Charging control method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a charging control method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: repeatedly executing simulation processing for N times to obtain N first charging current distributions; the simulation processing comprises the following steps: carrying out charging and discharging simulation according to the A charging current distributions and the battery performance parameters to obtain charging time and capacity loss corresponding to the A charging current distributions; according to the charging time and the capacity loss corresponding to the A charging current distributions, using an optimization algorithm to obtain a first charging current distribution corresponding to the minimum value of the objective function; the objective function is an optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and the capacity loss; and according to the N first charging current distributions, establishing charging current distributions of the battery in different life stages, and based on the charging current distributions in different life stages, performing battery charging control. According to the scheme, the accuracy of optimizing the charging current distribution can be improved.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a charging control method, device, electronic device, storage medium, and program product. Background Technology

[0002] With the increasing application of rechargeable batteries in electric vehicles and energy storage, fast charging is crucial for improving battery system efficiency and enhancing the user experience of electric vehicles. However, the increased charging current during fast charging can exacerbate battery capacity degradation and lead to increased battery temperature, potentially causing safety issues. Therefore, battery charging control methods have become a hot research topic in the battery field.

[0003] Currently, some charging control methods optimize the charging current distribution based on the initial stage of the battery's lifespan to obtain the optimal charging current distribution, and use this optimal charging current distribution to charge until the battery's lifespan ends. However, the optimal charging current distribution obtained by this approach is inaccurate, and using this charging current distribution to charge will accelerate battery aging. Summary of the Invention

[0004] This application provides a charging control method, apparatus, electronic device, storage medium, and program product to improve the accuracy of charging current distribution optimization.

[0005] In a first aspect, this application provides a charging control method, comprising: obtaining N first charging current distributions by repeatedly performing N simulation processes; the simulation process includes: performing charge-discharge simulations based on A charging current distributions and battery performance parameters to obtain the charging time and capacity loss corresponding to the A charging current distributions; using an optimization algorithm based on the charging time and capacity loss corresponding to the A charging current distributions to obtain the first charging current distribution corresponding to the minimum value of an objective function; the objective function is the optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and capacity loss; establishing charging current distributions under different life stages of the battery based on the N first charging current distributions, and performing battery charging control based on the charging current distributions under different life stages; the charging current distributions characterize the charging current under different state of charge intervals during battery charging; wherein, A and N are positive integers.

[0006] In one possible implementation, the capacity loss includes a first capacity loss and a second capacity loss; the first capacity loss characterizes the capacity loss caused by the growth of the solid electrolyte membrane, and the second capacity loss characterizes the capacity loss caused by ion deposition.

[0007] In one possible implementation, the method further includes: obtaining the temperature rise corresponding to the A charging current distributions based on a thermal model; the thermal model is used to characterize the relationship between the charging current distribution and the internal temperature change of the battery; the temperature rise characterizes the difference between the battery body temperature and the ambient temperature.

[0008] Based on the charging time and capacity loss corresponding to A charging current distributions, an optimization algorithm is used to obtain the first charging current distribution corresponding to the minimum value of the objective function; including: from the A charging current distributions, deleting the charging current distributions whose temperature rise exceeds a preset threshold, and retaining the charging current distributions whose temperature rise does not exceed the preset threshold; based on the charging time and capacity loss corresponding to the retained charging current distributions, an optimization algorithm is used to obtain the first charging current distribution corresponding to the minimum value of the objective function.

[0009] In one possible implementation, the method further includes: using the battery performance parameters under the current simulation as the initial battery performance parameters, performing M charge-discharge simulations based on the first charging current distribution obtained from the current simulation, and obtaining the first battery performance parameters after M charge-discharge simulations; using the first battery performance parameters as the battery performance parameters under the next simulation; where M is a positive integer.

[0010] In one possible implementation, based on N first charging current distributions, the charging current distribution under different life stages of the battery is established, including: dividing the battery's lifespan into N life stages; the lifespan represents the maximum number of charge-discharge cycles the battery can support, and each life stage contains M charge-discharge cycles; establishing the correspondence between the N first charging current distributions and the N life stages to obtain the charging current distribution under different life stages.

[0011] In one possible implementation, battery charging control is performed based on the charging current distribution under different life stages, including: determining the current life stage of the battery based on the battery's historical charge-discharge cycles; determining the charging current distribution corresponding to the current life stage of the battery based on the charging current distribution under different life stages; and performing charging control on the battery based on the charging current distribution corresponding to the current life stage of the battery.

[0012] In one possible implementation, charge-discharge simulation is performed based on A charging current distributions and battery performance parameters, including: inputting A charging current distributions and battery performance parameters into an equivalent circuit model and an aging model; the aging model is used to simulate the relationship between capacity loss and charging current distribution; the equivalent circuit model is used to simulate the relationship between charging time and charging current distribution.

[0013] The charging control method, device, electronic device, and storage medium provided in this application, as a program product, obtain N first charging current distributions by repeatedly executing N simulation processes. The simulation process includes: performing charge and discharge simulations based on A charging current distributions and battery performance parameters to obtain the charging time and capacity loss corresponding to the A charging current distributions; using an optimization algorithm to obtain the first charging current distribution corresponding to the minimum value of the objective function based on the charging time and capacity loss corresponding to the A charging current distributions; wherein, the objective function serves as the optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and capacity loss; based on the N first charging current distributions, establishing charging current distributions under different battery life stages, and performing battery charging control based on the charging current distributions under different life stages. The proposed solution performs charge-discharge simulations for each stage from the battery's initial stage to the end of its lifespan. The optimal charging current distribution, corresponding to the minimum objective function value related to battery charging time and capacity loss obtained from the simulation, is used as the first charging current distribution in this simulation. After N simulations, N first charging current distributions are obtained. Based on these N first charging current distributions, charging current distributions for different battery lifespan stages are established, and battery charging control is performed based on these distributions. The proposed solution optimizes the charging current distribution using the objective function as the optimization target and updates the current battery performance parameters for each simulation, resulting in a more accurate charging current distribution. Using this charging current distribution for charging control can improve battery lifespan. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0015] Figure 1 The diagram above illustrates a flowchart of a charging control method.

[0016] Figure 2 This is a schematic diagram illustrating how capacity retention varies with the number of cycles, as an example.

[0017] Figure 3 The diagram above illustrates a flowchart of another charging control method.

[0018] Figure 4 The diagram above illustrates a flowchart of yet another charging control method.

[0019] Figure 5 This is a schematic diagram of the equivalent circuit model for the charge-discharge simulation of this application;

[0020] Figure 6 This is a flowchart illustrating the dynamic optimization process of the charging control method of this application;

[0021] Figure 7 This is a dynamic flowchart illustrating the optimization of the charging current distribution in this application.

[0022] Figure 8 This is a schematic diagram illustrating the maximum temperature rise as a function of the number of cycles for six different charging strategies, as an example.

[0023] Figure 9 This is a schematic diagram illustrating the change in charging time as a function of the number of cycles for six different charging strategies, as an example.

[0024] Figure 10 This is a schematic diagram illustrating the capacity variation with the number of cycles for six charging strategies as an example.

[0025] Figure 11 The diagram above illustrates a schematic representation of the structure of a charging control device.

[0026] Figure 12 The diagram above illustrates the structure of an electronic device.

[0027] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0029] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning. The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to be omnipresent but not exclusive. For example, a product or device that comprises a series of components is not necessarily limited to those components that are explicitly listed, but may include other components that are not explicitly listed or that are inherent to such products or devices. The term "module" as used in this application refers to any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code capable of performing the functions associated with that element.

[0030] With the increasing application of rechargeable batteries in electric vehicles and energy storage, fast charging is crucial for improving battery system efficiency and enhancing the user experience of electric vehicles. However, the increased charging current during fast charging can exacerbate battery capacity degradation and lead to increased battery temperature, potentially causing safety issues. Therefore, battery charging control methods have become a hot research topic in the battery field.

[0031] Currently, some charging control methods optimize the charging current distribution based on the initial stage of the battery's lifespan to obtain the optimal charging current distribution, and use this optimal charging current distribution to charge until the battery's lifespan ends. However, the optimal charging current distribution obtained by this approach is inaccurate, and using this charging current distribution to charge will accelerate battery aging.

[0032] The technical content provided in this application aims to solve the aforementioned technical problems in related technologies. In the charging control method, device, electronic device, storage medium, and program product of this application, N first charging current distributions are obtained by repeatedly executing a simulation process N times. The simulation process includes: performing charge-discharge simulations based on A charging current distributions and battery performance parameters to obtain the charging time and capacity loss corresponding to the A charging current distributions; using an optimization algorithm based on the charging time and capacity loss corresponding to the A charging current distributions to obtain the first charging current distribution corresponding to the minimum value of the objective function; wherein, the objective function serves as the optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and capacity loss; based on the N first charging current distributions, charging current distributions under different battery life stages are established, and battery charging control is performed based on the charging current distributions under different life stages. The proposed solution performs charge-discharge simulations for each stage from the battery's initial stage to the end of its lifespan. The optimal charging current distribution, corresponding to the minimum objective function value related to battery charging time and capacity loss obtained from the simulation, is used as the first charging current distribution in this simulation. After N simulations, N first charging current distributions are obtained. Based on these N first charging current distributions, charging current distributions for different battery lifespan stages are established, and battery charging control is performed based on these distributions. The proposed solution optimizes the charging current distribution using the objective function as the optimization target and updates the current battery performance parameters for each simulation, resulting in a more accurate charging current distribution. Using this charging current distribution for charging control can improve battery lifespan.

[0033] The technical solutions of this application will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. In the description of this application, unless otherwise expressly specified and limited, the terms should be broadly understood within the art. The embodiments of this application will now be described with reference to the accompanying drawings.

[0034] Example 1

[0035] Figure 1 The diagram above illustrates a flowchart of a charging control method, such as... Figure 1 As shown, the method includes:

[0036] Step 101: By repeatedly performing the simulation process N times, N first charging current distributions are obtained; the simulation process includes: performing charge and discharge simulations based on A charging current distributions and battery performance parameters to obtain the charging time and capacity loss corresponding to A charging current distributions;

[0037] Step 102: Based on the charging time and capacity loss corresponding to the A charging current distributions, use an optimization algorithm to obtain the first charging current distribution corresponding to the minimum value of the objective function; the objective function is the optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and capacity loss.

[0038] Step 103: Based on the N first charging current distributions, establish the charging current distributions under different life stages of the battery, and perform battery charging control based on the charging current distributions under different life stages; the charging current distributions characterize the charging current under different states of charge intervals during battery charging; where A and N are positive integers.

[0039] Specifically, a charge-discharge simulation is performed on the battery. Through N simulations, N initial charging current distributions are obtained. This example does not restrict the type of battery. The number of simulations is related to the battery's lifespan; each simulation involves the same number of charge-discharge cycles until the battery reaches its end-of-life. The simulation process includes: based on the battery performance parameters and A charging current distributions, obtaining the charging time and battery capacity loss corresponding to the A current distributions; the battery performance parameters remain constant throughout the multiple charge-discharge cycles in a single simulation; based on the charging time and capacity loss corresponding to the A current distributions, an optimization algorithm is used to obtain the initial charging current distribution corresponding to the minimum objective function, which is the optimal charging current distribution under this simulation. The objective function is the optimization objective of the objective algorithm, and the optimization objective is related to the charging time and capacity loss. This example does not specifically limit the optimization algorithm, such as the second non-dominated sorting genetic algorithm, particle swarm optimization, etc. The objective function of this example is expressed as:

[0040]

[0041] Among them, t f t represents the charging time. max t0 represents the maximum charging time, t0 represents the minimum charging time, α and 1-α are the weighting factors for charging time and capacity loss, respectively, and Q is the maximum charging time. loss Q represents capacity loss. loss,max and Q loss,min These represent the maximum and minimum capacity losses. Specifically, in this example, the constraints for optimizing the objective function are as follows:

[0042] V min <V<V max

[0043] ΔT bat <ΔT bat,max

[0044] I NP,min NP NP,max

[0045] Where V represents the battery voltage, V min and V max Indicates the maximum and minimum values ​​of the battery voltage; ΔT bat and ΔT bat,max Indicates the maximum temperature rise and the maximum temperature rise value within one charge-discharge cycle of the battery; I NP I represents current. NP,min and I NP,max This indicates the maximum and minimum current values ​​during the constant current phase.

[0046] Furthermore, based on the N first charging current distributions obtained from N battery charge-discharge simulations, charging current distributions for different battery life stages are established, and battery charging control is performed based on these charging current distributions. In practical applications, when charging the battery, the obtained first charging current distributions are used for charging control at different life stages. It should be noted that the charging current distributions in this example represent the charging currents in different state-of-charge intervals during the battery charging process; A and N in this example are both positive integers. In this example, in one battery charge-discharge simulation, the charging current distributions are optimized based on A charging current distributions and battery performance parameters, using an objective function as the optimization objective. The current battery performance parameters are updated for each simulation, making the obtained charging current distributions more accurate. Using these charging current distributions for charging control can improve battery lifespan.

[0047] ​​Specifically, when using the objective function as the optimization target, this application considers the capacity loss corresponding to the two stages of aging during the battery aging process in the objective function. Accordingly, as an example, the capacity loss includes a first capacity loss and a second capacity loss; the first capacity loss characterizes the capacity loss caused by the growth of the solid electrolyte membrane, and the second capacity loss characterizes the capacity loss caused by ion deposition.

[0048] Specifically, the capacity loss in this example includes the first capacity loss caused by the growth of the solid electrolyte membrane and the second capacity loss caused by ion deposition. In practical applications, the aging process corresponding to the entire lifespan of a battery is divided into linear aging and accelerated aging stages. This example simulates the charge and discharge of a battery, calculating the capacity loss throughout the entire aging process from the battery's initial stage to the end of its lifespan, as a function of the number of charge and discharge cycles. Figure 2 This is a schematic diagram illustrating how capacity retention changes with cycle number as an example; such as... Figure 2 As shown, the horizontal axis represents the number of charge-discharge cycles of the battery, and the vertical axis represents the battery's capacity retention rate, which varies with the number of cycles. The Knee point is the inflection point between the linear aging stage and the accelerated aging stage. In the linear aging stage, the capacity loss due to battery aging is mainly caused by the growth of the solid electrolyte membrane. When aging reaches the Knee point, the battery begins to enter the accelerated aging stage, at which point the aging is mainly caused by ion deposition. In the process of optimizing the charging current distribution of the battery, this application considers both stages of aging for the objective function.

[0049] Specifically, in the battery charge-discharge simulation of this application, during the dynamic optimization process, the same number of charge-discharge cycles are performed in each simulation. Specifically, the battery capacity loss is calculated based on the input current distribution and current battery performance parameters during the simulation. This example does not limit the calculation method for battery capacity loss; for example, nonlinear fitting methods can be used to calculate the capacity loss. Specifically, the capacity loss calculation formula can be expressed as:

[0050]

[0051] Q Loss =Q SEI +Q x

[0052] Where N represents the number of charge-discharge cycles, a1 and b1 represent the coefficients of the fitting function corresponding to the solid electrolyte membrane growth stage, a2 and b2 represent the coefficients of the fitting function corresponding to the ion deposition stage, and a1, b1, a2, and b2 are all constants; SEI b SEI , represent the coefficients of the parametric correlation terms of the fitting function corresponding to the growth stages of the solid electrolyte membrane, respectively, a x bx Let a and b represent the coefficients of the parametric correlation terms of the fitting function corresponding to the ion deposition stage, respectively. SEI b SEI a x b x Both can be represented by functions of parameters such as C and T; SOC is the state of charge of the battery. The average SOC is the average of the maximum and minimum SOC; DOD is the depth of discharge, which is the difference between the maximum and minimum SOC. Q SEI Q represents the linear stage of battery aging caused by the growth of the solid electrolyte membrane. x This indicates aging caused by battery ion deposition; Q Loss This represents the total capacity loss, which is the aging Q caused by the growth of the solid electrolyte membrane. SEI and aging caused by ion deposition Q x In practical applications, for the linear stage, Q... SEI Plays a leading role, Q Loss =Q SEI For the acceleration phase, Q Loss =Q SEI +Q x This example considers the capacity loss during both stages of battery aging, improving the accuracy of the optimized battery charging current distribution.

[0053] Building upon the aforementioned example, in one example, the method further includes: obtaining the temperature rise corresponding to the A charging current distributions based on a thermal model; the thermal model characterizes the relationship between the charging current distribution and the internal temperature change of the battery; the temperature rise characterizes the difference between the battery body temperature and the ambient temperature; and, based on the charging time and capacity loss corresponding to the A charging current distributions, using an optimization algorithm to obtain the first charging current distribution corresponding to the minimum value of the objective function; including:

[0054] From A charging current distributions, delete the charging current distributions whose temperature rise exceeds the preset threshold, and retain the charging current distributions whose temperature rise does not exceed the preset threshold;

[0055] Based on the charging time and capacity loss corresponding to the retained charging current distribution, an optimization algorithm is used to obtain the first charging current distribution corresponding to the minimum value of the objective function.

[0056] Specifically, based on the charging current distribution input for each charge / discharge simulation, the temperature rise corresponding to A charging current distributions can be calculated using the thermal model of the charge / discharge simulation. In practical applications, the thermal model is used to characterize the relationship between the charging current distribution and the internal temperature change of the battery during charge / discharge. The temperature rise characterizes the difference between the battery's internal temperature and the ambient temperature. In this example, the thermal model for battery charge / discharge simulation is a lumped thermal model. The lumped thermal model treats the battery interior as a uniform internal heat source, does not consider the differences in internal temperature distribution, and only focuses on the overall or average temperature change of the battery. The governing equations for the thermal model in this example are:

[0057]

[0058] Where m is the mass of the battery; c p This indicates the specific heat capacity of the battery; in this paper, it is taken as 1000 J·kg⁻¹. -1 ·K -1 ;T bat q represents the overall temperature of the battery; q represents the internal heat generation rate of the battery; and h represents the equivalent heat transfer coefficient of the entire battery, which is taken as 10 W·m in this paper. -2 ·K -1 A bat The total heat exchange area of ​​the battery is represented by T; the ambient temperature is taken as 298K in this paper; U represents the output voltage of the equivalent circuit model for battery charging and discharging simulation; OCV represents the open-circuit voltage; and I represents the total heat exchange area of ​​the battery. N,P This represents the current. The temperature rise of the battery corresponding to different current distributions is calculated based on a thermal model. First, a temperature rise threshold is preset. The selection of this threshold depends on the type of battery and the ambient temperature, ensuring the battery operates within a safe temperature range. Specifically, when using an optimization algorithm to obtain the first charging current distribution corresponding to the minimum objective function based on the charging time and capacity loss corresponding to A charging current distributions, the temperature rise corresponding to each current distribution is calculated. Charging current distributions with temperature rises exceeding the preset threshold are deleted, while those with temperature rises below the preset threshold are retained. Based on the charging time and capacity loss corresponding to these retained charging current distributions, an optimization algorithm is used to calculate the minimum objective function, yielding the corresponding first charging current distribution. In this example, by limiting the optimization objective based on temperature rise, the accuracy of charging current distribution optimization is improved, further enhancing battery life.

[0059] Based on the aforementioned example, in one example, the method further includes: using the battery performance parameters under the current simulation as the initial battery performance parameters, performing M charge-discharge simulations according to the first charging current distribution obtained under the current simulation, and obtaining the first battery performance parameters after M charge-discharge simulations; using the first battery performance parameters as the battery performance parameters under the next simulation; where M is a positive integer.

[0060] Specifically, when simulating battery charge and discharge, the current battery performance parameters are calculated after each simulation. Based on the first charging current distribution obtained from this simulation, M charge and discharge simulations are performed to obtain the first performance parameters after M charge and discharge cycles, which are then used as the battery performance parameters for the next simulation. In practical applications, the main updates during battery charge and discharge simulation are to the battery capacity and the impedance during the charge and discharge simulation cycles. The formulas for calculating the remaining battery capacity and the impedance changing with the number of cycles are as follows:

[0061] R N =kN κ

[0062] C N =C0(1-Q) loss )

[0063] Among them, R N The impedance varies with the number of charge / discharge cycles; k is a constant, typically taken as 0.0015; κ is also a constant, typically taken as 0.5; N represents the number of simulated charge / discharge cycles; C N C0 represents the remaining battery capacity, and Q represents the initial battery capacity. loss This represents the battery's capacity loss. In this example, the current battery's remaining capacity and impedance as a function of the number of cycles are calculated after each simulation to update the current performance parameters. After updating the battery's current performance parameters, the charging current distribution is optimized to make the optimization results more accurate.

[0064] Furthermore, it is necessary to establish the charging current distribution under different battery life stages based on the first charging current distribution. Figure 3 The diagram illustrates a flowchart of another charging control method; based on any example, step 103 specifically includes:

[0065] Step 201: Divide the battery's lifespan into N lifespan stages; the lifespan stage represents the maximum number of charge-discharge cycles the battery can support, and each lifespan stage contains M charge-discharge cycles.

[0066] Step 202: Establish the correspondence between N first charging current distributions and N lifetime stages to obtain the charging current distribution under different lifetime stages.

[0067] Specifically, the battery is divided into N lifespan stages based on its lifespan, with each lifespan representing the maximum number of charge-discharge cycles the battery can support. Each lifespan stage undergoes M charge-discharge cycles for simulation. Based on the correspondence between N initial charging current distributions and N lifespan stages, each lifespan stage uses the corresponding initial charging current distribution for charging, thus obtaining the charging current distribution for different lifespan stages. This example divides the entire battery lifespan into different lifespan stages, with each stage representing the same number of charge-discharge cycles. By using the obtained initial charging current distribution for charging and discharging in each lifespan stage, the optimal charging current distribution can be obtained, improving the battery's charging efficiency and lifespan.

[0068] Furthermore, charging control is performed based on the charging current distribution at different life stages; Figure 4 The diagram illustrates a flowchart of yet another charging control method; based on any example, step 202 specifically includes:

[0069] Step 301: Determine the current lifespan stage of the battery based on its historical charge-discharge cycles;

[0070] Step 302: Determine the charging current distribution corresponding to the current life stage of the battery based on the charging current distribution under different life stages.

[0071] Step 303: Control the charging of the battery according to the charging current distribution corresponding to the current life stage of the battery.

[0072] In practical applications, charging control needs to be based on the optimized charging current distribution. First, the battery's current lifespan stage is determined based on its historical charge-discharge cycles. Then, the charging current distribution corresponding to the current battery lifespan stage is determined based on the charging current distribution under different lifespan stages. Finally, charging control is performed based on the charging current distribution corresponding to the current battery lifespan stage. In this example, by determining the battery's current lifespan stage and using the corresponding charging current distribution for charging control, the charging efficiency is improved, while capacity loss is reduced, thus extending the battery's lifespan.

[0073] Building upon the aforementioned example, in one instance, charge-discharge simulation is performed based on A charging current distributions and battery performance parameters, including: inputting A charging current distributions and battery performance parameters into an equivalent circuit model and an aging model; the aging model is used to simulate the relationship between capacity loss and charging current distribution; the equivalent circuit model is used to simulate the relationship between charging time and charging current distribution.

[0074] Specifically, when simulating the charging and discharging of a battery based on A charging current distributions and battery performance parameters, the A charging current distributions and battery performance parameters need to be input into the equivalent circuit model and the aging model. The aging model is used to simulate the relationship between capacity loss and charging current distribution, while the equivalent circuit model is used to simulate the relationship between charging time and charging current distribution. Figure 5 This is a schematic diagram of the equivalent circuit model for the charge-discharge simulation of this application; as shown. Figure 5 As shown, the equivalent circuit model is used to simulate the charging and discharging process of the battery. The equivalent circuit includes the polarization resistor R. p Capacitor C p Ohm resistance R ohm The impedance R varies with the number of cycles N And the power supply of the equivalent circuit. Its governing equation is:

[0075] U = OCV - I(R) ohm +R N )-U p

[0076] U p (t)=U p (t-1)·α p (t-1)-IR p ·(1-α(t-1))

[0077]

[0078] OCV = f ocv (SOC)

[0079]

[0080] Where U represents the output voltage; OCV represents the open-circuit voltage, which is a function of SOC; f ocv This represents the functional relationship between OCV and SOC; U p It is the voltage of an RC structure; R ohm and R p R represents the ohmic resistance and polarization resistance, respectively. N The impedance that varies with the number of cycles; α p The time coefficient for reaching the polarization voltage, Δt p τ represents the sampling time, typically 1 second; p This represents the time constant for the polarization process. In this example, the battery charge-discharge simulation uses an aging model and an equivalent circuit model, which improves the accuracy of the battery charge-discharge simulation and further enhances the accuracy of optimizing the battery charging current distribution.

[0081] Based on the examples above, Figure 6The flowchart of the dynamic optimization process of the charging control method of this application is as follows: Figure 6 As shown, firstly, an electrochemical model, equivalent circuit model, two-stage aging model, and battery heat generation model are constructed for battery charge-discharge simulation. Then, based on the charge-discharge model, battery charge-discharge simulation is performed, and calculations are conducted using Matlab-Simulink (simulation software) to determine the linear aging stage, inflection point, and accelerated aging stage of the battery. Based on the determined battery state, the charging strategy, i.e., the charging current distribution, is optimized using an objective function as the optimization goal; thus, the charging current distribution in the linear stage and the charging current distribution in the accelerated stage are obtained. Figure 6 This diagram illustrates an example of multi-stage charging current distribution. The horizontal axis represents the number of charge-discharge cycles (Cycles), and the vertical axis represents the charging current (Charging current). The transition point between stage 1 and stage 2 in the diagram is the knee point. This indicates the charging current distribution in the first stage; This indicates the charging current distribution in the second stage.

[0082] Figure 7 The following is a dynamic flowchart of the optimization of the charging current distribution in this application, such as... Figure 7 As shown, the specific optimization process in a single charge-discharge simulation is as follows: First, the battery performance parameters and A charging current distributions for this simulation are input into the aforementioned model. Then, the capacity loss and charging time are calculated based on the battery performance parameters and charging current distributions. Depending on the different stages of battery aging, the charging current distribution is optimized based on the objective function. For the linear stage, the optimization objective is primarily to minimize the capacity loss and charging time, where the capacity loss is Q caused by the growth of the solid electrolyte membrane. SEI For the accelerated aging stage, the optimization objective is also to minimize charging time and capacity loss. At this stage, the capacity loss is mainly caused by ion deposition, denoted as Q. x Based on the objective function constructed from the capacity loss and charging time, the charging current distribution is optimized using an optimization algorithm to obtain the optimal charging current distribution for the current simulation. The optimal charging current distribution for the current simulation is used to perform M charge and discharge cycles to update the current capacity and impedance. The next simulation optimization is then performed based on the current performance parameters and impedance until the termination condition is met, and the simulation ends.

[0083] Based on the aforementioned examples, the optimization strategy of this application is compared and optimized with other optimization strategies; specifically, step one: first, select the research object and obtain the corresponding battery performance parameters. In this embodiment, a 2.05Ah Sanyo UR18650E cylindrical battery is selected. The positive electrode material of the battery is LiNi. 0.33Co 0.33 Mn 0.33 The battery uses O2 (NCM111) as the cathode material and graphite as the anode material. Its charge / discharge voltage range is 2.75-4.2V, and its rated voltage is 3.6V. The battery has a height of 65mm, a radius of 9mm, and a surface area of ​​4.1846 × 10⁻⁶. -3 m -2 The cutoff current is 1 / 10C, ​​and the set temperature rise threshold is 10K.

[0084] Step Two: Determine the charging strategies for comparison. This embodiment sets up six charging strategies for comparison and analysis.

[0085] Table 1 compares six charging strategies. As shown in Table 1, this example presents six charging strategies: S1-1, S1-2, S2, S3, S4, and S5.

[0086]

[0087]

[0088] In this paper, charging strategies S1-1 and S1-2 are 1C and 2C CC-CV, respectively, serving as the baseline strategies for comparison. The initial charge of a new battery is optimized to obtain a charging strategy that satisfies the objective function; this is the statically optimized charging strategy, S2. A CC-CV strategy with the same initial charge time as S2 is used as another comparison strategy, S3. Optimization is performed every 50 cycles based on the battery's aging state, resulting in the dynamically optimized charging strategy S4. Further optimization is performed every 50 cycles based on the battery's aging state, with adjustments to the objective and weights based on the two aging stages of the battery, resulting in the improved dynamically optimized charging strategy S5. Furthermore, while the five charging strategies designed in this paper differ, the 1C CC-CV strategy is used for discharging, with a 100s interval between charging and discharging.

[0089] This example uses 0-100% SOC as the upper and lower limits of the charging range, dividing it into M constant current charging segments. The weights for strategies S2 and S4 in the multi-objective optimization are set to 0.6. For strategy S5, α = 0.6 in the first aging stage and α = 0.46 in the second aging stage. This embodiment selects 5 segments of MSCCCV as the optimized strategy.

[0090] Step 4: Optimize and calculate the six designed charging strategies to obtain corresponding data on battery aging, temperature rise, and charging time, such as... Figure 8 , Figure 9 and Figure 10 As shown in Table 3, the battery cycle performance is compared.

[0091] Figure 8 This is a schematic diagram illustrating the maximum temperature rise as a function of the number of cycles for six different charging strategies, as an example. Figure 9 This is a schematic diagram illustrating the change in charging time as a function of the number of cycles for six different charging strategies, as an example. Figure 10 This is a schematic diagram illustrating the capacity variation with the number of cycles for six charging strategies as an example.

[0092] Table 2 shows the battery performance indicators during the cycling process corresponding to the six charging current distributions.

[0093]

[0094] Combination Figure 8 , Figure 9 , Figure 10 Table 2 shows the absolute and relative values ​​of average charging time, capacity loss, and average maximum temperature rise calculated for six different charging current distributions. The relative values ​​are the average charging time and capacity loss, and the average maximum temperature rise under the latter five charging strategies, relative to the average charging time, capacity loss, and average maximum temperature rise under the first charging strategy. Compared to other optimization strategies, the charging current distribution obtained using the optimization scheme of this application results in smaller charging time and capacity loss, and a smaller corresponding temperature rise; it improves charging efficiency and extends battery life.

[0095] The optimized strategy proposed in this application leads to a slight increase in battery temperature rise, but the increase is within an acceptable range. Compared to S1-1, the average charging time of S4 is reduced by 21.017%, making it the shortest charging time among the six optimized strategies in this embodiment. However, this strategy also results in the largest average maximum temperature rise and capacity loss. In contrast, the S5 strategy of this application reduces the average charging time by 16.983% compared to S1-1, and the average maximum battery temperature rise is relatively smaller compared to other fast charging strategies. Furthermore, it suppresses battery aging in the second stage to a certain extent. Therefore, using the improved dynamic optimization method proposed in this application, the optimization objective and weights can be adjusted according to the different stages the battery is in, appropriately increasing the battery charging time while reducing the capacity loss during the two stages of battery aging and extending the battery's service life.

[0096] In the charging control method provided in this embodiment, N first charging current distributions are obtained by repeatedly performing simulation processing N times. The simulation processing includes: performing charge and discharge simulations based on A charging current distributions and battery performance parameters to obtain the charging time and capacity loss corresponding to the A charging current distributions; using an optimization algorithm to obtain the first charging current distribution corresponding to the minimum value of the objective function based on the charging time and capacity loss corresponding to the A charging current distributions; wherein, the objective function serves as the optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and capacity loss; based on the N first charging current distributions, charging current distributions under different battery life stages are established, and battery charging control is performed based on the charging current distributions under different life stages. The proposed solution performs charge-discharge simulations for each stage from the battery's initial stage to the end of its lifespan. The optimal charging current distribution, corresponding to the minimum objective function value related to battery charging time and capacity loss obtained from the simulation, is used as the first charging current distribution in this simulation. After N simulations, N first charging current distributions are obtained. Based on these N first charging current distributions, charging current distributions for different battery lifespan stages are established, and battery charging control is performed based on these distributions. The proposed solution optimizes the charging current distribution using the objective function as the optimization target and updates the current battery performance parameters for each simulation, resulting in a more accurate charging current distribution. Using this charging current distribution for charging control can improve battery lifespan.

[0097] Example 2

[0098] Figure 11 The diagram above illustrates a schematic representation of a charging control device, such as... Figure 11 As shown, the device includes:

[0099] Simulation module 21 is used to obtain N first charging current distributions by repeatedly executing simulation processing N times; the simulation processing includes: performing charge and discharge simulation based on A charging current distributions and battery performance parameters to obtain the charging time and capacity loss corresponding to A charging current distributions;

[0100] Optimization module 22 is used to obtain the first charging current distribution corresponding to the minimum value of the objective function based on the charging time and capacity loss corresponding to A charging current distributions using an optimization algorithm; the objective function is the optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and capacity loss;

[0101] The control module 23 is used to establish the charging current distribution under different life stages of the battery based on N first charging current distributions, and to perform battery charging control based on the charging current distribution under different life stages; the charging current distribution represents the charging current under different state of charge intervals during battery charging; where A and N are positive integers.

[0102] Specifically, a charge-discharge simulation is performed on the battery. Through N simulations, N initial charging current distributions are obtained. This example does not restrict the type of battery. The number of simulations is related to the battery's lifespan; each simulation involves the same number of charge-discharge cycles until the battery reaches its end-of-life. The simulation process includes: based on the battery performance parameters and A charging current distributions, obtaining the charging time and battery capacity loss corresponding to the A current distributions; the battery performance parameters remain constant throughout the multiple charge-discharge cycles in a single simulation; based on the charging time and capacity loss corresponding to the A current distributions, an optimization algorithm is used to obtain the initial charging current distribution corresponding to the minimum objective function, which is the optimal charging current distribution under this simulation. The objective function is the optimization objective of the objective algorithm, and the optimization objective is related to the charging time and capacity loss. This example does not specifically limit the optimization algorithm, such as the second non-dominated sorting genetic algorithm, particle swarm optimization, etc. The objective function of this example is expressed as:

[0103]

[0104] Among them, t f t represents the charging time. max t0 represents the maximum charging time, t0 represents the minimum charging time, α and 1-α are the weighting factors for charging time and capacity loss, respectively, and Q is the maximum charging time. loss Q represents capacity loss. loss,max and Q loss,min These represent the maximum and minimum capacity losses. Specifically, in this example, the constraints for optimizing the objective function are as follows:

[0105] V min <V<V max

[0106] ΔT bat <ΔT bat,max

[0107] I NP,min NP NP,max

[0108] Where V represents the battery voltage, V min and V max Indicates the maximum and minimum values ​​of the battery voltage; ΔT bat and ΔT bat,max Indicates the maximum temperature rise and the maximum temperature rise value within one charge-discharge cycle of the battery; I NP I represents current. NP,min and I NP,max This indicates the maximum and minimum current values ​​during the constant current phase.

[0109] ​​Furthermore, based on the N first charging current distributions obtained from N battery charge-discharge simulations, charging current distributions for different battery life stages are established, and battery charging control is performed based on these charging current distributions. In practical applications, when charging the battery, the obtained first charging current distributions are used for charging control at different life stages. It should be noted that the charging current distributions in this example represent the charging currents in different state-of-charge intervals during the battery charging process; A and N in this example are both positive integers. In this example, in one battery charge-discharge simulation, the charging current distributions are optimized based on A charging current distributions and battery performance parameters, using an objective function as the optimization objective. The current battery performance parameters are updated for each simulation, making the obtained charging current distributions more accurate. Using these charging current distributions for charging control can improve battery lifespan.

[0110] Specifically, when using the objective function as the optimization target, this application considers the capacity loss corresponding to the two stages of aging during the battery aging process in the objective function. Accordingly, as an example, the capacity loss includes a first capacity loss and a second capacity loss; the first capacity loss characterizes the capacity loss caused by the growth of the solid electrolyte membrane, and the second capacity loss characterizes the capacity loss caused by ion deposition.

[0111] Specifically, the capacity loss in this example includes a first capacity loss caused by the growth of the solid electrolyte membrane and a second capacity loss caused by ion deposition. In practical applications, the aging process corresponding to the entire lifespan of a battery is divided into a linear aging stage and an accelerated aging stage. This example simulates the charge and discharge of a battery, calculating the capacity loss throughout the entire aging process from the initial stage to the end of the battery's lifespan, as the number of charge and discharge cycles changes. In the linear aging stage, the capacity loss due to battery aging is mainly caused by the growth of the solid electrolyte membrane; when aging reaches a turning point, the battery begins to enter the accelerated aging stage, at which point the aging is mainly caused by ion deposition. In optimizing the charging current distribution of the battery, this application considers both stages of aging for the objective function.

[0112] Specifically, in the battery charge-discharge simulation of this application, during the dynamic optimization process, the same number of charge-discharge cycles are performed in each simulation. Specifically, the battery capacity loss is calculated based on the input current distribution and current battery performance parameters during the simulation. This example does not limit the calculation method for battery capacity loss; for example, nonlinear fitting methods can be used to calculate the capacity loss. Specifically, the capacity loss calculation formula can be expressed as:

[0113]

[0114] Q Loss =QSEI +Q x

[0115] Where N represents the number of charge-discharge cycles, a1 and b1 represent the coefficients of the fitting function corresponding to the solid electrolyte membrane growth stage, a2 and b2 represent the coefficients of the fitting function corresponding to the ion deposition stage, and a1, b1, a2, and b2 are all constants; SEI b SEI , represent the coefficients of the parametric correlation terms of the fitting function corresponding to the growth stages of the solid electrolyte membrane, respectively, a x b x Let a and b represent the coefficients of the parametric correlation terms of the fitting function corresponding to the ion deposition stage, respectively. SEI b SEI a x b x Both can be represented by functions of parameters such as C and T; SOC is the state of charge of the battery. The average SOC is the average of the maximum and minimum SOC; DOD is the depth of discharge, which is the difference between the maximum and minimum SOC. Q SEI Q represents the linear stage of battery aging caused by the growth of the solid electrolyte membrane. x This indicates aging caused by battery ion deposition; Q Loss This represents the total capacity loss, which is the aging Q caused by the growth of the solid electrolyte membrane. SEI and aging caused by ion deposition Q x In practical applications, for the linear stage, Q... SEI Plays a leading role, Q Loss =Q SEI For the acceleration phase, Q Loss =Q SEI +Q x This example considers the capacity loss during both stages of battery aging, improving the accuracy of the optimized battery charging current distribution.

[0116] Building upon the aforementioned example, in one example, the method further includes: obtaining the temperature rise corresponding to the A charging current distributions based on a thermal model; the thermal model characterizes the relationship between the charging current distribution and the internal temperature change of the battery; the temperature rise characterizes the difference between the battery body temperature and the ambient temperature; and, based on the charging time and capacity loss corresponding to the A charging current distributions, using an optimization algorithm to obtain the first charging current distribution corresponding to the minimum value of the objective function; including:

[0117] From A charging current distributions, delete the charging current distributions whose temperature rise exceeds the preset threshold, and retain the charging current distributions whose temperature rise does not exceed the preset threshold;

[0118] Based on the charging time and capacity loss corresponding to the retained charging current distribution, an optimization algorithm is used to obtain the first charging current distribution corresponding to the minimum value of the objective function.

[0119] Specifically, based on the charging current distribution input for each charge / discharge simulation, the temperature rise corresponding to A charging current distributions can be calculated using the thermal model of the charge / discharge simulation. In practical applications, the thermal model is used to characterize the relationship between the charging current distribution and the internal temperature change of the battery during charge / discharge. The temperature rise characterizes the difference between the battery's internal temperature and the ambient temperature. In this example, the thermal model for battery charge / discharge simulation is a lumped thermal model. The lumped thermal model treats the battery interior as a uniform internal heat source, does not consider the differences in internal temperature distribution, and only focuses on the overall or average temperature change of the battery. The governing equations for the thermal model in this example are:

[0120]

[0121] Where m is the mass of the battery; c p This indicates the specific heat capacity of the battery; in this paper, it is taken as 1000 J·kg⁻¹. -1 ·K -1 ;T bat q represents the overall temperature of the battery; q represents the internal heat generation rate of the battery; and h represents the equivalent heat transfer coefficient of the entire battery, which is taken as 10 W·m in this paper. -2 ·K -1 A bat The total heat exchange area of ​​the battery is represented by T; the ambient temperature is taken as 298K in this paper; U represents the output voltage of the equivalent circuit model for battery charging and discharging simulation; OCV represents the open-circuit voltage; and I represents the total heat exchange area of ​​the battery. N,P This represents the current. The temperature rise of the battery corresponding to different current distributions is calculated based on a thermal model. First, a temperature rise threshold is preset. The selection of this threshold depends on the type of battery and the ambient temperature, ensuring the battery operates within a safe temperature range. Specifically, when using an optimization algorithm to obtain the first charging current distribution corresponding to the minimum objective function based on the charging time and capacity loss corresponding to A charging current distributions, the temperature rise corresponding to each current distribution is calculated. Charging current distributions with temperature rises exceeding the preset threshold are deleted, while those with temperature rises below the preset threshold are retained. Based on the charging time and capacity loss corresponding to these retained charging current distributions, an optimization algorithm is used to calculate the minimum objective function, yielding the corresponding first charging current distribution. In this example, by limiting the optimization objective based on temperature rise, the accuracy of charging current distribution optimization is improved, further enhancing battery life.

[0122] Based on the aforementioned example, in one example, the method further includes: using the battery performance parameters under the current simulation as the initial battery performance parameters, performing M charge-discharge simulations according to the first charging current distribution obtained under the current simulation, and obtaining the first battery performance parameters after M charge-discharge simulations; using the first battery performance parameters as the battery performance parameters under the next simulation; where M is a positive integer.

[0123] Specifically, when simulating battery charge and discharge, the current battery performance parameters are calculated after each simulation. Based on the first charging current distribution obtained from this simulation, M charge and discharge simulations are performed to obtain the first performance parameters after M charge and discharge cycles, which are then used as the battery performance parameters for the next simulation. In practical applications, the main updates during battery charge and discharge simulation are to the battery capacity and the impedance during the charge and discharge simulation cycles. The formulas for calculating the remaining battery capacity and the impedance changing with the number of cycles are as follows:

[0124] R N =kN κ

[0125] C N =C0(1-Q) loss )

[0126] Among them, R N The impedance varies with the number of charge / discharge cycles; k is a constant, typically taken as 0.0015; κ is also a constant, typically taken as 0.5; N represents the number of simulated charge / discharge cycles; C N C0 represents the remaining battery capacity, and Q represents the initial battery capacity. loss This represents the battery's capacity loss. In this example, the current battery's remaining capacity and impedance as a function of the number of cycles are calculated after each simulation to update the current performance parameters. After updating the battery's current performance parameters, the charging current distribution is optimized to make the optimization results more accurate.

[0127] Furthermore, it is necessary to establish the charging current distribution under different battery life stages based on the first charging current distribution; based on any example, the control module 23 is specifically used for:

[0128] The battery's lifespan is divided into N lifespan stages; the lifespan stage represents the maximum number of charge-discharge cycles the battery can support, and each lifespan stage contains M charge-discharge cycles.

[0129] Establish the correspondence between N initial charging current distributions and N lifetime stages to obtain the charging current distribution under different lifetime stages.

[0130] Specifically, the battery is divided into N lifespan stages based on its lifespan, with each lifespan representing the maximum number of charge-discharge cycles the battery can support. Each lifespan stage undergoes M charge-discharge cycles for simulation. Based on the correspondence between N initial charging current distributions and N lifespan stages, each lifespan stage uses the corresponding initial charging current distribution for charging, thus obtaining the charging current distribution for different lifespan stages. This example divides the entire battery lifespan into different lifespan stages, with each stage representing the same number of charge-discharge cycles. By using the obtained initial charging current distribution for charging and discharging in each lifespan stage, the optimal charging current distribution can be obtained, improving the battery's charging efficiency and lifespan.

[0131] Furthermore, charging control is performed based on the charging current distribution at different life stages; in any example, the control module 23 is also specifically used for:

[0132] Determine the current lifespan stage of the battery based on its historical charge-discharge cycles;

[0133] Based on the charging current distribution at different life stages, determine the charging current distribution corresponding to the current life stage of the battery.

[0134] The charging control of the battery is based on the charging current distribution corresponding to the current stage of the battery's lifespan.

[0135] In practical applications, charging control needs to be based on the optimized charging current distribution. First, the battery's current lifespan stage is determined based on its historical charge-discharge cycles. Then, the charging current distribution corresponding to the current battery lifespan stage is determined based on the charging current distribution under different lifespan stages. Finally, charging control is performed based on the charging current distribution corresponding to the current battery lifespan stage. In this example, by determining the battery's current lifespan stage and using the corresponding charging current distribution for charging control, the charging efficiency is improved, while capacity loss is reduced, thus extending the battery's lifespan.

[0136] Building upon the aforementioned example, in one instance, charge-discharge simulation is performed based on A charging current distributions and battery performance parameters, including: inputting A charging current distributions and battery performance parameters into an equivalent circuit model and an aging model; the aging model is used to simulate the relationship between capacity loss and charging current distribution; the equivalent circuit model is used to simulate the relationship between charging time and charging current distribution.

[0137] Specifically, when simulating battery charge-discharge based on A charging current distributions and battery performance parameters, these A charging current distributions and battery performance parameters need to be input into the equivalent circuit model and aging model. The aging model is used to simulate the relationship between capacity loss and charging current distribution; the equivalent circuit model is used to simulate the relationship between charging time and charging current distribution. The governing equation of the equivalent circuit model is:

[0138] U = OCV - I(R) ohm +R N )-U p

[0139] U p (t)=U p (t-1)·α p (t-1)-IR p ·(1-α(t-1))

[0140]

[0141] OCV = f ocv (SOC)

[0142]

[0143] Where U represents the output voltage; OCV represents the open-circuit voltage, which is a function of SOC; f ocv This represents the functional relationship between OCV and SOC; U p It is the voltage of an RC structure; R ohm and R p R represents the ohmic resistance and polarization resistance, respectively. N The impedance that varies with the number of cycles; α p The time coefficient for reaching the polarization voltage, Δt p τ represents the sampling time, typically 1 second; p This represents the time constant for the polarization process. In this example, the battery charge-discharge simulation uses an aging model and an equivalent circuit model, which improves the accuracy of the battery charge-discharge simulation and further enhances the accuracy of optimizing the battery charging current distribution.

[0144] The charging control device provided in this embodiment obtains N first charging current distributions by repeatedly performing N simulation processes. The simulation process includes: performing charge and discharge simulations based on A charging current distributions and battery performance parameters to obtain the charging time and capacity loss corresponding to the A charging current distributions; using an optimization algorithm to obtain the first charging current distribution corresponding to the minimum value of the objective function based on the charging time and capacity loss corresponding to the A charging current distributions; wherein, the objective function serves as the optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and capacity loss; based on the N first charging current distributions, the charging current distributions under different battery life stages are established, and battery charging control is performed based on the charging current distributions under different life stages. The proposed solution performs charge-discharge simulations for each stage from the battery's initial stage to the end of its lifespan. The optimal charging current distribution, corresponding to the minimum objective function value related to battery charging time and capacity loss obtained from the simulation, is used as the first charging current distribution in this simulation. After N simulations, N first charging current distributions are obtained. Based on these N first charging current distributions, charging current distributions for different battery lifespan stages are established, and battery charging control is performed based on these distributions. The proposed solution optimizes the charging current distribution using the objective function as the optimization target and updates the current battery performance parameters for each simulation, resulting in a more accurate charging current distribution. Using this charging current distribution for charging control can improve battery lifespan.

[0145] Example 3

[0146] Figure 12 The diagram above illustrates the structure of an electronic device, which includes:

[0147] The device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke logical instructions stored in the memory 292 to execute the methods described in the example above.

[0148] Furthermore, the logic instructions in the aforementioned memory 292 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0149] The memory 292, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, it implements the methods in the above method examples.

[0150] The memory 292 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 292 may include high-speed random access memory and may also include non-volatile memory.

[0151] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method in any of the embodiments.

[0152] This application also provides a computer program product, including a computer program that, when executed by a processor, is used to implement the method in any of the embodiments.

[0153] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0154] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0155] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0156] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or in the form of software program modules.

[0157] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0158] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0159] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0160] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only.

[0161] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

[0162] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A charging control method, characterized in that, The method includes: By repeatedly performing the simulation process N times, N first charging current distributions are obtained; the simulation process includes: performing charge and discharge simulations based on A charging current distributions and battery performance parameters to obtain the charging time and capacity loss corresponding to the A charging current distributions; based on the charging time and capacity loss corresponding to the A charging current distributions, an optimization algorithm is used to obtain the first charging current distribution corresponding to the minimum value of the objective function; the objective function is the optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and capacity loss; Based on the N first charging current distributions, a charging current distribution under different life stages of the battery is established, and battery charging control is performed based on the charging current distribution under different life stages; the charging current distribution represents the charging current under different state of charge intervals during battery charging; where A and N are positive integers.

2. The method according to claim 1, characterized in that, The capacity loss includes a first capacity loss and a second capacity loss; the first capacity loss characterizes the capacity loss caused by the growth of the solid electrolyte membrane, and the second capacity loss characterizes the capacity loss caused by ion deposition.

3. The method according to claim 1, characterized in that, The method further includes: Based on the A charging current distributions, the temperature rise corresponding to the A charging current distributions is obtained based on a thermal model; the thermal model is used to characterize the relationship between the charging current distributions and the internal temperature change of the battery; the temperature rise characterizes the difference between the battery body temperature and the ambient temperature. The step of obtaining the first charging current distribution corresponding to the minimum value of the objective function using an optimization algorithm based on the charging time and capacity loss corresponding to the A charging current distributions includes: From the A charging current distributions, delete the charging current distributions whose temperature rise exceeds a preset threshold, and retain the charging current distributions whose temperature rise does not exceed the preset threshold; Based on the charging time and capacity loss corresponding to the retained charging current distribution, an optimization algorithm is used to obtain the first charging current distribution corresponding to the minimum value of the objective function.

4. The method according to claim 1, characterized in that, The method further includes: Using the battery performance parameters under this simulation as the initial battery performance parameters, and based on the first charging current distribution obtained from this simulation, M charge-discharge simulations are performed to obtain the first battery performance parameters after M charge-discharge simulations; the first battery performance parameters are used as the battery performance parameters under the next simulation; where M is a positive integer.

5. The method according to claim 1, characterized in that, The step of establishing the charging current distribution under different life stages of the battery based on the N first charging current distributions includes: The battery's lifespan is divided into N lifespan stages; the lifespan stage represents the maximum number of charge-discharge cycles the battery can support, and each lifespan stage contains M charge-discharge cycles. Establish the correspondence between the N first charging current distributions and the N lifetime stages to obtain the charging current distributions under different lifetime stages.

6. The method according to claim 5, characterized in that, The battery charging control based on the charging current distribution at different life stages includes: The current lifespan stage of the battery is determined based on the battery's historical charge-discharge cycles. Based on the charging current distribution under different life stages, determine the charging current distribution corresponding to the current life stage of the battery. The charging control of the battery is performed based on the charging current distribution corresponding to the current life stage of the battery.

7. The method according to any one of claims 1-6, characterized in that, The step of simulating charge and discharge based on A charging current distributions and battery performance parameters includes: inputting the A charging current distributions and battery performance parameters into an equivalent circuit model and an aging model; the aging model is used to simulate the relationship between the capacity loss and the charging current distribution; the equivalent circuit model is used to simulate the relationship between the charging time and the charging current distribution.

8. A charging control device, characterized in that, include: The simulation module is used to obtain N first charging current distributions by repeatedly executing the simulation process N times; the simulation process includes: performing charge and discharge simulations based on A charging current distributions and battery performance parameters to obtain the charging time and capacity loss corresponding to the A charging current distributions. The optimization module is used to obtain the first charging current distribution corresponding to the minimum value of the objective function based on the charging time and capacity loss corresponding to the A charging current distributions, using an optimization algorithm; the objective function is the optimization objective of the optimization algorithm, and the optimization objective is related to the charging time and capacity loss; The control module is used to establish the charging current distribution under different life stages of the battery based on the N first charging current distributions, and to perform battery charging control based on the charging current distribution under different life stages; the charging current distribution represents the charging current under different state of charge intervals during battery charging; where A and N are positive integers.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-8.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-8.

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