Battery parameter estimation method, device and storage medium

Abstract

The present application discloses a battery parameter estimation method, device and storage medium. The present application relates to the field of electrical digital data processing technology. The battery parameter estimation method includes: determining an iteration parameter according to the Jacobian matrix corresponding to the initial value of the battery parameter, wherein the initial value of the battery parameter is the historical battery parameter at the previous calculation moment; determining the battery parameter at the current calculation moment based on the iteration parameter and the historical battery parameter, wherein the battery parameter includes at least one of a concentration field variable, an electric potential field variable and a body current variable. The present application solves the technical problem in the related art that the battery state estimation takes a long time under the condition of strong nonlinearity of the battery parameters, thereby achieving the technical effect of reducing the computational overhead and improving the efficiency of battery state estimation.

Description

Technical Field

[0001] The present application relates to the technical field of electrical digital data processing, and in particular to a battery parameter estimation method, device and storage medium. Background Art

[0002] The equilibrium potential of the battery can provide a basis for the battery charging strategy and energy management, thereby improving the overall performance of the battery.

[0003] When determining the battery state in related technologies, the equations of the potential field and the concentration field are usually separated, that is, when performing nonlinear iterations of the potential field equations, they are not solved simultaneously with the iterative solution of the concentration field equations. However, under certain operating conditions, the equilibrium potential of the battery material will change dramatically with the change in the battery charge. Since the potential field equations and the concentration field equations are solved separately, the changes in the concentration field are not transmitted to the potential field, causing the equations to diverge easily. For this reason, an extremely small solution time step must be taken, which leads to a long battery state estimation time. Summary of the invention

[0004] The main purpose of the present application is to provide a battery parameter estimation method, device and storage medium, aiming to solve the technical problem that battery state estimation takes a long time under conditions where battery parameter nonlinearity is strong.

[0005] In a first aspect, the present application provides a method for estimating battery parameters, the method for estimating battery parameters comprising:

[0006] Determine the iteration parameter according to the Jacobian matrix corresponding to the initial value of the battery parameter, wherein the initial value of the battery parameter is the historical battery parameter at the last calculation moment;

[0007] Based on the iteration parameter and the historical battery parameter, the battery parameter at the current calculation moment is determined, wherein the battery parameter includes at least one of a concentration field variable, a potential field variable, and a body current variable.

[0008] In the technical solution of the embodiment of the present application, the initial value of the battery parameter at the current calculation moment is first determined based on the historical battery parameter at the previous calculation moment, and then the iteration parameter is determined based on the Jacobian matrix corresponding to the initial value of the battery parameter, and then the battery parameter at the current calculation moment is determined based on the iteration parameter and the historical battery parameter, wherein the battery parameter includes concentration field variable, potential field variable and body current variable. The technical problem of the battery state estimation taking a long time under the condition of strong nonlinearity of the battery parameter in the related technology is solved, thereby achieving the technical effect of reducing the calculation overhead and improving the efficiency of battery state estimation.

[0009] In some embodiments, the step of determining the iteration parameters according to the Jacobian matrix corresponding to the initial value of the battery parameter includes:

[0010] Determining the Jacobian matrix based on the initial values ​​of the battery parameters and a battery state equation group, wherein the battery state equation group describes the battery state from a concentration field, an electric potential field, and a connection relationship, and the connection relationship is a relationship between the concentration field and the electric potential field;

[0011] Constructing a first linear equation group according to the battery state equation group into which the initial values ​​of the battery parameters are substituted;

[0012] The first linear equation group is solved based on the Jacobian matrix, and the iteration parameter is determined according to the solution result.

[0013] In the technical solution of the embodiment of the present application, by using the Jacobian matrix to solve the linear equations involving the complex battery state equations, not only the calculation process is optimized, but also the accuracy and efficiency of battery state estimation are improved.

[0014] In some embodiments, the battery state equation group includes a concentration field equation, an electric potential field equation, and a connection equation, and the concentration field equation, the electric potential field equation, and the connection equation are all expressed by the concentration field variables, the electric potential field variables, and the body current variables. The step of determining the Jacobian matrix based on the initial values ​​of the battery parameters and the battery state equation group includes:

[0015] The Jacobian matrix is ​​determined by respectively taking partial derivatives of the variables of the initial values ​​of the battery parameters according to the concentration field equation, the potential field equation and the connection equation.

[0016] In the technical solution of the embodiment of the present application, the performance of the overall estimation of the battery state is improved by accurately constructing and calculating the battery state equation group and its corresponding Jacobian matrix.

[0017] In some embodiments, the step of determining the Jacobian matrix based on the initial values ​​of the battery parameters and the battery state equation group includes:

[0018] Based on each state equation in the battery state equation group, partial derivatives are calculated for each variable in the battery parameter initial value to determine an initial Jacobian matrix;

[0019] The Jacobian matrix is ​​determined by rearranging elements in the initial Jacobian matrix to reduce a bandwidth of the initial Jacobian matrix.

[0020] In the technical solution of the embodiment of the present application, the Jacobian matrix is ​​determined by rearranging the initial values ​​of the battery parameters, which reduces redundant calculations in the iterative process, speeds up the solution, reduces the accumulation of numerical errors, and improves the overall accuracy of battery state estimation.

[0021] In some embodiments, the step of rearranging the elements in the initial Jacobian matrix to determine the Jacobian matrix includes:

[0022] Rearrange the initial values ​​of the battery parameters based on the initial Jacobian matrix;

[0023] The order of elements in the initial Jacobian matrix is ​​adjusted based on the rearranged initial values ​​of the battery parameters to determine the Jacobian matrix.

[0024] In the technical solution of the embodiment of the present application, by rearranging the vectors corresponding to the initial values ​​of the battery parameters, the structure of the Jacobian matrix is ​​made more compact, the related variables are more concentrated, unnecessary calculation and storage overhead are reduced, and the speed and stability of the numerical solution are improved.

[0025] In some embodiments, the step of rearranging the initial values ​​of the battery parameters based on the initial Jacobian matrix includes:

[0026] Traversing the battery parameter initial values, and determining the target battery variable currently selected in the battery parameter initial values;

[0027] Constructing a variable set based on the adjacent variables of the target battery variable in the initial Jacobian matrix, wherein the adjacent variables are all other non-zero variables in the corresponding row of the target battery variable in the initial Jacobian matrix;

[0028] The target battery variables in the variable set are traversed and added to a target array, and the initial values ​​of the battery parameters are rearranged according to the order of the variables in the target array.

[0029] In the technical solution of the embodiment of the present application, the target queue is updated by judging whether the target battery variable is in the target array, thereby ensuring that each variable of the battery parameter is added to the target array and data integrity is guaranteed.

[0030] In some embodiments, the step of solving the first linear equations based on the Jacobian matrix to determine the iteration parameters includes:

[0031] Decomposing the Jacobian matrix into an upper triangular matrix and a lower triangular matrix;

[0032] The first linear equation group is solved based on the upper triangular matrix and the lower triangular matrix to determine the iteration parameter.

[0033] In the technical solution of the embodiment of the present application, by decomposing the Jacobian matrix into an upper triangular matrix and a lower triangular matrix, and solving the first linear equation group based on these decomposed matrices, the computational efficiency and accuracy of solving the related linear equation group are significantly improved, thereby improving the real-time performance of battery state estimation.

[0034] In some embodiments, after the step of determining the battery parameter at the current calculation moment based on the iteration parameter and the historical battery parameter, the method further comprises:

[0035] Determining a parameter change amount based on the battery parameter and the historical battery parameter;

[0036] If the parameter change is less than a preset tolerance, outputting the battery status corresponding to the current calculation time based on the battery parameter;

[0037] Otherwise, based on the battery parameter as the updated initial value of the battery parameter, the step of determining the iteration parameter according to the Jacobian matrix corresponding to the initial value of the battery parameter is performed.

[0038] In the technical solution of the embodiment of the present application, by accurately calculating and judging the parameter changes during the iterative calculation process of the battery state estimation, and deciding whether to update the battery state or continue to iterate based on the results, not only the accuracy and reliability of the battery state estimation are improved, but also the use of computing resources is optimized, thereby improving the overall efficiency and stability of the battery estimation.

[0039] In a second aspect, the present application provides a battery parameter estimation device, the battery parameter estimation device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the battery parameter estimation method as described above.

[0040] In a third aspect, the present application provides a storage medium, which is a computer-readable storage medium, on which is stored a program for implementing a method for estimating battery parameters, and the program for implementing the method for estimating battery parameters is executed by a processor to implement the steps of the method for estimating battery parameters as described above.

[0041] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0043] Figure 1 A schematic diagram of a flow chart of a method for estimating battery parameters according to a first embodiment of the present application;

[0044] Figure 2 A schematic diagram of battery segmentation provided for the second embodiment of the method for estimating battery parameters of the present application;

[0045] Figure 3 A schematic diagram of a flow chart of a third embodiment of a method for estimating battery parameters of the present application;

[0046] Figure 4 A comparison diagram of the bandwidth of the initial Jacobian matrix and the Jacobian matrix provided in Example 3 of the method for estimating battery parameters of the present application;

[0047] Figure 5 A schematic diagram of a simplified flow chart of a method for estimating battery parameters provided in Embodiment 5 of the method for estimating battery parameters of the present application;

[0048] Figure 6 A time-voltage simulation result comparison diagram provided for the sixth embodiment of the battery parameter estimation method of the present application;

[0049] Figure 7 A time-consuming statistical comparison chart of the present solution and the related technology provided in Example 6 of the method for estimating battery parameters of the present application;

[0050] Figure 8 This is a schematic diagram of the hardware structure involved in an embodiment of a battery parameter estimation device of the present application.

[0051] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] The following embodiments of the technical solution of the present application will be described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0054] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0055] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0056] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0057] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0058] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0059] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0060] At present, from the perspective of market development, the application of batteries is becoming more and more extensive. Batteries are not only used in energy storage power systems such as hydropower, thermal power, wind power and solar power stations, but also widely used in electric vehicles such as electric bicycles, electric motorcycles, electric cars, as well as military equipment and aerospace and other fields. With the continuous expansion of battery application areas, its market demand is also constantly expanding.

[0061] Related art proposes a separate method for quickly solving partial differential equations of a pseudo-two-dimensional model of lithium-ion battery electrochemistry. In this method, the potential field and concentration field equations are separated, and it is believed that the potential field and concentration field can be decoupled within a given solution time step, and the concentration field equations are no longer iterated and solved simultaneously when performing nonlinear iteration of the potential field equations. However, this solution method is only applicable to the range in which the equilibrium potential of the battery material does not change much with the battery charge. When the battery charge is low or high, the equilibrium potential of the battery material will change dramatically, and a small concentration change will cause a huge equilibrium potential change. At this time, since the potential field equation and the concentration field equation are not coupled and solved, the change in the concentration field is not transmitted to the potential field, and the equation is prone to divergence. At this time, a very small solution time step must be taken to still believe that the potential field and the concentration field can be decoupled when solving the equations, but the solution speed will drop to an unbearable level at this time. In actual calculation conditions, the equilibrium potential of the battery material changes dramatically with the battery charge in only 20% of the cases, and these 20% of the cases account for 80% of the total calculation time, which in turn leads to a long battery status estimation time.

[0062] The main solution of the present application is: determine the initial values ​​of the battery parameters at the current calculation moment based on the historical battery parameters at the previous calculation moment; determine the iteration parameters based on the Jacobian matrix corresponding to the initial values ​​of the battery parameters; determine the battery parameters at the current calculation moment based on the iteration parameters and the historical battery parameters, wherein the battery parameters include concentration field variables, potential field variables and body current variables.

[0063] This application estimates the battery state at the current calculation moment by combining the historical battery parameter initial values, Jacobian matrix and iteration method. It solves the technical problem of the battery state estimation taking a long time under the condition of strong nonlinear battery parameter in the related technology, thereby achieving the technical effect of reducing the calculation overhead and improving the efficiency of battery state estimation.

[0064] In the first aspect, the present application embodiment 1 proposes a method for estimating battery parameters, please refer to Figure 1 In this embodiment, the method for estimating battery parameters includes the following steps:

[0065] Step S10: determining iteration parameters according to the Jacobian matrix corresponding to the initial value of the battery parameter, wherein the initial value of the battery parameter is the historical battery parameter at the last calculation moment.

[0066] The execution subject of this embodiment can be a battery status estimation system, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a battery parameter estimation device capable of realizing the above functions, or an electronic device capable of realizing the above functions, a host computer, etc., which is not specifically limited in this embodiment. The following takes the host computer as an example to illustrate this embodiment and the following embodiments.

[0067] The application scenario of the embodiment of the present application is battery simulation under various working conditions. The current calculation moment is the moment when the battery is expected to operate in the battery simulation. For example, the state of the battery at 1500 seconds is simulated, and the current calculation moment is 1500 seconds. The previous calculation moment is the previous simulation moment, and the previous calculation moment differs from the current calculation moment by a time step length. The time step length is the battery state estimation system received by the user's set value, which is not specifically limited in this embodiment. The historical battery parameters are the battery parameters estimated at the previous calculation moment. The battery parameters include battery-related parameters in three dimensions: concentration field variables, potential field variables, and body current variables. The Jacobian matrix is ​​a matrix in which the first-order partial derivatives are arranged in a certain way, which reflects the local linear characteristics of the function at a certain point. The iteration parameter is a vector obtained by iterating using the Jacobian matrix. The battery parameters contain several variables, that is, the initial value of the battery parameter is a vector composed of several variables. The result obtained after iterating using the Jacobian matrix is ​​also a vector.

[0068] In order to further improve the accuracy of battery state estimation, the historical battery parameters at the previous calculation moment are used as the initial values ​​of the battery parameters at the current calculation moment.

[0069] Since the historical battery parameters at the previous calculation time are used as the initial values ​​at the current calculation time, a starting point that is closer to the true value can be provided, which helps to reduce the calculation error in the iterative process and improve the convergence speed and calculation accuracy.

[0070] In order to couple the potential field and the concentration field and improve the accuracy and real-time performance of the battery state estimation, the battery state equation group is determined based on the pseudo two-dimensional model before determining the Jacobian matrix. After determining the initial values ​​of the battery parameters, the battery state equation group is obtained, and the Jacobian matrix is ​​determined based on each state equation in the battery state equation group and each variable of the initial values ​​of the battery parameters. Then, the linear equation group is solved based on the Jacobian matrix to determine the iteration parameters.

[0071] In this embodiment, the battery state equation group is a group of equations that describes the battery state from three dimensions: concentration field, potential field and body current. That is, the battery state equation group is composed of the concentration field equation group, the potential field equation group and the body current equation group, and the concentration field equation group includes several concentration field equations, the potential field equation group includes several potential field equations, and the body current equation group includes several body current equations.

[0072] In order to reduce the bandwidth of the Jacobian matrix and reduce the time cost of battery state estimation, the initial Jacobian matrix is ​​determined based on the initial values ​​of the battery parameters, and then the initial values ​​of the battery parameters and the initial Jacobian matrix are rearranged to determine the Jacobian matrix, and then the iteration parameters are determined to reduce the bandwidth of the Jacobian matrix and reduce the computational cost.

[0073] Since the Jacobian matrix corresponding to the initial values ​​of the battery parameters is used, the influence of the battery parameter changes on the system state (such as concentration field, potential field, and body current) is reflected, thereby improving the stability and convergence of the iterative calculation.

[0074] Step S20: determining the battery parameters at the current calculation moment based on the iteration parameters and the historical battery parameters, wherein the battery parameters include at least one of a concentration field variable, an electric potential field variable, and a body current variable.

[0075] The battery parameters contain several variables, that is, the initial value of the battery parameters is a vector composed of several variables. The result obtained after iteration using the Jacobian matrix is ​​also a vector, and the dimension of the vector corresponding to the battery parameters is the same as the dimension of the vector corresponding to the iteration parameters. Therefore, the historical battery parameters are updated based on the iteration parameters to determine the battery parameters at the current calculation moment. Battery parameters are divided into three types according to their types: concentration field variables, potential field variables, and body current variables, which describe the state of the battery from the perspective of concentration, potential, and body current, respectively. Battery parameters include but are not limited to lithium ion concentration in solid particles, lithium ion concentration in liquid electrolyte, body current density, solid effective conductivity, solid potential, liquid effective conductivity, and liquid potential.

[0076] In this embodiment, each calculation moment only requires one iteration, and the time step length between two adjacent calculation moments is relatively large. Compared with the technical solutions in the related art that require a smaller time step length, the calculation cost is significantly reduced.

[0077] In order to accurately determine the battery parameters, the battery parameters at the current calculation moment are determined according to the sum of the historical battery parameters and the iteration parameters.

[0078] For ease of understanding, the following examples are given, but this application is not limited. As an example, assume that in battery simulation, it is necessary to estimate the state of the battery under various operating conditions and simulate the state of the battery at a specific time (such as 1500 seconds). Assume that the time step length is Δt = 10 seconds. Obtain the historical battery parameters of the last calculation moment: Assuming that the last calculation moment is 1490 seconds, the estimated battery parameters include: lithium ion concentration Cs in solid phase particles, 1490; lithium ion concentration Ce in liquid electrolyte, 1490; bulk current density J1490; effective conductivity σs of the solid phase, 1490; solid phase potential φs, 1490; liquid phase effective conductivity σe, 1490; liquid phase potential φe, 1490. Then, the initial values ​​of the battery parameters at the current calculation time are determined, and the battery parameters at 1490 seconds are used as the initial values ​​at 1500 seconds: Cs,1500initial=Cs,1490; Ce,1500initial=Ce,1490; J1500initial=J1490; σs,1500initial=σs,1490; ϕs,1500initial=ϕs,1490; σe,1500initial=σe,1490; ϕe,1500initial=ϕe,1490. Further, a battery state equation group is established. According to the pseudo two-dimensional model, a battery state equation group including the concentration field equation, the potential field equation and the body current equation is established. Based on the battery state equation group and the initial values ​​of the battery parameters, the Jacobian matrix J is constructed. The Jacobian matrix J is used to solve the linear equation group to obtain the iterative parameter vector ΔX. According to the iterative parameter vector ΔX, the initial value of the battery parameters is updated to obtain the battery parameters at 1500 seconds:

[0079] Cs,1500=Cs,1500initial+ΔCs;

[0080] Ce,1500=Ce,1500initial+ΔCe;

[0081] J1500=J1500initial+ΔJ;

[0082] σs,1500=σs,1500initial+Δσs;

[0083] ϕs,1500=ϕs,1500initial+Δϕs;

[0084] σe,1500=σe,1500initial+Δσe;

[0085] ϕe,1500=ϕe,1500initial+Δϕe. Finally, the battery parameters at 1500 seconds are output as simulation results. The battery state estimation method provided in this embodiment can accurately estimate the state of the battery under various working conditions at a larger time step length, while significantly reducing the calculation cost.

[0086] The present application provides a method for estimating battery parameters. The present application first determines the initial value of the battery parameters at the current calculation moment based on the historical battery parameters at the previous calculation moment, wherein the historical battery parameters simultaneously include all battery parameters in three dimensions: potential field, concentration field, and body current, that is, using the historical battery parameters at the previous calculation moment as the initial value at the current calculation moment, can provide a starting point that is closer to the true value. This helps to reduce the calculation error in the iteration process and improve the convergence speed and calculation accuracy. Then, the iteration parameters are determined based on the Jacobian matrix corresponding to the initial value of the battery parameter, that is, the full coupling method is used to solve the system of equations in a single time step, so that the system of equations can accept a larger time step length. Finally, based on the iteration parameters and the historical battery parameters, the battery parameters at the current calculation moment are determined, wherein the battery parameters include concentration field variables, potential field variables, and body current variables. The technical problem that the battery state estimation takes a long time under the condition of strong nonlinearity of the battery parameters in the related technology is solved, thereby achieving the technical effect of reducing the calculation overhead and improving the efficiency of battery state estimation.

[0087] In some embodiments, step S10 includes:

[0088] Step S11: Determine the Jacobian matrix based on the initial values ​​of the battery parameters and the battery state equation group, wherein the battery state equation group describes the battery state from the concentration field, the potential field and the connection relationship, and the connection relationship is the relationship between the concentration field and the potential field.

[0089] The battery state equation group is a group of equations that describes the battery state from three dimensions: concentration field, potential field and body current. That is, the battery state equation group is composed of the concentration field equation group, the potential field equation group and the body current equation group, and the concentration field equation group contains several concentration field equations, the potential field equation group contains several potential field equations, and the body current equation group contains several individual current equations.

[0090] In order to make the constructed battery state equation group closer to the operating environment of the battery and reduce the time cost of battery state estimation, before step S11, the battery state equation group is determined based on the pseudo two-dimensional model. First, the battery state equation of 5 parts is determined:

[0091] The first equation describes the solid phase lithium ion concentration in the spherical particles, where represents the lithium ion concentration in the solid particles, represents the radial distance, Indicates time, is the diffusion coefficient of the solid particles:

[0092] (1)

[0093] The second equation describes the lithium ion concentration in the electrolyte and separator, where represents the lithium ion concentration in the liquid electrolyte, Indicates distance, Indicates time, is the diffusion coefficient of lithium ions in the liquid electrolyte, is the migration number, is the volume current density, is the Faraday constant:

[0094] (2)

[0095] The third equation, describing the solid phase potential in the electrode, is the volume current density, is the effective conductivity of the solid phase, is the solid phase potential:

[0096] (3)

[0097] The fourth equation describes the liquid phase potential in the electrolyte and the isolation membrane, is the volume current density, is the liquid effective conductivity, is the liquid phase potential, is the gas constant, is the temperature, is the Faraday constant, is the activity coefficient, is the number of migrations:

[0098] (4)

[0099] The fifth equation describes the electrochemical reaction at the solid-liquid interface, is the volume current density, is the surface area of ​​the solid particles, is the reference exchange current density, and represents the forward and reverse reaction coefficients, usually taken as 0.5, is the equilibrium potential of the material itself, which usually varies with concentration Change, recorded as :

[0100] (5)

[0101] In these five parts, equations (1) and (2) are concentration field equations, equations (3) and (4) are potential field equations, and equation (5) connects the concentration field equation and the potential field equation.

[0102] Further, after constructing five battery state equations, taking equation (2) as an example, refer to Figure 2 , divided into n grids along the thickness direction of the battery, each grid has its own Variables, from arrive There are n variables in total. Take the i-th grid as an example, The variables also satisfy equation (2), that is:

[0103] (6)

[0104] Then, replace equation (6):

[0105]

[0106] use Instead, determine:

[0107] (7)

[0108] In short:

[0109]

[0110] By doing the same operation for each grid, we can get n equations of similar form:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] At this point, equation (2) is transformed from a partial differential equation into a set of ordinary differential equations with only time differential terms.

[0117] By performing the same operations on equations (1), (3), and (4) under the same grid division, we can also obtain a series of equations. Equations (3) and (4) do not contain time partial derivatives, and what is finally obtained is a series of algebraic equations.

[0118] Equation (5) and some additional equations are discretized into a series of algebraic equations under this operation. Taking equation (5) as an example, each grid has its own variables, and on the grid , , ,and After rearranging, we can get:

[0119] (8)

[0120] In short:

[0121] (9)

[0122] By performing the same operation on each grid, equation (5) can be transformed into a series of algebraic equations in the form of equation (9).

[0123] According to some algebraic equations attached to the pseudo-two-dimensional model, the variables are rearranged, and the entire set of partial differential equations can be transformed into a series of algebraic differential equations after spatial discretization, which can be expressed as:

[0124]

[0125]

[0126] …

[0127]

[0128]

[0129] …

[0130] Therefore, all concentration field variables from equations (1) and (2) are expressed as a vector , similarly, all the potential field variables from equations (3) and (4) are expressed as a vector , all the volume current density variables from equation (5) are recorded as a vector , the above equation can be written as:

[0131] (10)

[0132] (11)

[0133] (12)

[0134] Wherein equation (10) represents all the ordinary differential equations, which are derived from the concentration field equations (1) and (2). Equation (11) represents the algebraic equations derived from equations (3) and (4). The equations of equation (12) are all derived from equation (5).

[0135] To solve the equations (10), (11), and (12), further time discretization is still required to transform equation (10) into an algebraic equation system, so that the entire differential algebraic equation system becomes an algebraic equation system. The time partial derivative is discretized, and in equation (10):

[0136]

[0137] in, Superscript in Indicates that this is an unknown variable at the current moment, Represents the variable value at the previous moment, which is a known quantity. is the time step to be solved. The same is true in the above. The final equation can be transformed into a set of algebraic equations:

[0138] (13)

[0139] (14)

[0140] (15)

[0141] Finally, the vector is constructed based on the concentration field variables, potential field variables, and body current variables , that is, the vector corresponding to the initial value of the battery parameter, the battery state equation group is constructed according to the concentration field equation, the potential field equation and the connection equation, where the battery state equation group is:

[0142] (16)

[0143] Equation group (13) is the concentration field equation, equation group (14) is the potential field equation and equation group (14) is the connection equation.

[0144] In order to couple the potential field and concentration field equations, increase the time step length, and thereby reduce the overall time cost of battery state estimation, step S11 includes: calculating partial derivatives of each variable of the initial value of the battery parameter according to the concentration field equation, the potential field equation, and the connection equation, and determining the Jacobian matrix.

[0145] The battery state equation group includes a concentration field equation, a potential field equation and a connection equation, and the concentration field equation, the potential field equation and the connection equation are all expressed by the concentration field variable, the potential field variable and the body current variable. In lithium-ion batteries, the concentration field equation is used to describe the concentration change and distribution of lithium ions in the electrolyte. This equation is based on Fick's second law, that is, under certain conditions, the diffusion flow of a substance is proportional to the concentration gradient. In the case of a battery, this involves the diffusion process of lithium ions in electrodes and electrolytes. By solving this equation, the concentration distribution of lithium ions at different discharge stages and different battery states can be obtained, which is crucial to understanding the working mechanism of the battery. The potential field equation describes the distribution of the internal potential of the battery. In the electric field, the surface composed of points with equal potential is called an equipotential surface. In order to intuitively compare the potentials of each point in the electric field, the potential difference between adjacent equipotential surfaces is made a constant. Inside the battery, the distribution of potential directly affects the charge and discharge performance of the battery and the driving force of the electric field on lithium ions. The potential field equations are usually related to factors such as current density, electrode reaction kinetics, and battery internal resistance, and are essential for simulating the voltage response of the battery. In the battery state equations, the connecting equations act as a bridge to connect the individual equations.

[0146] For ease of understanding, the following examples are given, but this application is not limited. As an example, after determining that the initial value vector of the battery parameters is X0, it includes the initial values ​​of battery parameters such as the lithium ion concentration Cs,0 in the solid particles, the lithium ion concentration Ce,0 in the liquid electrolyte, the volume current density J0, the effective conductivity σs,0 of the solid phase, the solid phase potential φs,0, the effective conductivity σe,0 of the liquid phase, and the liquid phase potential φe,0. According to the pseudo-two-dimensional model of the battery or other equivalent circuit models, a battery state equation group F(X)=0 including the concentration field equation, the potential field equation, and the volume current equation is constructed. This set of equations describes the mathematical relationship of the battery under different states. The first-order partial derivatives of the battery state equation group F(X) are calculated at each component of the initial value X0 of the battery parameter to obtain the Jacobian matrix J. The size of the Jacobian matrix J is m×n, where m is the number of equations in the battery state equation group and n is the number of components in the battery parameter.

[0147] Step S12: constructing a first linear equation group according to the battery state equation group into which the initial values ​​of the battery parameters are substituted.

[0148] The first linear equation group is a linear equation group, one side of which is the Jacobian matrix and the iteration parameter, and the other side is the battery state equation group. The unknown quantity of each state equation in the battery state equation is the battery parameter, so the initial value of the battery parameter is substituted into the battery state equation group.

[0149] Step S13: Solving the first linear equation group based on the Jacobian matrix, and determining the iteration parameter according to the solution result.

[0150] The first linear equation group is a linear equation group which at least includes iteration parameters and the battery state equation group.

[0151] Solve the first linear equations based on the Jacobian matrix and determine the iteration parameters. First, construct the first linear equations:

[0152] Using the Jacobian matrix J and the function value F(X0) of the battery state equations at the initial value X0, construct the first linear equations:

[0153]

[0154] Among them, ΔX is the vector corresponding to the iteration parameter, which represents the correction amount from the current initial value X0 to the next iteration value.

[0155] Then, the first linear equation group is solved by using a numerical method, such as Gaussian elimination method, LU decomposition or conjugate gradient method, to obtain a vector ΔX corresponding to the iteration parameter.

[0156] For ease of understanding, the following examples are given, but are not intended to limit the present application. As an example, a first linear equation system is constructed:

[0157] (17)

[0158] in, is the determined Jacobian matrix, s is the iteration parameter, is the battery state equation set into which the initial values ​​of the battery parameters are substituted. That is, take a set of The initial value of the solution, the initial value of the battery parameters , and then calculate the Jacobian matrix under the initial value , then solve the first linear equation system to get the iteration parameters .

[0159] (18)

[0160] Then Plus Get the new solution, that is, the battery parameters at the current calculation time. When the change of is less than the tolerance, the solution of the equation is considered to converge, and we get .

[0161] Through the above implementation, based on the Jacobian matrix corresponding to the initial values ​​of the battery parameters, the correction amount of the battery parameters is determined by iteratively solving the first linear equation group, thereby gradually approaching the actual state of the battery and capturing the nonlinear impact of the battery parameter change on the system state.

[0162] In some embodiments, reference Figure 3 In this embodiment, step S11 includes:

[0163] Step S111: based on each state equation in the battery state equation group, partial derivatives are calculated for each variable in the battery parameter initial value to determine an initial Jacobian matrix.

[0164] The battery state equation group includes a concentration field equation, an electric potential field equation and a connection equation, and the concentration field equation, the electric potential field equation and the connection equation are all expressed by the concentration field variables, the electric potential field variables and the body current variables.

[0165] In order to couple the potential field and concentration field equations, increase the time step length, and thus reduce the overall time cost of battery state estimation, the battery state equation group determined based on the above embodiment is F(X)=0, where F(X) is a vector function containing m equations, and X is a vector containing n battery parameters. For each equation Fi(X) (i=1,2,…,m) in the battery state equation group, the partial derivative is calculated for each variable Xj (j=1,2,…,n) in the battery parameter initial value vector X0 to obtain the elements of the partial derivative matrix (i.e., the initial Jacobian matrix) Jinit.

[0166] For ease of understanding, the following examples are given, but are not intended to limit the present application. As an example, assume that the battery state equations include all algebraic equations, including nonlinear equations and linear equations. Determine the initial Jacobian matrix as:

[0167]

[0168] Among them, the definition The Jacobian matrix For each equation Pair Vector Each component The matrix obtained by taking partial derivatives.

[0169] Step S112: Rearrange the elements in the initial Jacobian matrix to determine the Jacobian matrix so as to reduce the bandwidth of the initial Jacobian matrix.

[0170] The elements in a matrix are the basic units that make up the matrix. They can be different values, including numbers, symbols, or expressions. The bandwidth is the maximum distance between a non-zero element in a matrix and the main diagonal. Reducing the bandwidth can improve computational efficiency and numerical stability.

[0171] In order to reduce the bandwidth of the initial Jacobian matrix, the initial values ​​of the battery parameters are rearranged based on the inverse Kushiel-McKee algorithm and the initial Jacobian matrix to determine the Jacobian matrix.

[0172] The Kushiel-McKee algorithm is used for matrix reordering, aiming to reduce the matrix bandwidth and improve the computational efficiency in the sparse matrix solution process. In this embodiment, based on the initial values ​​of the battery parameters and the actual content of the Jacobian matrix, the Kushiel-McKee algorithm is improved to optimize the matrix structure by reordering.

[0173] To increase the bandwidth of the initial Jacobian matrix, the matrix's rows and columns are swapped, effectively changing the layout of the non-zero elements, which may reduce the matrix's bandwidth. Using heuristics such as the minimum degree algorithm or approximation algorithms to automate the process of permuting rows and columns, the algorithm attempts to optimize both the fill factor and the overall matrix bandwidth.

[0174] In order to improve the bandwidth of the initial Jacobian matrix, for the large number of zero elements in the initial Jacobian matrix, compressed storage technology can be used to store only non-zero elements, thereby reducing storage requirements and indirectly reducing computational complexity. Advanced matrix decomposition methods such as LU decomposition are used to reduce padding in matrix operations, which helps maintain the sparsity of the matrix and reduce bandwidth.

[0175] According to the elements in the battery parameter initial value vector X0, select a starting point. For example, select the parameter with the smallest degree (that is, the least correlation with other parameters) as the starting point. Starting from the starting point, apply the reverse Kushiel-McGee algorithm to reorder the battery parameter initial value vector X0. At the same time, according to the reordering result of X0, rearrange the rows and columns of the initial Jacobian matrix Jinit accordingly. After rearrangement by the reverse Kushiel-McGee algorithm, a new battery parameter vector XRCM and a rearranged Jacobian matrix JRCM are obtained. Take JRCM as the final Jacobian matrix J.

[0176] Through the above optional implementation, based on the battery state equations and the initial values ​​of the battery parameters, the Jacobian matrix for iterative parameter solution is determined by calculating partial derivatives and rearranging the initial Jacobian matrix. This method can optimize the structure of the sparse matrix and improve the efficiency of subsequent calculations.

[0177] In some embodiments, step S112 includes:

[0178] Step S1121: Rearrange the initial values ​​of the battery parameters based on the initial Jacobian matrix.

[0179] In order to reduce the bandwidth of the initial Jacobian matrix, the initial Jacobian matrix is ​​analyzed and the distribution of non-zero elements in the initial Jacobian matrix is ​​observed. The size of the bandwidth depends on the distribution of these non-zero elements. According to the distribution of non-zero elements in the Jacobian matrix, a rearrangement strategy is selected. The rearrangement strategy includes minimum degree sorting, approximate minimum degree sorting, or any of the sorting algorithms in other graph theory, which can reduce the bandwidth of the matrix. The initial values ​​of the battery parameters are reordered using the selected rearrangement strategy, and the order of the elements in the parameter vector is adjusted according to the strategy.

[0180] Step S1122: adjusting the order of elements in the initial Jacobian matrix based on the rearranged initial values ​​of the battery parameters to determine the Jacobian matrix.

[0181] element order is the order of the elements in the Jacobian matrix.

[0182] After the initial values ​​of the battery parameters are rearranged, the Jacobian matrix is ​​updated accordingly. Since the rows and columns of the Jacobian matrix are directly related to the battery parameters, changing the order of the parameters will affect the structure of the matrix. According to the rearranged order of the battery parameters, the rows and columns of the Jacobian matrix are rearranged.

[0183] By rearranging the vectors corresponding to the initial values ​​of the battery parameters, the structure of the Jacobian matrix is ​​made more compact, the related variables are more concentrated, unnecessary calculation and storage overhead are reduced, and the speed and stability of the numerical solution are improved.

[0184] In some embodiments, step S1121 includes:

[0185] Step S11211: traverse the battery parameter initial values ​​to determine the target battery variable currently selected in the battery parameter initial values.

[0186] The battery parameter initial value vector is a vector containing all the parameters of the battery model, and each parameter is an element of the vector. The target battery variable is the parameter currently selected in the process of traversing the battery parameter initial value vector, which is called the target battery variable.

[0187] In one example, the battery parameter initial values ​​are traversed, and the battery parameter initial value vector is traversed. This vector contains all the parameters in the battery model, such as voltage, current, temperature, etc. The purpose of the traversal is to find the corresponding row and column of each parameter in the Jacobian matrix. During the traversal process, the currently selected battery parameter is used as the target battery variable. This variable will be used to construct the adjacent variable set in the subsequent steps.

[0188] Step S11212: construct a variable set based on the adjacent variables of the target battery variable in the initial Jacobian matrix, wherein the adjacent variables are all other non-zero variables in the corresponding rows of the target battery variable in the initial Jacobian matrix.

[0189] Adjacent variables, in the initial Jacobian matrix, the parameters corresponding to all non-zero elements in the row where the target battery variable is located are called adjacent variables. Variable set, a set consisting of all adjacent variables of the target battery variable.

[0190] In one example, look at the row corresponding to the target battery variable in the initial Jacobian matrix. In this row, all non-zero elements represent other battery parameters that are directly related to the target battery variable. These parameters are called adjacent variables. Collect these adjacent variables and build a variable set.

[0191] Step S11213: traverse the target battery variables in the variable set and add them to the target array, and rearrange the initial values ​​of the battery parameters according to the order of the variables in the target array.

[0192] The target array is a temporary array used to store the updated variable order based on the adjacent variable relationship.

[0193] In one example, we traverse the variable set constructed in the previous step. During the traversal process, we will check the relationship between each adjacent variable and the target battery variable and update the target array accordingly. Rearrange the initial values ​​of the battery parameters and rearrange the initial value vector of the battery parameters according to the order of the variables in the target array. This means that the battery parameters will be rearranged in a new order, which will affect the structure of the Jacobian matrix.

[0194] Through the above scheme, the initial values ​​of battery parameters are systematically rearranged to optimize the bandwidth of the initial Jacobian matrix and improve the efficiency and stability of numerical calculations.

[0195] In some embodiments, step S1121 includes:

[0196] Step S1: Get the initialization queue and the initialization array.

[0197] Initialize the queue to an empty queue and the array to an empty array.

[0198] Step S2: Determine the target battery variable in the battery parameter initial value that is not in the initialization array, and add the target battery variable to the initialization array to determine the target array.

[0199] The target array is the array obtained by adding the target battery variable to the initialization array.

[0200] In one example, an empty initialization queue and an empty initialization array are obtained. A target battery variable that is not in the initialization array is determined from the initial values ​​of the battery parameters. The target battery variable is added to the initialization array to form a target array.

[0201] In another example, the degree of a variable is defined as the number of non-diagonal elements in the corresponding row of the variable in the Jacobian matrix. An empty initialization queue and an empty initialization array are obtained. The first battery variable that is not in the initialization array is determined from the initial values ​​of the battery parameters, and then the variable with the smallest degree among the first battery variables is determined as the target battery variable, and finally the target battery variable is added to the initialization array to form a target array.

[0202] Step S3: Add the adjacent variables of the target battery variable to the initialization queue according to a preset order to determine the target queue.

[0203] The adjacent variables are all other non-zero variables in the corresponding row of the target battery variable in the Jacobian matrix. The target queue is the queue after the critical variables of the target battery variable are added to the initialization queue.

[0204] In order to improve the efficiency of rearrangement of the initial values ​​of the battery parameters, find all other non-zero variables, i.e., adjacent variables, in the corresponding rows of the target battery variable in the Jacobian matrix. Add these adjacent variables to the initialization queue in a preset order, such as from small to large, to form a target queue. After determining the adjacent variables, determine the degree of each of the adjacent variables, and add the adjacent variables to the initialization queue in ascending order according to the numerical value of the degree to construct the target queue.

[0205] Step S4: extracting a target variable based on the target queue;

[0206] In this embodiment, a variable is extracted from the target queue as a target variable. After the extraction, the target variable does not exist in the target queue.

[0207] Step S5: If the target variable exists in the target array, the step of extracting the target variable based on the target array is executed in a loop until the target array is empty.

[0208] In this embodiment, if the target variable already exists in the target array, the next target variable is continuously extracted from the target queue until the target queue is empty.

[0209] In order to improve the efficiency of rearranging the initial values ​​of the battery parameters, after extracting the target variable based on the target queue, if the target variable is not in the target array, the target array is updated based on the target variable, and the target queue is updated based on the adjacent variables of the target variable. The step of extracting the target variable based on the target queue is performed based on the updated target queue.

[0210] If the target variable is not in the target array, add it to the end of the target array and update the target queue based on the adjacent variables of the target variable, that is, find all other non-zero variables in the corresponding row of the target variable in the Jacobian matrix, that is, adjacent variables. Add these adjacent variables to the end of the target queue in a preset order, such as the numerical value of the degree, from small to large.

[0211] If the adjacent variable of the target variable does not exist in the target queue, it is added to the target queue; if the adjacent variable of the target variable exists in the target queue, the adjacent variable is skipped so that there are no duplicate variables in the target queue, thereby improving the rearrangement efficiency.

[0212] Based on the updated target queue, step S4 is repeated.

[0213] For ease of understanding, the following examples are given, but they are not intended to limit this application. As an example, create an empty initialization queue Q and an empty initialization array A. Select a starting point (such as the variable with the smallest degree) from the initial values ​​of the battery parameters, add it to array A, and find all non-zero variables in the corresponding rows in the Jacobian matrix, and add these variables to queue Q. Loop processing: When queue Q is not empty, perform the following steps:

[0214] a. Extract a target variable v from the queue Q.

[0215] b. Check if variable v is already in array A:

[0216] If v is in A, then continue to extract the next variable from Q.

[0217] If v is not in A, add it to the end of array A and find all non-zero variables in the corresponding row of v's Jacobian matrix, i.e., the adjacent variables of v. Add these adjacent variables to the end of the queue Q in a preset order, but only if they are not already in Q.

[0218] Step S6: If all the initial values ​​of the battery parameters exist in the target array, the initial values ​​of the battery parameters are rearranged based on the order of the variables in the target array to determine the Jacobian matrix.

[0219] In one example, if the target queue is empty, it is determined whether all the initial values ​​of the battery parameters exist in the target array. When all the initial values ​​of the battery parameters exist in the target array, the initial values ​​of the battery parameters are rearranged according to the order of the variables in the target array, and then the corresponding Jacobian matrix is ​​determined according to the rearranged initial values ​​of the battery parameters.

[0220] In order to improve the efficiency of rearrangement of the initial values ​​of the battery parameters, it is further determined whether all the vectors corresponding to the initial values ​​of the battery parameters exist in the target array. If the initial values ​​of the battery parameters are not in the target array, the step of determining the target battery variables that are not in the initialization array in the initial values ​​of the battery parameters is performed, and the target battery variables are added to the initialization array to determine the target array.

[0221] In the technical solution of the embodiment of the present application, by judging whether the target queue is empty, when the target queue is empty, repeatedly executing the step of determining the target battery variables that are not in the initialization array in the initial value of the battery parameters, and adding the target battery variables to the initialization array, the step of determining the target array is performed, so that all variables corresponding to the battery parameters are added to the target data, thereby ensuring the integrity of the data and thereby improving the accuracy of battery status estimation.

[0222] For ease of understanding, the following examples are given, but are not intended to limit the present application. As an example, the variables in the battery parameter initial values ​​are arranged in reverse order according to the variable order in the target array to obtain the rearranged battery parameter initial values, and then the Jacobian matrix corresponding to the rearranged battery parameter initial values ​​is determined.

[0223] In this embodiment, if there are variables in the initial values ​​of the battery parameters that have not been added to the target array, based on the currently empty target queue and the updated target array, the steps of determining the target battery variables in the initial values ​​of the battery parameters that are not in the initialization array are re-executed, and the target battery variables are added to the initialization array to determine the target array.

[0224] For ease of understanding, the following examples are given, but the present application is not limited thereto. As an example, assume that in a simulation estimation scenario of a lithium-ion battery, a set of nonlinear equations including parameters such as the internal temperature of the battery, electrolyte concentration, and electrode potential need to be solved. These equations constitute the state equations of the battery, and the battery parameters, such as temperature, concentration, and potential, constitute the vector corresponding to the initial values ​​of the battery parameters. In order to improve computational efficiency, the reverse Kushiel-McGee algorithm is used to rearrange the initial values ​​of the battery parameters and the initial Jacobian matrix. Refer to Figure 4 , Figure 4 This is a comparison diagram of the initial Jacobian matrix and the Jacobian matrix. It is obvious that the bandwidth of the Jacobian matrix after rearrangement is greatly reduced.

[0225] In the above manner, all battery parameters are systematically traversed, and the parameters and matrices are rearranged according to the correlation between them, thereby reducing the loan of the Jacobian matrix and improving the estimation efficiency of the battery state.

[0226] In some embodiments, the step S12 includes:

[0227] Step S121: decomposing the Jacobian matrix into an upper triangular matrix and a lower triangular matrix.

[0228] An upper triangular matrix is ​​a special square matrix whose elements below the main diagonal are all zero, while the elements on and above the main diagonal can be arbitrary. In contrast to the upper triangular matrix, the lower triangular matrix is ​​also a special square matrix, but its elements above the main diagonal are all zero, while the elements on and below the main diagonal can be arbitrary.

[0229] LU decomposition is based on representing a matrix as the product of a lower triangular matrix L and an upper triangular matrix U, that is, A=LU. This decomposition uses the principle of Gaussian elimination to transform the matrix into the product of two triangular matrices through row transformation. Compared with directly operating on the original matrix, it is more efficient to calculate the upper and lower triangular matrices because such matrices have fewer non-zero elements and can quickly solve certain types of linear equations, such as through forward and backward substitution methods.

[0230] In an example, let the Jacobian matrix be J, and its dimension is n×n. Initialize the lower triangular matrix L as the identity matrix, and the upper triangular matrix U as the 0 matrix. The identity matrix keeps the main diagonal elements of the original matrix as 1, and the rest of the positions are 0; while the 0 matrix has all elements as 0. For each row i from 1 to n (row-dominated), select the i-th element of the row as the main element. Then use the elimination operation in the Gaussian elimination method to eliminate the lower elements of the current column by subtracting multiples of the upper row from the lower row. Then update L and U. In each elimination operation, the multiples of the subtracted rows actually constitute the elements in the lower triangular matrix L, and the rows after elimination directly constitute the upper triangular matrix U. When all rows are processed, the matrix J is successfully decomposed into the product of L and U, that is, J=LU

[0231] Step S122: Solve the first linear equation group based on the upper triangular matrix and the lower triangular matrix to determine the iteration parameters.

[0232] In one example, a first linear equation group is constructed based on the Jacobian matrix, the iteration parameters, and the battery state equation group, and then the Jacobian matrix is ​​decomposed into an upper triangular matrix and a lower triangular matrix. Then, the first linear equation group is:

[0233] (19)

[0234] Among them, the rearranged Jacobian matrix The matrix is ​​decomposed into an upper triangular matrix L and a lower triangular matrix U. Both L and U are band matrices with a bandwidth half the matrix bandwidth of the rearranged Jacobian matrix.

[0235] Then, the first linear equation group is solved according to the upper triangular matrix and the lower triangular matrix to determine the iteration parameters.

[0236] Since the Jacobian matrix is ​​decomposed into an upper triangular matrix and a lower triangular matrix; the first linear equations are solved based on the upper triangular matrix and the lower triangular matrix to determine the iteration parameter. The time complexity of solving the band matrix with a fixed bandwidth is O(n), while in the related art, for example, the time complexity of solving the Gaussian elimination method is O(n 3 ), that is, this embodiment greatly reduces the time consumption of battery status estimation.

[0237] In some embodiments, after step S20, the following steps are included:

[0238] Step S30: Determine a parameter change based on the battery parameter and the historical battery parameter.

[0239] The parameter change is a vector representing the difference between the battery parameter at the current calculation moment and the historical battery parameter at the previous calculation moment.

[0240] In order to accurately describe the change of battery parameters, in this embodiment, based on each variable in the battery parameters and historical battery parameters, the difference is calculated according to the parameter value corresponding to the variable, and the parameter change amount is determined according to the vector corresponding to the obtained difference sequence.

[0241] In order to increase the speed of determining the change in battery parameters and reduce the time cost of battery status estimation, in this embodiment, the characterization type of the battery status is obtained, and the characterization variables associated with the characterization type are determined, wherein the association between the characterization type and the characterization variable is predetermined, that is, the characterization type is the specific manifestation of the battery status that the user is concerned about, including but not limited to voltage, remaining power, battery temperature, etc. For example, the equilibrium potential of the battery is associated with the lithium ion concentration, potential, and body current density. After determining the characterization variables, the parameter values ​​corresponding to each characterization variable in the battery parameters and historical battery parameters are subtracted, and the parameter change is determined according to the vector corresponding to the obtained difference sequence.

[0242] Step S40: If the parameter change is less than a preset tolerance, output the battery status corresponding to the current calculation time based on the battery parameter.

[0243] The preset tolerance is a set value. When the parameter change is less than the preset tolerance, the current iteration is considered to have converged.

[0244] Step S50: Otherwise, based on the battery parameter as the updated initial value of the battery parameter, the step of determining the iteration parameter based on the Jacobian matrix corresponding to the initial value of the battery parameter is performed.

[0245] In one example, the battery status characterization type that the user is concerned about is determined, such as voltage, remaining power, battery temperature, etc. According to the predetermined association relationship, the characterization variables associated with these characterization types are found. For each characterization variable, the difference between the parameter value at the current calculation moment and the parameter value at the previous calculation moment is calculated. The difference values ​​of all characterization variables are combined into a vector, which is the parameter change. Then, the modulus or norm of the parameter change is calculated. The preset tolerance associated with the battery model is obtained. If the modulus is less than the preset tolerance, it is considered that the current iteration has converged and the battery parameters have stabilized. The battery status at the current calculation moment is output, including voltage, remaining power, battery temperature, etc. If the modulus of the parameter change is not less than the preset tolerance, it is considered that the battery parameters are not yet stable. The current battery parameters are used as the new battery parameter initial values. Return to the step of "determining the iteration parameters based on the Jacobian matrix corresponding to the battery parameter initial values" to perform the next round of iterative calculations.

[0246] For ease of understanding, the following examples are given, but this application is not limited. As an example, assume that a lithium-ion battery is discharged and simulated to simulate the change of its battery state under 1C rate discharge, and pay attention to the changes in battery voltage and temperature. Get the initial value of the battery parameter setting: voltage is 4.2V, current is -1C (indicating discharge), and temperature is 25°C. Set historical battery parameters: voltage is 4.2V, current is 0A, and temperature is 25°C (initial state). Set the preset tolerances to 0.01V and 1°C (corresponding to the tolerances of voltage and temperature, respectively). Calculate the Jacobian matrix using the battery model and initial values. Perform iterative calculations to update the battery parameters. Pay attention to the changes in battery voltage and temperature, and calculate the difference between the current voltage and the historical voltage, as well as the difference between the current temperature and the historical temperature. Combine these two differences into a vector as the parameter change. Calculate the modulus of the parameter change, considering the weights of voltage and temperature at this time. If the modulus is less than the preset tolerance, i.e. 0.01V and 1°C, output the battery state at the current calculation moment. For example, the output voltage is 3.9V and the temperature is 35℃, which means that the battery voltage drops and the temperature rises during the discharge process. If the modulus of the parameter change is not less than the preset tolerance, the current battery parameter is used as the new initial value for the next round of iteration. Repeat the above steps until the parameter change is less than the preset tolerance or the set number of iterations is reached.

[0247] To help understand the technical concept or technical principle of the battery parameter estimation method after combining this embodiment with the above embodiment, please refer to Figure 5 , Figure 5A brief flowchart of a battery parameter estimation method is provided, as follows:

[0248] S501: Start

[0249] S502: Obtaining the historical battery parameters at the last moment Composition vector , as the initial value of the solution process at that moment.

[0250] S503: Obtaining the initial value of the battery parameter obtained in the previous iteration , and calculate the battery state equations The initial Jacobian matrix .

[0251] S504: Rearrange the initial values ​​of battery parameters using the reverse Kushiel-McKee algorithm The order and initial Jacobian matrix .

[0252] S505: Use the band matrix solution method to solve the rearranged Jacobian matrix:

[0253] ,

[0254] Determine iteration parameters .

[0255] S506: Calculate the battery parameters at the current calculation time:

[0256] .

[0257] S507: If If the corresponding parameter variation is less than the tolerance, it is considered that the solution has converged to a solution, otherwise the process returns to S503.

[0258] S508: End.

[0259] Since the parameter change is determined based on the battery parameters and the historical battery parameters; if the parameter change is less than the preset tolerance, the battery state corresponding to the current calculation moment is output based on the battery parameters; otherwise, based on the battery parameters as the updated initial values ​​of the battery parameters, the step of determining the iteration parameters based on the Jacobian matrix corresponding to the initial values ​​of the battery parameters is executed, so that the battery state can be accurately estimated through a smaller number of iterations within a single time step length.

[0260] In some embodiments, before step S10, it includes: if the current calculation time is the initial calculation time, obtaining preset battery parameters associated with the battery model as the initial values ​​of the battery parameters.

[0261] In one example, if the current calculation time is the initial calculation time, the battery model is obtained, the real operating data associated with the battery model is obtained, and the average of the real operating data based on a preset number is used as the initial value of the battery parameter to improve the accuracy of the battery state estimation.

[0262] In another example, the battery parameter estimation method further includes: after determining the battery parameters at the current calculation moment, obtaining the characterization type of the battery status input by the user, determining the target battery parameters based on the characterization type, and determining the characterization value corresponding to the characterization type as the battery status based on the mapping relationship corresponding to the characterization type and the target battery parameters.

[0263] For ease of understanding, the following examples are given, but the present application is not limited. As an example, assume that in a scenario where a power control strategy is determined based on obtaining the battery status, the battery is installed in a car, mobile terminal, energy storage system or ship and other equipment to power the equipment. It is necessary to adjust the output parameters of the battery based on the battery status. Therefore, if the calculation moment is triggered, determine whether the current calculation moment is the initial calculation moment; if it is the initial calculation moment, obtain the battery model and the characterization type of the battery status; determine the target real operating data based on the characterization type and the battery model, and use the mean of the target real operating data as the initial value of the battery parameter; determine the iteration parameters based on the Jacobian matrix corresponding to the initial value of the battery parameter; determine the battery parameters at the current calculation moment based on the iteration parameters and the historical battery parameters; determine the target battery parameters according to the characterization type; determine the battery status according to the mapping relationship between the target battery parameters and the characterization type.

[0264] Reference Figure 6 In a constant current charging condition simulation: Matlab is used to implement the solution methods of the original solution and this solution respectively. When the grid division is consistent, the simulation results are compared as shown in the figure below. Figure 6 As shown. In this scheme, the time step length dt is selected as 5s, which can successfully calculate the discharge cut-off time. However, the time step length dt in the related art is 0.5s, but it still cannot converge at 1500s, resulting in calculation crash. The time statistics of the two schemes are shown in Figure 7 . Although this solution performs iterative calculations in each time step, thanks to the fully coupled solution, the average number of iterations does not exceed 3 times, and a larger time step length (5s) can be used, with a total number of iterations of only 837 times. In the related technology, it can be considered that there is no iteration in each time step, so the number of time steps is the total number of iterations, but the related technology has a high requirement for the time step length (0.5s), so the total number of iterations reaches 3030 times. Therefore, the total calculation time is far less than this solution. Even at a time step length of 0.5s, the related technology still cannot converge at the end of the discharge, and the step length needs to be further reduced.

[0265] In this embodiment, when solving the equations obtained by the full coupling method, the variables are rearranged so that the Jacobian matrix becomes a banded matrix. Then the banded matrix solution method is used to solve the problem, which greatly reduces the solution time, thereby enabling the rapid response to the synchronous output of the battery status in each time step. The rapid update of the battery status further provides a data basis for battery use management at the application layer, thereby improving the utilization efficiency of the battery.

[0266] In a second aspect, an embodiment of the present application provides a battery parameter estimation device, the battery parameter estimation device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the battery parameter estimation method in the above-mentioned embodiment one.

[0267] Reference below Figure 8 , which shows a schematic diagram of the structure of a battery parameter estimation device suitable for implementing an embodiment of the present application. The battery parameter estimation device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The battery parameter estimation device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0268] like Figure 8As shown, the battery parameter estimation device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the battery parameter estimation device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the battery parameter estimation device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a battery parameter estimation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0269] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0270] The battery parameter estimation device provided in the present application adopts the battery parameter estimation method in the above embodiment, which can solve the technical problem that the battery state estimation takes a long time under the working condition where the battery parameter nonlinearity is strong. Compared with the prior art, the beneficial effects of the battery parameter estimation device provided in the present application are the same as the beneficial effects of the battery parameter estimation device provided in the above embodiment, and the other technical features in the battery parameter estimation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0271] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0272] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0273] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, the computer-readable program instructions being used to execute the battery parameter estimation method in the above-mentioned embodiment.

[0274] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0275] The computer-readable storage medium may be included in the battery parameter estimation device; or may exist independently without being assembled into the battery parameter estimation device.

[0276] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the battery parameter estimation device, the battery parameter estimation device: determines an iteration parameter according to a Jacobian matrix corresponding to an initial value of the battery parameter, wherein the initial value of the battery parameter is a historical battery parameter at a previous calculation moment;

[0277] Based on the iteration parameters and the historical battery parameters, the battery parameters at the current calculation moment are determined, wherein the battery parameters include at least one of a concentration field variable, an electric potential field variable, and a body current variable.

[0278] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0279] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0280] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0281] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned battery parameter estimation method, and can solve the technical problem that the battery state estimation takes a long time under the condition of strong nonlinear battery parameter. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the battery parameter estimation method provided in the above-mentioned embodiment, and will not be repeated here.

[0282] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements the steps of the battery parameter estimation method as described above when executed by a processor.

[0283] The computer program product provided by the present application can solve the technical problem that the battery state estimation takes a long time under the condition of strong nonlinearity of battery parameters. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as the beneficial effects of the battery parameter estimation method provided by the above embodiment, which will not be repeated here.

[0284] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A method for estimating battery parameters, characterized in that: The battery parameter estimation method includes: Determine the Jacobian matrix based on the initial values ​​of the battery parameters and the battery state equation group, wherein the battery state equation group describes the battery state from the concentration field, the potential field and the connection relationship, the connection relationship is the relationship between the concentration field and the potential field, and the initial values ​​of the battery parameters are the historical battery parameters at the last calculation time; Constructing a first linear equation group according to the battery state equation group into which the initial values ​​of the battery parameters are substituted; Solving the first linear equations based on the Jacobian matrix, and determining an iteration parameter according to the solution result; Based on the iteration parameters and the historical battery parameters, the battery parameters at the current calculation moment are determined, wherein the battery parameters include at least one of a concentration field variable, an electric potential field variable, and a body current variable.

2. The method for estimating battery parameters according to claim 1, wherein: The battery state equation group includes a concentration field equation, an electric potential field equation and a connection equation, wherein the concentration field equation, the electric potential field equation and the connection equation are all expressed by the concentration field variables, the electric potential field variables and the body current variables. The step of determining the Jacobian matrix based on the initial values ​​of the battery parameters and the battery state equation group includes: The Jacobian matrix is ​​determined by respectively taking partial derivatives of the variables of the initial values ​​of the battery parameters according to the concentration field equation, the potential field equation and the connection equation.

3. The method for estimating battery parameters according to claim 1, wherein: The step of determining the Jacobian matrix based on the initial values ​​of the battery parameters and the battery state equation group includes: Based on each state equation in the battery state equation group, partial derivatives are calculated for each variable in the battery parameter initial value to determine an initial Jacobian matrix; The Jacobian matrix is ​​determined by rearranging elements in the initial Jacobian matrix to reduce a bandwidth of the initial Jacobian matrix.

4. The method for estimating battery parameters according to claim 3, characterized in that: The step of rearranging the elements in the initial Jacobian matrix to determine the Jacobian matrix comprises: Rearrange the initial values ​​of the battery parameters based on the initial Jacobian matrix; The order of elements in the initial Jacobian matrix is ​​adjusted based on the rearranged initial values ​​of the battery parameters to determine the Jacobian matrix.

5. The method for estimating battery parameters according to claim 4, characterized in that: The step of rearranging the initial values ​​of the battery parameters based on the initial Jacobian matrix comprises: Traversing the battery parameter initial values, and determining the target battery variable currently selected in the battery parameter initial values; Constructing a variable set based on the adjacent variables of the target battery variable in the initial Jacobian matrix, wherein the adjacent variables are all other non-zero variables in the corresponding row of the target battery variable in the initial Jacobian matrix; The target battery variables in the variable set are traversed and added to a target array, and the initial values ​​of the battery parameters are rearranged according to the order of the variables in the target array.

6. The method for estimating battery parameters according to claim 1, wherein: The step of solving the first linear equations based on the Jacobian matrix and determining an iteration parameter according to the solution result comprises: Decomposing the Jacobian matrix into an upper triangular matrix and a lower triangular matrix; The first linear equation group is solved based on the upper triangular matrix and the lower triangular matrix to determine the iteration parameter.

7. The method for estimating battery parameters according to claim 1, characterized in that: After the step of determining the battery parameters at the current calculation moment based on the iteration parameters and the historical battery parameters, the method further comprises: Determining a parameter change amount based on the battery parameter and the historical battery parameter; If the parameter change is less than a preset tolerance, outputting the battery status corresponding to the current calculation time based on the battery parameter; Otherwise, based on the battery parameters as the updated initial values ​​of the battery parameters, the step of determining the Jacobian matrix based on the initial values ​​of the battery parameters and the battery state equation group is performed.

8. A battery parameter estimation device, characterized in that: The battery parameter estimation device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the battery parameter estimation method according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the battery parameter estimation method according to any one of claims 1 to 7 are implemented.

Images