Multi-order decreasing constant heat production charging method, device, equipment and medium

Abstract

The invention discloses a multi-order decreasing constant heat production charging method and device, equipment and a medium. Comprising the following steps: randomly generating a plurality of initial parameter groups according to a pre-specified charging stage number to form an initial parameter group set; performing charging simulation on each initial parameter group, and obtaining the fitness and constraint condition of each initial parameter group according to the simulation result of each initial parameter group; updating the initial parameter group set, and performing charging simulation on the updated parameter group set again so as to perform multi-round iterative updating on the parameter group set; when it is determined that a termination condition is met, obtaining an optimal parameter group set; and selecting a target parameter group from the optimal parameter group set, obtaining a current change curve corresponding to the target parameter group, and charging the battery according to the current change curve. By adopting the technical scheme, the heating rate of the battery in the charging process can be inhibited to the greatest extent on the premise of improving the charging speed, so that the safety and health degree of the battery are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery charging control, and in particular to a multi-stage decreasing constant heat generation charging method, device, equipment and medium. BACKGROUND

[0002] In the actual application process of lithium ion batteries, especially in the scenario of being used as a power component of an electric vehicle, lithium ion batteries generally face the problems of long charging time and poor thermal stability during charging. Therefore, obtaining a lithium ion battery charging strategy that can cooperatively improve the charging speed and effectively control the temperature rise is of great significance to the development of the energy storage and electric vehicle industries.

[0003] At present, the most widely used charging method is the constant current-constant voltage charging method. This method divides the lithium ion charging process into two stages. The first stage is constant current charging, that is, a fixed current is used to charge the battery to quickly supplement the battery power. When the battery terminal voltage rises to a preset voltage threshold, the second stage of constant voltage charging is entered, and the charging voltage is kept constant. The charging current gradually decreases with the increase of the battery power until the charging current decreases to a preset termination current value, and the charging is ended.

[0004] However, although the constant current-constant voltage charging method achieves a balance between charging effect and battery protection to a certain extent, the fixed current in the early charging period is used to adapt to the highly dynamic changes in the electrochemical state of the battery during the charging process, which can cause the battery heat generation to increase in a quadratic manner in the middle and late charging periods. This not only easily leads to high-temperature safety risks, but also triggers the conservative current reduction mechanism of the battery management system, ultimately significantly prolonging the overall charging time. SUMMARY

[0005] The present application provides a multi-stage decreasing constant heat generation charging method, device, equipment and medium, which can maximize the inhibition of the battery heat generation rate during the charging process under the premise of improving the charging speed, thereby improving the safety and health of the battery.

[0006] According to an aspect of the present application, a multi-stage decreasing constant heat generation charging method is provided, comprising:

[0007] According to the number of charging stages specified in advance, a plurality of initial parameter groups are randomly generated to form an initial parameter group set. Each parameter group includes a state of charge point for switching between charging stages and a constant heat generation power target value that decreases step by step in each charging stage.

[0008] Through an electro-thermal simulation model, the charging simulation of each initial parameter group is performed, and the fitness and constraint conditions of each initial parameter group are obtained according to the simulation results of each initial parameter group.

[0009] The initial parameter set collection is updated according to the fitness and constraint conditions of each initial parameter set, and the updated parameter set collection is simulated again to perform multiple rounds of iterative updating on the parameter set collection;

[0010] When it is determined that the termination condition is met, the optimal parameter set collection is obtained;

[0011] A target parameter set is selected from the optimal parameter set collection, and a current change curve corresponding to the target parameter set is obtained, and the battery is charged according to the current change curve.

[0012] According to another aspect of the present application, a multi-stage decreasing constant heat production charging device is provided, comprising:

[0013] An initial parameter set generation module is configured to randomly generate a plurality of initial parameter sets according to a pre-specified number of charging stages to form an initial parameter set collection, wherein each parameter set includes state of charge points for switching between charging stages and constant heat production power target values for gradually decreasing charging stages;

[0014] A charging simulation module is configured to simulate charging of each initial parameter set through an electrothermal simulation model, and obtain fitness and constraint conditions of each initial parameter set according to simulation results of each initial parameter set;

[0015] A parameter set updating module is configured to update the initial parameter set collection according to the fitness and constraint conditions of each initial parameter set, and simulate charging of the updated parameter set collection again to perform multiple rounds of iterative updating on the parameter set collection;

[0016] An optimal parameter set selection module is configured to obtain the optimal parameter set collection when it is determined that the termination condition is met;

[0017] A charging strategy generation module is configured to select a target parameter set from the optimal parameter set collection, obtain a current change curve corresponding to the target parameter set, and charge the battery according to the current change curve.

[0018] According to another aspect of the present application, an electronic device is provided, comprising:

[0019] At least one processor; and

[0020] A memory connected in communication with the at least one processor; wherein,

[0021] 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 perform the multi-stage decreasing constant heat production charging method according to any one of the embodiments of the present application.

[0022] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the multi-stage decreasing constant heat generation charging method according to any of the embodiments of the present application when executed.

[0023] The technical solution of the embodiments of the present application generates a plurality of initial parameter sets according to a pre-specified number of charging stages, forms an initial parameter set collection, performs charging simulation on each initial parameter set, obtains the fitness and constraint conditions of each initial parameter set according to the simulation results of each initial parameter set, updates the initial parameter set collection according to the fitness and constraint conditions of each initial parameter set, performs charging simulation again on the updated parameter set collection to iteratively update the parameter set collection for multiple rounds, obtains an optimal parameter set collection when a termination condition is determined to be met, selects a target parameter set from the optimal parameter set collection, and obtains a current change curve corresponding to the target parameter set. Compared with the constant current-constant voltage charging method of the prior art, the battery charging method according to the current change curve can optimize power distribution and avoid unnecessary current limiting, thereby significantly shortening the total charging time, and the multi-stage decreasing constant heat generation charging method can homogenize the heat generation during the charging process, thereby maximizing the suppression of the heat generation rate during the battery charging process under the premise of improving the charging speed, and improving the safety and health of the battery.

[0024] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0026] Figure 1 is a flowchart of a multi-stage decreasing constant heat generation charging method according to an embodiment of the present application;

[0027] Figure 2 is a current change curve diagram according to an embodiment of the present application;

[0028] Figure 3 is a flowchart of another multi-stage decreasing constant heat generation charging method according to an embodiment of the present application;

[0029] Figure 4is a structural schematic diagram of a multi-stage decreasing constant heat generation charging device according to embodiment three of the present application;

[0030] Figure 5 is a structural schematic diagram of an electronic device implementing a multi-stage decreasing constant heat generation charging method according to the present application. DETAILED DESCRIPTION

[0031] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the terms thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product, or device.

[0033] Embodiment one

[0034] Figure 1 A flowchart of a multi-stage decreasing constant heat generation charging method according to embodiment one of the present application is provided. The present embodiment can be applied to generate a charging strategy for charging a power battery using a multi-stage decreasing constant heat generation charging method. The method can be executed by a multi-stage decreasing constant heat generation charging device, which can be implemented in the form of hardware and / or software, and can generally be configured in a computer or processor with data processing function. As shown in the figure, the method comprises: Figure 1

[0035] S110, randomly generating a plurality of initial parameter groups according to a pre-specified number of charging stages to form an initial parameter group set.

[0036] Each parameter group includes the state of charge points for switching between charging stages and the target values of the constant heat generation power for each charging stage.

[0037] ​Optionally, the pre-specified number of charging stages can divide the charging process into a plurality of continuous charging stages with different constant heat generation, and the application considers that if the charging process is a single-stage constant heat generation, in order to shorten the charging time, the charging current needs to be increased, which will result in a decrease in the actual amount of charge when the power battery reaches the cut-off voltage. If the number of constant heat generation stages is too high, the calculation difficulty will undoubtedly increase. Therefore, in the embodiment of the application, the charging process can be divided into three charging stages, and the battery heat generation power in each stage is maintained at a pre-set constant value by adjusting the charging current in real time. The constant heat generation power of each stage is gradually decreased according to the principle of high in front and low in back, and finally the dual goals of shortening the charging time and controlling the charging temperature rise are balanced.

[0038] Optionally, the state of charge points can be used to divide the state of charge (SOC) threshold of the charging stage. When the real-time SOC of the battery reaches the threshold, the charging is switched from the current stage to the next stage. In the initial parameter set, the constant heat generation power target value of each charging stage is specified, and the power values of the stages are sequentially decreased according to the order of the previous stage being greater than the next stage.

[0039] In an optional example, when the pre-specified number of charging stages is three, the initial parameter set includes two state of charge points and three constant heat generation power target values of the stages. The state of charge point from the first stage to the second stage can be represented as SOC1, the state of charge point from the second stage to the third stage can be represented as SOC2, the constant heat generation power target value of the first stage can be represented as Q1, the constant heat generation power target value of the second stage can be represented as Q2, and the constant heat generation power target value of the third stage can be represented as Q3. Q1 is greater than Q2, and Q2 is greater than Q3. The above characters will be used in the subsequent examples of the embodiment of the application.

[0040] Optionally, according to the state of the battery to be charged, the maximum state of charge SOCmax and the minimum state of charge SOCmin can be determined, and according to the battery performance, the maximum heat generation power Qmax and the minimum heat generation power Qmin can be pre-specified. The initial parameter set can be used as the initial population of the genetic algorithm, and the initial parameter set can be updated by the genetic algorithm. The maximum state of charge, the minimum state of charge, the maximum heat generation power, and the minimum heat generation power can be used as the gene boundary. Each gene randomly takes a value within the gene boundary to generate an initial parameter set, which can be represented as [SOC11, SOC21, Q11, Q21, Q31], …, [SOC1 N ,SOC2 N ,Q1 N ,Q2 N ,Q3 N ].

[0041] Optionally, by the above setting parameter group, the charging current of each stage can be actively controlled, so that the internal heat generation rate of the battery is maintained at a preset, lower constant level, thereby achieving the purpose of uniformizing the heat generation of the charging process as much as possible, and fundamentally reducing the charging temperature rise of the power battery.

[0042] In S120, the charging simulation is performed on each initial parameter group through the electro-thermal simulation model, and the fitness and the constraint condition of each initial parameter group are obtained according to the simulation result of each initial parameter group.

[0043] In S120, the charging simulation is performed on each initial parameter group through the electro-thermal simulation model, and the fitness and the constraint condition of each initial parameter group are obtained according to the simulation result of each initial parameter group.

[0044] In each charging stage, the charging simulation is performed according to the constant heat generation power target value in the charging stage, and the stage current change is obtained.

[0045] According to the stage current change, the real-time state of charge is calculated, and the switching of the charging stage is completed according to the real-time state of charge and the state of charge points between the charging stages.

[0046] When it is detected that the battery terminal voltage reaches the maximum cutoff voltage or the real-time state of charge reaches the state of charge peak value, the charging simulation of the initial parameter group is completed and the simulation result of the initial parameter group is obtained.

[0047] Optionally, the electro-thermal simulation model is a mathematical model integrating the electrical characteristics and thermal characteristics of the battery, which is used to simulate the charging process under different parameter groups. The input of the electro-thermal simulation model is the initial parameter group, and the output simulation result can include the total charging time, the charging temperature rise, the full-stage current change and the full-stage battery terminal voltage.

[0048] Optionally, in order to realize the constant heat generation in each charging stage, it is necessary to adjust the charging current in real time. The real-time current I(t) in each charging stage can be calculated by the formula , wherein Q is the constant heat generation power target value specified for the current charging stage in the parameter group, R is the internal resistance of the battery, and the internal resistance R changes with the real-time state of charge SOC and temperature T. The real-time current change in each charging stage can be used to represent the stage current change.

[0049] Optionally, after obtaining the stage current change, the real-time state of charge SOC can be calculated according to the formula dSOC / dt = I(t) / C, wherein I(t) is the stage current change in the current charging stage, and C is the rated capacity of the battery.

[0050] Optionally, when the real-time state of charge SOC reaches the state of charge point, the next charging stage is switched to.

[0051] Optionally, the state of charge peak value can be determined according to the charging demand, and the state of charge peak value can be the maximum state of charge that the battery can support if it is a full charging scenario; the maximum cutoff voltage can be specified in advance according to the battery characteristics, and the battery terminal voltage can be determined according to the real-time current, the state of charge and the battery internal resistance.

[0052] According to the simulation results of each initial parameter group, the fitness and the constraint condition of each initial parameter group can be obtained, which can include:

[0053] According to the simulation results of the initial parameter group, the total charging time and the charging temperature rise are obtained, and the fitness is calculated according to the total charging time and the charging temperature rise;

[0054] According to the simulation results of the initial parameters, the full-stage current change and the full-stage battery terminal voltage are obtained, and whether the initial parameter group meets the multi-dimensional constraint condition is judged according to the full-stage current change, the full-stage battery terminal voltage, the charging temperature rise and the parameter value in the initial parameter group.

[0055] Optionally, the charging temperature rise can refer to the difference between the highest temperature in the charging process and the initial temperature of the battery, and the fitness can be calculated according to the following formula: F = (1-β) t total / a + β△T / b, wherein F is the fitness, β is a preset weight coefficient, a and b are normalization processing coefficients, the normalization processing coefficients are used to normalize the total charging time and the charging temperature rise, t total is the total charging time, and △T is the charging temperature rise.

[0056] Optionally, the multi-dimensional constraint condition can include current constraint, voltage constraint, state of charge constraint, temperature rise constraint and power constraint; the current constraint can refer to that the current at any time point in the charging full stage does not exceed the maximum allowable charging current I c,max of the battery, which can be expressed as 0 < I ≤ I c,max ; the voltage constraint can refer to that the battery terminal voltage Ut at any time point in the charging full stage does not exceed the specified maximum cutoff voltage U t,max , and is not lower than the specified minimum cutoff voltage U t,min , which can be expressed as U t,min ≤ Ut ≤ U t,max ; the state of charge constraint can refer to that the first SOC switching point SOC1 is less than the second SOC switching point SOC2, and both are between the specified minimum state of charge SOCmin and the maximum state of charge SOCmax, which can be expressed as SOCmin < SOC1 < SOC2 < SOCmax; the temperature rise constraint can refer to that the charging temperature rise △T is less than the maximum charging temperature rise △T max , and the maximum charging temperature rise can be specified in advance, which can be expressed as △T < △T maxThe power constraint can refer to a step-by-step decrease of the constant heat generation power in each charging stage, for example, the first-order constant heat generation power is less than the second-order constant heat generation power, which is less than the third-order constant heat generation power, and the constant heat generation power in each stage is between the pre-specified maximum heat generation power Qmax and the minimum heat generation power Qmin, which can be expressed as Qmin < Q1 < Q2 < Q3 < Qmax.

[0057] In S130, the initial parameter set is updated according to the fitness of each initial parameter group and the constraint condition, and the updated parameter set is simulated again to perform multiple rounds of iterative updating of the parameter set.

[0058] The updating of the initial parameter set according to the fitness of each initial parameter group and the constraint condition can include:

[0059] According to the fitness of each initial parameter group, a plurality of first parameter groups are selected, and the first parameter groups are subjected to gene crossover processing according to a preset crossover probability to generate a plurality of second parameter groups.

[0060] According to a preset mutation probability, the second parameter groups are subjected to random mutation processing to generate a plurality of third parameter groups.

[0061] The first parameter groups, the second parameter groups and the third parameter groups are combined, and the combined parameter set is subjected to elimination processing according to the constraint condition of each initial parameter group to obtain a parameter set for the next round of iteration.

[0062] Optionally, according to the fitness of each initial parameter group, a parameter group with better performance can be selected as a parent, which can specifically include that in the initial parameter set, 2 parameter groups are randomly selected each round, the fitness of the two is compared, and the parameter group with better fitness is selected into a mating pool, and the initial parameter group remaining in the mating pool is the first parameter group.

[0063] Optionally, after the first parameter group is selected, two parent individuals are randomly selected from the first parameter group in the mating pool, a part of genes of the two parent individuals are exchanged at a preset crossover probability to generate a new child individual, the child inherits the characteristics of the parent, and the newly generated child individual can be used as the second parameter group.

[0064] In an optional example, the first parameter groups in the mating pool can be randomly grouped two by two, for each group of parents, a random number r between 0 and 1 is generated, if r is greater than the crossover probability, the crossover operation is performed; if r is less than the crossover probability, no crossover operation is performed, when performing crossover, the gene positions of two parent individuals are randomly selected, the corresponding gene positions of the two groups of parents are exchanged, for example, SOC1A of parent A and SOC1B of parent B are exchanged, Q2A of parent A and Q2B of parent B are exchanged, to generate offspring A' [SOC1B, SOC2A, Q1A, Q2B, Q3A] and offspring B' [SOC1A, SOC2B, Q1B, Q2A, Q3B], i.e. the second parameter group.

[0065] Optionally, after generating the second parameter group, part of the genes of the offspring parameter group can be randomly changed according to a preset mutation probability, so as to introduce new parameter combinations and avoid the population from falling into a local optimal solution.

[0066] In an optional example, for each second parameter group, each gene position thereof is checked one by one, for each gene position, a random number p between 0 and 1 is generated, if p is greater than the mutation probability, the mutation operation is performed; if p is less than the mutation probability, the original gene value is retained, when performing mutation, the parameter value can be randomly fine-tuned within the parameter boundary of the gene position, for example, the boundary of SOC1 is 20%-50%, if the original SOC1=30%, the parameter value can be fine-tuned to 32% after mutation, and the parameter group after mutation is the third parameter group.

[0067] Further, the first parameter group, the second parameter group and the third parameter group can be combined to form a temporary set, and the parameter groups in the temporary set that do not satisfy the multi-dimensional constraint condition are removed to obtain a parameter group set for the next round of iteration.

[0068] It can be understood that the above iteration process of the parameter group takes the initial parameter group as an example, but is also applicable to each subsequent round of iteration, and the iteration process of each subsequent round of parameter group is the same as that of the initial parameter group, which will not be described here.

[0069] S140, when it is determined that the termination condition is satisfied, an optimal parameter group set is obtained.

[0070] Optionally, when a preset number of iterations is reached, it is determined that the termination condition is satisfied, and the parameter group set after the last round of crossover, mutation and elimination processing is determined as the optimal parameter group set.

[0071] S150, a target parameter group is selected from the optimal parameter group set, and a current change curve corresponding to the target parameter group is obtained, and the battery is charged according to the current change curve.

[0072] The process of selecting a target parameter set from the optimal parameter set and obtaining the corresponding current variation curve, and then charging the battery based on the current variation curve, may include:

[0073] Using an electrothermal simulation model, charging simulations are performed on each optimal parameter group, and the fitness and constraints of each optimal parameter group are obtained based on the simulation results.

[0074] Based on the fitness and constraints of each optimal parameter group, the optimal parameter group with the highest fitness is selected as the target parameter group among the optimal parameter groups that meet the multi-dimensional constraints. Based on the real-time current changes during the charging simulation, a current change curve is generated, and the battery is charged according to the current change curve.

[0075] Optionally, the current change curve is a staged current trajectory generated by an electrothermal simulation model.

[0076] Figure 2 This is a schematic diagram of one possible current variation curve. (Example) Figure 2 As shown, I1 is the optimal charging current curve obtained using the constant heat generation power Q1 in the first stage within the range of SOCmin-SOC1, I2 is the optimal charging current curve obtained using the constant heat generation power Q2 in the second stage within the range of SOC1-SOC2, and I3 is the optimal charging current curve obtained using the constant heat generation power Q3 in the third stage within the range of SOC2-SOCmax.

[0077] It is understandable that using the current change curve corresponding to the target parameter set for charging can achieve the best balance between total charging time and charging temperature rise. While increasing the charging speed, it can suppress the heat generation rate of the battery during the charging process to the greatest extent, thereby improving the safety and health of the battery.

[0078] The technical scheme of the embodiment of the present application comprises the following steps: generating a plurality of initial parameter groups according to a pre-specified number of charging stages, composing an initial parameter group set, performing charging simulation on each initial parameter group, obtaining the fitness and constraint conditions of each initial parameter group according to the simulation results of each initial parameter group, updating the initial parameter group set according to the fitness and constraint conditions of each initial parameter group, performing charging simulation on the updated parameter group set again to perform multi-round iteration and update on the parameter group set, obtaining an optimal parameter group set when a termination condition is met, selecting a target parameter group from the optimal parameter group set, and obtaining a current change curve corresponding to the target parameter group. Compared with the constant current-constant voltage charging method of the prior art, the battery charging method according to the current change curve can optimize power distribution and avoid unnecessary current limiting, thereby significantly shortening the total charging time, and the step-by-step decreasing constant heat generation charging method can homogenize heat generation during the charging process, thereby maximizing the inhibition of the heat generation rate during the battery charging process under the premise of improving the charging speed, and improving the safety and health of the battery.

[0079] Embodiment two

[0080] Figure 3 A flowchart of a multi-stage decreasing constant heat generation charging method provided by the second embodiment of the present application is shown in the figure. Based on the above-mentioned embodiment, the multi-stage decreasing constant heat generation charging method is specifically described. As shown in the figure, the method comprises the following steps: Figure 3

[0081] S210, generating a plurality of initial parameter groups according to a pre-specified number of charging stages, and composing an initial parameter group set.

[0082] S220, in each charging stage, performing charging simulation according to the constant heat generation power target value in the charging stage, and obtaining stage current change.

[0083] S230, calculating the real-time state of charge according to the stage current change, and completing the switching of the charging stage according to the real-time state of charge and the state of charge points between the charging stages.

[0084] S240, when it is detected that the battery terminal voltage reaches the maximum cutoff voltage or the real-time state of charge reaches the state of charge peak value, completing the charging simulation on the initial parameter group and obtaining the simulation results of the initial parameter group.

[0085] S250, obtaining the total charging time and the charging temperature rise according to the simulation results of the initial parameter group, and calculating the fitness according to the total charging time and the charging temperature rise.

[0086] ​S260, obtaining the full-stage current change and the full-stage battery terminal voltage according to the simulation result of the initial parameters, and determining whether the initial parameter set meets the multi-dimensional constraint condition according to the full-stage current change, the full-stage battery terminal voltage, the charging temperature rise, and the parameter values in the initial parameter set.

[0087] S270, selecting a plurality of first parameter sets according to the fitness of each initial parameter set, and performing gene crossover processing on the first parameter sets according to a preset crossover probability to generate a plurality of second parameter sets.

[0088] According to the fitness of each initial parameter set, a plurality of first parameter sets are selected, and gene crossover processing is performed on the first parameter sets according to a preset crossover probability to generate a plurality of second parameter sets, which can include:

[0089] In the initial parameter set collection, at least two target initial parameter sets are randomly selected in each round, and the fitness of each target initial parameter set in each round is compared, and the target initial parameter set with the highest fitness is determined as the first parameter set.

[0090] The first parameter sets are grouped two by two, and gene exchange is performed on the first parameter sets in the same group according to a preset crossover probability to generate second parameter sets.

[0091] Optionally, in the initial parameter set collection, 2 parameter sets can be randomly selected without replacement in each round, the fitness of the two parameter sets is compared, and the initial parameter set with higher fitness is used as the first parameter set.

[0092] S280, performing random mutation processing on the second parameter sets according to a preset mutation probability to generate a plurality of third parameter sets.

[0093] According to the preset mutation probability, the second parameter sets are randomly mutated to generate a plurality of third parameter sets, which can include:

[0094] Randomly selecting a target gene in the second parameter set, and changing the gene value of the target gene according to a preset mutation probability to obtain a third parameter set after mutation.

[0095] Optionally, the target gene can refer to a specific parameter position to be mutated randomly selected from 5 gene positions of the second parameter set.

[0096] S290, merging the first parameter sets, the second parameter sets, and the third parameter sets, and performing elimination processing on the merged parameter set collection according to the constraint conditions of each initial parameter set to obtain a parameter set collection for the next round of iteration.

[0097] S2100, performing charging simulation on the updated parameter set collection again to perform multi-round iteration update on the parameter set collection.

[0098] S2110, when it is determined that the termination condition is met, obtaining the optimal parameter group set.

[0099] S2120, simulating charging for each optimal parameter group through the electrothermal simulation model, and obtaining the fitness and constraint condition of each optimal parameter group according to the simulation result of each optimal parameter group.

[0100] S2130, selecting the optimal parameter group with the highest fitness as the target parameter group from the optimal parameter groups meeting the multi-dimensional constraint condition according to the fitness and constraint condition of each optimal parameter group, and generating a current change curve according to the real-time current change in the charging simulation process, and charging the battery according to the current change curve.

[0101] The technical scheme of the embodiment of the application comprises the following steps: generating a plurality of initial parameter groups according to a pre-specified number of charging stages, composing an initial parameter group set, simulating charging for each initial parameter group, obtaining the fitness and constraint condition of each initial parameter group according to the simulation result of each initial parameter group, updating the initial parameter group set according to the fitness and constraint condition of each initial parameter group, and again simulating charging for the updated parameter group set to iteratively update the parameter group set for multiple rounds, obtaining the optimal parameter group set when it is determined that the termination condition is met, selecting a target parameter group from the optimal parameter group set, and obtaining the current change curve corresponding to the target parameter group, and charging the battery according to the current change curve. Compared with the constant current-constant voltage charging method of the prior art, the method can optimize power distribution and avoid unnecessary current limiting, thereby significantly shortening the total charging time, and the step-by-step decreasing constant heat generation charging method can homogenize heat generation during the charging process, thereby maximizing the suppression of the heat generation rate during the battery charging process under the premise of improving the charging speed, and improving the safety and health of the battery.

[0102] Embodiment three

[0103] Figure 4 A structure schematic diagram of a multi-stage decreasing constant heat generation charging device provided by the embodiment three of the application is shown in FIG. 3. Figure 4 As shown in the figure, the device comprises an initial parameter group generation module 310, a charging simulation module 320, a parameter group updating module 330, an optimal parameter group selection module 340, and a charging strategy generation module 350.

[0104] The initial parameter group generation module 310 is configured to generate a plurality of initial parameter groups according to a pre-specified number of charging stages, and compose an initial parameter group set; wherein each parameter group comprises a state of charge point for switching between charging stages and a step-by-step decreasing constant heat generation power target value for each charging stage.

[0105] The charging simulation module 320 is configured to perform charging simulation on each initial parameter group by using an electric heating simulation model, and obtain fitness and constraint conditions of each initial parameter group according to simulation results of the initial parameter groups.

[0106] The parameter group updating module 330 is configured to update the initial parameter group set according to the fitness and constraint conditions of the initial parameter groups, and perform charging simulation on the updated parameter group set again to perform multi-round iteration updating on the parameter group set.

[0107] The optimal parameter group selecting module 340 is configured to obtain an optimal parameter group set when it is determined that a termination condition is met.

[0108] The charging strategy generating module 350 is configured to select a target parameter group from the optimal parameter group set, obtain a current change curve corresponding to the target parameter group, and perform battery charging according to the current change curve.

[0109] The technical scheme of the embodiment of the application comprises the following steps: a plurality of initial parameter groups are randomly generated according to a pre-specified number of charging stages, and the initial parameter groups are combined to form an initial parameter group set; charging simulation is performed on each initial parameter group; fitness and constraint conditions of each initial parameter group are obtained according to simulation results of the initial parameter groups; the initial parameter group set is updated according to the fitness and constraint conditions of the initial parameter groups; charging simulation is performed on the updated parameter group set again to perform multi-round iteration updating on the parameter group set; when it is determined that a termination condition is met, an optimal parameter group set is obtained; a target parameter group is selected from the optimal parameter group set; a current change curve corresponding to the target parameter group is obtained; and battery charging is performed according to the current change curve. Compared with the constant current-constant voltage charging method of the prior art, the technical scheme can optimize power distribution and avoid unnecessary current limiting, thereby significantly shortening the total charging time, and the constant heat production charging method with step-by-step decrease can homogenize heat production in the charging process, thereby maximizing the suppression of the heat generation rate in the battery charging process under the premise of improving the charging speed, and improving the safety and health of the battery.

[0110] On the basis of the above-mentioned embodiments, the charging simulation module 320 can comprise a simulation unit, which is specifically configured to:

[0111] In each charging stage, charging simulation is performed according to a constant heat production power target value in the charging stage, and a stage current change is obtained;

[0112] The real-time state of charge is calculated according to the stage current change, and the switching of the charging stages is completed according to the real-time state of charge and the state of charge points between the charging stages;

[0113] When it is detected that the battery terminal voltage reaches the maximum cut-off voltage or the real-time state of charge reaches the state of charge peak value, the charging simulation of the initial parameter set is completed and the simulation result of the initial parameter set is obtained.

[0114] On the basis of the above embodiments, the charging simulation module 320 can further include a parameter calculation unit, specifically configured to:

[0115] According to the simulation result of the initial parameter set, the total charging time and the charging temperature rise are obtained, and the fitness is calculated according to the total charging time and the charging temperature rise;

[0116] According to the simulation result of the initial parameter, the full-stage current change and the full-stage battery terminal voltage are obtained, and whether the initial parameter set meets the multi-dimensional constraint condition is judged according to the full-stage current change, the full-stage battery terminal voltage, the charging temperature rise and the parameter value in the initial parameter set.

[0117] On the basis of the above embodiments, the parameter set updating module 330 can include:

[0118] The second parameter set generation unit is configured to select a plurality of first parameter sets according to the fitness of each initial parameter set, and perform gene crossover processing on the first parameter sets according to a preset crossover probability to generate a plurality of second parameter sets;

[0119] The third parameter set generation unit is configured to perform random mutation processing on the second parameter sets according to a preset mutation probability to generate a plurality of third parameter sets;

[0120] The parameter set elimination unit is configured to combine the first parameter sets, the second parameter sets and the third parameter sets, and eliminate the combined parameter set collection according to the constraint condition of each initial parameter set to obtain a parameter set collection for the next round of iteration.

[0121] On the basis of the above embodiments, the second parameter set generation unit can be specifically configured to:

[0122] In the initial parameter set collection, at least two target initial parameter sets are randomly selected in each round, and the fitness of each target initial parameter set in each round is compared, and the target initial parameter set with the maximum fitness is determined as the first parameter set;

[0123] The first parameter sets are grouped two by two, and the first parameter sets in the same group are exchanged according to a preset crossover probability to generate second parameter sets.

[0124] On the basis of the above embodiments, the third parameter set generation unit can be specifically configured to:

[0125] Randomly select a target gene in the second parameter group, and change the gene value of the target gene according to a preset variation probability to obtain a third parameter group after variation.

[0126] On the basis of the above-mentioned embodiments, the charging strategy generation module 350 can be specifically used for:

[0127] The charging simulation is performed on each optimal parameter group through the electrothermal simulation model, and the fitness and the constraint condition of each optimal parameter group are obtained according to the simulation results of each optimal parameter group.

[0128] According to the fitness and the constraint condition of each optimal parameter group, the optimal parameter group with the highest fitness is selected as the target parameter group from the optimal parameter groups satisfying the multi-dimensional constraint condition, and the current change curve is generated according to the real-time current change in the charging simulation process, and the battery is charged according to the current change curve.

[0129] The multi-stage decreasing constant heat production charging device provided in the embodiments of the present application can execute the multi-stage decreasing constant heat production charging method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0130] Embodiment Four

[0131] Figure 5 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0132] As Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0133] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, a loudspeaker, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0134] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the charging method of multi-stage decreasing constant heat generation according to embodiments of the present application. That is:

[0135] According to a predetermined number of charging stages, a plurality of initial parameter groups are randomly generated to form an initial parameter group set; wherein each parameter group includes the state of charge points for switching between stages and the constant heat generation power target values for each stage decreasing step by step;

[0136] Through an electrothermal simulation model, charging simulation is performed on each initial parameter group, and according to the simulation results of each initial parameter group, the fitness and constraint conditions of each initial parameter group are obtained;

[0137] According to the fitness and constraint conditions of each initial parameter group, the initial parameter group set is updated, and the updated parameter group set is again subjected to charging simulation to perform multi-round iteration update on the parameter group set;

[0138] When it is determined that the termination condition is met, the optimal parameter group set is obtained;

[0139] In the optimal parameter set collection, a target parameter set is selected, and a current change curve corresponding to the target parameter set is obtained, and the battery is charged according to the current change curve.

[0140] In some embodiments, the charging method with multi-stage decreasing constant heat generation can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the charging method with multi-stage decreasing constant heat generation described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the charging method with multi-stage decreasing constant heat generation by any other appropriate means, for example, by means of firmware.

[0141] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0142] Computer programs used to implement the processes of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0143] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0145] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0146] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0147] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, and this is not limited herein.

[0148] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A charging method of a multi-step decreasing constant heat generation, characterized by, The application relates to a battery charging parameter optimization method and device. According to a pre-specified number of charging stages, a plurality of initial parameter groups are randomly generated to form an initial parameter group set; each parameter group includes state of charge switching points between stages and constant heat generation power target values gradually decreasing in stages; Through an electro-thermal simulation model, each initial parameter group is simulated for charging, and according to the simulation results of each initial parameter group, the fitness and constraint conditions of each initial parameter group are obtained; According to the fitness and constraint conditions of each initial parameter group, the initial parameter group set is updated, and the updated parameter group set is simulated for charging again to iteratively update the parameter group set for multiple rounds; When the termination condition is met, the optimal parameter group set is obtained; In the optimal parameter group set, a target parameter group is selected, and a current change curve corresponding to the target parameter group is obtained, and the battery is charged according to the current change curve.

2. The method of claim 1, wherein, Through an electro-thermal simulation model, each initial parameter group is simulated for charging, including: In each charging stage, the constant heat generation power target value in the stage is used to simulate charging, and the stage current change is obtained; According to the stage current change, the real-time state of charge is calculated, and the switching of the charging stage is completed according to the real-time state of charge and the state of charge switching points between stages; When the battery terminal voltage reaches the maximum cutoff voltage or the real-time state of charge reaches the state of charge peak, the charging simulation of the initial parameter group is completed, and the simulation result of the initial parameter group is obtained.

3. The method of claim 1, wherein, According to the simulation results of each initial parameter group, the fitness and constraint conditions of each initial parameter group are obtained, including: According to the simulation results of the initial parameter group, the total charging time and the charging temperature rise are obtained, and the fitness is calculated according to the total charging time and the charging temperature rise; According to the simulation results of the initial parameter, the full-stage current change and the full-stage battery terminal voltage are obtained, and whether the initial parameter group meets the multi-dimensional constraint condition is judged according to the full-stage current change, the full-stage battery terminal voltage, the charging temperature rise and the parameter values in the initial parameter group.

4. The method of claim 1, wherein, According to the fitness and constraint conditions of each initial parameter group, the initial parameter group set is updated, including: According to the fitness of each initial parameter group, a plurality of first parameter groups are selected, and gene crossover processing is performed on the first parameter groups according to a preset crossover probability to generate a plurality of second parameter groups; According to a preset mutation probability, the second parameter groups are randomly mutated to generate a plurality of third parameter groups; The first parameter groups, the second parameter groups and the third parameter groups are combined, and the combined parameter group set is eliminated according to the constraint conditions of each initial parameter group to obtain a parameter group set for the next round of iteration.

5. The method of claim 4, wherein, According to the fitness of each initial parameter group, a plurality of first parameter groups are selected, and gene crossover processing is performed on the first parameter groups according to a preset crossover probability to generate a plurality of second parameter groups, including: In the initial parameter group set, at least two target initial parameter groups are randomly selected in each round, and the fitness of each target initial parameter group in each round is compared, and the target initial parameter group with the highest fitness is determined as the first parameter group; The first parameter groups are grouped two by two, and the first parameter groups in the same group are exchanged according to a preset crossover probability to generate second parameter groups.

6. The method of claim 4, wherein, According to a preset mutation probability, the second parameter groups are randomly mutated to generate a plurality of third parameter groups, including: Randomly selecting a target gene in the second parameter group, and changing the gene value of the target gene according to a preset mutation probability to obtain a third parameter group after mutation.

7. The method of claim 1, wherein, In the optimal parameter group set, a target parameter group is selected, and a current change curve corresponding to the target parameter group is obtained, and the battery is charged according to the current change curve, including: Through the electrothermal simulation model, the charging simulation of each optimal parameter group is performed, and the fitness and constraint conditions of each optimal parameter group are obtained according to the simulation results of each optimal parameter group; According to the fitness and constraint conditions of each optimal parameter group, the optimal parameter group with the highest fitness is selected as the target parameter group under the condition that the multi-dimensional constraint condition is met, and a current change curve is generated according to the real-time current change in the charging simulation process, and the battery is charged according to the current change curve.

8. A charging device of multi-step decreasing constant heat generation, characterized by, Including: An initial parameter group generation module is configured to randomly generate a plurality of initial parameter groups according to a pre-specified number of charging stages to form an initial parameter group set; wherein each parameter group includes state of charge points for switching between charging stages and constant heat generation power target values that decrease step by step in each charging stage; A charging simulation module is configured to perform charging simulation of each initial parameter group through an electrothermal simulation model, and obtain the fitness and constraint conditions of each initial parameter group according to the simulation results of each initial parameter group; A parameter group updating module is configured to update the initial parameter group set according to the fitness and constraint conditions of each initial parameter group, and perform charging simulation on the updated parameter group set again to iteratively update the parameter group set for multiple rounds; An optimal parameter group selection module is configured to obtain an optimal parameter group set when a termination condition is met; A charging strategy generation module is configured to select a target parameter group from the optimal parameter group set, obtain a current change curve corresponding to the target parameter group, and charge the battery according to the current change curve.

9. An electronic device, comprising: The electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein 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 to enable the at least one processor to execute the charging method with multiple stages of decreasing constant heat generation according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the charging method with multiple stages of decreasing constant heat generation according to any one of claims 1-7 when executed.

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