Charging optimization method for inhibiting lithium electroplating based on mechanism model

Abstract

The invention discloses a charging optimization method for inhibiting lithium electroplating based on a mechanism model, and belongs to the technical field of battery charging. According to the method, firstly, a lithium ion battery electrochemical model P2D model is constructed and linearized to serve as a state space of the model prediction control method so as to obtain response of lithium electroplating overpotential to charging current, and the response is one of main factors for promoting battery aging. And then, designing a multi-objective optimization task for rapid charging and limiting the lithium electroplating potential, and improving the charging current to the greatest extent while inhibiting the lithium electroplating reaction in the lithium battery, so that a rapid charging scheme for inhibiting the lithium electroplating reaction of the battery is realized. Besides, by constructing an LSTM model and utilizing current and voltage data of the battery to identify the aging condition of the battery, an experimental result shows that the charging strategy can improve the charging speed under the condition of keeping the lithium plating potential of the battery within a certain range, and can effectively adjust the aged batteries of different degrees, so that the universality of the charging strategy is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the field of charging strategies and is a charging optimization method for inhibiting lithium electroplating based on a mechanism model. Background Art

[0002] In recent years, fast-charging technology has garnered significant attention due to concerns among electric vehicle users about insufficient range and long charging times. While increasing the charging current can shorten charging time, it can lead to irreversible degradation of battery capacity and even failure. To balance charging efficiency with battery durability, this study designed a charging scheme based on battery aging mechanisms that balances charging speed with battery life preservation. This scheme dynamically adjusts the charging current by constructing a state-space model of lithium plating overpotential and combining multi-objective optimization with model predictive control techniques. Furthermore, addressing the limitations of traditional charging strategies in adapting to battery aging, this study extracted core aging-related parameters from an electrochemical model and trained aging data using an LSTM network to achieve real-time identification of battery status and model updates. This fusion of a mechanistic model and data-driven approach not only improves charging speed while slowing down battery performance degradation, but also dynamically optimizes the charging strategy based on battery aging, achieving a balance between battery life and charging efficiency.

[0003] After searching, the application publication number CN116683069A is a dynamic optimization charging method for actively improving the life of lithium-ion batteries in the field of battery charging technology, which includes the following steps: constructing a battery electrochemistry-heat generation-aging coupling model, and constructing a quantitative mathematical relationship in the model based on the battery heat generation sub-model, voltage sub-model, and battery aging sub-model; based on the proposed model, combined with the Kalman filter algorithm, an accurate comprehensive state observer is constructed, based on the measurable state quantity, the internal state quantity that cannot be measured inside the battery is accurately estimated to accurately track the current state of the battery, and some state parameters inside the model are closed-loop state correction. Based on the proposed model and state observer, combined with the model predictive control algorithm, an optimized charging controller is designed.

[0004] This method, published in Patent Application No. CN116683069A, modifies the state space based on a Kalman filter and requires real-time calculation of data across the entire battery lifecycle to ensure model accuracy. This results in a heavy computational burden, particularly when frequent updates are required, and may not be suitable for resource-constrained devices. Furthermore, closely monitoring the voltage and current throughout the battery lifecycle not only increases the complexity of data processing but can also affect processing speed due to the sheer volume of data.

[0005] In contrast, the present invention uses an LSTM model to periodically update the state space, focusing only on the voltage and current data at the end of charging for parameter identification, avoiding the real-time capture of data throughout the entire life cycle, thereby greatly reducing the computational burden. By performing parameter identification over a longer period, this method reduces the demand for computing resources, allowing the device to operate efficiently with lower power consumption and computing power. At the same time, this method can also be processed in the cloud, promoting its promotion in practical applications. Summary of the Invention

[0006] The present invention aims to solve the above problems of the prior art. It proposes a charging optimization method based on a mechanism model to inhibit lithium plating. The technical solution of the present invention is as follows:

[0007] A charging optimization method for inhibiting lithium plating based on a mechanism model comprises the following steps:

[0008] Step 1: Simplify and linearize the battery electrochemical model;

[0009] Step 2: Based on the simplified battery electrochemical model, the state space of the model predictive control method is designed to obtain the response of the battery lithium plating overpotential to the charging current;

[0010] Step 3: By limiting the lithium plating overpotential and accelerating the charging speed, a multi-objective optimization task is designed, and a quadratic programming method is used to solve the battery charging current under the constraints;

[0011] Step 4: Use the LSTM model to identify battery aging parameters through the battery current and voltage data, and update the state space equations in the model predictive control accordingly, so that the charging strategy can dynamically adapt to the battery aging status.

[0012] Furthermore, the Step 1: simplifies and linearizes the battery electrochemical model, specifically including: assuming that the concentration distribution inside the particle is a parabolic function and combining it with the solid phase concentration description of the single particle model, it can be deduced that:

[0013]

[0014]

[0015] Where J_n is the pore wall flux, C avg represents the average lithium ion concentration in the solid particles, C surf represents the surface lithium ions of solid particles, where R represents the radius of solid particles, and D eff represents the solid-phase diffusion coefficient, and J represents the concentration flux of the lithium ion intercalation reaction.

[0016] Furthermore, in Step 2, a model predictive control method is constructed based on the battery electrochemical model, and its state space matrix related to the lithium plating overpotential is:

[0017] x(k+1)=Ax(k)+Bi(k)+C

[0018] y(k+1)=Dx(k+1)

[0019] Where x(k) is the value of the state space at time k, x = [C avg C surf JU ocp U sei η] T , where C avg represents the average lithium ion concentration in the solid particles, C surf represents the surface lithium ion concentration of solid particles, J represents the concentration flux of lithium ion intercalation reaction, U ocp Indicates the battery open circuit voltage, U sei represents the voltage drop caused by the SEI layer of the battery, η represents the intercalation reaction overpotential of the lithium-ion battery, i(k) represents the charging current at time k, which is the control target, and y(k) represents the lithium plating overpotential at time k, where the A matrix is:

[0020]

[0021] Where R represents the solid particle radius, D eff represents the solid phase diffusion coefficient, C max represents the maximum lithium insertion concentration in the solid phase, a is the linear term of the fitting function between the open circuit potential of the electrode material and the amount of lithium insertion, and its B matrix is:

[0022]

[0023] Among them A cell Represents the battery electrode area, a s represents the specific surface area of the battery material, L represents the electrode length, F represents the Faraday constant, R sei Represents the battery SEI layer resistance, R ct represents the charge transfer impedance, and its C matrix is

[0024] C=[0 b 0 0 0 0] T

[0025] Where b is the constant term of the relationship between the open circuit potential of the electrode material and the amount of lithium inserted, and the D matrix is expressed as follows:

[0026] D=[0 0 0 1 1 1] T .

[0027] 4. Furthermore, in step 3, a multi-objective optimization task is designed by limiting the lithium plating overpotential and accelerating the charging speed. According to the incremental MPC, the prediction domain is Y p (k+1|k)=S x Δx(k)+y(k)+S u ΔI(k), S x is the state gain matrix, S u is the control gain matrix, ΔI(k) is the control quantity, that is, the change in charging current at each time step.

[0028] in:

[0029]

[0030] In the above formula, DA represents the output matrix D multiplied by the system matrix A, DB represents the output matrix D multiplied by the input matrix B, i represents the summation index, p represents the prediction domain size of the MPC method, and m represents the control domain size.

[0031] Finally, β is the weight of the objective function, and its value ranges from 0 to 1. It can be adjusted according to actual needs. The final objective function is designed as follows:

[0032] J(ΔI(k))=||(1-β)Y p (k+1|k)|| 2 +||βΔI(k)|| 2

[0033] Through its Y p Both (k+1|k) and ΔI(k) will be restricted to a certain range, and the minimum loss function value will be sought within this range. The core idea is to find the optimal control current and reduce the charging current fluctuation amplitude by limiting the lithium plating overpotential range.

[0034] Furthermore, in Step 4, the LSTM model is used to process the battery current and voltage data to identify the battery aging parameters, and based on this parameter estimation, the state space equation in the model predictive control is updated. According to the battery aging mechanism, the growth of the battery SEI layer and lithium plating are the main factors leading to battery aging. Therefore, the LSTM model is used to perform parameter identification on the battery charging current and voltage data to evaluate the key parameters currently characterizing battery aging, including the SEI layer film resistance R sei and the volume proportion of battery solid active materials ε s Based on these parameters, the state space equation is updated, where the variable that needs to be modified is the SEI layer resistance R sei and the material specific surface area a s , R represents the radius of the solid phase particle:

[0035] a s =3εs / R.

[0036] An electronic device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, it implements the charging optimization method for suppressing lithium plating based on a mechanism model as described in any one of the above.

[0037] A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any one of the charging optimization methods for suppressing lithium plating based on a mechanism model.

[0038] The advantages and beneficial effects of the present invention are as follows:

[0039] The present invention and method provide a lithium-ion battery charging strategy based on model prediction and machine learning that considers battery aging. The innovation of this method lies in designing a model predictive control (MPC) strategy based on the response characteristics of lithium plating potential to charging current, enabling real-time regulation of the charging current. Furthermore, the method utilizes an LSTM network to regularly identify aging parameters to update the model in real time, preventing model distortion and ensuring that the charging strategy is consistent with the actual battery performance, thereby improving charging efficiency. Compared to traditional methods, this method has the following advantages: Traditional methods often use fixed charging strategies that cannot promptly adjust the charging current to respond to changes in battery status. This method, through model predictive control, can respond to changes in battery status in real time. It can optimize between multiple objectives (such as the balance between charging speed and battery life), ensuring that charging targets meet user needs. Using an LSTM network to regularly identify aging parameters enables the model to adapt to the battery aging process, thereby improving the accuracy and effectiveness of the charging strategy and avoiding algorithm failure caused by ignoring battery aging. In summary, this method not only improves charging efficiency but also effectively adapts to battery aging, demonstrating a more advanced and scientific advantage over traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention provides a preferred embodiment as the technical roadmap of the present invention.

[0041] Figure 2 Flowchart for battery pack short circuit diagnosis.

[0042] Figure 3 Graph of the current during battery charging.

[0043] Figure 4 This is a diagram showing the change in lithium plating overpotential during battery charging.

[0044] Figure 5 To use LSTM for ε s The identification results.

[0045] Figure 6 To use LSTM for R sei The identification results. DETAILED DESCRIPTION

[0046] The following will describe the technical solutions in the embodiments of the present invention in detail with reference to the accompanying drawings. The described embodiments are only a part of the embodiments of the present invention.

[0047] The technical solution of the present invention to solve the above technical problems is:

[0048] A charging optimization method for inhibiting lithium plating based on a mechanism model is characterized in that the method comprises the following steps:

[0049] Step 1: Simplify and linearize the battery electrochemical model;

[0050] Step 2: Based on the simplified battery electrochemical model, the state space of the model predictive control method is designed to obtain the response of the battery lithium plating overpotential to the charging current;

[0051] Step 3: By limiting the lithium plating overpotential and accelerating the charging speed, a multi-objective optimization task is designed, and a quadratic programming method is used to solve the battery charging current under the constraints;

[0052] Step 4: Use the LSTM model to identify battery aging parameters through the battery current and voltage data, and update the state space equations in the model predictive control accordingly, so that the charging strategy can dynamically adapt to the battery aging status.

[0053] In Step 2, a model predictive control method is constructed based on the battery electrochemical model, and its state space matrix related to the lithium plating overpotential is:

[0054] x(k+1)=Ax(k)+Bi(k)+C

[0055] y(k+1)=Dx(k+1)

[0056] Where x(k) is the value of the state space at time k, x = [C avg C surf JU ocp U sei η] T , where C avg represents the average lithium ion concentration in the solid particles, C surf represents the surface lithium ion concentration of solid particles, J represents the concentration flux of lithium ion intercalation reaction, U ocp Indicates the battery open circuit voltage, U seirepresents the voltage drop caused by the SEI layer of the battery, η represents the intercalation reaction overpotential of the lithium-ion battery. i(k) represents the charging current at time k, which is the control target, and y(k) represents the lithium plating overpotential at time k, where the A matrix is:

[0057]

[0058] Where R represents the solid particle radius, D eff represents the solid phase diffusion coefficient, C max represents the maximum lithium insertion concentration in the solid phase, and a is the linear term of the fitting function relationship between the open circuit potential of the electrode material and the amount of lithium insertion. Its B matrix is:

[0059] Among them A cell Represents the battery electrode area, a s represents the specific surface area of the battery material, L represents the electrode length, F represents the Faraday constant, R sei Represents the battery SEI layer resistance, R ct represents the charge transfer impedance. Its C matrix is

[0060] C=[0b 0 0 0 0] T

[0061] Where b is the constant term of the relationship between the open circuit potential of the electrode material and the amount of lithium inserted, and the D matrix is expressed as follows:

[0062] D=[0 0 0 1 1 1] T

[0063] In step 3, a multi-objective optimization task is designed by limiting the lithium plating overpotential and accelerating the charging speed. According to the incremental MPC, the prediction domain is Y p (k+1|k)=S x Δx(k)+y(k)+S u ΔI(k), where:

[0064]

[0065] Finally, β is the weight of the loss function and its loss function is designed as:

[0066] J(ΔI(k))=||(1-β)Y p (k+1|k)|| 2 +||βΔI(k)|| 2

[0067] Through its Y pBoth (k+1|k) and ΔI(k) are constrained within a certain range, and the minimum loss function value is sought within this range. The core idea is to find the optimal control current and reduce the charging current fluctuation amplitude by limiting the lithium plating overpotential range.

[0068] In Step 4, the LSTM model is used to process the battery current and voltage data to identify battery aging parameters. Based on these parameters, the state space equations in the model predictive control are updated. According to the battery aging mechanism, the growth of the battery SEI layer and lithium plating are the main factors leading to battery aging. Therefore, the LSTM model is used to perform parameter identification on the battery charging current and voltage data to evaluate the key parameters that currently characterize battery aging, including the SEI layer film resistance R sei and the volume proportion of battery solid active materials ε s Based on these parameters, the state space equation is updated, where the variable that needs to be modified is R sei and a s :

[0069] a s =3ε s / R

[0070] This paper constructs a simulation platform to test a lithium-ion battery simulation model, and uses simulation data to describe the specific implementation of this method in detail. First, a P2D model is constructed using COMSOL. This paper uses the preset parameters of the battery electrochemical model within COMSOL software to construct the state space equations of the model prediction method.

[0071] The implementation method of the present invention takes the above simulated battery as the object, and its implementation process includes the following steps:

[0072] Step 1: Construct the state space equation of the model predictive control method based on the battery electrochemical model parameters.

[0073] Step 2: Set the objective function and boundary conditions based on the actual application requirements for battery charging speed, avoiding capacity decay, and controlling the current fluctuation amplitude. This paper limits the lithium plating potential to between 0.1 and -0.1, and limits the current fluctuation for each time step to |ΔI(k)|<1A.

[0074] Step 3: Use the battery electrochemical aging coupling model to simulate the charge and discharge cycle conditions, collect the current and voltage conditions of 500s at the end of each charging condition and the battery parameters R of each cycle sei and ε s Generate battery aging parameter dataset.

[0075] Step 4: Use the LSTM model to learn and identify key parameters R using the battery aging datasetsei and ε s like Figure 5 Figure 6 shown.

[0076] Step 5: Enable the model prediction algorithm to control the charging current in real time. Figure 3 shown.

[0077] Step 6: After the battery has run for a certain life cycle, input the current and voltage data of the last 500 seconds of charging into the LSTM model, and adjust the parameters R in the battery electrochemical model. sei and ε s Parameter identification is performed to update the spatial state equation. Regular updates are performed to incorporate battery aging into the charging strategy, preventing model distortion caused by battery aging. To account for computational resource consumption in practical applications, this paper performs parameter identification every 50 charge and discharge cycles.

[0078] In the experiment, by limiting the range of lithium plating potential and current fluctuations, a high charging speed of 6C-5C was achieved in the initial charging stage. At the same time, throughout the charging process, the lithium plating potential was maintained at around 0V, which can be regarded as no lithium plating phenomenon. Overall, high-speed charging with suppressed lithium plating reaction was achieved. At the same time, the LSTM algorithm was successfully used to identify the aging parameters of the current battery, with an error controlled at 4%, ensuring that the entire charging strategy would not fail due to model distortion caused by battery aging.

[0079] It should be noted that for different batteries, it is necessary to perform parameter identification on the battery to obtain model parameters close to the real battery. For different batteries, it is also necessary to retrain the LSTM network to estimate the aging parameters based on the battery model after parameter identification.

[0080] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0081] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0082] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0083] The above embodiments should be understood as merely illustrating the present invention and not as limiting the scope of protection of the present invention. After reading the contents of the present invention, technicians may make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A charging optimization method for inhibiting lithium plating based on a mechanism model, characterized in that: The following steps are involved: Step 1: Simplify the battery electrochemical model; Step 2: Based on the simplified battery electrochemical model, the state space of the model predictive control method is designed to obtain the response of the battery lithium plating overpotential to the charging current; Step 3: By limiting the lithium plating overpotential and accelerating the charging speed, a multi-objective optimization task is designed, and a quadratic programming method is used to solve the battery charging current under the constraints; Step 4: Use the LSTM model to identify battery aging parameters through battery current and voltage data, and update the state space equations in the model predictive control accordingly, so that the charging strategy can dynamically adapt to the battery aging status.

2. The charging optimization method for suppressing lithium plating based on a mechanism model according to claim 1, characterized in that: Step 1: Simplify and linearize the battery electrochemical model, specifically including: assuming that the concentration distribution inside the particle is a parabolic function and combining it with the solid phase concentration description of the single particle model, it can be deduced that: Where J_n is the pore wall flux, C avg represents the average lithium ion concentration in the solid particles, C surf represents the surface lithium ions of solid particles, where R represents the radius of solid particles, and D eff represents the solid-phase diffusion coefficient, and J represents the concentration flux of the lithium ion intercalation reaction.

3. The charging optimization method for suppressing lithium plating based on a mechanism model according to claim 1, characterized in that: In Step 2, a model predictive control method is constructed based on the battery electrochemical model, and its state space matrix related to the lithium plating overpotential is: x(k+1)=Ax(k)+Bi(k)+C y(k+1)=Dx(k+1) Where x(k) is the value of the state space at time k, x = [C avg C surf JU ocp U sei η] T , where C avg represents the average lithium ion concentration in the solid particles, C surf represents the surface lithium ion concentration of solid particles, J represents the concentration flux of lithium ion intercalation reaction, U ocp Indicates the battery open circuit voltage, U sei represents the voltage drop caused by the SEI layer of the battery, η represents the intercalation reaction overpotential of the lithium-ion battery, i(k) represents the charging current at time k, which is the control target, and y(k) represents the lithium plating overpotential at time k, where the A matrix is: Where R represents the solid particle radius, D eff represents the solid phase diffusion coefficient, C max represents the maximum lithium insertion concentration in the solid phase, a is the linear term of the fitting function relationship between the open circuit potential of the electrode material and the amount of lithium insertion, and its B matrix is: Among them A cell Represents the battery electrode area, a s represents the specific surface area of the battery material, L represents the electrode length, F represents the Faraday constant, R sei Represents the battery SEI layer resistance, R ct represents the charge transfer impedance, and its C matrix is C=[0b 0 0 0 0] T Where b is the constant term of the relationship between the open circuit potential of the electrode material and the amount of lithium inserted, and the D matrix is expressed as follows: D＝[0 0 0 1 1 1] T 。 4. The charging optimization method for suppressing lithium plating based on a mechanism model according to claim 3, characterized in that: In step 3, a multi-objective optimization task is designed by limiting the lithium plating overpotential and accelerating the charging speed. According to the incremental MPC, its prediction domain is Y p (k+1|k)=S x Δx(k)+y(k)+S u ΔI(k), S x is the state gain matrix, S u is the control gain matrix, ΔI(k) is the control quantity, that is, the change in charging current at each time step. in: In the above formula, DA represents the output matrix D multiplied by the system matrix A, DB represents the output matrix D multiplied by the input matrix B, i represents the summation index, p represents the prediction domain size of the MPC method, and m represents the control domain size; Finally, β is the function weight in the objective function, and its value ranges from 0 to 1. It can be adjusted according to actual needs. The final objective function is designed as follows: J(ΔI(k))=||(1-β)Y p (k+1|k)|| 2 +||βΔI(k)|| 2 Through its Y p Both (k+1|k) and ΔI(k) will be restricted to a certain range, and the minimum loss function value will be sought within this range. The core idea is to find the optimal control current and reduce the charging current fluctuation amplitude by limiting the lithium plating overpotential range.

5. The charging optimization method for suppressing lithium plating based on a mechanism model according to claim 1, characterized in that: In Step 4, the LSTM model is used to process the battery current and voltage data to identify the battery aging parameters, and based on this parameter estimation, the state space equation in the model predictive control is updated. According to the battery aging mechanism, the growth of the battery SEI layer and lithium plating are the main factors leading to battery aging. Therefore, the LSTM model is used to perform parameter identification on the battery charging current and voltage data to evaluate the key parameters that currently characterize battery aging, including the SEI layer film resistance R sei and the volume proportion of battery solid active materials ε s ; Based on these parameters, the state space equation is updated, where the variable that needs to be modified is the SEI layer resistance R sei and the material specific surface area a s , R represents the radius of the solid phase particle: a s =3ε s / R。 6. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the charging optimization method for suppressing lithium plating based on a mechanism model as described in any one of claims 1 to 5 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the charging optimization method for suppressing lithium plating based on a mechanism model as described in any one of claims 1 to 5 is implemented.

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