A battery charging optimization method, device, equipment and storage medium

Abstract

The application discloses a battery charging optimization method and device, equipment and storage medium, relate to power electronic technical field, this method includes: according to the relationship of the charging current of battery and charging time and charging temperature rise establishes double target optimization function;Determine the multiple charging currents of battery;Each charging current is used as habitat, each habitat is migrated and varied, and the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat are determined according to the double target optimization function;Return to execute migration and variation to each habitat, and the target charging current is determined according to the migration fitness index and the variation fitness index after meeting the preset stop condition.The above technical scheme realizes the determination of the target charging current, when charging the battery based on the target charging current, the charging time and the charging temperature rise of the battery reach balance, and the charging time and the charging temperature rise are smaller, and the optimization of battery charging is realized.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of power electronics, and particularly relate to a battery charging optimization method, device, equipment and storage medium. BACKGROUND

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

[0003] Therefore, there is an urgent need for a battery charging optimization method to reduce charging time and charging temperature rise. SUMMARY

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

[0005] In a first aspect, embodiments of the present application provide a battery charging optimization method, comprising:

[0006] establishing a double-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise;

[0007] determining a plurality of charging currents of 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 of the migrated habitats and a mutation fitness index corresponding to each of the mutated habitats according to the double-objective optimization function;

[0009] returning to migrate and mutate 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.

[0010] The technical scheme of the embodiment of the application provides a battery charging optimization method, comprising: establishing a double-target optimization function according to the relationship 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, migrating and mutating each habitat, and determining a migration fitness index corresponding to each migrated habitat and a mutation fitness index corresponding to each mutated habitat according to the double-target optimization function; returning to perform migration and mutation on 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. The above technical scheme can first establish a double-target optimization function according to the relationship between the charging current of a battery and the charging time and the charging temperature rise, secondly determine a plurality of charging currents of the battery, and then optimize the double-target optimization function based on a biogeography-based optimization (BBO) algorithm. Specifically, each charging current is taken as a habitat, migration and mutation are repeatedly performed on each habitat, a migration fitness index corresponding to each migrated habitat and a mutation fitness index corresponding to each mutated habitat are determined according to the double-target optimization function, until a preset stop condition is met, a current corresponding to the minimum fitness index in the migration fitness index corresponding to each migrated habitat and the mutation fitness index corresponding to each mutated habitat determined when the preset stop condition is met is determined as the target charging current, the determination of the target charging current is realized, and when the battery is charged based on the target charging current, the charging time and the charging temperature rise of the battery reach a balance, and the charging time and the charging temperature rise are both small, thereby optimizing the charging of the battery.

[0011] Further, the double-target optimization function is established according to the relationship between the charging current of the battery and the charging time and the charging temperature rise, comprising:

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

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

[0014] the double-target optimization function is established according to a first weight time corresponding to the charging time and the time optimization function and a second weight corresponding to the charging temperature rise and the temperature rise optimization function.

[0015] Further, the plurality of charging currents of the battery are determined, comprising:

[0016] the plurality of charging currents are determined according to the current constraint of the battery.

[0017] Further, the migration of each habitat comprises:

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

[0019] For each of the habitats, determine a migration source habitat according to the initial fitness index of the current habitat, and determine a migration habitat according to the current habitat and the migration source habitat;

[0020] For each of the habitats, determine a variation habitat by varying the current habitat based on a selected variation mode.

[0021] Further, 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 with an initial fitness index greater than the initial fitness index of the current habitat as the migration source habitat;

[0023] determine the migration habitat according to the current habitat, the migration source habitat, and the migration rate corresponding to the current habitat.

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

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

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

[0028] determine the target charging current corresponding 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 of the migration habitats and the variation fitness index corresponding to each of the variation habitats after the preset stop condition is met.

[0029] In a second aspect, an embodiment of the present application also provides a battery charging optimization device, including:

[0030] The establishing module is configured to 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;

[0031] The determining module is configured to determine a plurality of charging currents of the battery;

[0032] A first execution module is configured 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 a preset stop condition is met.

[0034] In a third aspect, an embodiment of the present invention further provides an 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 the 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 descriptions of the second, third, fourth and fifth aspects of this application can refer to the detailed description of the first aspect; and the beneficial effects of the descriptions of the second, third, fourth and fifth aspects can refer to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0041] In the present application, the names of the above-mentioned battery charging optimization apparatus do not constitute a limitation on the devices or functional modules themselves, and in actual implementation, these devices or functional modules can appear under other names. As long as the functions of the respective devices or functional modules are similar to those of the present application, they fall within the scope of the claims of the present application and equivalent technologies.

[0042] These aspects or other aspects of the present application will be more apparent in the following description. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0044] Figure 1 A flow chart of a battery charging optimization method provided by an embodiment of the present application;

[0045] Figure 2 A flow chart of another battery charging optimization method provided by an embodiment of the present application;

[0046] Figure 3 A structural schematic diagram of a battery charging optimization apparatus provided by an embodiment of the present application;

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

[0048] The present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, and not to limit the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings, not all the structures.

[0049] The term "and / or" in the present document is only used to describe the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone.

[0050] The terms "first" and "second" and the like in the specification and drawings of the present application are used to distinguish different objects or to distinguish different treatments of the same object, and are not used to describe the specific order of the objects.

[0051] Moreover, the terms "comprising" and "having" and any variations thereof in the description and in the claims are intended to cover both the inclusive and exclusive aspects thereof. For example, a process, method, system, product, or apparatus that comprises a list of steps or elements is not necessarily limited to only those steps or elements but can include other steps or elements not expressly listed or inherent to such process, method, system, product, or apparatus.

[0052] Before some example embodiments are discussed in further detail, it should be noted that some example embodiments are described as processes or methods depicted as flow diagrams. Although the processes are described in a certain order, many of the operations can be performed concurrently, in parallel, or simultaneously. In addition, the order of the operations can be re-arranged. The processes can terminate when their operations are completed, but can also have additional steps not included in the figure. The processes can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, embodiments and features of the present application can be combined with each other as mutually exclusive combinations, if not in conflict.

[0053] It should be noted that the terms "exemplary" and / or "example" are used herein to mean an instance of the general case, an implementation, an example, etc. Any embodiment or implementation described herein as "exemplary" and / or as an "example" should not be construed as preferred or advantageous over other embodiments or implementations.

[0054] In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0055] Figure 1 A flowchart of a battery charging optimization method provided by an embodiment of the present application, the embodiment can be applicable to the case of simultaneously reducing charging time and charging temperature rise, and the method can be executed by a battery charging optimization device, as shown in the figure, and specifically includes the following steps: Figure 1 As shown in the figure, the embodiment specifically includes the following steps:

[0056] In step 110, a double-target optimization function is established according to the relationship between the charging current of the battery and the charging time and the charging temperature rise.

[0057] Specifically, the purpose of optimizing the battery charging is to determine the target charging current while reducing the charging time and the charging temperature rise. First, the relationship between the charging current and the charging time can be determined according to the charging properties of the battery, and the relationship between the charging current and the charging temperature rise can be determined according to the temperature rise properties of the battery, and then a double-target optimization function can be constructed according to the pre-set weight corresponding to the charging time, the weight corresponding to the charging temperature rise, and 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 application, a double-target optimization function is established according to the relationship between the charging current of the battery and the charging time and the charging temperature rise.

[0059] In step 120, a plurality of charging currents of the battery are determined.

[0060] The charging process of the battery has a plurality of constraints, for example, current constraint, voltage constraint, State of Charge (SOC) range constraint, temperature constraint, and charging time constraint. Therefore, when determining the plurality of charging currents of the battery, the current constraint needs to be considered.

[0061] Specifically, the plurality of charging currents can be determined according to the current constraint of the battery and a preset current interval, for example, the current constraint is I min ≤I≤I max , I min represents the minimum charging current of the battery, I max represents the maximum charging current of the battery, and when the preset current interval is ΔI, the plurality of charging currents can be determined as I min , I min +ΔI, I min +2ΔI, …, I max .

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

[0063] In step 130, each of the charging currents is taken as a habitat, each of the habitats is migrated and mutated, and the migration fitness index corresponding to each of the migrated habitats and the mutation fitness index corresponding to each of the mutated habitats are determined according to the double-target optimization function.

[0064] Since the target charging current needs to be determined while reducing the charging time and the charging temperature rise, the double-target optimization function can be optimized based on the BBO algorithm to determine the target charging current. Therefore, the plurality of charging currents of the battery determined in the foregoing can be taken as the habitats in the BBO algorithm, the double-target optimization function is used to determine the fitness index of the habitats, and by migrating and mutating the habitats, the double-target optimization function can be optimized.

[0065] The migration of the habitats can be understood as migrating part of the species of the habitats with high fitness to the habitats with low fitness to update the migrated habitats. The mutation of the habitats can be understood as mutating the habitats based on a preset mutation manner.

[0066] Specifically, since the migration of the habitat needs to refer to the fitness of the habitat, the initial fitness of each habitat needs to be determined, specifically, each charging current can be substituted into the double-objective optimization function, the function value of each charging current is determined, and the function value of each charging current is determined as the initial fitness of the habitat corresponding to each charging current. Further, for each habitat, any habitat with an initial fitness greater than the initial fitness of the current habitat is taken as a migration source habitat, and the migration habitat corresponding to the current habitat is determined according to the current habitat and the migration source habitat. For each habitat, the target variation mode can be determined in the preset variation mode, and the current habitat is varied based on the target variation mode to obtain the variation habitat corresponding to the current habitat.

[0067] Of course, after the migration habitat corresponding to the current habitat and the variation habitat are determined, the migration charging current corresponding to the migration habitat and the variation charging current corresponding to the variation habitat can be determined. By substituting the migration charging current into the double-objective optimization function, the migration fitness index corresponding to the migration habitat can be determined, and by substituting the variation charging current into the target optimization function, the variation fitness index corresponding to the variation habitat can be determined.

[0068] In the embodiment of the present application, after the migration habitat and the variation habitat are determined by migrating and varying each habitat, the migration charging current corresponding to each migration habitat and the variation charging current corresponding to each variation habitat are determined, and the migration fitness index corresponding to each migration habitat and the variation fitness index corresponding to each variation habitat are determined according to each migration charging current and each variation charging current and the double-objective optimization function, thereby optimizing the double-objective optimization function.

[0069] Step 140, return to execute migration and variation of each habitat, and determine the target charging current according to the migration fitness index and the variation fitness index after the preset stop condition is met.

[0070] The preset stop condition can be that the number of migrations and variations is greater than the number threshold, or the migration fitness index and / or the variation fitness index converges.

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

[0072] In the embodiment of the present application, the current corresponding to the minimum fitness index among the migration fitness indexes corresponding to each migration habitat and the mutation fitness indexes corresponding to each mutation habitat determined when the preset stop condition is met is determined as the target charging current, the determination of the target charging current is realized, when the battery is charged based on the target charging current, the charging time and the charging temperature rise of the battery reach a balance, and both the charging time and the charging temperature rise are small, and the optimization of the battery charging is realized.

[0073] The battery charging optimization method provided by the embodiment of the present application comprises: establishing a double-target optimization function according to the relationship between the charging current of the 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, 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 double-target optimization function; returning to perform migration and mutation on 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. The above technical solution can first establish a double-target optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise, secondly can determine a plurality of charging currents of the battery, and then can optimize the double-target optimization function based on the BBO algorithm. Specifically, each charging current is taken as a habitat, migration and mutation of each habitat are repeatedly performed, a migration fitness index corresponding to each migration habitat and a mutation fitness index corresponding to each mutation habitat are determined according to the double-target optimization function, until a preset stop condition is met, a current corresponding to the minimum fitness index among the migration fitness indexes corresponding to each migration habitat and the mutation fitness indexes corresponding to each mutation habitat determined when the preset stop condition is met is determined as the target charging current, the determination of the target charging current is realized, when the battery is charged based on the target charging current, the charging time and the charging temperature rise of the battery reach a balance, and both the charging time and the charging temperature rise are small, and the optimization of the battery charging is realized.

[0074] Figure 2 The flowchart of another battery charging optimization method provided by the embodiment of the present application is based on the embodiment described above. As shown in FIG. 10, in the embodiment, the method can further comprise: Figure 2

[0075] Step 210: establishing a double-target optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise.

[0076] In an embodiment, step 210 can specifically comprise:

[0077] ​establish a time optimization function according to the relationship between the charging current and the charging time, and establish a temperature rise optimization function according to the relationship between the charging current and the charging temperature rise; and establish the double-target optimization function according to the first weight 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 the charging current and the battery capacity and other factors. The charging process of the battery follows a constant current-constant voltage charging model, and the charging time can be determined according to the relationship between the charging current and the charging time wherein C represents the battery capacity, I represents the charging current, and t(I) represents the charging time required for charging the battery at I. The charging temperature rise is related to the charging current, the battery internal resistance, the heat dissipation condition and other factors. According to the Joule law, the heat generation power P(I) = IR during the charging process, wherein R represents the battery internal resistance, and P(I) represents the heat generation power of the battery when charging the battery at I. The charging process of the battery follows a first-order thermal model, and the charging temperature rise can be determined according to the relationship between the charging current and the charging temperature rise wherein 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] Further, the double-target optimization function is determined according to the first weight 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. That is, the double-target optimization function can be determined as wherein 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 at an acceptable current, and 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 double-target optimization function will only target the charging time and ignore the charging temperature rise, at this time, the battery can be charged at the maximum charging current corresponding to the current constraint, and when the current conversion condition of the multi-stage charging is reached, the battery is switched to the next stage, and the battery is charged at the maximum charging current of each stage. When β=1, the double-target optimization function will only target the charging temperature rise and ignore the charging time, at this time, the battery can be charged at the minimum charging current corresponding to the current constraint, and when the current conversion condition of the multi-stage charging is reached, the battery is switched to the next stage, and the battery is charged at the minimum charging current of each stage.

[0081] In the embodiment of the application, the double-target optimization function is established according to the relationship between the charging current and the charging time and the charging temperature rise of the battery.

[0082] Step 220, determining a plurality of charging currents of the battery.

[0083] In an embodiment, step 220 can specifically include:

[0084] determining the plurality of charging currents according to a current constraint of the battery.

[0085] As mentioned before, the current constraint is I min ≤I≤I max , and when a preset current interval is ΔI, the plurality of charging currents can be determined as I min , I min +ΔI, I min +2ΔI, …, I max .

[0086] In an embodiment of the present application, the plurality of 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 of the migrated habitats and a mutation fitness index corresponding to each of the mutated habitats according to the double-objective optimization function.

[0088] In an embodiment, step 230 can specifically include:

[0089] taking each of the charging currents as a habitat, determining an initial fitness index of each of the habitats according to the double-objective optimization function, for each of the habitats, determining a migration source habitat according to the initial fitness index of the current habitat, and determining a migrated habitat according to the current habitat and the migration source habitat, for each of the habitats, determining a mutated habitat by mutating the current habitat based on a selected mutation mode, substituting a migration charging current corresponding to each of the migrated habitats into the double-objective optimization function to determine the migration fitness index corresponding to each of the migrated habitats, and substituting a mutation charging current corresponding to each of the mutated habitats into the double-objective optimization function to determine the mutation fitness index corresponding to each of the mutated habitats.

[0090] Further, determining the migration source habitat according to the initial fitness index of the current habitat, and determining the migrated habitat according to the current habitat and the migration source habitat, includes:

[0091] determining any habitat with an initial fitness index greater than the initial fitness index of the current habitat as the migration source habitat, and determining the migrated habitat according to the current habitat, the migration source habitat, and a migration rate corresponding to the current habitat.

[0092] Specifically, first, each charging current is substituted into the double-objective optimization function, the function value of each charging current is determined, 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 the initial fitness of the current habitat is taken as a migration source habitat, and the migration habitat corresponding to the current habitat is determined according to the current habitat and the migration source habitat. Specifically, the migration habitat corresponding to the current habitat can be obtained by migrating part of the characteristics of the migration source habitat to the current habitat. The habitat is the charging current, and the current charging current I t corresponding to the current habitat and the migration source charging current I s corresponding to the migration source habitat are determined. t The corresponding migration charging current I is determined. Wherein, r represents the migration rate, which 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 a preset mutation mode, and a mutation habitat is determined by mutating the current habitat based on the selected mutation mode. The preset mutation mode can be random mutation and neighborhood mutation. Specifically, the mutation habitat corresponding to the current habitat can be obtained by mutating the current habitat based on random mutation or neighborhood mutation. The habitat is the charging current, and the current charging current I t is mutated based on random mutation, and the mutated charging current I is determined. Wherein, 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. The current charging current I t is mutated based on neighborhood mutation, and the mutated charging current I is determined. Wherein, 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 guide BBO algorithm to search in a better direction by using the charging current corresponding to lower charging time and charging temperature rise in the neighborhood.

[0095] Further, the migration charging current corresponding to each migration habitat is substituted into the double-objective optimization function, and the migration fitness index corresponding to each migration habitat is determined. The mutated charging current corresponding to each mutated habitat is substituted into the double-objective optimization function, and the migration fitness index corresponding to each mutated habitat is determined.

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

[0097] In the embodiment of the application, after determining the migration habitats and the variation habitats by migration and variation of each habitat, the migration charging currents corresponding to each migration habitat and the variation charging currents corresponding to each variation habitat are determined, the migration fitness indexes corresponding to each migration habitat and the variation fitness indexes corresponding to each variation habitat are determined according to the migration charging currents and the variation charging currents and the double-target optimization function, and the optimization of the double-target optimization function is realized.

[0098] Step 240, determining whether a preset stop condition is met.

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

[0100] Specifically, step 230 is repeatedly executed, and after each execution of step 230, it is determined whether the preset stop condition is met. It is determined that the preset stop condition is met when the number of migrations and variations is greater than a threshold or the migration fitness index and / or the variation fitness index converges, and then step 250 can be continuously executed; otherwise, step 230 is executed.

[0101] Step 250, determining a target charging current according to the migration fitness index and the variation fitness index.

[0102] In an implementation, step 250 can specifically include:

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

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

[0105] In the embodiment of the present application, the current corresponding to the minimum fitness index among the migration fitness indexes corresponding to the migration habitats and the variation fitness indexes corresponding to the variation habitats determined when the preset stop condition is met is determined as the target charging current, so that the determination of the target charging current is realized. When the battery is charged based on the target charging current, the charging time and the charging temperature rise of the battery reach a balance, and both the charging time and the charging temperature rise are small, so that the optimization of the battery charging is realized.

[0106] A battery charging optimization method provided by an embodiment of the present invention includes: establishing a dual-objective optimization function based on the relationship between the battery's charging current, charging time, and charging temperature rise; determining multiple charging currents of the battery; using 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; 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 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; if it is determined that the preset stop condition is met, determining a target charging current based on the migration fitness index and the mutation fitness index. The above technical solution can first establish a dual-objective optimization function based on the relationship between the battery's charging current and the charging time and charging temperature rise. Secondly, it can 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, thereby realizing the determination of the target charging current. When the battery is charged based on the target charging current, the battery's charging time and charging temperature rise are balanced, and the charging time and charging temperature rise are both small, thereby realizing the optimization of 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. This device is suitable for situations where it is necessary to reduce charging time and charging temperature rise. The device can be implemented through software and / or hardware and is generally integrated into electronic devices such as vehicles.

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

[0109] Establishing module 310, for establishing a dual-objective optimization function based on the relationship between the battery's charging current, charging time, and charging temperature rise;

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

[0111] The first execution module 330 is configured to take each of the charging currents as a habitat, migrate and mutate each of the habitats, and determine a migration fitness index corresponding to each of the migrated habitats and a mutation fitness index corresponding to each of the mutated habitats according to the double-target optimization function.

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

[0113] The battery charging optimization device provided in the embodiment is configured to establish a double-target optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise, determine a plurality of charging currents of the battery, take each of the charging currents as a habitat, migrate and mutate each of the habitats, and determine a migration fitness index corresponding to each of the migrated habitats and a mutation fitness index corresponding to each of the mutated habitats according to the double-target optimization function. The device returns to execute the migration and mutation of each of the habitats, and determines 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 solution can first establish a double-target optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise, secondly determine a plurality of charging currents of the battery, and then optimize the double-target optimization function based on the BBO algorithm. Specifically, each charging current is taken as a habitat, the migration and mutation of each habitat are repeatedly executed, a migration fitness index corresponding to each of the migrated habitats and a mutation fitness index corresponding to each of the mutated habitats are determined according to the double-target optimization function, until a preset stop condition is met. The current corresponding to the minimum fitness index in the migration fitness index corresponding to each of the migrated habitats and the mutation fitness index corresponding to each of the mutated habitats determined when the preset stop condition is met is determined as the target charging current, the determination of the target charging current is realized, and when the battery is charged based on the target charging current, the charging time and the charging temperature rise of the battery reach a balance, and the charging time and the charging temperature rise are both small, thereby optimizing the charging of the battery.

[0114] On the basis of the above embodiment, the establishing module 310 is specifically configured to:

[0115] establish a time optimization function according to the relationship between the charging current and the charging time, establish a temperature rise optimization function according to the relationship between the charging current and the charging temperature rise, and establish the double-target optimization function 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] On the basis of the above embodiment, the determining module 320 is specifically configured to:

[0117] determine the plurality of charging currents according to the current constraints of the battery.

[0118] On the basis of the above-mentioned embodiments, the first execution module 330 is specifically configured to:

[0119] determine the initial fitness index of each habitat according to the double-objective optimization function; for each habitat, determine the migration source habitat according to the initial fitness index of the current habitat, and determine the migration habitat according to the current habitat and the migration source habitat; for each habitat, determine the variation habitat by performing variation on the current habitat based on the selected variation mode; determine the migration fitness index corresponding to each migration habitat by substituting the migration charging current corresponding to each migration habitat into the double-objective optimization function; and determine the migration fitness index corresponding to each variation habitat by substituting the variation charging current corresponding to each variation habitat into the double-objective optimization function.

[0120] In an implementation form, 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] determining any habitat with an initial fitness index greater than the initial fitness index of the current habitat as the migration source habitat; and determining the migration habitat according to the current habitat, the migration source habitat, and the migration rate corresponding to the current habitat.

[0122] On the basis of the above-mentioned embodiments, the second execution module 340 is specifically configured to:

[0123] determining 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 as the target charging current after determining that a preset stop condition is met.

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

[0125] It should be noted that, in the embodiments of the above-mentioned battery charging optimization device, each unit and module included is only divided according to the functional logic, but is not limited to the above-mentioned division, as long as the corresponding functions can be implemented; in addition, the specific names of each functional unit are only for the convenience of mutual differentiation, and are not used to limit the protection scope of the present application.

[0126] Figure 4A schematic structural diagram 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, electronic device 4 is in the form of a general-purpose computing electronic device. Components of electronic device 4 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and a bus 18 connecting various system components (including system memory 28 and 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. Examples of 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 configured to read and write non-removable, non-volatile magnetic media ( Figure 4 Not shown, often called a "hard drive"). Although Figure 4 Not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic 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, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0131] Program / utility 40 having a set of program modules 42 can be stored in system memory 28 by way of example, such program modules 42 include an operating system, one or more application programs, other program modules, and program data, each of which or a combination thereof, can include implementation of the network environment as each or a combination of these examples. Program modules 42 generally carry out the functions and / or methodologies of embodiments of the application as described herein.

[0132] Electronic device 4 can also communicate with one or more external devices 14 such as a keyboard or pointing device, a display 24, etc. ; other devices such as are well known in the art. Communication with such devices can be effected by input / output (I / O) interface(s) 22. Additionally, electronic device 4 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet, by way of network adapter 20. As Figure 4 illustrated, network adapter 20 communicates with other modules of electronic device 4 by way of bus 18. It will be appreciated that, although not shown, other hardware and / or software well known in the art can be typically used, such as an operating system, middleware, device drivers, etc. in conjunction with electronic device 4. ​ It should be appreciated that, although not shown in FIG. 1, other hardware and / or software modules can be used in conjunction with electronic device 4, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0133] Processing unit 16 performs various functions and displays pages by running programs stored in system memory 28, such as implementing the battery charging optimization method provided by any embodiment of the present application, which includes:

[0134] establishing a double-target optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise;

[0135] determining a plurality of charging currents of the battery;

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

[0137] returning to migrating and mutating 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.

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

[0139] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize a battery charging optimization method provided by the embodiment of the present application, the method comprises the following steps:

[0140] establishing a double-target optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise;

[0141] determining a plurality of charging currents of 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 of the migrated habitats and a mutation fitness index corresponding to each of the mutated habitats according to the double-target optimization function;

[0143] returning to the step of migrating and mutating 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.

[0144] The computer storage medium of the embodiment of the present application 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, but is not limited to, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, 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, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, device or component.

[0145] The computer readable signal medium can include a data signal propagated in a baseband or as a part of a carrier wave, in which a computer readable program code is borne. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can send, propagate or transmit a program for use by or in connection with an instruction execution system, device or component.

[0146] Program code embodied on a 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 stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving 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., through the Internet using an Internet service provider).

[0148] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computer device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. 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 comply 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 employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments, and substitutions are possible for 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 detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method of optimizing battery charging, the method comprising: The method comprises the following steps: establishing a double-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise; determining a plurality of charging currents of the battery; migrating and mutating each of the habitats, and determining a migration fitness index corresponding to each of the migrated habitats and a mutation fitness index corresponding to each of the mutated habitats according to the double-objective optimization function; returning to perform migration and mutation on 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.

2. The battery charge optimization method of claim 1, wherein, The method for establishing a double-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise comprises the following steps: 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; establishing the double-objective optimization function according to a first weight time corresponding to the charging time and the time optimization function and a second weight corresponding to the charging temperature rise and the temperature rise optimization function.

3. The battery charge optimization method of claim 1, wherein, The method for determining a plurality of charging currents of the battery comprises the following steps: determining the plurality of charging currents according to the current constraint of the battery.

4. The battery charge optimization method of claim 1, wherein, The method for migrating and mutating each of the habitats comprises the following steps: determining an initial fitness index of each of the habitats according to the double-objective optimization function; for each of the habitats, determining a migration source habitat according to the initial fitness index of the current habitat, and determining a migrated habitat according to the current habitat and the migration source habitat; for each of the habitats, determining a mutated habitat by mutating the current habitat based on a selected mutation mode.

5. The battery charge optimization method of claim 4, wherein, The method for determining a migration source habitat according to the initial fitness index of the current habitat and determining a migrated habitat according to the current habitat and the migration source habitat comprises the following steps: determining any habitat with an initial fitness index greater than the initial fitness index of the current habitat as the migration source habitat; determining the migrated habitat according to the current habitat, the migration source habitat and a migration rate corresponding to the current habitat.

6. The battery charge optimization method of claim 1, wherein, The method for determining a migration fitness index corresponding to each of the migrated habitats and a mutation fitness index corresponding to each of the mutated habitats according to the double-objective optimization function comprises the following steps: determining the migration fitness index corresponding to each of the migrated habitats by substituting the migration charging current corresponding to each of the migrated habitats into the double-objective optimization function; determining the mutation fitness index corresponding to each of the mutated habitats by substituting the mutation charging current corresponding to each of the mutated habitats into the double-objective optimization function.

7. The battery charge optimization method of claim 1, wherein, The method for determining a target charging current according to the migration fitness index and the mutation fitness index after a preset stop condition is met comprises the following steps: determining the target charging current as the current corresponding to the migrated habitat or the mutated habitat with the minimum fitness index among the migration fitness index corresponding to each of the migrated habitats and the mutation fitness index corresponding to each of the mutated habitats after the preset stop condition is met.

8. A battery charge optimization apparatus, characterized by, The method comprises the following steps: a establishing module is configured to establish a double-objective optimization function according to the relationship between the charging current of the battery and the charging time and the charging temperature rise; determining a plurality of charging currents of the battery; a first execution module configured to take each of the charging currents as a habitat, migrate and mutate each of the habitats, and determine a migration fitness index corresponding to each of the migrated habitats and a mutation fitness index corresponding to each of the mutated habitats according to the double-objective optimization function; a second execution module configured to return to execute the migration and mutation of each of the habitats, and determine a target charging current according to the migration fitness index and the mutation fitness index after a preset stop condition is met.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein 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 to enable the at least one processor to execute the battery charging optimization method according to any one of claims 1-7.

10. A storage medium containing computer-executable instructions, wherein: The computer executable instructions, when executed by a computer processor, are configured to execute the battery charging optimization method according to any one of claims 1-7.

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