration habitat corresponding to the current habitat. t The charging current I corresponding to the migration source habitat s Determine the current charging current I t The corresponding migration charging current To determine Here, r represents the mobility, and the mobility can be set according to actual needs, for example, it can be 0.15.

[0094] For each habitat, a mutation mode can be selected from the preset mutation modes, and the current habitat is mutated based on the selected mutation mode to determine the mutated habitat. The preset mutation modes can be random mutation and neighborhood mutation, etc. Specifically, the current habitat can be mutated based on random mutation or neighborhood mutation to obtain the mutated habitat corresponding to the current habitat. The habitat is the charging current, and the current charging current I is mutated based on random mutation. t By performing mutation, the variation charging current can be determined To determine Where ΔI=λ(I max -I min ), λ∈[-a,a], a is a coefficient set according to actual needs, for example, it can be 0.1. Based on the neighborhood variation, the current charging current I t By performing mutation, the variation charging current can be determined To determine in, is a coefficient set according to actual needs, for example, 0.2, and I' represents the charging current with lower charging time and charging temperature rise. Neighborhood mutation can use the charging current corresponding to the lower charging time and charging temperature rise in the neighborhood to guide the BBO algorithm to search in a better direction.

[0095] Furthermore, the migration charging current corresponding to each migration habitat is substituted into the dual-objective optimization function to determine the migration fitness index corresponding to each migration habitat; the variant charging current corresponding to each variant habitat is substituted into the dual-objective optimization function to determine the migration fitness index corresponding to each variant habitat.

[0096] It should be noted that in the optimization process, reference voltage constraints, SOC range constraints, temperature constraints, and charging time constraints are also required. Among them, the voltage constraint is V min ≤V≤V max , where V min Indicates the voltage lower limit, V max Indicates the voltage lower limit, and the SOC range constraint can be SOC min ≤SOC≤SOC max , where SOC min Indicates the initial SOC, SOC max represents the target SOC, and the temperature constraint is T min ≤T≤T max , where T min Indicates the minimum allowable current temperature, T max represents the maximum allowable current temperature, and the charging time constraint is t min ≤t≤t max , where t min It represents the time required to charge the battery based on the maximum current in multi-stage charging, t max Indicates the time required to charge the battery based on the minimum current in multi-stage charging.

[0097] In an embodiment of the present invention, after determining the migration habitat and the variant habitat by migrating and mutating each habitat, the migration charging current corresponding to each migration habitat and the variant charging current corresponding to each variant habitat are determined, and the migration fitness index corresponding to each migration habitat and the variant fitness index corresponding to each variant habitat are determined according to each migration charging current and each variant charging current and a dual-objective optimization function, thereby achieving optimization of the dual-objective optimization function.

[0098] Step 240: Determine whether a preset stop condition is met.

[0099] If it is determined that the preset stop condition is met, step 250 is executed; otherwise, the process returns to step 230 .

[0100] Specifically, step 230 is repeatedly executed, and after each execution of step 230, it is determined whether the preset stop condition is met. After determining that the number of migrations and mutations is greater than the number threshold, or the migration fitness index and / or the mutation fitness index converges, it is determined that the preset stop condition is met, and then step 250 can be continued. Otherwise, return to execute step 230.

[0101] Step 250: Determine a target charging current according to the migration fitness index and the mutation fitness index.

[0102] In one implementation, step 250 may specifically include:

[0103] The target charging current is determined according to the current corresponding to the migration habitat or the variation habitat corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat.

[0104] Specifically, after the preset stop condition is met, the final migration habitat and variation habitat can be obtained. The target charging current is determined according to the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat. Specifically, the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat can be sorted, and the current corresponding to the minimum fitness index is determined as the target charging current.

[0105] In an embodiment of the present invention, the current corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat determined when the preset stop condition is met is determined as the target charging current, so as to achieve the determination of the target charging current. When the battery is charged based on the target charging current, the charging time and the charging temperature rise of the battery are balanced, and both the charging time and the charging temperature rise are small, thereby achieving optimization of battery charging.

[0106] The battery charging optimization method provided by an embodiment of the present invention includes: establishing a dual-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise; determining multiple charging currents of the battery; taking each of the charging currents as a habitat, migrating and mutating each of the habitats, and determining the migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function; determining whether a preset stop condition is met; if it is determined that the preset stop condition is not met, returning to execute the migration and mutation of each of the habitats, and determining the migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function; if it is determined that the preset stop condition is met, determining the target charging current according to the migration fitness index and the mutation fitness index. The above technical scheme can first establish a dual-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise, and secondly determine multiple charging currents of the battery, and then optimize the dual-objective optimization function based on the BBO algorithm. Specifically, each charging current is used as a habitat, and the migration and mutation of each habitat are repeatedly performed. The migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat are determined according to the dual-objective optimization function until the preset stop condition is met. The current corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat determined when the preset stop condition is met is determined as the target charging current, so as to determine the target charging current. When the battery is charged based on the target charging current, the charging time of the battery and the charging temperature rise are balanced, and the charging time and the charging temperature rise are both small, thereby optimizing the battery charging.

[0107] Figure 3 This is a schematic diagram of the structure of a battery charging optimization device provided by an embodiment of the present invention, which can be applied to situations where charging time and charging temperature rise need to be reduced. The device can be implemented by software and / or hardware and is generally integrated in electronic equipment, such as a vehicle.

[0108] like Figure 3 As shown, the device comprises:

[0109] Establishing module 310, for establishing a dual-objective optimization function according to the relationship between the charging current, charging time and charging temperature rise of the battery;

[0110] A determination module 320, configured to determine a plurality of charging currents of a battery;

[0111] A first execution module 330 is used to use each charging current as a habitat, migrate and mutate each habitat, and determine a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function;

[0112] The second execution module 340 is used to return to execute the migration and mutation of each habitat, and determine the target charging current according to the migration fitness index and the mutation fitness index after the preset stop condition is met.

[0113] The battery charging optimization device provided in this embodiment establishes a dual-objective optimization function according to the relationship between the battery's charging current, charging time, and charging temperature rise; determines multiple charging currents of the battery; uses each of the charging currents as a habitat, migrates and mutates each of the habitats, and determines a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function; returns to execute the migration and mutation of each of the habitats, and determines a target charging current according to the migration fitness index and the mutation fitness index after a preset stop condition is met. The above technical scheme can first establish a dual-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise, and secondly determine multiple charging currents of the battery, and then optimize the dual-objective optimization function based on the BBO algorithm. Specifically, each charging current is used as a habitat, and the migration and mutation of each habitat are repeatedly performed. The migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat are determined according to the dual-objective optimization function until the preset stop condition is met. The current corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the mutation fitness index corresponding to each mutation habitat determined when the preset stop condition is met is determined as the target charging current, so as to determine the target charging current. When the battery is charged based on the target charging current, the charging time of the battery and the charging temperature rise are balanced, and the charging time and the charging temperature rise are both small, thereby optimizing the battery charging.

[0114] Based on the above embodiment, a module 310 is established, which is specifically used for:

[0115] A time optimization function is established according to the relationship between the charging current and the charging time; a temperature rise optimization function is established according to the relationship between the charging current and the charging temperature rise; the dual-objective optimization function is established according to the first weight time corresponding to the charging time and the time optimization function and the second weight corresponding to the charging temperature rise and the temperature rise optimization function.

[0116] Based on the above embodiment, the determination module 320 is specifically used for:

[0117] The plurality of charging currents are determined based on a current constraint of the battery.

[0118] Based on the above embodiment, the first execution module 330 is specifically configured to:

[0119] Taking each of the charging currents as a habitat, the initial fitness index of each of the habitats is determined according to the dual-objective optimization function; for each of the habitats, the migration source habitat is determined according to the initial fitness index of the current habitat, and the migration habitat is determined according to the current habitat and the migration source habitat; for each of the habitats, the current habitat is mutated based on the selected mutation method to determine the variant habitat; the migration charging current corresponding to each of the migration habitats is substituted into the dual-objective optimization function to determine the migration fitness index corresponding to each of the migration habitats; the variant charging current corresponding to each of the variant habitats is substituted into the dual-objective optimization function to determine the migration fitness index corresponding to each of the variant habitats.

[0120] In one embodiment, determining the migration source habitat according to the initial fitness index of the current habitat, and determining the migration habitat according to the current habitat and the migration source habitat, comprises:

[0121] Any habitat whose initial fitness index is greater than the initial fitness index of the current habitat is determined as the migration source habitat; and the migration habitat is determined according to the current habitat, the migration source habitat and the migration rate corresponding to the current habitat.

[0122] Based on the above embodiment, the second execution module 340 is specifically configured to:

[0123] After determining that the preset stop condition is met, the target charging current is determined as the current corresponding to the migration habitat or the variation habitat corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat.

[0124] The battery charging optimization device provided in the embodiment of the present invention can execute the battery charging optimization method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the battery charging optimization method.

[0125] It is worth noting that in the embodiment of the above-mentioned battery charging optimization device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0126] Figure 4A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 A block diagram of an exemplary electronic device 4 suitable for implementing embodiments of the present invention is shown. Figure 4 The electronic device 4 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0127] like Figure 4 As shown, the electronic device 4 is in the form of a general purpose computing electronic device. The components of the electronic device 4 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0128] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0129] The electronic device 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 4, including volatile and non-volatile media, removable and non-removable media.

[0130] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0131] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0132] The electronic device 4 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), one or more devices that enable a user to interact with the electronic device 4, and / or any device that enables the electronic device 4 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 4 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. Figure 4 As shown, the network adapter 20 communicates with other modules of the electronic device 4 via the bus 18. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 4, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0133] The processing unit 16 executes various functional applications and page displays by running the program stored in the system memory 28, for example, implementing the battery charging optimization method provided in the embodiment of the present invention, the method comprising:

[0134] A dual-objective optimization function is established based on the relationship between the battery's charging current, charging time, and charging temperature rise;

[0135] determining multiple charging currents for the battery;

[0136] Taking each of the charging currents as a habitat, migrating and mutating each of the habitats, and determining a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function;

[0137] Return to execute migration and mutation of each habitat, and determine the target charging current according to the migration fitness index and the mutation fitness index after the preset stop condition is met.

[0138] Of course, those skilled in the art can understand that the processor can also implement the technical solution of the battery charging optimization method provided in any embodiment of the present invention.

[0139] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, for example, a battery charging optimization method provided by an embodiment of the present invention is implemented. The method includes:

[0140] A dual-objective optimization function is established based on the relationship between the battery's charging current, charging time, and charging temperature rise;

[0141] determining multiple charging currents for the battery;

[0142] Taking each of the charging currents as a habitat, migrating and mutating each of the habitats, and determining a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function;

[0143] Return to execute migration and mutation of each habitat, and determine the target charging current according to the migration fitness index and the mutation fitness index after the preset stop condition is met.

[0144] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0145] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0146] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0147] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0148] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0149] In addition, the acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of national laws and regulations.

[0150] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A battery charging optimization method, characterized in that: include: A dual-objective optimization function is established based on the relationship between the battery's charging current, charging time, and charging temperature rise; determining multiple charging currents for the battery; Taking each of the charging currents as a habitat, migrating and mutating each of the habitats, and determining a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function; Return to execute migration and mutation of each habitat, and determine the target charging current according to the migration fitness index and the mutation fitness index after the preset stop condition is met.

2. The battery charging optimization method according to claim 1, characterized in that: A dual-objective optimization function is established based on the relationship between the battery's charging current, charging time, and charging temperature rise, including: Establishing a time optimization function according to the relationship between the charging current and the charging time; Establishing a temperature rise optimization function according to the relationship between the charging current and the charging temperature rise; The dual-objective optimization function is established according to the first weight time corresponding to the charging time and the time optimization function, and the second weight corresponding to the charging temperature rise and the temperature rise optimization function.

3. The battery charging optimization method according to claim 1, characterized in that: Determine multiple charging currents for the battery, including: The plurality of charging currents are determined based on a current constraint of the battery.

4. The battery charging optimization method according to claim 1, characterized in that: Movement and modification of each of the habitats mentioned, including: Determining an initial fitness index of each of the habitats according to the dual-objective optimization function; For each of the habitats, the migration source habitat is determined based on the initial fitness index of the current habitat, and the migration habitat is determined based on the current habitat and the migration source habitat; For each of the habitats, the current habitat is mutated based on the selected mutation method to determine a variant habitat.

5. The battery charging optimization method according to claim 4, characterized in that: Determine the migration source habitat based on the initial fitness index of the current habitat, and determine the migration habitat based on the current habitat and the migration source habitat, including: Determine any habitat whose initial fitness index is greater than the initial fitness index of the current habitat as the migration source habitat; The migration habitat is determined according to the current habitat, the migration source habitat and the migration rate corresponding to the current habitat.

6. The battery charging optimization method according to claim 1, characterized in that: Determining the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat according to the dual-objective optimization function includes: Substituting the migration charging current corresponding to each of the migration habitats into the dual-objective optimization function to determine the migration fitness index corresponding to each of the migration habitats; The variable charging current corresponding to each of the variable habitats is substituted into the dual-objective optimization function to determine the migration fitness index corresponding to each of the variable habitats.

7. The battery charging optimization method according to claim 1, characterized in that: After a preset stop condition is met, determining a target charging current according to the migration fitness index and the variation fitness index includes: After determining that the preset stop condition is met, the target charging current is determined as the current corresponding to the migration habitat or the variation habitat corresponding to the minimum fitness index among the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat.

8. A battery charging optimization device, characterized in that: include: Establishing a module for establishing a dual-objective optimization function according to the relationship between the charging current, charging time and charging temperature rise of the battery; A determination module, used for determining a plurality of charging currents of the battery; A first execution module is used to use each charging current as a habitat, migrate and mutate each habitat, and determine a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat according to the dual-objective optimization function; The second execution module is used to return to execute the migration and mutation of each habitat, and determine the target charging current according to the migration fitness index and the mutation fitness index after the preset stop condition is met.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the battery charging optimization method as described in any one of claims 1-7.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to perform the battery charging optimization method as described in any one of claims 1-7 when executed by a computer processor.

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