Lithium battery fast charging method, system and device based on multi-objective optimization

Abstract

The present application discloses a fast charging method, system and device for lithium batteries based on multi-objective optimization. The charging method includes: establishing a temperature rise rate model of the lithium battery by using an interpolation method; dividing the charging process of the lithium battery into n stages at a preset power difference interval according to the preset power range of the lithium battery and calculating the initial candidate solution set of the charging rates corresponding to the n stages; establishing a minimum total temperature rise amount function and a total charging time function; iteratively optimizing the charging rate combination during the charging process of the lithium battery according to the initial candidate solution set and a preset fitness function; until the number of iterations reaches a preset value or the maximum fitness converges, screening out the charging rate combination corresponding to the minimum total temperature rise amount according to the temperature rise rate model; verifying whether the total charging time of the lithium battery meets the constraint conditions according to the total charging time function. This charging method reduces the maximum temperature rise and capacity loss and improves the safety of the lithium battery.

Description

Technical Field

[0001] This application relates to the technical field of lithium batteries, and specifically relates to a rapid charging method, system and device for lithium batteries based on multi-objective optimization. Background Art

[0002] With the popularization of electric vehicles and portable electronic devices, the charging efficiency and safety of batteries have become the focus of attention. Traditional battery charging strategies usually adopt a fixed charging rate, which may cause excessive temperature rise in the battery during charging, affecting the battery life and safety.

[0003] In the prior art, there have been some methods attempting to optimize the charging process by adjusting the charging rate, but these methods lack systematicness and global optimization ability, and it is difficult to achieve an optimal charging method when the lithium battery is in different states of charge (SOC).

[0004] Therefore, how to optimize the battery charging method to reduce the temperature rise of the lithium battery during charging has become an urgent problem to be solved. Summary of the Invention

[0005] This application provides a rapid charging method, system and device for lithium batteries based on multi-objective optimization, aiming to reduce the temperature rise of the lithium battery during charging, thereby improving the safety of the lithium battery.

[0006] On the one hand, an embodiment of the present application provides a rapid charging method for a lithium battery based on multi-objective optimization, including the following steps: obtaining the current battery level, charging rate, and temperature rise rate of the lithium battery, and establishing a temperature rise rate model of the lithium battery by using the interpolation method. The temperature rise rate model includes the mapping relationship among the current battery level, the charging rate, and the temperature rise rate of the lithium battery; dividing the charging process of the lithium battery into n stages at intervals of a preset battery level difference according to the preset battery level range of the lithium battery; generating an initial candidate solution set of the charging rate corresponding to the lithium battery in n stages according to the preset total charging time range of the lithium battery and the preset charging rate range of the lithium battery; setting a charging target, the charging target including a preset minimum total temperature rise amount and a preset total charging time, and respectively establishing a minimum total temperature rise amount function and a total charging time function based on the preset minimum total temperature rise amount and the preset total charging time; iteratively optimizing the charging rate combination in the charging process of the lithium battery according to the initial candidate solution set and a preset fitness function, the fitness function including the mapping relationship among the minimum total temperature rise amount function, a dynamic penalty coefficient, and a constraint violation amount calculation function; until the number of iterations reaches a preset value or the maximum fitness converges, screening out the optimal charging rate combination according to the temperature rise rate model, the optimal charging rate combination being the charging rate combination corresponding to the minimum total temperature rise amount; verifying whether the total charging time required for the lithium battery to be charged based on the optimal charging rate combination meets the constraint conditions according to the total charging time function. If it meets, an optimal lithium battery charging control scheme is obtained.

[0007] Optionally, in some embodiments of the present application, the temperature rise rate model is:

[0008] ,

[0009] where is the temperature rise rate (°C / s) of the lithium battery, is the current battery level (SOC) of the lithium battery, is the charging rate of the lithium battery in the current charging stage, is a bilinear interpolation function;

[0010] The is calculated by the following formula:

[0011] ,

[0012] where is the index number of the experimental data discrete point, are two adjacent SOC discrete points, are two adjacent charging rate discrete points, is corresponding to ( , ) of the measured temperature rise rate.

[0013] Optionally, in some embodiments of the present application, the step of dividing the charging process of the lithium battery into n stages according to the preset power range of the lithium battery and at intervals of preset power difference specifically includes:

[0014] The charging process of the lithium battery is divided into n stages according to the preset power range of the lithium battery and the preset power difference is used as an interval, and the preset power range of the lithium battery in any stage is: [y / n(i-1)%, y / n*i%], (i=1,2,3...,n);

[0015] An initial charging rate combination is randomly generated according to the preset charging rate range of the lithium battery and the total charging time function.

[0016] Optionally, in some embodiments of the present application, the step of generating an initial candidate solution set of the charge rate corresponding to the lithium battery in n stages according to the preset total charging time range of the lithium battery and the preset charge rate range of the lithium battery specifically includes:

[0017] Randomly generate the charging rate combination C of the lithium battery rand ;

[0018] Adjust the charge rate combination by the scaling factor C rand To meet the time constraint:

[0019] ,

[0020] in, is the randomly generated initial charging rate, is the scaled feasible initial candidate solution, is the preset minimum charging rate, The preset maximum charge rate.

[0021] Optionally, in some embodiments of the present application, the expression of the minimum total temperature rise function is:

[0022] ,

[0023] in, is the total temperature rise of the lithium battery during charging (°C), is the target charging capacity of the lithium battery, is the total number of charging stages, i is the number of the current charging stage (1 to n), and ds is the change in the amount of charge of the lithium battery;

[0024] The expression of the total charging time function is as follows:

[0025] ,

[0026] where, is the total charging time (seconds), is the target charging power of the lithium battery, is the total number of charging stages, and i is the current charging stage number (1~n), is the maximum allowed charging time (seconds).

[0027] Optionally, in some embodiments of the present application, the fitness function has the following expression:

[0028] ,

[0029] where, is the total temperature rise (°C), λ is the dynamic penalty coefficient, and max is the constraint violation calculation function, is the target charging power of the lithium battery, is the total number of charging stages, is the charging rate of the lithium battery in the current charging stage, is the maximum allowed charging time (seconds).

[0030] Optionally, in some embodiments of the present application, the dynamic penalty coefficient λ is adaptively adjusted according to the optimization progress. The adaptive adjustment includes the following two stages: initial stage (iteration number < 30% of the total number), set λ = 2000 to strengthen the constraints; later stage (iteration number ≥ 30% of the total number), set λ = 500 to accelerate convergence.

[0031] Optionally, in some embodiments of the present application, the steps of iteratively optimizing the charging rate combination during the charging process of the lithium battery according to the initial candidate solution set and the preset fitness function and until the iteration number reaches the preset value or the maximum fitness converges, and screening out the optimal charging rate combination according to the temperature rise rate model specifically include: evaluating whether the fitness of the current solution in the initial candidate solution set meets the preset conditions; if it does not meet the preset conditions, generating new candidate solutions based on the preset algorithm rules, and the preset algorithm rules include one of the particle swarm algorithm, genetic algorithm, simulated annealing algorithm, and gradient descent algorithm; scaling and adjusting the new candidate solutions that do not meet the total time constraint; until the iteration number reaches the preset value or the maximum fitness converges, and screening out the optimal charging rate combination according to the temperature rise rate model, and the optimal charging rate combination is the charging rate combination corresponding to the minimum total temperature rise.

[0032] Optionally, in some embodiments of the present application, the new candidate solution has a scaling adjustment expression as follows:

[0033] ,

[0034] where clip is the boundary constraint function of the charging rate, is the target charging power of the lithium battery, is the total number of charging stages, is the maximum allowed charging time (seconds), is the preset minimum charging rate, is the preset maximum charging rate, is the current solution in the initial candidate solution set.

[0035] Optionally, in some embodiments of the present application, the preset algorithm rule is the particle swarm algorithm. The steps to meet the total time constraint include: introducing a penalty term in the fitness function, where the penalty term includes a dynamic penalty coefficient λ, and the value of the dynamic penalty coefficient λ includes 1000; after each update of the particle position, force the scaling of the charging rate to meet the total time constraint:

[0036] ,

[0037] where, is the new candidate solution obtained after forced scaling, is the current solution in the initial candidate solution set.

[0038] Optionally, in some embodiments of the present application, the steps to establish the temperature rise rate model of the lithium battery by using the interpolation method include: obtaining the current power of the lithium battery, where the value of the current power ranges from 0% to 80%; obtaining the charging rate of the lithium battery; obtaining the temperature rise rate of the lithium battery at the corresponding current power and charging rate; storing the obtained current power data s j , the charging rate data C k and the temperature rise rate data dT / dt j as a discrete data set {(s j , C k , dT / dt j , k)}, where j = 1, 2, 3,..., n, C min ≤ k ≤ C max， is the preset minimum charging rate, is the preset maximum charging rate; using the bilinear interpolation method to generate a continuous temperature rise surface, thereby establishing the temperature rise rate model of the lithium battery.

[0039] Optionally, in some embodiments of the present application, 100 of the power values are sampled at each of the n stages, and the trapezoidal rule is used to calculate the average temperature rise rate of the lithium battery. The average temperature rise rate is calculated by the formula:

[0040] ,

[0041] where is the start and end value of the power of the current charging stage of the lithium battery, is the bilinear interpolation function, and ds is the change in the power of the lithium battery;

[0042] The total temperature rise amount of the lithium battery is calculated by the formula:

[0043] ,

[0044] where is the total temperature rise amount (°C) of the lithium battery during charging, is the target charging power of the lithium battery, is the total number of charging stages, i is the current charging stage number (1 to n), is the temperature rise rate (°C / s) of the lithium battery, is the charging rate of the lithium battery in the current charging stage, and 3600 is the unit conversion coefficient (converting from hours to seconds).

[0045] On the other hand, the present application provides a lithium battery system, including a modeling module, a definition module, an optimization module, and a verification module; the modeling module is used to obtain the current battery level, charging rate, and temperature rise rate of the lithium battery, and establish a temperature rise rate model of the lithium battery by using the interpolation method. The temperature rise rate model includes the mapping relationship among the current battery level, the charging rate, and the temperature rise rate of the lithium battery; the definition module is used to divide the charging process of the lithium battery into n stages at intervals of a preset battery level difference according to the preset battery level range of the lithium battery, generate an initial candidate solution set of the charging rate corresponding to the lithium battery in the n stages according to the preset total charging time range and the preset charging rate range of the lithium battery, and is further used to set a charging target, the charging target includes a preset minimum total temperature rise amount and a preset total charging time, and respectively establish a minimum total temperature rise amount function and a total charging time function based on the preset minimum total temperature rise amount and the preset total charging time; the optimization module is used to iteratively optimize the charging rate combination in the charging process of the lithium battery according to the initial candidate solution set and a preset fitness function. The fitness function includes the mapping relationship among the minimum total temperature rise amount function, a dynamic penalty coefficient, and a constraint violation amount calculation function, until the number of iterations reaches a preset value or the maximum fitness converges, and screen out an optimal charging rate combination according to the temperature rise rate model. The optimal charging rate combination is the charging rate combination corresponding to the minimum total temperature rise amount; the verification module is used to verify whether the total charging time required for the lithium battery to be charged based on the optimal charging rate combination meets the constraint conditions. If it meets the conditions, an optimal lithium battery charging control scheme is obtained.

[0046] On the other hand, the present application further provides an electronic device, which is used to run a program. When the running program runs, it executes the above-mentioned lithium battery fast charging method based on multi-objective optimization.

[0047] On the other hand, the present application further provides a computer storage medium, which includes a stored program. When the stored program runs, it controls the device where the storage medium is located to execute the above-mentioned lithium battery fast charging method based on multi-objective optimization.

[0048] Compared with the prior art, the lithium battery fast charging method based on multi-objective optimization provided by the present application shortens the charging time of the lithium battery, reduces the total temperature rise amount during the charging process of the lithium battery, and reduces the capacity loss compared with the charging method using a fixed charging rate, achieving the multi-objective optimized lithium battery charging effect. Description of the Drawings

[0049] Figure 1 is a schematic diagram of the lithium battery system provided by the present application;

[0050] Figure 2 It is a flowchart of the lithium battery fast charging method based on multi-objective optimization provided by this application;

[0051] Figure 3 is Figure 2 A schematic diagram of the temperature rise rate model in the lithium battery fast charging method based on multi-objective optimization provided;

[0052] Figure 4 It is a comparison chart of the charging rate and total temperature rise of a lithium battery in the prior art during charging and the charging rate and total temperature rise of a lithium battery in this application during charging;

[0053] Figure 5 It is a comparison chart of the total temperature rise of a lithium battery in the prior art and the total temperature rise of a lithium battery after using the lithium battery fast charging method based on multi-objective optimization provided by this application;

[0054] Figure 6 It is a flowchart of the lithium battery fast charging method based on multi-objective optimization of the particle swarm optimization algorithm provided by this application. Specific embodiments

[0055] Next, the technical solutions in the embodiments of this application will be described in conjunction with the accompanying drawings in the embodiments of this application. The described technical solutions are only used to explain and illustrate the idea of this application, and should not be regarded as a limitation on the protection scope of this application.

[0056] The various embodiments provided by this application are similar, and the features in different embodiments can be combined with each other.

[0057] Such as Figure 1 As shown, an embodiment of this application provides a lithium battery system 100, including a modeling module 10, a definition module 20, an optimization module 30, and a verification module 40.

[0058] In an embodiment of this application, the modeling module 10 is used to obtain the current battery level, charging rate, and temperature rise rate of the lithium battery, and establish a temperature rise rate model of the lithium battery by using the interpolation method. The temperature rise rate model includes the mapping relationship of the current battery level, charging rate, and temperature rise rate of the lithium battery.

[0059] In an embodiment of the present application, a definition module 20 is configured to divide the charging process of a lithium battery into n stages at intervals of a preset power difference according to a preset power range of the lithium battery, generate an initial candidate solution set of the charging rates corresponding to the lithium battery in the n stages according to a preset total charging time range of the lithium battery and a preset charging rate range of the lithium battery, and is further configured to set a charging target, where the charging target includes a preset minimum total temperature rise and a preset total charging time, and establish a minimum total temperature rise function and a total charging time function based on the preset minimum total temperature rise and the preset total charging time respectively.

[0060] In an embodiment of the present application, an optimization module 30 is configured to iteratively optimize the charging rate combination during the charging process of the lithium battery according to the initial candidate solution set and a preset fitness function, where the fitness function includes a mapping relationship of a minimum total temperature rise function, a dynamic penalty coefficient, and a constraint violation amount calculation function, until the number of iterations reaches a preset value or the maximum fitness converges, and screen out an optimal charging rate combination according to a temperature rise rate model, and the optimal charging rate combination is the charging rate combination corresponding to the minimum total temperature rise.

[0061] In an embodiment of the present application, a verification module 40 is configured to verify whether the total charging time required for the lithium battery to charge based on the optimal charging rate combination meets the constraint conditions according to the total charging time function. If it meets the conditions, an optimal lithium battery charging control scheme is obtained.

[0062] The lithium battery system provided by the present application realizes multi-objective optimization based on the setting of a modeling module 10, a definition module 20, an optimization module 30, and a verification module 40, thereby realizing fast charging of the lithium battery, shortening the charging time of the lithium battery, reducing the total temperature rise of the lithium battery during the charging process, and reducing the capacity loss, achieving the charging effect of multi-objective optimized lithium battery.

[0063] As Figure 2 shown, an embodiment of the present application provides a method for fast charging a lithium battery based on multi-objective optimization, including the following steps:

[0064] S10. Obtain the current power, charging rate, and temperature rise rate of the lithium battery, and establish a temperature rise rate model of the lithium battery by using an interpolation method, as Figure 3 shown, the temperature rise rate model includes a mapping relationship of the current power, charging rate, and temperature rise rate of the lithium battery.

[0065] In an embodiment of the present application, based on the experimental data of 18650LFP batteries, a mapping relationship between the temperature rise rate and the state of charge (SOC) and the charging rate is established.

[0066] In an embodiment of the present application, the temperature rise rate model is:

[0067] ,

[0068] Among them, is the temperature rise rate of the lithium battery (℃ / s), is the current state of charge (SOC) of the lithium battery, is the charging rate of the lithium battery at the current charging stage, is a bilinear interpolation function.

[0069] It is calculated by the following formula:

[0070] ,

[0071] Among them, is the index number of the discrete points of the experimental data, are two adjacent SOC discrete points, are two adjacent charging rate discrete points, is the corresponding actual measured temperature rise rate for ([[]] , ).

[0072] In the embodiments of the present application, the steps of establishing a temperature rise rate model of a lithium battery by using an interpolation method include:

[0073] S101. Obtain the current state of charge of the lithium battery, and the value of the current state of charge is between 0% and 80%.

[0074] S102. Obtain the charging rate of the lithium battery.

[0075] S103. Obtain the temperature rise rate of the lithium battery at the corresponding current state of charge and charging rate.

[0076] S104. Store the obtained current state of charge data s[[[]] j , charging rate data C[[[]] k and temperature rise rate data dT / dt[[[]] j as a discrete data set {(s[[[]] j , C[[[]] k , dT / dt[[[]] j , k)}, where j = 1,..., n, C[[[]] min ≤ k ≤ C[[[]] max， is the preset minimum charging rate, is the preset maximum charging rate.

[0077] S105. Use the bilinear interpolation method to generate a continuous temperature rise surface, thereby establishing a temperature rise rate model of the lithium battery.

[0078] In an embodiment of the present application, 100 power values are sampled at each of the n stages, and the trapezoidal rule is used to calculate the average temperature rise rate of the lithium battery. The average temperature rise rate is calculated by the formula:

[0079] ,

[0080] where is the start and end value of the power of the current charging stage of the lithium battery, is the bilinear interpolation function, and ds is the change in the power of the lithium battery.

[0081] The total temperature rise of the lithium battery is calculated by the formula:

[0082] ,

[0083] where is the total temperature rise (°C) of the lithium battery during charging, is the target charging power of the lithium battery, is the total number of charging stages, i is the current charging stage number (1~n), is the temperature rise rate (°C / s) of the lithium battery, is the charging rate of the lithium battery in the current charging stage, and 3600 is the unit conversion coefficient (converting from hours to seconds).

[0084] S20. Divide the charging process of the lithium battery into n stages at intervals of a preset power difference according to the preset power range of the lithium battery.

[0085] In an embodiment of the present application, step S20 specifically includes:

[0086] S201. Divide the charging process of the lithium battery into n stages at intervals of a preset power difference according to the preset power range of the lithium battery. The preset power range of the lithium battery in any stage is: [y / n(i−1)%, y / n*i%], (i = 1, 2, 3... n).

[0087] S202. Randomly generate an initial charging rate combination according to the preset charging rate range and total charging time function of the lithium battery.

[0088] Exemplarily, in the present application, the preset power range of the lithium battery is 0% to 80%, the preset power difference is 10%, and n is 8, that is, 8 stages. By dynamically optimizing the charging rate of each stage to minimize the temperature rise, the total charging time is constrained to 3C (960 seconds). Specifically, the charging rate of each stage is C ratei , and a series of random numbers are generated that satisfy the condition (a: 1C < C ratei<5C; b: The total charging time t < 960 s) of C ratei Combination.

[0089] S30. Generate an initial candidate solution set of the charging rate corresponding to the lithium battery in n stages according to the preset total charging time range and the preset charging rate range of the lithium battery.

[0090] In an embodiment of the present application, step S30 specifically includes:

[0091] S301. Randomly generate a charging rate combination C of the lithium battery rand .

[0092] S302. Adjust the charging rate combination C by a scaling factor rand to meet the time constraint:

[0093] ,

[0094] where is the randomly generated initial charging rate, is the scaled feasible initial candidate solution, is the preset minimum charging rate, is the preset maximum charging rate.

[0095] S40. Set a charging target, the charging target includes a preset minimum total temperature rise and a preset total charging time, and establish a minimum total temperature rise function and a total charging time function based on the preset minimum total temperature rise and the preset total charging time respectively.

[0096] In an embodiment of the present application, the expression of the minimum total temperature rise function is:

[0097] ,

[0098] where is the total temperature rise (°C) of the lithium battery during charging, is the target charging power of the lithium battery, is the total number of charging stages, i is the current charging stage number (1~n), and ds is the power change amount of the lithium battery.

[0099] The expression of the total charging time function is:

[0100] ,

[0101] where is the total charging time (seconds), is the target charging power of the lithium battery, is the total number of charging stages, i is the current charging stage number (1~n), is the maximum allowable charging time (seconds).

[0102] Exemplarily, .

[0103] As Figure 4 and Figure 5 shown, through experimental verification, in 8 stages, the charging rate of the first 8 stages before optimization is 3C, and the total temperature rise of the lithium battery is 11.53 degrees Celsius (°C). After optimization according to the fast charging method provided by this application, the charging rates of the 8 stages are respectively: 2.0C, 3.02C, 3.0C, 3.03C, 4.0C, 3.0C, 3.91C, 3.01C, and the total temperature rise of the lithium battery is 10.97 degrees Celsius (°C). It can be seen that the total temperature rise of the lithium battery after optimization is significantly reduced.

[0104] S50. Iteratively optimize the charging rate combination during the charging process of the lithium battery according to the initial candidate solution set and the preset fitness function. The fitness function includes the mapping relationship of the minimum total temperature rise function, the dynamic penalty coefficient, and the constraint violation amount calculation function.

[0105] In the embodiment of this application, the fitness function has the expression of:

[0106] ,

[0107] wherein, is the total temperature rise (°C), λ is the dynamic penalty coefficient, max is the constraint violation amount calculation function, is the target charging power of the lithium battery, is the total number of charging stages, is the charging rate of the lithium battery at the current charging stage, is the maximum allowable charging time (seconds).

[0108] In the embodiment of this application, the dynamic penalty coefficient λ is adaptively adjusted according to the optimization progress. The adaptive adjustment includes the following two stages:

[0109] Initial stage (iteration number < 30% of the total number), set λ = 2000 to strengthen the constraint.

[0110] Later stage (iteration number ≥ 30% of the total number), set λ = 500 to accelerate convergence.

[0111] In the embodiment of this application, step S50 specifically includes:

[0112] S501. Evaluate whether the fitness of the current solution in the initial candidate solution set meets the preset conditions.

[0113] S502. If the preset conditions are not met, generate a new candidate solution based on the preset algorithm rules, which include one of the particle swarm optimization algorithm, genetic algorithm, simulated annealing algorithm, and gradient descent algorithm.

[0114] Exemplarily, as Figure 6 shown (where some of the aforementioned repeated steps are not described in detail here to avoid redundancy), the preset algorithm rule is the particle swarm optimization algorithm:

[0115] In the embodiment of the present application, initialize the particle swarm, encode the particle position as X = [C1, C2,..., C8], and randomly generate the velocity;

[0116] In the embodiment of the present application, define the fitness function as the weighted sum of the total temperature rise and the time constraint:

[0117] ,

[0118] In the embodiment of the present application, update the particle velocity and position iteratively according to the following rules:

[0119] ,

[0120] where clip is the magnification boundary constraint function;

[0121] Result verification: Output the optimal charging rate combination X*, and verify that the total charging time ≤ 960 seconds.

[0122] Specifically, the steps of the particle swarm optimization algorithm include:

[0123] In the embodiment of the present application, the particle swarm size is 256, and the maximum number of iterations is 300.

[0124] In the embodiment of the present application, the inertia weight w = 0.6, and the individual and social learning factors c1 = c2 = 1.8.

[0125] In the embodiment of the present application, the initial particle position is adjusted by a scaling factor to meet the time constraint, and its expression is:

[0126] ,

[0127] In the embodiment of the present application, the interpolation model is constructed through the following steps:

[0128] First, collect experimental data: Measure the temperature rise rate at every 10% SOC interval within the range of 1 - 5C rate.

[0129] Second, store the data as a discrete data set {(sj, Ck, dT / dtj, k)}, where j = 1,..., 8, k = 1,..., 5;

[0130] Next, bilinear interpolation is used to generate a continuous temperature rise surface.

[0131] S503. Scale and adjust the new candidate solutions that do not meet the total time constraint.

[0132] In the embodiment of the present application, the new candidate solutions The expression for the scaling adjustment is:

[0133] ,

[0134] where clip is the boundary constraint function of the charging rate, is the target charging power of the lithium battery, is the total number of charging stages, is the maximum allowable charging time (seconds), is the preset minimum charging rate, is the preset maximum charging rate, is the current solution in the initial candidate solution set.

[0135] S60. Until the number of iterations reaches the preset value or the maximum fitness converges, the optimal charging rate combination is selected according to the temperature rise rate model. The optimal charging rate combination is the charging rate combination corresponding to the minimum total temperature rise.

[0136] S70. Verify whether the total charging time required for the lithium battery to be charged based on the optimal charging rate combination meets the constraint conditions according to the total charging time function. If it meets the conditions, the optimal lithium battery charging control scheme is obtained.

[0137] In the embodiment of the present application, the preset algorithm rule is the particle swarm algorithm. The steps to meet the total time constraint include: introducing a penalty term in the fitness function. The penalty term includes a dynamic penalty coefficient λ, and the value of the dynamic penalty coefficient λ includes 1000. After each update of the particle position, the charging rate is forced to be scaled to meet the total time constraint:

[0138] ,

[0139] where is the new candidate solution obtained after forced scaling, is the current solution in the initial candidate solution set.

[0140] The lithium battery fast charging method based on multi-objective optimization provided by the present application shortens the charging time of the lithium battery, reduces the total temperature rise of the lithium battery during the charging process, reduces the capacity loss, and achieves the multi-objective optimized lithium battery charging effect.

[0141] On the other hand, the present application also provides an electronic device for running a program. When the program runs, it executes the above-mentioned method for fast charging a lithium battery based on multi-objective optimization.

[0142] On the other hand, the present application also provides a computer storage medium including a stored program. When the stored program runs, it controls the device where the storage medium is located to execute the above-mentioned method for fast charging a lithium battery based on multi-objective optimization.

[0143] The above has introduced in detail a method, system and device for fast charging a lithium battery based on multi-objective optimization input in the embodiments of the present application. The description of the above embodiments is only used to help understand the core idea of the present application, and the above description should not be construed as a limitation on the protection scope of the present application.

Claims

1. A lithium battery fast charging method based on multi-objective optimization, characterized in that: The following steps are involved: Obtaining the current power, charge rate, and temperature rise rate of the lithium battery, and establishing a temperature rise rate model of the lithium battery using an interpolation method, wherein the temperature rise rate model includes a mapping relationship between the current power, the charge rate, and the temperature rise rate of the lithium battery; Dividing the charging process of the lithium battery into n stages according to a preset power range of the lithium battery and at intervals of a preset power difference; Generating an initial candidate solution set of the charging rate corresponding to the lithium battery in n stages according to the preset total charging time range of the lithium battery and the preset charging rate range of the lithium battery; Setting a charging target, the charging target including a preset minimum total temperature rise and a preset total charging time, and establishing a minimum total temperature rise function and a total charging time function based on the preset minimum total temperature rise and the preset total charging time, respectively; Iteratively optimizing the charge rate combination during the charging process of the lithium battery according to the initial candidate solution set and a preset fitness function, wherein the fitness function includes a mapping relationship between the minimum total temperature rise function, a dynamic penalty coefficient, and a constraint violation calculation function; Until the number of iterations reaches a preset value or the maximum fitness converges, the optimal charging rate combination is screened out according to the temperature rise rate model, and the optimal charging rate combination is the charging rate combination corresponding to the minimum total temperature rise; The total charging time function is used to verify whether the total charging time required for charging the lithium battery based on the optimal charging rate combination meets the constraint conditions. If so, the optimal lithium battery charging control scheme is obtained.

2. The lithium battery fast charging method based on multi-objective optimization according to claim 1, characterized in that: The temperature rise rate model is: ， in, is the temperature rise rate of the lithium battery, is the current power of the lithium battery, is the charging rate of the lithium battery in the current charging stage, is the bilinear interpolation function, The target charging capacity of the lithium battery; described Calculated by the following formula: ， in, is the index number of the discrete point of the experimental data, are two adjacent SOC discrete points, are two adjacent discrete charging rate points, For the corresponding ( , ) of the measured temperature rise rate.

3. The lithium battery fast charging method based on multi-objective optimization according to claim 1 or 2, characterized in that: The step of dividing the charging process of the lithium battery into n stages according to the preset power range of the lithium battery and at intervals of preset power difference specifically includes: The charging process of the lithium battery is divided into n stages according to the preset power range of the lithium battery and the preset power difference is used as an interval. The preset power range of the lithium battery in any stage is: [y / n(i-1)%, y / n*i%], is the target charging capacity of the lithium battery, i=1,2,3...,n; An initial charging rate combination is randomly generated according to the preset charging rate range of the lithium battery and the total charging time function.

4. The lithium battery fast charging method based on multi-objective optimization according to claim 1, characterized in that: The step of generating an initial candidate solution set of the charging rate corresponding to the lithium battery in n stages according to the preset total charging time range of the lithium battery and the preset charging rate range of the lithium battery specifically includes: Randomly generate the charging rate combination C of the lithium battery rand ; Adjust the charge rate combination by the scaling factor C rand To meet the time constraint: ， in, is the randomly generated initial charging rate, is the scaled feasible initial candidate solution, is the preset minimum charging rate, is the preset maximum charging rate, is the target charging capacity of the lithium battery, is the total number of charging stages, The maximum allowed charging time.

5. The lithium battery fast charging method based on multi-objective optimization according to claim 2, characterized in that: The expression of the minimum total temperature rise function is: ， in, is the total temperature rise of the lithium battery during the charging process, is the target charging capacity of the lithium battery, is the total number of charging stages, i is the number of the current charging stage, i=1,2,3...,n, ds is the change in the amount of charge of the lithium battery; The expression of the total charging time function is: ， in, is the total charging time, is the target charging capacity of the lithium battery, is the total number of charging stages, i is the number of the current charging stage, i=1,2,3...,n, is the maximum allowed charging time, is the preset minimum charging rate, The preset maximum charge rate.

6. The lithium battery fast charging method based on multi-objective optimization according to claim 1, characterized in that: The fitness function The expression is: ， in, is the total temperature rise, λ is the dynamic penalty coefficient, and max is the constraint violation calculation function. is the target charging capacity of the lithium battery, is the total number of charging stages, is the charging rate of the lithium battery in the current charging stage, The maximum allowed charging time.

7. The lithium battery fast charging method based on multi-objective optimization according to claim 6, characterized in that: The dynamic penalty coefficient λ is adaptively adjusted according to the optimization progress, and the adaptive adjustment includes the following two stages: In the initial stage, λ=2000 is set to strengthen the constraint; the number of iterations < 30% of the total number of iterations is the initial stage; In the late stage, λ=500 is set to accelerate convergence; the number of iterations ≥ 30% of the total number is the late stage.

8. The lithium battery fast charging method based on multi-objective optimization according to claim 1, characterized in that: The steps of iteratively optimizing the charging rate combination during the charging process of the lithium battery according to the initial candidate solution set and a preset fitness function, and selecting the optimal charging rate combination according to the temperature rise rate model until the number of iterations reaches a preset value or the maximum fitness converges, specifically include: Evaluate whether the fitness of the current solution in the initial candidate solution set meets a preset condition; If the preset conditions are not met, a new candidate solution is generated based on a preset algorithm rule, wherein the preset algorithm rule includes one of a particle swarm algorithm, a genetic algorithm, a simulated annealing algorithm, and a gradient descent algorithm; performing scaling adjustments on the new candidate solutions that do not satisfy the total time constraint; Until the number of iterations reaches a preset value or the maximum fitness converges, the optimal charging rate combination is screened out according to the temperature rise rate model, and the optimal charging rate combination is the charging rate combination corresponding to the minimum total temperature rise.

9. The lithium battery fast charging method based on multi-objective optimization according to claim 8, characterized in that: The new candidate solution The expression for scaling adjustment is: ， Wherein, clip is the boundary constraint function of the charging rate, is the target charging capacity of the lithium battery, is the total number of charging stages, is the maximum allowed charging time, is the preset minimum charging rate, is the preset maximum charging rate, is the current solution in the initial candidate solution set.

10. The lithium battery fast charging method based on multi-objective optimization according to claim 8, characterized in that: The preset algorithm rule is a particle swarm algorithm, and the steps of satisfying the total time constraint include: Introducing a penalty term into the fitness function, the penalty term includes a dynamic penalty coefficient λ, and the value of the dynamic penalty coefficient λ includes 1000; After each particle position update, the charge rate is forced to scale to meet the total time constraint: ， in, is the new candidate solution obtained after forced scaling, is the current solution in the initial candidate solution set, is the maximum allowed charging time, is the current solution in the initial candidate solution set.

11. The lithium battery fast charging method based on multi-objective optimization according to claim 1 or 2, characterized in that: The steps of establishing the temperature rise rate model of the lithium battery using the interpolation method include: Obtaining the current power level of the lithium battery, where the current power level is between 0% and 80%; Obtaining the charge rate of the lithium battery; Obtaining the temperature rise rate of the lithium battery corresponding to the current power and the charge rate; The current power data s obtained j , the charging rate data C k and the temperature rise rate data dT / dt j Stored as discrete data sets {(s j , C k , dT / dt j ,k)}, where j=1,2,3,...,n, C min ≤k≤C max， is the preset minimum charging rate, It is the preset maximum charging rate; A bilinear interpolation method is used to generate a continuous temperature rise surface, thereby establishing a temperature rise rate model of the lithium battery.

12. The lithium battery fast charging method based on multi-objective optimization according to claim 1 or 2, characterized in that: 100 power values are sampled in each of the n stages, and the average temperature rise rate of the lithium battery is calculated using the trapezoidal rule. The average temperature rise rate The calculation formula is: ， in, are the starting and ending values of the current charging stage of the lithium battery, is a bilinear interpolation function, ds is the change in the amount of charge of the lithium battery, is the current power of the lithium battery; The total temperature rise of the lithium battery The calculation formula is: ， in, is the total temperature rise of the lithium battery during charging, is the target charging capacity of the lithium battery, is the total number of charging stages, i is the number of the current charging stage, i=1,2,3...,n, is the temperature rise rate of the lithium battery, is the charging rate of the lithium battery in the current charging stage.

13. A lithium battery system, characterized in that: Includes modeling module, definition module, optimization module and verification module; The modeling module is used to obtain the current power, charging rate and temperature rise rate of the lithium battery, and establish a temperature rise rate model of the lithium battery using an interpolation method, wherein the temperature rise rate model includes a mapping relationship between the current power, the charging rate and the temperature rise rate of the lithium battery; The definition module is configured to divide the charging process of the lithium battery into n stages based on a preset power range of the lithium battery and at intervals of a preset power difference, generate an initial candidate solution set of the charging rate corresponding to the lithium battery in the n stages based on a preset total charging time range of the lithium battery and a preset charging rate range of the lithium battery, and further configured to set a charging target, the charging target including a preset minimum total temperature rise and a preset total charging time, and establish a minimum total temperature rise function and a total charging time function based on the preset minimum total temperature rise and the preset total charging time, respectively; The optimization module is used to iteratively optimize the charging rate combination during the charging process of the lithium battery according to the initial candidate solution set and a preset fitness function, wherein the fitness function includes a mapping relationship between the minimum total temperature rise function, a dynamic penalty coefficient, and a constraint violation calculation function, until the number of iterations reaches a preset value or the maximum fitness converges, and the optimal charging rate combination is screened out according to the temperature rise rate model, wherein the optimal charging rate combination is the charging rate combination corresponding to the minimum total temperature rise; The verification module is used to verify whether the total charging time required for charging the lithium battery based on the optimal charging rate combination meets the constraint conditions according to the total charging time function. If it does, the optimal lithium battery charging control solution is obtained.

14. An electronic device, characterized in that: The electronic device is used to run a program, wherein the running program executes the lithium battery fast charging method based on multi-objective optimization according to any one of claims 1 to 12 when running.

15. A computer storage medium, characterized in that The storage medium includes a storage program, wherein, when the storage program is running, the device where the storage medium is located is controlled to execute the lithium battery fast charging method based on multi-objective optimization according to any one of claims 1 to 12.

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