A method and device for evaluating battery state

Abstract

The present invention provides a method and device for evaluating the state of a battery. The method includes: obtaining the design parameters and process conditions of a target battery, and establishing an equivalent circuit model of the target battery; determining, based on the design parameters and process conditions, the ohmic resistance, the first time constant corresponding to the electrochemical polarization resistance in the equivalent circuit model, and the second time constant corresponding to the concentration polarization resistance of the battery; calculating, based on the first time constant, the parameter values of the ohmic resistance, the electrochemical polarization resistance, and the first capacitor in the RC circuit where the electrochemical polarization resistance is located; calculating, based on the second time constant, the parameter values of the concentration polarization resistance of the battery and the second capacitor in the RC circuit where the concentration polarization resistance of the battery is located; and evaluating the state of the battery based on the above parameter values. By identifying the electrochemical characteristics of the battery in the high-frequency and low-frequency response regions through two time constants, the identification result is accurate and stable, improving the accuracy of the evaluation result.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular, to a method and device for evaluating the state of a battery. Background Art

[0002] In recent years, with the increasing consumption of non-renewable energy and environmental pollution problems, the replacement of traditional non-renewable energy with green new energy has become a hot topic of current research. Therefore, new energy electric vehicles have developed rapidly. Lithium-ion batteries have been widely used in the promotion of electric vehicles due to their advantages such as high energy density, long cycle life, wide operating temperature range, and no memory effect.

[0003] The state evaluation of lithium-ion batteries, such as the estimation of the state of charge (SOC) and the state of health (SOH) of the battery, is the core of the battery management system of electric vehicles. The estimation accuracy directly affects the charge and discharge limits, life, and driving safety of lithium batteries.

[0004] In the engineering application of automotive power battery systems, the SOC of lithium-ion batteries is currently usually estimated by establishing a battery parameter model and then estimating the SOC of the lithium-ion battery through the method of model parameter identification, and then estimating the SOH of the battery. However, in the existing model estimation methods, whether using the general Kalman filter or the extended Kalman filter, there is an inseparable core problem, that is, the accurate acquisition of the battery state space equation. In the case of the same equivalent circuit model structure, the accurate identification of battery model parameters often determines the success or failure of battery state estimation.

[0005] Due to its own characteristics, lithium-ion batteries have the characteristics of high-frequency response and low-frequency response during use, that is, the model needs to have both short-cycle model parameters and long-cycle model parameters. In the conventional DC pulse test, if the parameter identification uses a short-cycle time scale, the battery characteristics of lithium-ion battery diffusion control cannot be reflected. If a long-cycle time scale is used, the phenomenon of model data oversaturation will occur; in the AC impedance test, the second-order RC circuit presents an arc-shaped RC loop in the low-frequency region of the impedance spectrum, which does not fully conform to the battery characteristics in diffusion control; in short, no matter which method is used, the charge and discharge characteristics of the battery in the high-frequency band and the low-frequency band cannot be fully reflected, resulting in inaccurate identification of battery model parameters, and further affecting the accuracy of the final state evaluation result of the lithium-ion battery. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a method and device for evaluating the state of a battery to overcome the problem in the prior art that the data generated by the battery in the energy storage scenario is not aligned on the time axis, resulting in inconvenience for subsequent data analysis.

[0007] An embodiment of the present invention provides a method for evaluating the state of a battery, including:

[0008] Obtain the design parameters and process conditions of the target battery, and establish an equivalent circuit model of the target battery;

[0009] Based on the design parameters and process conditions, determine the ohmic resistance, the first time constant corresponding to the electrochemical polarization resistance, and the second time constant corresponding to the battery concentration polarization resistance in the equivalent circuit model, where the second time constant is greater than the first time constant;

[0010] Based on the first time constant, calculate the parameter values of the ohmic resistance, the electrochemical polarization resistance, and the first capacitor in the RC circuit where the electrochemical polarization resistance is located;

[0011] Based on the second time constant, the ohmic resistance, the electrochemical polarization resistance, and the parameter values corresponding to the first capacitor, calculate the parameter values of the battery concentration polarization resistance and the second capacitor in the RC circuit where the battery concentration polarization resistance is located;

[0012] Based on the parameter values of the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor, perform battery state evaluation to obtain the battery state evaluation result of the target battery.

[0013] Optionally, the performing battery state evaluation based on the parameter values of the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor to obtain the battery state evaluation result of the target battery includes:

[0014] Use the parameter values of the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor to initialize the model parameters of the equivalent circuit model;

[0015] Take the battery state and model parameters of the target battery as the state variables of the dual Kalman filter algorithm, and perform online parameter identification on the initialized parameter identification model to obtain the first battery state evaluation result of the target battery, where the first battery state evaluation result includes: the estimated result of the state of charge of the battery.

[0016] Optionally, before taking the battery state and model parameters of the target battery as the state variables of the dual Kalman filter algorithm and performing online parameter identification on the initialized parameter identification model, the method further includes:

[0017] Obtain the current operating condition of the target battery;

[0018] Determine whether the current operating condition belongs to a preset condition;

[0019] When the current operating condition belongs to the preset condition, obtain the current open-circuit voltage of the target battery;

[0020] Based on the relationship curve between the open-circuit voltage of the target battery and the state of charge of the battery and the current open-circuit voltage, determine the estimated result of the state of charge of the target battery.

[0021] Optionally, when the current operating condition does not belong to the preset condition, use the battery state and model parameters of the target battery as the state variables of the dual Kalman filter algorithm to perform online parameter identification on the initialized parameter identification model.

[0022] Optionally, the first battery state evaluation result further includes: the current model parameters of the parameter identification model, and the method further includes:

[0023] Obtain the current cumulative usage time and the current cumulative charge-discharge capacity of the target battery;

[0024] Based on the current cumulative usage time and the current cumulative charge-discharge capacity, determine the first battery health state of the target battery;

[0025] Calculate the second battery health state of the target battery based on the current model parameters;

[0026] Based on the first battery health state and the second battery health state, determine the estimated result of the battery health state of the target battery.

[0027] Optionally, the determining the estimated result of the battery health state of the target battery based on the first battery health state and the second battery health state includes:

[0028] Obtain the battery health state evaluation weight corresponding to the target battery;

[0029] Based on the first battery health state, the second battery health state and the battery health state evaluation weight, calculate the estimated result of the battery health state of the target battery.

[0030] Optionally, the obtaining the battery health state evaluation weight corresponding to the target battery includes:

[0031] Obtain the battery type of the target battery;

[0032] Based on the battery type, determine the battery health state evaluation weight corresponding to the target battery.

[0033] An embodiment of the present invention further provides a battery state evaluation device, including:

[0034] An acquisition module, configured to acquire design parameters and process conditions of a target battery, and establish an equivalent circuit model of the target battery;

[0035] A first processing module, configured to determine an ohmic resistance, a first time constant corresponding to an electrochemical polarization resistance, and a second time constant corresponding to a battery concentration polarization resistance in the equivalent circuit model based on the design parameters and the process conditions, where the second time constant is greater than the first time constant;

[0036] A second processing module, configured to calculate parameter values of the ohmic resistance, the electrochemical polarization resistance, and a first capacitor in an RC circuit where the electrochemical polarization resistance is located based on the first time constant;

[0037] A third processing module, configured to calculate parameter values of the battery concentration polarization resistance and a second capacitor in an RC circuit where the battery concentration polarization resistance is located based on the second time constant, the ohmic resistance, the electrochemical polarization resistance, and the parameter values corresponding to the first capacitor;

[0038] A fourth processing module, configured to perform battery state evaluation based on the parameter values of the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor, and obtain a battery state evaluation result of the target battery.

[0039] An embodiment of the present invention further provides an electronic device, including: a memory and a processor, where the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the method provided by the embodiment of the present invention.

[0040] An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method provided by the embodiment of the present invention.

[0041] The technical solution of the present invention has the following advantages:

[0042] An embodiment of the present invention provides a method and device for evaluating the state of a battery. By obtaining the design parameters and process conditions of the target battery, an equivalent circuit model of the target battery is established; based on the design parameters and process conditions, the ohmic resistance, the first time constant corresponding to the electrochemical polarization resistance, and the second time constant corresponding to the concentration polarization resistance of the battery in the equivalent circuit model are determined, and the second time constant is greater than the first time constant; based on the first time constant, the parameter values of the ohmic resistance, the electrochemical polarization resistance, and the first capacitor in the RC circuit where the electrochemical polarization resistance is located are calculated; based on the second time constant, the ohmic resistance, the electrochemical polarization resistance, and the parameter values corresponding to the first capacitor, the parameter values of the concentration polarization resistance of the battery and the second capacitor in the RC circuit where the concentration polarization resistance of the battery is located are calculated; based on the parameter values of the ohmic resistance, the electrochemical polarization resistance, the concentration polarization resistance of the battery, the first capacitor, and the second capacitor, the state of the battery is evaluated to obtain the battery state evaluation result of the target battery. Thus, by respectively determining the two time constants in the equivalent circuit model of the battery according to the actual design parameters and process conditions of the battery, which respectively correspond to the electrochemical characteristics of the battery in the high-frequency response region and the low-frequency response region, that is, the RC loop module region in the second-order equivalent circuit model, the off-line identification of the circuit equivalent model parameters is carried out in these two regions respectively, and during the off-line parameter identification process, each model parameter is associated, fully reflecting the characteristics of battery ohmic polarization, electrochemical polarization, concentration polarization, etc. Through the parameter identification of the two time constants, the identification result is accurate and stable, and then the off-line identification result is used as the initial parameter value for on-line parameter identification, further improving the accuracy of the on-line parameter identification result, that is, the battery state evaluation result. Description of the Drawings

[0043] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1 It is a flowchart of the method for evaluating the state of the battery in the embodiment of the present invention;

[0045] Figure 2 It is a schematic diagram of the second-order equivalent circuit model of the battery in the embodiment of the present invention;

[0046] Figure 3 It is a Nyquist diagram in the embodiment of the present invention;

[0047] Figure 4 It is a schematic diagram of the relationship curve between the open-circuit voltage and the state of charge of the battery in the embodiment of the present invention;

[0048] Figure 5 It is a schematic diagram of the specific working process of battery state evaluation in the embodiments of the present invention;

[0049] Figure 6 It is a schematic diagram of the structure of the battery state evaluation device in the embodiments of the present invention;

[0050] Figure 7 It is a schematic diagram of the structure of the electronic device in the embodiments of the present invention. Detailed implementation manners

[0051] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and should not be construed as indicating or implying relative importance.

[0053] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" 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 directly connected or indirectly connected through an intermediate medium, and it can also be the internal connection of two elements. It can be a wireless connection or a wired connection. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0054] The technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] Lithium-ion batteries have the characteristics of high-frequency response and low-frequency response during use due to their own characteristics, that is, the model needs to have both short-cycle model parameters and long-cycle model parameters. In a conventional DC pulse test, if the parameter identification uses a short-cycle time scale, the diffusion control characteristics of the lithium-ion battery cannot be reflected. If a long-cycle time scale is used, the phenomenon of model data oversaturation will occur; in an AC impedance test, the second-order RC circuit presents an arc-shaped RC loop in the low-frequency region of the impedance spectrum, which does not fully conform to the battery characteristics under diffusion control; in short, no matter which method is used, the charge and discharge characteristics of the battery in the high-frequency and low-frequency bands cannot be fully reflected, resulting in inaccurate identification of battery model parameters, and further affecting the accuracy of the final state evaluation result of the lithium-ion battery.

[0056] Based on the above problems, the embodiments of the present invention provide a battery state evaluation method, as Figure 1 shown, the battery state evaluation method specifically includes the following steps:

[0057] Step S101: Obtain the design parameters and process conditions of the target battery, and establish an equivalent circuit model of the target battery.

[0058] Among them, the equivalent circuit model of the battery is the basis for battery parameter identification. The electronic components in the model correspond to the internal reaction process of the battery during charge and discharge;

[0059] The first-order equivalent circuit model often does not have the ability to reflect battery characteristics, and the identification process of the multi-order equivalent circuit model is complex. Therefore, the second-order equivalent circuit model is widely used in the engineering field, as Figure 2 shown. The above design parameters and process conditions include: during the production and manufacturing of the battery, material parameters such as copper foil, aluminum foil, and separator used, as well as corresponding production process conditions, etc. The physical properties of these substances extended to the battery will cause a certain ohmic resistance. The positive and negative active materials of the battery will form a double-layer capacitance during charge and discharge. The double-layer capacitance will, to a certain extent, hinder the occurrence of electrochemical reactions and form an electrochemical polarization resistance; furthermore, the propagation rate of lithium ions in the battery is affected by indicators such as electrolyte temperature, viscosity, and conductivity, and will also affect the charge and discharge of the battery within a certain range, resulting in a concentration polarization resistance.

[0060] Step S102: Determine the first time constant corresponding to the ohmic resistance and electrochemical polarization resistance in the equivalent circuit model and the second time constant corresponding to the concentration polarization resistance of the battery based on the design parameters and process conditions.

[0061] Among them, the second time constant is greater than the first time constant. In practical applications, according to the reaction rate of the above-mentioned electrochemical reaction, the first time constant corresponding to the ohmic resistance and the electrochemical polarization resistance can be set as τ1, and the second time constant corresponding to the battery concentration polarization resistance can be set as τ2; parameter identification is carried out in these two time domains respectively. Exemplarily, for a 51Ah NCM battery cell, the range of τ1 can be set from 1 to 5 s, and the range of τ2 can be set from 100 to 500 s. The present invention is only an example and is not limited thereto.

[0062] Step S103: Based on the first time constant, calculate the parameter values of the ohmic resistance, the electrochemical polarization resistance, and the first capacitor in the RC circuit where the electrochemical polarization resistance is located.

[0063] Specifically, in practical applications, the ohmic resistance R0, the electrochemical polarization resistance R1, and the specific value of the first capacitor C1 in the equivalent circuit model as shown in Figure 2 are calculated by the AC impedance method. Exemplarily, the AC impedance test is generally directly measured using an electrochemical workstation. Since the upper limit of τ1 is 5 s, considering the equipment capabilities, the scanning range of the equipment is set to 0.2 to 1000 Hz, and the obtained Nyquist plot is as shown in Figure 3 . From Figure 3 , R0 and R1 can be directly obtained, ZRe = R0 + R1 / 2, and C1 can be obtained from the ω of the semicircle vertex P, C1 = 1 / (ω*R1).

[0064] Step S104: Based on the second time constant, the parameter values of the ohmic resistance, the electrochemical polarization resistance, and the first capacitor, calculate the parameter values of the battery concentration polarization resistance and the second capacitor in the RC circuit where the battery concentration polarization resistance is located.

[0065] Specifically, the DC pulse method can be used to calculate the specific values of the battery concentration polarization resistance R2 and the second capacitor C2 as shown in Figure 2 on the basis of the above parameter calculation results.

[0066] Exemplarily, according to Kirchhoff's law, under a certain SOC condition, when the input current of the battery is I, the output terminal voltage is:

[0067] U(t) = U oc -IR 0 -U 1 -U 2

[0068] The open-circuit voltage is:

[0069] U oc = U 0

[0070] The identification parameters R0, R1, and C1 of the high-frequency region associated with τ1 have been obtained, and the identification parameters R2 and C2 of the low-frequency region associated with τ2 can be obtained by using convolution coupling calculation. The calculation process is as follows:

[0071] Let the value range of τ1 be [1, 5], and the value range of τ2 be [100, 500].

[0072] There is a capacitance voltage in the RC loop of the equivalent circuit model. The charge accumulation of the capacitance voltage on τ1 and τ2 can be expressed by a convolution model:

[0073]

[0074] where f(τ) is the state equation:

[0075] f 1 (τ) = IR 1 (1 - e -t / (R1*C1) )

[0076] f 2 (τ) = IR 2 (1 - e -t / (R2*C2) )

[0077] where g(t - τ) is the attenuation equation:

[0078] g 1 (t - τ) = e -t / (R1*C1)

[0079] g 2 (t - τ) = e -t / (R2*C2)

[0080] Through the least squares fitting, the model parameters R2 and C2 within the time constant range τ2 can be obtained.

[0081] Through the above process, the off-line parameter identification of the battery equivalent circuit model is completed. In actual applications, by using simulation software, the results of the above dual-time-constant coupling parameter identification are imported into the equivalent circuit model and compared with the test results. The experimental results show that the simulated voltage of the model is very close to the measured voltage. Thus, it can be seen that the above dual-time-constant coupling parameter identification can ensure the accuracy of the off-line parameter identification results, and further improve the accuracy of subsequent battery state estimation.

[0082] Step S105: Based on the parameter values corresponding to the ohmic resistance, electrochemical polarization resistance, battery concentration polarization resistance, first capacitor, and second capacitor, evaluate the battery state to obtain the battery state evaluation result of the target battery.

[0083] By performing the above steps, the battery state evaluation method provided by the embodiments of the present invention determines two time constants in the equivalent circuit model of the battery according to the actual design parameters and process conditions of the battery, respectively corresponding to the electrochemical characteristics of the battery in the high-frequency response region and the low-frequency response region, that is, the RC loop module region in the second-order equivalent circuit model. Circuit equivalent model parameter offline identification is performed in these two regions, and during the offline parameter identification process, each model parameter is associated, fully reflecting the characteristics of battery ohmic polarization, electrochemical polarization, concentration polarization, etc. Through the parameter identification of the two time constants, the identification result is accurate and stable. Then, the offline identification result is used as the initial parameter value for online parameter identification, further improving the accuracy of the online parameter identification result, that is, the battery state evaluation result.

[0084] Specifically, in one embodiment, step S105 above specifically includes the following steps:

[0085] Step S501: Initialize the model parameters of the equivalent circuit model with the parameter values corresponding to the ohmic resistance, electrochemical polarization resistance, battery concentration polarization resistance, first capacitor, and second capacitor.

[0086] Specifically, the above offline identification result data is used as the initialization parameters of the above equivalent circuit model.

[0087] Step S502: Use the battery state and model parameters of the target battery as the state variables of the dual Kalman filter algorithm to perform online parameter identification on the initialized parameter identification model, and obtain the first battery state evaluation result of the target battery. The first battery state evaluation result includes: the estimated result of the state of charge of the battery.

[0088] Specifically, the specific implementation process of performing online parameter identification using the dual Kalman filter algorithm in step S502 above is a prior art, and can be specifically implemented with reference to the online identification process based on the Kalman filter algorithm in the prior art, and will not be elaborated here.

[0089] Specifically, in one embodiment, before performing step S502 above, the battery state estimation method provided by the embodiments of the present invention further includes the following steps:

[0090] Step S503: Obtain the current operating condition of the target battery.

[0091] Step S504: Determine whether the current operating condition belongs to a preset condition.

[0092] Among them, the preset conditions include: the full charge condition of the battery and the full static condition of the battery.

[0093] Specifically, when the current operating condition does not belong to the preset condition, step S502 above is executed, otherwise step S505 is executed.

[0094] Step S505: When the current operating condition belongs to a preset condition, obtain the current open-circuit voltage of the target battery.

[0095] Step S506: Based on the relationship curve between the open-circuit voltage of the target battery and the state of charge of the battery and the current open-circuit voltage, determine the estimated result of the state of charge of the target battery.

[0096] Specifically, no matter whether Kalman filtering or other methods are adopted, errors will be brought due to the accuracy of the equivalent circuit model structure and the accuracy of the observation sensor. Errors and interferences are ubiquitous in nature. Under the preset conditions, the state of charge (SOC) of the battery and the battery model parameters can be corrected with reference to the relationship curve between the open-circuit voltage of the battery and the state of charge of the battery, that is, the SOC-OCV curve, so as to improve the accuracy of the final estimation result. Exemplarily, Figure 4 FIG. 10 is a schematic diagram of an SOC-OCV curve of a certain battery under preset conditions.

[0097] In practical applications, due to the high dimension of the solution of online parameter identification, R2 and C2 will jump within a large range, resulting in slow convergence or ineffective convergence of the entire model algorithm, wasting the computing power of the chip. In addition, the synchronous estimation of the battery state of health (SOH) is based on the calculation of battery model parameters, and the abnormal movement of the model parameters will also make it difficult to estimate the SOH. The present invention uses a double time constant identification method to artificially delimit the high-frequency response area and the low-frequency response area in the time dimension, so that the battery model parameters are confined within a reasonable range, thereby improving the accuracy of the final estimation result.

[0098] Specifically, in one embodiment, the above first battery state evaluation result further includes: the current model parameters of the parameter identification model. The battery state estimation method provided by the embodiment of the present invention further includes the following steps:

[0099] Step S106: Obtain the current cumulative usage time and the current cumulative charge and discharge capacity of the target battery.

[0100] Step S107: Determine the first battery health state of the target battery based on the current cumulative usage time and the current cumulative charge and discharge capacity.

[0101] Step S108: Calculate the second battery health state of the target battery based on the current model parameters;

[0102] Step S109: Based on the first battery health state and the second battery health state, determine the estimated result of the battery health state of the target battery.

[0103] Specifically, the above step S109 obtains the battery health state evaluation weight corresponding to the target battery; based on the first battery health state, the second battery health state, and the battery health state evaluation weight, calculates the battery health state estimation result of the target battery. In practical applications, the battery type of the target battery can be obtained; based on the battery type, the battery health state evaluation weight corresponding to the target battery is determined.

[0104] Specifically, the SOH of the battery is mainly affected by two aspects: one is the capacity attenuation of the battery (SOHC), and SOHC is generally jointly affected by two indicators of the battery's throughput cycle and calendar cycle. The value of SOHC is obtained by recording the cumulative usage time and cumulative charge-discharge capacity of the battery. The other is the increase in the internal resistance of the battery (SOHR), and SOHR needs to refer to the internal resistance parameters output by the dual Kalman filter. It can be set that the final health state of the battery is SOH = k * SOHR + (1 - k) * SOHC. The selection of the battery health state evaluation weight k follows the battery design characteristics. Generally, for energy-type batteries and power-type batteries, the value of k ranges from 0 to 1, and the value of k for power-type batteries is generally larger. The specific value can be selected according to the actual battery design characteristics, and the present invention is not limited thereto.

[0105] Thus, by comprehensively considering the two influencing factors of the battery's SOH and determining the influence degree of the two on the battery health state according to the battery design characteristics, the SOH of the battery is estimated, thereby improving the accuracy of the battery SOH estimation result. The present invention adopts a dual-time-constant coupling identification method to improve the accuracy of model parameters, and improves the accuracy of the final battery state estimation result by introducing operating condition correction in the dual Kalman filter. The battery state evaluation method provided by the present invention has the characteristics of high equivalent circuit model parameter identification accuracy, strong normalization effect of model parameters under various operating conditions, and stable parameters, and can simultaneously estimate the battery SOC and SOH, which is beneficial to the energy management and service life optimization of power batteries.

[0106] Figure 5 It is a schematic diagram of the specific working process of the battery state evaluation provided by the embodiment of the present invention. Figure 6It can be seen that the present invention artificially sets two time constants, τ1 and τ2, corresponding to the electrochemical characteristics of the battery in the high-frequency response region and the low-frequency response region, that is, the RC loop module region in the second-order equivalent circuit model. The battery model parameter identification is carried out in these two regions respectively. By considering the technical characteristics of the DC pulse test method and the AC impedance test method, during the offline parameter identification process, the convolution mathematical operation is used to correlate the two test data, fully reflecting the characteristics of the battery such as ohmic polarization, electrochemical polarization, and concentration polarization. The identification result is accurate and stable. In addition, based on the offline model parameters, the online parameter identification of closed-loop control is carried out. The battery SOC and the model dynamic parameters are used as the state quantities of the dual Kalman filter, and the calculation is carried out in an alternating recursive form. Under specific working conditions, the result is corrected based on the offline model parameters, and finally the high-precision and strong correlation of SOC and SOH are achieved.

[0107] It should be noted that in practical applications, the parameter identification of more time constants can also be realized according to the parameter identification method of the two time constants provided in the embodiments of the present invention to further improve the accuracy of the parameter identification result. Specifically, the more time constants are set, the more accurate the final parameter identification result is, and the higher the calculation complexity is. Usually, the parameter identification accuracy of two time constants can already meet the actual requirements, and the present invention is not limited thereto.

[0108] By performing the above steps, the battery state evaluation method provided by the embodiments of the present invention determines the two time constants in the equivalent circuit model of the battery according to the actual design parameters and process conditions of the battery, corresponding to the electrochemical characteristics of the battery in the high-frequency response region and the low-frequency response region, that is, the RC loop module region in the second-order equivalent circuit model. The off-line identification of the equivalent circuit model parameters of the circuit is carried out in these two regions respectively. During the off-line parameter identification process, each model parameter is correlated, fully reflecting the characteristics of the battery such as ohmic polarization, electrochemical polarization, and concentration polarization. Through the parameter identification of the two time constants, the identification result is accurate and stable. Then, the off-line identification result is used as the initial parameter value for on-line parameter identification, further improving the accuracy of the on-line parameter identification result, that is, the battery state evaluation result.

[0109] The embodiments of the present invention also provide a battery state evaluation device, as Figure 6 shown. The battery state evaluation device specifically includes:

[0110] An acquisition module 101, configured to acquire the design parameters and process conditions of the target battery and establish an equivalent circuit model of the target battery. For the detailed content, refer to the relevant description of step S101 in the above method embodiment, and details are not described herein again.

[0111] The first processing module 102 is configured to determine the ohmic resistance in the equivalent circuit model, the first time constant corresponding to the electrochemical polarization resistance, and the second time constant corresponding to the battery concentration polarization resistance based on design parameters and process conditions, where the second time constant is greater than the first time constant. For detailed content, refer to the relevant description of step S102 in the above method embodiment, which will not be elaborated here.

[0112] The second processing module 103 is configured to calculate the parameter values of the ohmic resistance, the electrochemical polarization resistance, and the first capacitor in the RC circuit where the electrochemical polarization resistance is located based on the first time constant. For detailed content, refer to the relevant description of step S103 in the above method embodiment, which will not be elaborated here.

[0113] The third processing module 104 is configured to calculate the parameter values of the battery concentration polarization resistance and the second capacitor in the RC circuit where the battery concentration polarization resistance is located based on the second time constant, the ohmic resistance, the electrochemical polarization resistance, and the parameter values corresponding to the first capacitor. For detailed content, refer to the relevant description of step S104 in the above method embodiment, which will not be elaborated here.

[0114] The fourth processing module 105 is configured to perform battery state evaluation based on the parameter values of the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor, and obtain the battery state evaluation result of the target battery. For detailed content, refer to the relevant description of step S105 in the above method embodiment, which will not be elaborated here.

[0115] Through the collaborative cooperation of the above-mentioned various components, the battery state evaluation device provided by the embodiment of the present invention determines two time constants in the equivalent circuit model of the battery according to the actual design parameters and process conditions of the battery, which respectively correspond to the electrochemical characteristics of the battery in the high-frequency response region and the low-frequency response region, that is, the RC loop module region in the second-order equivalent circuit model. Circuit equivalent model parameter offline identification is carried out in these two regions, and during the offline parameter identification process, each model parameter is associated, fully reflecting the characteristics of battery ohmic polarization, electrochemical polarization, concentration polarization, etc. Through the parameter identification of the two time constants, the identification result is accurate and stable. Then, the offline identification result is used as the initial parameter value for online parameter identification, further improving the accuracy of the online parameter identification result, that is, the battery state evaluation result.

[0116] According to an embodiment of the present invention, there is also provided an electronic device, as Figure 7 shown, the electronic device may include a processor 901 and a memory 902, where the processor 901 and the memory 902 may be connected through a bus or other means, Figure 7 taking the connection through the bus as an example here.

[0117] The processor 901 may be a Central Processing Unit (CPU). The processor 901 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., in the form of chips, or combinations of the above types of chips.

[0118] The memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the method embodiments of the present invention. The processor 901 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 902, that is, implements the methods in the above method embodiments.

[0119] The memory 902 may include a program storage area and a data storage area. Among them, the program storage area can store an operating device and application programs required for at least one function; the data storage area can store data created by the processor 901, etc. In addition, the memory 902 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 902 may optionally include a memory remotely located relative to the processor 901, and these remote memories can be connected to the processor 901 through a network. Examples of the above networks include, but are not limited to, the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof.

[0120] One or more modules are stored in the memory 902 and, when executed by the processor 901, implement the methods in the above method embodiments.

[0121] For specific details of the above electronic device, reference may be made to the corresponding related descriptions and effects in the above method embodiments for understanding, and details are not elaborated herein.

[0122] Those skilled in the art can understand that to implement all or part of the processes in the above-described embodiment methods, it can be completed by instructing relevant hardware through a computer program. The implemented program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memories.

[0123] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for evaluating the state of a battery, characterized in that, it includes: Obtain the design parameters and process conditions of the target battery, and establish an equivalent circuit model of the target battery; Based on the design parameters and process conditions, determine the ohmic resistance in the equivalent circuit model, the first time constant corresponding to the electrochemical polarization resistance, and the second time constant corresponding to the battery concentration polarization resistance, where the second time constant is greater than the first time constant; By means of the AC impedance method, based on the first time constant, calculate the parameter values of the ohmic resistance, the electrochemical polarization resistance, and the first capacitor in the RC circuit where the electrochemical polarization resistance is located; By means of the DC pulse method, based on the second time constant, the ohmic resistance, the electrochemical polarization resistance, and the parameter values corresponding to the first capacitor, calculate the parameter values of the battery concentration polarization resistance and the second capacitor in the RC circuit where the battery concentration polarization resistance is located; Based on the parameter values of the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor, conduct a battery state evaluation to obtain the battery state evaluation result of the target battery.

2. The method according to claim 1, characterized in that, The battery state evaluation is carried out based on the parameter values of the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor to obtain the battery state evaluation result of the target battery, including: Initialize the model parameters of the equivalent circuit model with the parameter values of the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor; Use the battery state and model parameters of the target battery as the state variables of the dual Kalman filter algorithm to perform online parameter identification on the initialized parameter identification model, and obtain the first battery state evaluation result of the target battery. The first battery state evaluation result includes: the estimated result of the state of charge of the battery.

3. The method according to claim 2, characterized in that, Before using the battery state and model parameters of the target battery as the state variables of the dual Kalman filter algorithm to perform online parameter identification on the initialized parameter identification model, the method further includes: Obtain the current operating condition of the target battery; Judge whether the current operating condition belongs to a preset condition; When the current operating condition belongs to a preset condition, obtain the current open-circuit voltage of the target battery; Based on the relationship curve between the open-circuit voltage of the target battery and the state of charge of the battery and the current open-circuit voltage, determine the estimated result of the state of charge of the target battery.

4. The method according to claim 3, characterized in that, When the current operating condition does not belong to a preset condition, use the battery state and model parameters of the target battery as the state variables of the dual Kalman filter algorithm to perform online parameter identification on the initialized parameter identification model.

5. The method according to claim 3, characterized in that, The first battery state evaluation result further includes: the current model parameters of the parameter identification model, and the method further includes: Obtain the current cumulative usage time and the current cumulative charge-discharge capacity of the target battery; Determine the first battery health state of the target battery based on the current cumulative usage time and the current cumulative charge-discharge capacity; Calculate the second battery health state of the target battery based on the current model parameters; Determine the battery health state estimation result of the target battery based on the first battery health state and the second battery health state.

6. The method according to claim 5, wherein, the determining the battery health state estimation result of the target battery based on the first battery health state and the second battery health state includes: Obtain the battery health state evaluation weight corresponding to the target battery; Calculate the battery health state estimation result of the target battery based on the first battery health state, the second battery health state, and the battery health state evaluation weight.

7. The method according to claim 6, wherein, the obtaining the battery health state evaluation weight corresponding to the target battery includes: Obtain the battery type of the target battery; Determine the battery health state evaluation weight corresponding to the target battery based on the battery type.

8. A battery state evaluation device, wherein, it includes: An acquisition module, configured to acquire the design parameters and process conditions of the target battery, and establish an equivalent circuit model of the target battery; A first processing module, configured to determine the ohmic resistance, the first time constant corresponding to the electrochemical polarization resistance, and the second time constant corresponding to the battery concentration polarization resistance in the equivalent circuit model based on the design parameters and process conditions, and the second time constant is greater than the first time constant; A second processing module, configured to calculate the parameter values of the ohmic resistance, the electrochemical polarization resistance, and the first capacitor in the RC circuit where the electrochemical polarization resistance is located based on the first time constant by using the alternating current impedance method; A third processing module, configured to calculate the parameter values of the battery concentration polarization resistance and the second capacitor in the RC circuit where the battery concentration polarization resistance is located based on the second time constant, the ohmic resistance, the electrochemical polarization resistance, and the parameter values of the first capacitor by using the direct current pulse method; A fourth processing module, configured to perform battery state evaluation based on the parameter values of the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor, and obtain the battery state evaluation result of the target battery.

9. An electronic device, wherein, it includes: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein, the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1-7.

Images