Method and device for determining SOH of lithium battery, storage medium and electronic equipment

Abstract

The invention discloses a lithium battery SOH determination method and device, a storage medium and electronic equipment. The method relates to the technical field of batteries, and comprises the steps that target battery model parameters of a target lithium battery are determined based on charge and discharge data of the target lithium battery through a least recursive square method, and the charge and discharge data at least comprise a voltage value and a current value of the target lithium battery; the target battery model parameters at least comprise a polarization resistance value and a polarization capacitance value corresponding to the target lithium battery; sOC estimation is carried out based on the target battery model parameters through a Kalman filtering algorithm, and a target SOC is obtained; and determining the SOH of the target lithium battery according to the target SOC. Through the method and the device, the problem that the accuracy of determining the SOH of the lithium battery is relatively low due to the fact that the SOH of the lithium battery is estimated through a coulomb counting method in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method and device for determining SOH of a lithium battery, a storage medium, and an electronic device. Background Art

[0002] With the rapid development of new energy vehicles, driverless cars and artificial intelligence technologies, the market demand for power batteries that provide energy platforms for these technologies is increasing, but at the same time, the control of battery use is becoming more and more sophisticated. The purpose of control is, on the one hand, to use the battery correctly and reasonably, avoid damage to the battery, and thus reduce costs; on the other hand, to reasonably manage the battery, control its energy distribution to achieve high efficiency, and meet the user's requirements for vehicle comfort and safety. However, fine control of battery use is based on real-time estimation of the instantaneous state of the battery; generally including battery state of charge, state of health and power state. In the prior art, coulomb counting can be used to estimate the SOH of the battery. Coulomb counting relies on current monitoring and calculates the net charge transferred to estimate the SOC. However, the cumulative error caused by current integration in coulomb counting seriously affects the accuracy of estimating the SOH of lithium batteries. Summary of the invention

[0003] The main purpose of the present application is to provide a method and device for determining the SOH of a lithium battery, a storage medium and an electronic device, so as to solve the problem in the related art of estimating the SOH of a lithium battery by the Coulomb counting method, resulting in low accuracy in determining the SOH of a lithium battery.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for determining the SOH of a lithium battery is provided. The method comprises: determining the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive square method, wherein the charge and discharge data at least include the voltage value and current value of the target lithium battery, and the target battery model parameters at least include the polarization resistance value and polarization capacitance value corresponding to the target lithium battery; performing SOC estimation based on the target battery model parameters by the Kalman filter algorithm to obtain the target SOC; and determining the SOH of the target lithium battery according to the target SOC.

[0005] Furthermore, determining the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive squares method includes: determining a second-order RC equivalent circuit model based on the target lithium battery; determining the target function of the target lithium battery based on the second-order RC equivalent circuit model, wherein the target function is a relationship function between the battery model parameters, the voltage value and the current value; and iteratively solving the battery model parameters in the target function by the least recursive squares method to obtain the target battery model parameters.

[0006] Furthermore, based on the second-order RC equivalent circuit model, determining the target function of the target lithium battery includes: determining the initial function between the battery model parameters, the voltage value and the current value in the time domain based on the second-order RC equivalent circuit model; transforming the initial function to obtain the initial function in the complex frequency domain; arranging the initial function in the complex frequency domain to obtain the arranged initial function; transforming the arranged initial function to obtain the target function.

[0007] Furthermore, the battery model parameters in the objective function are iteratively solved by the least recursive squares method to obtain the target battery model parameters, including: obtaining a first battery model parameter, and calculating based on the first battery model parameter and the first charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain a first voltage prediction value; obtaining a first real voltage value at the current moment in the charge and discharge data, and performing error calculation based on the first real voltage value and the first voltage prediction value to obtain a first error; calculating based on the first error and a preset first covariance matrix to obtain a first gain; updating the first battery model parameter based on the first gain and the first error to obtain the target battery model parameter.

[0008] Further, updating the first battery model parameters according to the first gain and the first error to obtain the target battery model parameters includes: updating the first battery model parameters according to the first gain and the first error to obtain second battery model parameters; calculating according to the first battery model parameters and the second charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain a second voltage prediction value; obtaining the second true voltage value at the current moment in the charge and discharge data, and performing error calculation based on the second true voltage value and the second voltage prediction value to obtain a second error; calculating according to the preset first covariance matrix, the preset forgetting factor and the second charge and discharge data to obtain a second covariance matrix, and calculating according to the second error and the second covariance matrix to obtain a second gain; updating the second battery model parameters according to the second gain and the second error to obtain the target battery model parameters.

[0009] Furthermore, SOC estimation is performed based on the target battery model parameters through the Kalman filter algorithm to obtain the target SOC, including: constructing the system state equation and the system output observation equation in the Kalman filter algorithm according to the target battery model parameters and the SOC function of the target lithium battery constructed based on the ampere-hour integration method; determining the system state transfer matrix, system input matrix, system output observation matrix and system feedforward matrix in the Kalman filter algorithm according to the system state equation and the system output observation equation; SOC estimation is performed based on the system state transfer matrix, the system input matrix, the system output observation matrix and the system feedforward matrix to obtain the target SOC.

[0010] Further, determining the SOH of the target lithium battery according to the target SOC includes: acquiring the current capacity and the nominal capacity of the target lithium battery; and determining the SOH of the target lithium battery according to the current capacity, the nominal capacity and the target SOC.

[0011] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for determining the SOH of a lithium battery is provided. The device comprises: a first determination unit, which is used to determine the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive square method, wherein the charge and discharge data at least include the voltage value and current value of the target lithium battery, and the target battery model parameters at least include the polarization resistance value and polarization capacitance value corresponding to the target lithium battery; an estimation unit, which is used to perform SOC estimation based on the target battery model parameters by the Kalman filter algorithm to obtain the target SOC; and a second determination unit, which is used to determine the SOH of the target lithium battery according to the target SOC.

[0012] Furthermore, the first determination unit includes: a first determination subunit, used to determine a second-order RC equivalent circuit model based on the target lithium battery; a second determination subunit, used to determine the target function of the target lithium battery based on the second-order RC equivalent circuit model, wherein the target function is a relationship function between the battery model parameters, the voltage value and the current value; and a solution subunit, used to iteratively solve the battery model parameters in the target function through the least recursive squares method to obtain the target battery model parameters.

[0013] Furthermore, the second determination subunit includes: a first determination module, used to determine the initial function between the battery model parameters, the voltage value and the current value in the time domain based on the second-order RC equivalent circuit model; a first transformation module, used to transform the initial function to obtain the initial function in the complex frequency domain; a conversion module, used to common denominator the initial function in the complex frequency domain to obtain the arranged initial function; a second transformation module, used to transform the arranged initial function to obtain the target function.

[0014] Furthermore, the solution subunit includes: a first acquisition module, used to acquire a first battery model parameter, and to perform calculations based on the first battery model parameter and the first charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain a first voltage prediction value; a second acquisition module, used to acquire a first real voltage value at the current moment in the charge and discharge data, and to perform error calculations based on the first real voltage value and the first voltage prediction value to obtain a first error; a calculation module, used to perform calculations based on the first error and a preset first covariance matrix to obtain a first gain; an update module, used to update the first battery model parameter based on the first gain and the first error to obtain the target battery model parameter.

[0015] Furthermore, the update module includes: a first update submodule, used to update the first battery model parameter according to the first gain and the first error to obtain a second battery model parameter; a first calculation submodule, used to calculate according to the first battery model parameter and the second charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain a second voltage prediction value; an acquisition submodule, used to obtain the second real voltage value at the current moment in the charge and discharge data, and perform error calculation based on the second real voltage value and the second voltage prediction value to obtain a second error; a second calculation submodule, used to calculate according to the preset first covariance matrix, the preset forgetting factor and the second charge and discharge data to obtain a second covariance matrix, and calculate according to the second error and the second covariance matrix to obtain a second gain; a second update submodule, used to update the second battery model parameter according to the second gain and the second error to obtain the target battery model parameter.

[0016] Furthermore, the estimation unit includes: a construction subunit, which is used to construct the system state equation and the system output observation equation in the Kalman filter algorithm according to the target battery model parameters and the SOC function of the target lithium battery constructed based on the ampere-hour integration method; a third determination subunit, which is used to determine the system state transfer matrix, system input matrix, system output observation matrix and system feedforward matrix in the Kalman filter algorithm according to the system state equation and the system output observation equation; an estimation subunit, which is used to perform SOC estimation based on the system state transfer matrix, the system input matrix, the system output observation matrix and the system feedforward matrix to obtain the target SOC.

[0017] Furthermore, the second determination unit includes: an acquisition subunit, used to acquire the current capacity and nominal capacity of the target lithium battery; and a fourth determination subunit, used to determine the SOH of the target lithium battery based on the current capacity, the nominal capacity and the target SOC.

[0018] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned methods for determining the SOH of a lithium battery.

[0019] In order to achieve the above-mentioned purpose, according to another aspect of the present application, an electronic device is also provided, the electronic device includes one or more processors and a memory, and the memory is used to store the method for determining the SOH of a lithium battery described in any one of the above-mentioned methods implemented by one or more processors.

[0020] Through this application, the following steps are adopted: determine the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive square method, wherein the charge and discharge data at least include the voltage value and current value of the target lithium battery, and the target battery model parameters at least include the polarization resistance value and polarization capacitance value corresponding to the target lithium battery; perform SOC estimation based on the target battery model parameters by the Kalman filter algorithm to obtain the target SOC; determine the SOH of the target lithium battery based on the target SOC, solving the problem of low accuracy in determining the SOH of the lithium battery by estimating the SOH of the lithium battery by the coulomb counting method in the related art. In this scheme, the charge and discharge data of the target lithium battery are processed by the least recursive square method to obtain the target battery model parameters of the target lithium battery, and then the SOC is estimated based on the target battery model parameters by the Kalman filter algorithm, thereby improving the accuracy of the target SOC, thereby achieving the effect of improving the accuracy of determining the SOH of the lithium battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 The process of the method for determining the SOH of a lithium battery provided in the embodiment of the present application is as follows Figure 1 ;

[0023] Figure 2 is a schematic diagram of a battery model provided according to an embodiment of the present application;

[0024] Figure 3 The process of the method for determining the SOH of a lithium battery provided in the embodiment of the present application is as follows Figure 2 ;

[0025] Figure 4 is a schematic diagram of a device for determining SOH of a lithium battery provided in an embodiment of the present application;

[0026] Figure 5 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0031] SOC, state of charge, that is, the available state of the remaining power in the battery;

[0032] SOH, state of health The health status or state of the battery is used to describe the performance and remaining life of the battery.

[0033] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0034] The present invention is described below in conjunction with preferred implementation steps. Figure 1 The process of the method for determining the SOH of a lithium battery provided in the embodiment of the present application is as follows Figure 1 ,like Figure 1 As shown, the method comprises the following steps:

[0035] Step S101, determining the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive square method, wherein the charge and discharge data at least include the voltage value and the current value of the target lithium battery, and the target battery model parameters at least include the polarization resistance value and the polarization capacitance value corresponding to the target lithium battery.

[0036] Optionally, for the target lithium battery, its charge and discharge data, such as the voltage and current value of the target lithium battery, are collected. The battery model parameters are identified based on the charge and discharge data of the target lithium battery by the least recursive square method to obtain the target battery model parameters, such as the polarization resistance value and polarization capacitance value corresponding to the target lithium battery, and the internal resistance value of the target lithium battery may also be included.

[0037] Step S102, performing SOC estimation based on the target battery model parameters by using a Kalman filter algorithm to obtain a target SOC.

[0038] Optionally, after obtaining the above-mentioned target battery model parameters, the SOC is estimated based on the target battery model parameters by a Kalman filter algorithm to obtain the target SOC of the target lithium battery. For example, the system state transfer matrix, system input matrix, system output observation matrix and system feedforward matrix in the Kalman filter algorithm are obtained by the target battery model parameters, thereby achieving accurate evaluation of the SOC parameters.

[0039] Step S103, determining the SOH of the target lithium battery according to the target SOC.

[0040] Optionally, the SOH of the target lithium battery is obtained according to the estimated target SOC, for example, the current capacity of the battery is estimated according to the target SOC, and then the SOH of the target lithium battery is determined according to the current capacity.

[0041] In summary, the charge and discharge data of the target lithium battery are processed by the least recursive squares method to obtain the target battery model parameters of the target lithium battery, and then the SOC is estimated based on the target battery model parameters through the Kalman filter algorithm, which improves the accuracy of the target SOC, thereby achieving the effect of improving the accuracy of determining the SOH of the lithium battery.

[0042] Optionally, in the method for determining the SOH of a lithium battery provided in an embodiment of the present application, determining the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery through the least recursive squares method includes: determining a second-order RC equivalent circuit model based on the target lithium battery; determining an objective function of the target lithium battery based on the second-order RC equivalent circuit model, wherein the objective function is a relationship function between battery model parameters, voltage values, and current values; and iteratively solving the battery model parameters in the objective function through the least recursive squares method to obtain the target battery model parameters.

[0043] In an optional embodiment, first, a second-order RC equivalent circuit model is determined according to the target lithium battery. The second-order RC equivalent circuit model is a simplified circuit used to simulate battery behavior, which approximates the charge and discharge characteristics of the battery through a combination of resistors and capacitors. The relationship function between the battery model parameters, voltage values, and current values ​​(i.e., the above-mentioned objective function) can be obtained through the second-order RC equivalent circuit model, and finally the battery model parameters in the objective function are iteratively solved according to the least recursive squares method to obtain the target battery model parameters.

[0044] The second-order RC equivalent circuit model can be used to accurately simulate the voltage response and internal dynamics of the battery, thereby improving the accuracy of the battery model parameters.

[0045] Optionally, in the method for determining the SOH of a lithium battery provided in an embodiment of the present application, determining the target function of a target lithium battery based on a second-order RC equivalent circuit model includes: determining an initial function between battery model parameters, voltage values, and current values ​​in the time domain based on the second-order RC equivalent circuit model; transforming the initial function to obtain an initial function in the complex frequency domain; arranging the initial function in the complex frequency domain to obtain a common denominator initial function; and transforming the arranged initial function to obtain a target function.

[0046] In an optional embodiment, the objective function is obtained by taking the following steps: Figure 2 The second-order RC equivalent circuit model shown in the figure, where Voc represents the open circuit voltage OCV, which has a very strong nonlinear relationship with SOC and is closely related to temperature; I is the load current, V represents the terminal voltage of the battery; Re is the ohmic internal resistance of the battery; the RC parallel network composed of Rs and Cs describes the electrochemical polarization, that is, the part of the battery terminal voltage that changes rapidly, and Vs represents its voltage; the RC parallel network composed of R1 and C1 describes the concentration polarization, that is, the part of the battery terminal voltage that changes slowly, and V1 represents its voltage.

[0047] Through Figure 2 The second-order RC equivalent circuit model shown in FIG. 1 obtains the initial relationship function between the battery model parameters, the voltage value and the current value. For example, the initial relationship function is shown in formula (1):

[0048]

[0049] The initial function is transformed to obtain the initial function in the complex frequency domain. For example, the initial function in the complex frequency domain is shown in formula (2):

[0050]

[0051] Where, E(s) = V(s) - V oc (s).

[0052] The initial function in the complex frequency domain is common-denominated to obtain the sorted initial function, and then the sorted initial function is transformed to obtain the target function. For example, the derivation process of the above-mentioned target function is as follows:

[0053] (as 2 +bs+1)E(s)=-I(s)(cs 2 +ds+e) (3)

[0054]

[0055] Among them, formula (3) is the above-mentioned rearranged initial function. Then the following formula (5) is substituted into formula (3) to obtain the difference equation required for deriving the least squares method, as shown in formula (6):

[0056]

[0057] E k =k1E k-1 +k2E k-2 +k3I k +k4I k-1 +k5I k-2(6)

[0058] in,

[0059] The system model is shown in formula (7):

[0060] Y k =φ k θ k (7)

[0061] in,

[0062] The battery model parameters in the objective function are iteratively solved based on the least recursive square method using the above formula (7) to obtain the target battery model parameters.

[0063] Optionally, in the method for determining the SOH of a lithium battery provided in an embodiment of the present application, the battery model parameters in the objective function are iteratively solved by the least recursive squares method to obtain the target battery model parameters, including: obtaining the first battery model parameter, and calculating based on the first battery model parameter and the first charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain a first voltage prediction value; obtaining the first true voltage value at the current moment in the charge and discharge data, and performing error calculation based on the first true voltage value and the first voltage prediction value to obtain a first error; calculating based on the first error and a preset first covariance matrix to obtain a first gain; updating the first battery model parameter based on the first gain and the first error to obtain the target battery model parameter.

[0064] In an optional embodiment, a first battery model parameter, i.e., an initial first battery model parameter, is obtained, and a first voltage prediction value is calculated based on the first battery model parameter and the first charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data, and then, the first real voltage value at the current moment is obtained, and the error is calculated by the first real voltage value and the first voltage prediction value to obtain the first error, and the first gain is calculated by the first error and the preset first covariance matrix, and finally, the first battery model parameter is updated according to the first gain and the first error to obtain the target battery model parameter. It should be noted that the first covariance matrix can be a diagonal matrix or a unit matrix.

[0065] In an optional embodiment, updating the first battery model parameters according to the first gain and the first error to obtain the target battery model parameters includes: updating the first battery model parameters according to the first gain and the first error to obtain the second battery model parameters; calculating according to the first battery model parameters and the second charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain the second voltage prediction value; obtaining the second real voltage value at the current moment in the charge and discharge data, and performing error calculation based on the second real voltage value and the second voltage prediction value to obtain the second error; calculating according to a preset first covariance matrix, a preset forgetting factor and the second charge and discharge data to obtain the second covariance matrix, and calculating according to the second error and the second covariance matrix to obtain the second gain; updating the second battery model parameters according to the second gain and the second error to obtain the target battery model parameters.

[0066] When the first battery model parameter is updated by the first gain and the first error, it includes: calculating the second voltage prediction value by the first battery model parameter and the second charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data, and calculating the second error by the second real voltage value at the current moment.

[0067] In order to avoid the covariance matrix gradually approaching zero as the recursion proceeds, the final algorithm will lose the ability to correct the parameters, resulting in the so-called "filter saturation" phenomenon. The introduction of the forgetting factor (value between 0.95-0.99) can weaken the effect of old data and enhance the effect of new data. Therefore, by calculating the first covariance matrix, the preset forgetting factor and the second charge and discharge data, the second covariance matrix is ​​obtained, and then the second gain is obtained according to the second error and the second covariance matrix, and the second battery model parameters are updated again by the second gain and the second error to obtain the target battery model parameters.

[0068] In an optional embodiment, the gain matrix is ​​calculated as shown in formula (8), the parameter vector is updated as shown in formula (9), and the covariance matrix is ​​updated as shown in formula (10):

[0069]

[0070] Among them, γ is the forgetting factor, K(k) is the gain, y(k) is the actual observation value (i.e. the true voltage value mentioned above), and P(k) is the covariance matrix, which is continuously updated during the operation of the system to make the parameters converge. is the battery model parameter at the current moment. is the transposed matrix of the charge and discharge data.

[0071] Optionally, in the method for determining the SOH of a lithium battery provided in an embodiment of the present application, SOC estimation is performed based on target battery model parameters through a Kalman filter algorithm to obtain a target SOC, including: constructing a system state equation and a system output observation equation in the Kalman filter algorithm based on the target battery model parameters and the SOC function of the target lithium battery constructed based on the ampere-hour integration method; determining a system state transfer matrix, a system input matrix, a system output observation matrix and a system feedforward matrix in the Kalman filter algorithm based on the system state equation and the system output observation equation; and estimating SOC based on the system state transfer matrix, the system input matrix, the system output observation matrix and the system feedforward matrix to obtain the target SOC.

[0072] In an optional embodiment, the target SOC mentioned above is obtained by the following steps: constructing a SOC function of the target lithium battery based on the ampere-hour integration method, and then constructing a system state equation and a system output observation equation through the SOC function and the target battery model parameters, and then determining the system state transfer matrix, system input matrix, system output observation matrix and system feedforward matrix in the Kalman filter algorithm through the system state equation and the system output observation equation, and then estimating the SOC through the system state transfer matrix, the system input matrix, the system output observation matrix and the system feedforward matrix to obtain the target SOC.

[0073] The target SOC of the target lithium battery can be accurately estimated through the Kalman filter algorithm.

[0074] Optionally, in the method for determining the SOH of a lithium battery provided in an embodiment of the present application, determining the SOH of a target lithium battery based on a target SOC includes: obtaining a current capacity and a nominal capacity of the target lithium battery; and determining the SOH of the target lithium battery based on the current capacity, the nominal capacity and the target SOC.

[0075] In an optional embodiment, the current capacity of the target lithium battery is obtained, for example, the current capacity is measured by an open circuit voltage method, the nominal capacity of the target lithium battery is obtained, and finally the SOH of the target lithium battery is determined by the current capacity, the nominal capacity and the target SOC.

[0076] In an optional embodiment, SOH is calculated using formula (11):

[0077]

[0078] Among them, Q store is the amount of electricity stored in the current state of the lithium-ion battery, and C0 is the nominal capacity of the lithium-ion battery. The impact of the lithium-ion battery SOH on SOC changes with the change of SOH.

[0079] In an optional embodiment, the Figure 3The flowchart shown in the figure obtains the SOH of the lithium battery, specifically including: collecting actual value data of current, voltage and temperature, performing parameter identification of the battery model using a recursive least squares algorithm containing a forgetting factor based on the actual value data to obtain the battery model parameters, performing SOC estimation based on the target battery model parameters using a Kalman filter algorithm to obtain the target SOC, and finally, using the relationship between the SOC and SOH of the lithium-ion battery to estimate the SOH.

[0080] The method for determining the SOH of a lithium battery provided in an embodiment of the present application determines the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive square method, wherein the charge and discharge data at least include the voltage value and current value of the target lithium battery, and the target battery model parameters at least include the polarization resistance value and polarization capacitance value corresponding to the target lithium battery; the SOC is estimated based on the target battery model parameters by the Kalman filter algorithm to obtain the target SOC; based on the target SOC, the SOH of the target lithium battery is determined, which solves the problem of low accuracy in determining the SOH of the lithium battery by estimating the SOH of the lithium battery by the coulomb counting method in the related art. In this scheme, the charge and discharge data of the target lithium battery are processed by the least recursive square method to obtain the target battery model parameters of the target lithium battery, and then the SOC is estimated based on the target battery model parameters by the Kalman filter algorithm, which improves the accuracy of the target SOC, thereby achieving the effect of improving the accuracy of determining the SOH of the lithium battery.

[0081] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0082] The present application also provides a device for determining the SOH of a lithium battery. It should be noted that the device for determining the SOH of a lithium battery in the present application can be used to execute the method for determining the SOH of a lithium battery provided in the present application. The following is an introduction to the device for determining the SOH of a lithium battery provided in the present application.

[0083] Figure 4 Schematic diagram of a device for determining SOH of a lithium battery according to an embodiment of the present application. Figure 4 As shown, the device includes: a first determining unit 401, an estimating unit 402 and a second determining unit 403.

[0084] A first determining unit 401 is used to determine a target battery model parameter of a target lithium battery based on the charge and discharge data of the target lithium battery by a least recursive square method, wherein the charge and discharge data at least include a voltage value and a current value of the target lithium battery, and the target battery model parameter at least includes a polarization resistance value and a polarization capacitance value corresponding to the target lithium battery;

[0085] An estimation unit 402 is used to estimate the SOC based on the target battery model parameters by using a Kalman filter algorithm to obtain a target SOC;

[0086] The second determining unit 403 is used to determine the SOH of the target lithium battery according to the target SOC.

[0087] The device for determining the SOH of a lithium battery provided in the embodiment of the present application determines the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the first determination unit 401 through the least recursive square method, wherein the charge and discharge data at least include the voltage value and current value of the target lithium battery, and the target battery model parameters at least include the polarization resistance value and polarization capacitance value corresponding to the target lithium battery; the estimation unit 402 estimates the SOC based on the target battery model parameters through the Kalman filter algorithm to obtain the target SOC; the second determination unit 403 determines the SOH of the target lithium battery based on the target SOC, which solves the problem of low accuracy in determining the SOH of the lithium battery by estimating the SOH of the lithium battery through the coulomb counting method in the related art. In this scheme, the charge and discharge data of the target lithium battery are processed by the least recursive square method to obtain the target battery model parameters of the target lithium battery, and then the SOC is estimated based on the target battery model parameters through the Kalman filter algorithm, thereby improving the accuracy of the target SOC, thereby achieving the effect of improving the accuracy of determining the SOH of the lithium battery.

[0088] Optionally, in the device for determining the SOH of a lithium battery provided in an embodiment of the present application, the first determination unit includes: a first determination subunit, used to determine a second-order RC equivalent circuit model based on a target lithium battery; a second determination subunit, used to determine an objective function of the target lithium battery based on the second-order RC equivalent circuit model, wherein the objective function is a relationship function between battery model parameters, voltage values, and current values; and a solution subunit, used to iteratively solve the battery model parameters in the objective function by a least recursive squares method to obtain target battery model parameters.

[0089] Optionally, in the device for determining the SOH of a lithium battery provided in an embodiment of the present application, the second determination subunit includes: a first determination module, used to determine the initial function between the battery model parameters, voltage values ​​and current values ​​in the time domain based on a second-order RC equivalent circuit model; a first transformation module, used to transform the initial function to obtain the initial function in the complex frequency domain; a conversion module, used to common-denominator the initial function in the complex frequency domain to obtain the arranged initial function; and a second transformation module, used to transform the arranged initial function to obtain the target function.

[0090] Optionally, in the device for determining the SOH of a lithium battery provided in an embodiment of the present application, a solution subunit includes: a first acquisition module, used to acquire a first battery model parameter, and to perform calculations based on the first battery model parameter and the first charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain a first voltage prediction value; a second acquisition module, used to acquire the first true voltage value at the current moment in the charge and discharge data, and to perform error calculation based on the first true voltage value and the first voltage prediction value to obtain a first error; a calculation module, used to perform calculations based on the first error and a preset first covariance matrix to obtain a first gain; an update module, used to update the first battery model parameters based on the first gain and the first error to obtain target battery model parameters.

[0091] Optionally, in the device for determining the SOH of a lithium battery provided in an embodiment of the present application, the update module includes: a first update submodule, used to update the first battery model parameters according to the first gain and the first error to obtain the second battery model parameters; a first calculation submodule, used to calculate according to the first battery model parameters and the second charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain the second voltage prediction value; an acquisition submodule, used to obtain the second true voltage value at the current moment in the charge and discharge data, and perform error calculation based on the second true voltage value and the second voltage prediction value to obtain the second error; a second calculation submodule, used to calculate according to a preset first covariance matrix, a preset forgetting factor and the second charge and discharge data to obtain the second covariance matrix, and calculate according to the second error and the second covariance matrix to obtain the second gain; a second update submodule, used to update the second battery model parameters according to the second gain and the second error to obtain the target battery model parameters.

[0092] Optionally, in the device for determining the SOH of a lithium battery provided in an embodiment of the present application, the estimation unit includes: a construction subunit, used to construct a system state equation and a system output observation equation in a Kalman filter algorithm based on target battery model parameters and a SOC function of a target lithium battery constructed based on the ampere-hour integration method; a third determination subunit, used to determine the system state transfer matrix, system input matrix, system output observation matrix and system feedforward matrix in the Kalman filter algorithm based on the system state equation and the system output observation equation; an estimation subunit, used to perform SOC estimation based on the system state transfer matrix, system input matrix, system output observation matrix and system feedforward matrix to obtain a target SOC.

[0093] Optionally, in the device for determining the SOH of a lithium battery provided in an embodiment of the present application, the second determination unit includes: an acquisition subunit, used to acquire the current capacity and nominal capacity of the target lithium battery; and a fourth determination subunit, used to determine the SOH of the target lithium battery based on the current capacity, nominal capacity and target SOC.

[0094] The device for determining the SOH of a lithium battery includes a processor and a memory. The first determination unit 401, the estimation unit 402 and the second determination unit 403 are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

[0095] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the accurate evaluation of the battery SOH can be achieved by adjusting the kernel parameters.

[0096] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0097] An embodiment of the present invention provides a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, a method for determining the SOH of a lithium battery is implemented.

[0098] An embodiment of the present invention provides a processor, which is used to run a program, wherein the method for determining the SOH of a lithium battery is executed when the program is run.

[0099] like Figure 5As shown, an embodiment of the present invention provides an electronic device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: determining the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive squares method, wherein the charge and discharge data at least include the voltage value and the current value of the target lithium battery, and the target battery model parameters at least include the polarization resistance value and the polarization capacitance value corresponding to the target lithium battery; performing SOC estimation based on the target battery model parameters by the Kalman filtering algorithm to obtain the target SOC; determining the SOH of the target lithium battery based on the target SOC.

[0100] Optionally, determining the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive squares method includes: determining a second-order RC equivalent circuit model based on the target lithium battery; determining the target function of the target lithium battery based on the second-order RC equivalent circuit model, wherein the target function is a relationship function between battery model parameters, voltage values, and current values; and iteratively solving the battery model parameters in the target function by the least recursive squares method to obtain the target battery model parameters.

[0101] Optionally, determining the target function of the target lithium battery based on the second-order RC equivalent circuit model includes: determining the initial function between the battery model parameters, voltage values ​​and current values ​​in the time domain based on the second-order RC equivalent circuit model; transforming the initial function to obtain the initial function in the complex frequency domain; commonizing the initial function in the complex frequency domain to obtain the organized initial function; transforming the organized initial function to obtain the target function.

[0102] Optionally, the battery model parameters in the objective function are iteratively solved by the least recursive squares method to obtain the target battery model parameters, including: obtaining the first battery model parameter, and calculating according to the first battery model parameter and the first charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain a first voltage prediction value; obtaining the first true voltage value at the current moment in the charge and discharge data, and performing error calculation according to the first true voltage value and the first voltage prediction value to obtain a first error; calculating according to the first error and a preset first covariance matrix to obtain a first gain; updating the first battery model parameter according to the first gain and the first error to obtain the target battery model parameter.

[0103] Optionally, updating the first battery model parameters according to the first gain and the first error to obtain the target battery model parameters includes: updating the first battery model parameters according to the first gain and the first error to obtain the second battery model parameters; calculating according to the first battery model parameters and the second charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain the second voltage prediction value; obtaining the second real voltage value at the current moment in the charge and discharge data, and performing error calculation based on the second real voltage value and the second voltage prediction value to obtain the second error; calculating according to a preset first covariance matrix, a preset forgetting factor and the second charge and discharge data to obtain the second covariance matrix, and calculating according to the second error and the second covariance matrix to obtain the second gain; updating the second battery model parameters according to the second gain and the second error to obtain the target battery model parameters.

[0104] Optionally, SOC estimation is performed based on the target battery model parameters through the Kalman filter algorithm to obtain the target SOC, including: constructing the system state equation and the system output observation equation in the Kalman filter algorithm according to the target battery model parameters and the SOC function of the target lithium battery constructed based on the ampere-hour integration method; determining the system state transfer matrix, system input matrix, system output observation matrix and system feedforward matrix in the Kalman filter algorithm according to the system state equation and the system output observation equation; and performing SOC estimation based on the system state transfer matrix, the system input matrix, the system output observation matrix and the system feedforward matrix to obtain the target SOC.

[0105] Optionally, determining the SOH of the target lithium battery according to the target SOC includes: acquiring the current capacity and the nominal capacity of the target lithium battery; and determining the SOH of the target lithium battery according to the current capacity, the nominal capacity and the target SOC.

[0106] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0107] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with the following method steps: determining the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive squares method, wherein the charge and discharge data at least include the voltage value and current value of the target lithium battery, and the target battery model parameters at least include the polarization resistance value and polarization capacitance value corresponding to the target lithium battery; estimating the SOC based on the target battery model parameters by the Kalman filtering algorithm to obtain the target SOC; determining the SOH of the target lithium battery based on the target SOC.

[0108] Optionally, determining the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive squares method includes: determining a second-order RC equivalent circuit model based on the target lithium battery; determining the target function of the target lithium battery based on the second-order RC equivalent circuit model, wherein the target function is a relationship function between battery model parameters, voltage values, and current values; and iteratively solving the battery model parameters in the target function by the least recursive squares method to obtain the target battery model parameters.

[0109] Optionally, determining the target function of the target lithium battery based on the second-order RC equivalent circuit model includes: determining the initial function between the battery model parameters, voltage values ​​and current values ​​in the time domain based on the second-order RC equivalent circuit model; transforming the initial function to obtain the initial function in the complex frequency domain; commonizing the initial function in the complex frequency domain to obtain the organized initial function; transforming the organized initial function to obtain the target function.

[0110] Optionally, the battery model parameters in the objective function are iteratively solved by the least recursive squares method to obtain the target battery model parameters, including: obtaining the first battery model parameter, and calculating according to the first battery model parameter and the first charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain a first voltage prediction value; obtaining the first true voltage value at the current moment in the charge and discharge data, and performing error calculation according to the first true voltage value and the first voltage prediction value to obtain a first error; calculating according to the first error and a preset first covariance matrix to obtain a first gain; updating the first battery model parameter according to the first gain and the first error to obtain the target battery model parameter.

[0111] Optionally, updating the first battery model parameters according to the first gain and the first error to obtain the target battery model parameters includes: updating the first battery model parameters according to the first gain and the first error to obtain the second battery model parameters; calculating according to the first battery model parameters and the second charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain the second voltage prediction value; obtaining the second real voltage value at the current moment in the charge and discharge data, and performing error calculation based on the second real voltage value and the second voltage prediction value to obtain the second error; calculating according to a preset first covariance matrix, a preset forgetting factor and the second charge and discharge data to obtain the second covariance matrix, and calculating according to the second error and the second covariance matrix to obtain the second gain; updating the second battery model parameters according to the second gain and the second error to obtain the target battery model parameters.

[0112] Optionally, SOC estimation is performed based on the target battery model parameters through the Kalman filter algorithm to obtain the target SOC, including: constructing the system state equation and the system output observation equation in the Kalman filter algorithm according to the target battery model parameters and the SOC function of the target lithium battery constructed based on the ampere-hour integration method; determining the system state transfer matrix, system input matrix, system output observation matrix and system feedforward matrix in the Kalman filter algorithm according to the system state equation and the system output observation equation; and performing SOC estimation based on the system state transfer matrix, the system input matrix, the system output observation matrix and the system feedforward matrix to obtain the target SOC.

[0113] Optionally, determining the SOH of the target lithium battery according to the target SOC includes: acquiring the current capacity and the nominal capacity of the target lithium battery; and determining the SOH of the target lithium battery according to the current capacity, the nominal capacity and the target SOC.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0119] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0120] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0121] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0122] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0123] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining SOH of a lithium battery, characterized in that: include: Determine the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive square method, wherein the charge and discharge data at least include the voltage value and the current value of the target lithium battery, and the target battery model parameters at least include the polarization resistance value and the polarization capacitance value corresponding to the target lithium battery; Estimating the SOC based on the target battery model parameters by using a Kalman filter algorithm to obtain a target SOC; The target SOH of the lithium battery is determined according to the target SOC.

2. The method according to claim 1, characterized in that Determining the target battery model parameters of the target lithium battery based on the charge and discharge data of the target lithium battery by the least recursive square method includes: Determine a second-order RC equivalent circuit model according to the target lithium battery; Determine the objective function of the target lithium battery according to the second-order RC equivalent circuit model, wherein the objective function is a relationship function between battery model parameters, the voltage value and the current value; The battery model parameters in the objective function are iteratively solved by the least recursive square method to obtain the target battery model parameters.

3. The method according to claim 2, characterized in that According to the second-order RC equivalent circuit model, determining the objective function of the target lithium battery includes: Determine an initial function between the battery model parameters, the voltage value, and the current value in the time domain according to the second-order RC equivalent circuit model; Transforming the initial function to obtain an initial function in the complex frequency domain; Performing common denominator arrangement on the initial function in the complex frequency domain to obtain an arranged initial function; The rearranged initial function is transformed to obtain the target function.

4. The method according to claim 3, characterized in that The battery model parameters in the objective function are iteratively solved by the least recursive square method to obtain the target battery model parameters, including: Acquire a first battery model parameter, and perform calculation based on the first battery model parameter and first charge and discharge data corresponding to a previous moment corresponding to the current moment in the charge and discharge data to obtain a first voltage prediction value; Acquire a first real voltage value at a current moment in the charge and discharge data, and perform an error calculation based on the first real voltage value and the first voltage prediction value to obtain a first error; Calculating according to the first error and a preset first covariance matrix to obtain a first gain; The first battery model parameters are updated according to the first gain and the first error to obtain the target battery model parameters.

5. The method according to claim 4, characterized in that Updating the first battery model parameter according to the first gain and the first error to obtain the target battery model parameter includes: updating the first battery model parameter according to the first gain and the first error to obtain a second battery model parameter; Calculating according to the first battery model parameter and the second charge and discharge data corresponding to the previous moment corresponding to the current moment in the charge and discharge data to obtain a second voltage prediction value; Acquire a second real voltage value at a current moment in the charge and discharge data, and perform error calculation based on the second real voltage value and the second voltage prediction value to obtain a second error; Calculating according to the preset first covariance matrix, the preset forgetting factor and the second charge and discharge data to obtain a second covariance matrix, and calculating according to the second error and the second covariance matrix to obtain a second gain; The second battery model parameters are updated according to the second gain and the second error to obtain the target battery model parameters.

6. The method according to claim 1, characterized in that The SOC is estimated based on the target battery model parameters by using the Kalman filter algorithm to obtain the target SOC, including: Constructing the system state equation and the system output observation equation in the Kalman filter algorithm according to the target battery model parameters and the SOC function of the target lithium battery constructed based on the ampere-hour integration method; Determine the system state transfer matrix, system input matrix, system output observation matrix and system feedforward matrix in the Kalman filter algorithm according to the system state equation and the system output observation equation; The SOC is estimated according to the system state transfer matrix, the system input matrix, the system output observation matrix and the system feedforward matrix to obtain the target SOC.

7. The method according to claim 1, characterized in that Determining the SOH of the target lithium battery according to the target SOC includes: Obtaining the current capacity and nominal capacity of the target lithium battery; The SOH of the target lithium battery is determined according to the current capacity, the nominal capacity and the target SOC.

8. A device for determining SOH of a lithium battery, characterized in that: include: A first determination unit, configured to determine a target battery model parameter of the target lithium battery based on the charge and discharge data of the target lithium battery by a least recursive square method, wherein the charge and discharge data at least include a voltage value and a current value of the target lithium battery, and the target battery model parameter at least includes a polarization resistance value and a polarization capacitance value corresponding to the target lithium battery; An estimation unit, configured to estimate the SOC based on the target battery model parameters by using a Kalman filter algorithm to obtain a target SOC; The second determining unit is used to determine the SOH of the target lithium battery according to the target SOC.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the storage medium is controlled to execute the method for determining the SOH of a lithium battery according to any one of claims 1 to 7 in a device.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the SOH of a lithium battery as described in any one of claims 1 to 7.

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