Method for estimating soh using fractional order model considering lithium battery aging mechanism

Abstract

The application discloses a method for estimating SOH by using a fractional order model considering a lithium battery aging mechanism, and belongs to the technical field of battery management and battery state of health evaluation. The application simplifies solid-phase diffusion of a lithium ion battery by using a fractional order Padé approximation method, proposes a fractional order lithium ion battery model, establishes a fractional order lithium ion battery model considering an aging mechanism by considering generation of an electrolyte interface film and a lithium dendrite side reaction, and can not only realize prediction of a voltage of an aging battery, but also realize prediction of an intermediate state quantity of the aging battery. The model outputs multiple battery internal states to evaluate the battery state of health. The model reduces calculation complexity of an electrochemical model and can be applied to an Arduino development board. The SOH estimation method has simple principles, is easy to implement, and has high practical value in engineering.

Description

Technical Field

[0001] This invention belongs to the field of battery management technology and battery health status assessment technology, and relates to a method for estimating SOH using a fractional-order model that takes into account the aging mechanism of lithium batteries. Background Technology

[0002] To address the crises of dwindling fossil fuels, increasing greenhouse gas emissions, and continued global warming, transportation electrification is imperative. The development of electric vehicles is key to this electrification, and lithium-ion batteries are the most important and expensive component in electric vehicles. Accurate and efficient State of Health (SOH) estimation is crucial to ensuring safe battery operation and maximizing battery performance and lifespan.

[0003] Chinese invention application CN106980091A discloses a method for estimating the health status of a power battery system based on a fractional-order model. The method obtains the open-circuit voltage of the battery based on the fractional-order model and further combines the capacity increment method to estimate the health status online.

[0004] Chinese invention application CN114091282A discloses a method and system for estimating the state of a lithium-ion battery based on a fractional-order model. The method includes: constructing a lithium-ion battery coupled model; estimating the parameters of the lithium-ion battery coupled model; estimating the state using a two-layer unscented Kalman joint estimation method, using the output of the Kalman filter for estimating the state of charge (SOC) of the lithium-ion battery as the input of the Kalman filter for estimating the state of equilibrium (SOH), and using the output SOH as the input of the Kalman filter for estimating the SOC at the next time step, and iterating continuously to update the SOC and SOH values ​​in real time.

[0005] Chinese invention application CN115436806A discloses an adaptive collaborative estimation method for the State of Charge (SOC) and State of Hypothesis (SOH) of a lithium-ion battery. The steps are as follows: First, the terminal voltage and load current data of the lithium-ion battery are measured through a hybrid power pulse characteristic experiment; then, a fractional-order equivalent circuit model of the lithium-ion battery is established offline; next, based on the state space of the established model, a dual adaptive square root capacitive Kalman filter for state estimation of the lithium-ion battery is constructed; finally, the SOC and SOH of the lithium-ion battery are estimated online by simulating actual working conditions through a random walk charge-discharge experiment.

[0006] Chinese invention application CN116413608A discloses a method for jointly estimating the state of charge (SOC) and state of health (SOH) of a lithium-ion battery. The specific steps are as follows: Step 1: Establish a fractional-order equivalent circuit model of the lithium-ion battery, and then establish a mathematical model of the model parameters; Step 2: Use a dual extended Kalman filter algorithm to estimate the parameters of the fractional-order equivalent circuit model and the SOC and maximum usable capacity of the battery under the current state online; Step 3: Use a time series weighted method to calculate the average maximum usable capacity and SOH of the battery, and combine the estimated ohmic internal resistance with the multiple relationship between the ohmic internal resistance of the new battery to comprehensively judge the health status of the battery.

[0007] Lithium-ion batteries age during charging and discharging, resulting in a decrease in usable energy and power with increasing cycle count. This is directly related to battery health (SOH), a crucial indicator of battery performance. SOH typically manifests as a decrease in battery capacity and an increase in internal resistance. The main reasons for this are adverse side reactions leading to the loss of lithium ions and active materials, consumption of electrolyte solvents, and an increase in the thickness of the solid electrolyte interphase (SEI) film. These changes result in alterations to aging parameters such as the number and volume fraction of cyclic ions and the battery's internal resistance.

[0008] The side reactions of solid electrolyte interfacial film growth mainly occur at the interface between the negative electrode particles and the electrolyte. Typically, the negative electrode material in lithium-ion batteries is graphite, and the electrolyte solvent is ethylene carbonate (EC). The main products of the solid electrolyte interfacial film formation side reactions are Li₂CO₃ and (CH₂OCO₂Li)₂. As the battery ages, the solid electrolyte interfacial film continuously grows on the surface of the negative electrode particles, reducing the contact area between lithium ions and the electrode active material, and increasing the battery's internal resistance. The solid electrolyte interfacial film formation side reactions consume lithium ions, causing some electrode particles to be isolated by the side reaction products, resulting in the loss of lithium ions and active material, thus leading to a decrease in battery charge / discharge power and capacity.

[0009] When a battery is charged at low temperatures, a lithium plating-stripping side reaction is prone to occur inside the battery. This reaction is a reversible electrochemical process. When the overpotential is less than zero, lithium dendrites form. When the overpotential is greater than zero, lithium stripping occurs, meaning the dendritic lithium redissolves into the electrolyte. Some undissolved lithium ions on the surface of the dendrites react with the electrolyte to form a secondary solid electrolyte interface film, preventing further dendrite stripping. This encapsulated metallic lithium and the secondary solid electrolyte interface film are collectively referred to as the deposition layer. The occurrence of the lithium plating-stripping side reaction leads to lithium ion loss, increased internal resistance, and reduced electrolyte volume, resulting in decreased battery charge / discharge power and capacity.

[0010] Physically based lithium-ion electrochemical models can accurately estimate both battery capacity and internal state variables. However, traditional electrochemical models are computationally complex and therefore difficult to apply in battery management systems. To simplify traditional lithium-ion battery electrochemical models, the fractional Padé approximation method is used to simplify the lithium-ion solid-phase diffusion process. The fractional Padé approximation method is fast and accurate, accelerating the computation of electrochemical models and enabling their application in practical battery management systems.

[0011] Arduino is an open-source electronic prototyping platform with flexible and easy-to-use hardware and software. Its fractional-order model, which considers aging mechanisms, is computationally simple and easy to implement. Arduino development boards can accurately perform model simulations and achieve precise estimation of the battery's internal state.

[0012] Traditional battery health assessment methods rely solely on capacity degradation rate to evaluate battery health. However, electrochemical models based on physical mechanisms can output multiple internal battery health parameters, allowing for a multi-dimensional assessment of battery health. This results in more accurate battery health assessments, ensuring safe battery operation and maximizing battery performance and lifespan.

[0013] In summary, most existing battery state of health (SOH) estimation methods can only assess battery health from the perspective of battery capacity alone, and their calculation speed is slow, resulting in low accuracy. There is a need to propose a SOH estimation method based on a fractional-order lithium-ion battery model that considers aging mechanisms. This method can assess battery health from multiple perspectives of internal battery state, and its calculation speed is fast, making it suitable for use in battery management systems. Summary of the Invention

[0014] Existing battery health state estimation methods suffer from problems such as limited assessment data features, slow computation speed, and insufficient progress in assessing health status. In practical engineering applications, traditional lithium-ion electrochemical models based on physical mechanisms cannot be applied to battery management systems due to the computational speed limitations of these systems, and empirical models for estimating battery health state lack sufficient prediction accuracy. To address these issues, this invention aims to provide a method for estimating state of health (SOH) using a fractional-order model that considers the aging mechanism of lithium batteries. This invention simplifies the lithium-ion electrochemical model using the fractional-order Padé approximation method and considers the influence of side reactions such as solid electrolyte interfacial film formation and lithium dendrite formation on the electrochemical model parameters. Finally, the fractional-order model based on the aging mechanism is applied to an Arduino development board, and the battery health state is assessed based on the multi-state quantities output by the model. The model provided by this invention can efficiently and accurately predict the health status of aged batteries, ensuring safe battery operation and maximizing battery performance and lifespan. The objective of this invention is achieved through the following technical solutions.

[0015] The method for estimating SOH using a fractional-order model that considers the aging mechanism of lithium batteries is characterized by the following steps:

[0016] S1 inputs the initial electrochemical parameters of the lithium battery;

[0017] S2 simplifies the solid-phase diffusion process inside lithium-ion batteries using the fractional Padé approximation method, considering the influence of electrolyte interface film growth side reactions and lithium dendrite side reactions on battery model parameters, and establishes a fractional lithium battery model that considers aging mechanism.

[0018] S3 applies the fractional-order lithium battery model considering the aging mechanism obtained in step S2 to the Arduino development board and calculates and outputs multiple state variables of the lithium battery.

[0019] S4 comprehensively evaluates the battery's SOH based on the multiple state variables of the lithium battery obtained in step S3.

[0020] Furthermore, it also includes step S5:

[0021] Based on the battery SOH assessment results obtained in step S4, S5 formulates a battery usage strategy to ensure safe battery operation and maximize battery performance and lifespan.

[0022] Furthermore, the initial electrochemical parameters mentioned in step S1 include: positive and negative electrode capacities, initial lithium intercalation amounts of the positive and negative electrodes, solid-phase diffusion time constants of the positive and negative electrodes, equivalent internal resistance of the battery, side reaction rate of SEI film growth, and side reaction rate of lithium dendrite formation.

[0023] Furthermore, the specific steps in step S2 to simplify the solid-phase diffusion process inside the lithium-ion battery using the fractional Padé approximation method include:

[0024] The relationship between lithium concentration difference and current on the solid surface of electrode particles is described by the fractional Padé approximation transfer function, and the lithium ion concentration on the surface of electrode particles is calculated by adding the average lithium ion concentration of electrode particles.

[0025] The SOC of the battery electrode surface is calculated based on the lithium ion concentration on the battery electrode surface and the maximum lithium ion concentration on the electrode.

[0026] Based on the SOC and equilibrium potential curves of the positive and negative terminals of the battery, the open-circuit voltages of the positive and negative terminals of the battery can be obtained by referring to the table.

[0027] The battery open-circuit voltage is obtained by adding the difference between the positive and negative open-circuit voltages to the lumped ohmic polarization voltage.

[0028] Furthermore, the consideration of the influence of electrolyte interface film growth side reactions and lithium dendrite side reactions on battery model parameters in step S2 is specifically as follows: the Tafel equation is used to describe the solid electrolyte interface film formation side reactions, the amount of solid electrolyte interface film formed is calculated based on the current density of the solid electrolyte interface film side reactions, and the resistance of the solid electrolyte interface film is calculated based on the amount of solid electrolyte interface film formed; the BV equation is used to describe the lithium dendrite side reactions, and the dendrite lithium thickness and the resistance of the dendrite lithium are calculated.

[0029] Furthermore, the specific calculation process for step S2 is as follows:

[0030] Based on the fundamental principle of side reactions in solid electrolyte interfacial film growth, S2-1 calculates the products generated by these side reactions and their impact on the internal electrochemical parameters of the battery.

[0031] The basic principle of solid electrolyte interfacial film growth is shown in equation (1), and the reaction rate of solid electrolyte interfacial film formation is shown in equation (2).

[0032] 2Li + +2e - +EC→C2H4↑+Li2CO3↓ (1)

[0033]

[0034] Where, j SEI This is the SEI film growth reaction rate, in A / m. 2 ;α SEI It is the transmission coefficient, dimensionless; i 0,SEI This is the reference exchange current density for the reaction, in A / m. 2 F is the Faraday constant, with units of C / mol; R g It is the ideal gas constant, with units of J / mol / K; T is the temperature, with units of K; n SEI It is the molar ratio of the substances participating in the SEI formation reaction, dimensionless; η SEI This is the overpotential of the SEI film formation reaction, in V; a s It is the specific surface area, in units of 1 / m², which can be calculated using equation (3).

[0035] a s =3ε / R s (3)

[0036] Where ε is the solid volume fraction, which is dimensionless; R s This refers to the radius of the electrode particles, in meters (m).

[0037] Based on the lithium dendrite side reaction mechanism, S2-2 calculates the lithium dendrite side reaction products and their impact on the internal electrochemical parameters of the battery:

[0038] The chemical equation for the lithium dendrite side reaction is shown in equation (4), and the lithium dendrite reaction rate is shown in equation (5).

[0039] Li + +e - →z1Li rev +z2Li dead +z3SEI sec (4)

[0040] Among them, Li rev Li is a reversible lithium, Li dead For dead lithium, SEI sec It is a secondary SEI membrane.

[0041]

[0042] Where, j LP / S The rate of lithium dendrite formation is expressed in A / m. 2 i 0,LP The reference exchange current density for lithium dendrite side reactions is given in A / m. 2 ;α a,LS and α c,LP η represents the charge transfer coefficients of the anode and cathode, respectively, both dimensionless; LP / S The side reaction overpotential, in V, is given when η LP / S When η is less than zero, lithium dendrite formation occurs as lithium is deposited. LP / S When the value is greater than zero, lithium dendrite side reaction occurs, resulting in lithium stripping;

[0043] S2-3 Quantitatively analyzes the effects of solid electrolyte interfacial film growth side reactions and lithium dendrite side reactions on electrochemical model parameters based on battery aging mechanisms:

[0044] The formula for calculating the increase in SEI film thickness is shown in Equation (6), and the formula for calculating the increase in dendritic lithium thickness is shown in Equation (7).

[0045]

[0046] Where, ρ SEI This is the density of the SEI membrane, in kg / m³. 3 M SEI This refers to the molar mass of the SEI membrane, expressed in kg / mol; L SEI t is the SEI film thickness in meters (m); t is time in seconds (s); j SEI,sec This is the secondary SEI film growth reaction rate, in A / m. 2 ;

[0047]

[0048] Where, ρ LiThe density of dendritic lithium, in kg / m³ 3 M Li L is the molar mass of lithium, expressed in kg / mol. Li The thickness of the lithium dendrites is in meters (m); n Li It is the molar ratio of the substances participating in the dendrite formation reaction, dimensionless; j LP and j LS These represent the reaction rates for lithium dendrite precipitation and exfoliation, respectively, both in A / m. 2 ;

[0049] The resistance of the SEI film and the resistance of the lithium dendrite can be calculated using formulas (8) and (9), respectively.

[0050]

[0051]

[0052] Among them, R SEI and R Li These are the resistance values ​​of the SEI film and the lithium dendrite, respectively, both in Ω; κ SEI and κ Li The values ​​are the conductivity of the SEI film and the dendritic lithium, respectively, both in S / m.

[0053] S2-4 inputs the updated battery electrochemical model parameters into the fractional-order lithium-ion battery model that considers the aging mechanism. The output voltage of the fractional-order lithium-ion battery model is calculated by formula (10).

[0054] V cell =E+η ce +η ct +R ohmI (10)

[0055] Among them, V cell Output voltage, unit: V; η ce The concentration overpotential of the liquid phase is expressed in V; η ct R is the overpotential of an electrochemical reaction, measured in V. ohm I is the lumped ohmic internal resistance of the battery, in Ω; I is the current intensity, in A; E is the electromotive force of the battery, in V, which can be calculated by formula (11).

[0056]

[0057] Among them, U p and U n These represent the positive and negative potentials, both in V. and , respectively, are the stoichiometric coefficients of the positive and negative electrode solid phase surfaces, both of which are dimensionless.

[0058] Furthermore, the multiple state quantities of the lithium battery mentioned in step S3 include: the lithium intercalation range of the positive and negative electrodes, the total amount of lithium ion loss, the total amount of solid electrolyte interface film generated, the battery capacity, and the increase in internal resistance.

[0059] Compared with existing battery health state estimation methods, the present invention has the following advantages:

[0060] (1) This invention proposes a fractional-order lithium-ion battery model based on physical mechanisms. This model simplifies the solid-phase diffusion process of lithium-ion batteries using the fractional-order Padé approximation method. The fractional-order lithium-ion battery model based on physical mechanisms improves the calculation speed of the electrochemical model, enabling it to be applied to Arduino development boards, and obtains accurate internal state variables of the battery.

[0061] (2) This invention proposes a fractional-order lithium-ion battery model that considers the battery aging mechanism. By analyzing the side reactions of solid electrolyte interfacial film growth and lithium dendrite formation, the relationship between electrochemical model parameters and aging is established. This model can not only accurately predict the battery aging state, but also predict the changes in the intermediate state inside the battery during the aging process, providing multi-dimensional battery state data.

[0062] (3) This invention proposes a method for estimating battery health status using multiple state variables. It utilizes various internal battery states output by a fractional-order lithium-ion battery model considering aging mechanisms, combined with the production of byproducts from solid electrolyte interface film growth and lithium dendrite formation, to comprehensively evaluate battery health status, ensuring safe battery operation and maximizing battery performance and lifespan. The battery health status assessment method proposed in this invention is simple to calculate, easy to implement, and has high practical value. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of a fractional-order lithium battery model;

[0064] Figure 2 Flowchart for outputting a fractional-order lithium battery model that takes into account aging mechanisms;

[0065] Figure 3 Flowchart for multi-state quantity battery health state estimation;

[0066] Figure 4 The graph shows the change in internal resistance of an LMO cell as it ages.

[0067] Figure 5 The graph shows the change in liquid phase volume fraction of LMO batteries with aging.

[0068] Figure 6 The graph shows the change of electrostoichiometry on the solid phase surface of LMO cell electrodes with aging.

[0069] Figure 7 This is a graph showing how the health status of an LMO battery changes with battery aging. Detailed Implementation

[0070] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0071] Taking a 17Ah lithium manganese oxide (LMO) battery as an example, after 1000 cycles at room temperature (298.15K) under constant current and constant voltage charge-discharge conditions at a 1C rate, the battery health status was evaluated using a fractional-order model considering the aging mechanism of lithium batteries. The fractional-order lithium battery model is as follows: Figure 1 As shown, the output flow of the fractional-order lithium battery model considering the aging mechanism is as follows: Figure 2 As shown, the multi-state quantity battery health state estimation process is as follows: Figure 3 As shown. The specific implementation steps are as follows:

[0072] Step 1: Input the initial electrochemical parameters of the LMO battery. The initial parameters of the LMO battery are shown in Table 1.

[0073] Table 1 Initial electrochemical parameters of LMO cells

[0074]

[0075]

[0076] Step 2: Based on the basic principle of side reactions in solid electrolyte interfacial film growth, calculate the products generated by the side reactions in solid electrolyte interfacial film growth and their impact on the internal electrochemical parameters of the battery. The basic principle of solid electrolyte interfacial film growth is shown in Equation (1), and the electrochemical reaction for solid electrolyte interfacial film formation is shown in Equation (2):

[0077] 2Li + +2e - +EC→C2H4↑+Li2CO3↓ (1)

[0078]

[0079] Where, j SEI This is the SEI film growth reaction rate, in A / m. 2 ;α SEI It is the transmission coefficient, dimensionless; i 0,SEI This is the reference exchange current density for the reaction, in A / m. 2 F is Faraday's constant, with units of C / mol; nSEI It is the molar ratio of the substances participating in the SEI formation reaction, dimensionless; R g η is the ideal gas constant, with units of J / mol / K; T is temperature, with units of K; SEI This is the overpotential of the SEI film formation reaction, in V; a s It is the specific surface area, in units of 1 / m², which can be calculated using equation (3).

[0080] a s =3ε / R g (12)

[0081] Where ε is the solid volume fraction, which is dimensionless; R s It is the radius of the electrode particles, in meters (m).

[0082] Step 3: Based on the lithium dendrite side reaction mechanism, calculate the lithium dendrite side reaction products and their impact on the internal electrochemical parameters of the battery. The chemical equation for the lithium dendrite side reaction is shown in equation (4), and the lithium dendrite reaction mechanism is shown in equation (5).

[0083] Li + +e - →z1Li rev +z2Li dead +z3SEI sec (13)

[0084] Among them, Li rev Li is a reversible lithium, Li dead For dead lithium, SEI sec It is a secondary SEI membrane.

[0085]

[0086] Where, j LP / S The rate of lithium dendrite formation is expressed in A / m. 2 i 0,LP The reference exchange current density for lithium dendrite side reactions is given in A / m. 2 ;α a,LS and α c,LP η represents the charge transfer coefficients of the anode and cathode, respectively, both dimensionless; LP / S The side reaction overpotential, in V, is given when η LP / S When η is less than zero, lithium dendrite formation occurs as lithium is deposited. LP / S When the value is greater than zero, lithium dendrite side reaction occurs and lithium stripping occurs.

[0087] Step 4: Based on the battery aging mechanism, quantitatively analyze the effects of the solid electrolyte interfacial film growth side reaction and the lithium dendrite side reaction on the electrochemical model parameters. The calculation formula for SEI film thickness growth is shown in Equation (6), and the calculation formula for lithium dendrite thickness growth is shown in Equation (7).

[0088]

[0089] Where, ρ SEI This is the density of the SEI membrane, in kg / m³. 3 M SEI This is the SEI molar mass, in kg / mol; L SEI The thickness of the SEI film is in meters (m).

[0090]

[0091] Where, ρ Li The density of dendritic lithium, in kg / m³ 3 M Li L is the molar mass of lithium, expressed in kg / mol. Li The thickness of the lithium dendrites is in meters (m); n Li It is the molar ratio of the substances participating in the dendrite formation reaction, dimensionless; j LP and j LS These represent the reaction rates for lithium dendrite precipitation and exfoliation, respectively, both in A / m. 2 .

[0092] The resistance of the SEI film and the resistance of the dendritic lithium can be calculated using formulas (8) and (9), respectively.

[0093]

[0094]

[0095] Among them, R SEI and R Li These are the resistance values ​​of the SEI film and the lithium dendrite, respectively, both in Ω; κ SEI and κ Li The values ​​are the conductivity of the SEI film and the lithium dendrite, respectively, both in S / m.

[0096] Step 5: Input the updated battery electrochemical model parameters into the fractional-order lithium-ion battery model that considers the aging mechanism. The output voltage of the fractional-order lithium-ion battery model can be calculated using formula (10).

[0097]

[0098] Among them, V cell Output voltage, in volts (V). The concentration overpotential of the liquid phase is expressed in V; η ct R is the overpotential of an electrochemical reaction, measured in V. ohmI is the lumped ohmic internal resistance of the battery, in Ω; I is the current intensity, in A; E is the electromotive force of the battery, in V, which can be calculated by formula (11).

[0099]

[0100] Among them, U p and U n These represent the positive and negative potentials, both in V. and , respectively, are the stoichiometric coefficients of the positive and negative electrode solid phase surfaces, both of which are dimensionless.

[0101] Step 6: Apply the fractional-order model considering the aging mechanism to the Arduino development board to output multiple internal state variables of the battery, including the internal resistance of the battery. Figure 4 ), battery liquid phase volume fraction ( Figure 5 ), electrode lithium intercalation region ( Figure 6 ), and changes in health status caused by capacity decay ( Figure 7 ).

[0102] Step 7: Assess the battery's health status based on multiple internal state variables and select an appropriate operating strategy. Ensure safe battery operation and maximize battery performance and lifespan.

[0103] Although embodiments of the present invention have been shown and described above, it is understood that these embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and alterations to the above embodiments within the scope of the present invention without departing from its principles and spirit. The scope of protection of the present invention is defined by the claims and their equivalents.

Claims

1. A method for estimating SOH using a fractional-order model considering the aging mechanism of lithium batteries, characterized in that, Includes the following steps: S1 inputs the initial electrochemical parameters of the lithium battery; S2 simplifies the solid-phase diffusion process inside a lithium-ion battery using the fractional Padé approximation method. The specific steps include: using the fractional Padé approximation transfer function to describe the relationship between the lithium concentration difference and current at the solid-phase surface of the electrode particles, and adding the average lithium-ion concentration of the electrode particles to calculate the lithium-ion concentration at the electrode particle surface; calculating the state of charge (SOC) at the battery electrode surface based on the lithium-ion concentration and the maximum lithium-ion concentration at the electrode; obtaining the open-circuit voltages of the positive and negative electrodes from a table based on the SOC and equilibrium potential curves of the positive and negative electrodes; and obtaining the battery open-circuit voltage by adding the lumped ohmic polarization voltage to the difference between the positive and negative open-circuit voltages. The effects of electrolyte interface film growth side reactions and lithium dendrite side reactions on battery model parameters are considered. Specifically, the Tafel equation is used to describe the solid electrolyte interface film formation side reactions, the amount of solid electrolyte interface film formed is calculated based on the current density of the solid electrolyte interface film side reactions, and the resistance of the solid electrolyte interface film is calculated based on the amount of solid electrolyte interface film formed; the BV equation is used to describe the lithium dendrite side reactions, and the dendrite thickness and the resistance of the dendrite lithium are calculated. Establish a fractional-order lithium battery model that considers aging mechanisms; S3 calculates and outputs multiple state variables of the lithium battery based on the fractional-order lithium battery model considering the aging mechanism obtained in step S2. S4 comprehensively evaluates the battery's SOH based on the multiple state variables of the lithium battery obtained in step S3.

2. The method according to claim 1, characterized in that, Step S3 applies the fractional-order lithium battery model considering the aging mechanism obtained in step S2 to the Arduino development board to calculate and output multiple state variables of the lithium battery.

3. The method according to claim 1 or 2, characterized in that, It also includes step S5: Based on the battery SOH assessment results obtained in step S4, S5 formulates a battery usage strategy to ensure safe battery operation and maximize battery performance and lifespan.

4. The method according to claim 1 or 2, characterized in that, The initial electrochemical parameters mentioned in step S1 include: positive and negative electrode capacities, initial lithium intercalation amounts of the positive and negative electrodes, solid-phase diffusion time constants of the positive and negative electrodes, equivalent internal resistance of the battery, side reaction rate of SEI film growth, and side reaction rate of lithium dendrite formation.

5. The method according to claim 1 or 2, characterized in that, The specific calculation process for step S2 is as follows: S2-1 Calculation of side reaction products generated during solid electrolyte interfacial film growth and their impact on internal electrochemical parameters of the battery: The reaction rate for the formation of the solid electrolyte interfacial film is shown in equation (1). ， in, j SEI This is the SEI film growth reaction rate, in A / m. 2 ; α SEI It is the transmission coefficient, which is dimensionless; i 0,SEI This is the reference exchange current density for the reaction, in A / m. 2 F is Faraday's constant, with units of C / mol. n SEI It is the molar ratio of the substances participating in the SEI formation reaction, and it is dimensionless. R g T is the ideal gas constant, with units of J / mol / K; T is the temperature, with units of K. η SEI This is the overpotential of the SEI film formation reaction, in V; a s It is the specific surface area, in units of 1 / m², which can be calculated using equation (2). ， in, ε It is the volume fraction of the solid phase, which is dimensionless. R s This refers to the radius of the electrode particles, in meters (m). S2-2 Calculation of lithium dendrite by-reaction products and their impact on battery internal electrochemical parameters: The lithium dendrite reaction rate is shown in equation (3). ， in, j LP / S The rate of lithium dendrite formation is expressed in A / m. 2 ; i 0,LP The reference exchange current density for lithium dendrite side reactions is given in A / m. 2 ; α a,LS and α c,LP are the charge transfer coefficients of the anode and cathode, respectively, both of which are dimensionless; η LP / S This is the overpotential of the side reaction, in V, when... η LP / S When the value is less than zero, lithium dendrite formation occurs as lithium is deposited. η LP / S When the value is greater than zero, lithium dendrite side reaction occurs, resulting in lithium stripping; S2-3 Quantitative analysis of the effects of solid electrolyte interfacial film growth side reactions and lithium dendrite side reactions on electrochemical model parameters: The formula for calculating the increase in SEI film thickness is shown in equation (4), and the formula for calculating the increase in dendritic lithium thickness is shown in equation (5). ， in, ρ SEI This is the density of the SEI membrane, in kg / m³. 3 ; M SEI This is the molar mass of the SEI membrane, expressed in kg / mol. L SEI The thickness of the SEI film is in meters (m); t is time in seconds (s). j SEI,sec This is the secondary SEI film growth reaction rate, in A / m. 2 ; ， in, ρ Li The density of dendritic lithium, in kg / m³ 3 ; M Li This is the molar mass of lithium, expressed in kg / mol. L Li The thickness of the lithium dendrite is in meters (m). n Li It is the molar ratio of participants in the dendrite lithium formation reaction, and is dimensionless. j Lp and j LS These represent the reaction rates for lithium dendrite precipitation and exfoliation, respectively, both in A / m. 2 ; The resistance of the SEI film and the resistance of the lithium dendrite were calculated using formulas (6) and (7), respectively. ， in, R SEI and R Li These are the resistance values ​​of the SEI film and the lithium dendrite, respectively, both in Ω; κ SEI and κ Li The values ​​are the conductivity of the SEI film and the dendritic lithium, respectively, both in S / m. S2-4 inputs the updated battery electrochemical model parameters into the fractional-order lithium battery model that considers the aging mechanism. The output voltage of the fractional-order lithium battery model is calculated by formula (8). ， in, V cell Output voltage, in volts (V). η ce This refers to the concentration overpotential of the liquid phase, expressed in V. η ct This is the overpotential of an electrochemical reaction, expressed in V. R ohm I is the total lumped ohmic resistance of the battery, in Ω; I is the current intensity, in A; E is the electromotive force of the battery, in V, which can be calculated by formula (9). ， in, U p and U n These represent the positive and negative potentials, both in V. and , respectively, are the stoichiometric coefficients of the positive and negative electrode solid phase surfaces, both of which are dimensionless.

6. The method according to claim 1, characterized in that, The multiple state quantities of the lithium battery mentioned in step S3 include: lithium intercalation range of the positive and negative electrodes, total lithium ion loss, total amount of solid electrolyte interface film generated, battery capacity, and internal resistance increment.

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