CN114720898A - Battery state evaluation method and device - Google Patents
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Abstract
The invention provides a battery state evaluation method and a device, wherein the method comprises the following steps: obtaining design parameters and process conditions of a target battery, and establishing an equivalent circuit model of the target battery; determining ohmic resistance, a first time constant corresponding to electrochemical polarization resistance and a second time constant corresponding to battery concentration polarization resistance in an equivalent circuit model based on design parameters and process conditions; calculating the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitance in the RC loop where the electrochemical polarization resistance is located based on the first time constant; calculating a battery concentration polarization resistance and a parameter value corresponding to a second capacitor in an RC loop where the battery concentration polarization resistance is located based on the second time constant, the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitor; and performing battery state evaluation based on the parameter values. The electrochemical characteristics of the battery in high-frequency and low-frequency response areas are identified through the two time constants, the identification result is accurate and stable, and the accuracy of the evaluation result is improved.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a battery state evaluation method and device.
Background
In recent years, as the consumption of non-renewable energy and the problem of environmental pollution become more and more severe, a new green energy source replacing the traditional non-renewable energy source is a major focus of current research, and therefore, new energy electric vehicles have been developed rapidly. The lithium ion battery has the advantages of high energy density, long cycle life, wide use temperature range, no memory effect and the like, and is widely applied to the popularization process of the electric automobile.
The state evaluation of the lithium ion battery comprises the following steps: the estimation Of the State Of Charge (SOC) and the State Of Health (SOH) Of the battery is used as the core Of the battery management system Of the electric vehicle, and the estimation accuracy directly influences the charging and discharging limit, the service life and the driving safety Of the lithium battery.
In the engineering application of an automobile power battery system, currently, the SOC of a lithium ion battery is estimated generally by establishing a battery parameter model, and then finally estimating the SOC of the lithium ion battery by a model parameter identification method, so as to estimate the SOH of the battery. However, in the existing model estimation method, no matter general kalman filtering or extended kalman filtering is used, a core problem cannot be solved, namely accurate acquisition of a battery state space equation, and under the condition that the equivalent circuit model has the same structure, accurate identification of battery model parameters often determines success or failure of battery state estimation.
The lithium ion battery has the characteristics of high-frequency response and low-frequency response in the using process due to the characteristics of the lithium ion battery, namely, the model needs to have two parts of short-period model parameters and long-period model parameters at the same time, in the conventional direct current pulse test, if the parameter identification adopts a short-period time scale, the battery characteristics of the part of the lithium ion battery diffusion control cannot be embodied, and if the long-period time scale is adopted, the phenomenon of supersaturation of model data can occur; in an alternating current impedance test, a second-order RC circuit presents an arc-shaped RC ring in a low-frequency region of an impedance spectrum, and the RC ring does not completely conform to the characteristics of a battery in diffusion control; in a word, no matter which method is adopted, the charging and discharging characteristics of the battery at a high frequency band and a low frequency band can not be completely reflected, so that the identification of parameters of a battery model is not accurate, and the accuracy of a final state evaluation result of the lithium ion battery is further influenced.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for evaluating a battery state, so as to overcome a problem in the prior art that data generated by a battery in an energy storage scene are not aligned on a time axis, which causes inconvenience in subsequent data analysis.
The embodiment of the invention provides a battery state evaluation method, which comprises the following steps:
obtaining design parameters and process conditions of a target battery, and establishing an equivalent circuit model of the target battery;
determining 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 process conditions, wherein the second time constant is greater than the first time constant;
calculating parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance and a first capacitance in an RC loop in which the electrochemical polarization resistance is positioned based on the first time constant;
calculating a parameter value corresponding to a second capacitor in an RC loop where the battery concentration polarization resistance and the battery concentration polarization resistance are located based on the second time constant, the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitor;
and performing battery state evaluation based on the parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitance and the second capacitance to obtain a battery state evaluation result of the target battery.
Optionally, the performing battery state evaluation based on the parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitance, and the second capacitance to obtain a battery state evaluation result of the target battery includes:
initializing model parameters of the equivalent circuit model according to parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitance and the second capacitance;
taking the battery state and the model parameters of the target battery as state quantities of a double-Kalman filtering algorithm, and performing online parameter identification on the initialized parameter identification model to obtain a first battery state evaluation result of the target battery, wherein the first battery state evaluation result comprises: and estimating the state of charge of the battery.
Optionally, before performing online parameter identification on the initialized parameter identification model by using the battery state and the model parameter of the target battery as state quantities of the dual kalman filtering algorithm, the method further includes:
acquiring the current operation condition of the target battery;
judging whether the current operation working condition belongs to a preset working condition or not;
when the current operating condition belongs to a preset condition, acquiring the current open-circuit voltage of the target battery;
and determining a battery state of charge estimation result of the target battery based on the relation curve of the open-circuit voltage and the battery state of charge of the target battery and the current open-circuit voltage.
Optionally, when the current operating condition does not belong to a preset condition, performing online parameter identification on the initialized parameter identification model by using the battery state and the model parameter of the target battery as state quantities of the dual-kalman filtering algorithm.
Optionally, the first battery state evaluation result further includes: the parameters identify current model parameters of the model, the method further comprising:
acquiring the current accumulated service time and the current accumulated charge-discharge capacity of the target battery;
determining a first battery health state of the target battery based on the current accumulated usage time and the current accumulated charge-discharge capacity;
calculating a second battery state of health of the target battery based on the current model parameters;
determining a battery state of health estimation of the target battery based on the first battery state of health and the second battery state of health.
Optionally, the determining a battery state of health estimate of the target battery based on the first battery state of health and the second battery state of health comprises:
acquiring a battery health state evaluation weight corresponding to the target battery;
calculating a battery state of health estimation result of the target battery based on the first and second battery states of health and the battery state of health estimation weight.
Optionally, the obtaining of the battery state of health evaluation weight corresponding to the target battery includes:
acquiring the battery type of the target battery;
and determining a battery state of health evaluation weight corresponding to the target battery based on the battery type.
An embodiment of the present invention further provides a battery state evaluation device, including:
the acquisition module is used for acquiring design parameters and process conditions of a target battery and establishing an equivalent circuit model of the target battery;
a first processing module, configured to determine, based on the design parameters and process conditions, an ohmic resistance in the equivalent circuit model, a first time constant corresponding to an electrochemical polarization resistance, and a second time constant corresponding to a battery concentration polarization resistance, where the second time constant is greater than the first time constant;
the second processing module is used for calculating parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance and the first capacitance in an RC loop where the electrochemical polarization resistance is located based on the first time constant;
the third processing module is used for calculating a battery concentration polarization resistance and a parameter value corresponding to a second capacitor in an RC loop where the battery concentration polarization resistance is located based on the second time constant, the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitor;
and the fourth processing module is used for performing battery state evaluation on the basis of the parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor and the second capacitor to obtain a battery state evaluation result of the target battery.
An embodiment of the present invention further provides an electronic device, including: the device comprises a memory and a processor, wherein the memory and the processor are connected with each other in a communication mode, computer instructions are stored in the memory, and the processor executes the computer instructions so as to execute the method provided by the embodiment of the invention.
The embodiment of the invention also provides a computer-readable storage medium, which stores computer instructions for enabling a computer to execute the method provided by the embodiment of the invention.
The technical scheme of the invention has the following advantages:
the embodiment of the invention provides a battery state evaluation method and a battery state evaluation device, wherein an equivalent circuit model of a target battery is established by acquiring design parameters and process conditions of the target battery; determining ohmic resistance in an equivalent circuit model, a first time constant corresponding to electrochemical polarization resistance and a second time constant corresponding to battery concentration polarization resistance based on design parameters and process conditions, wherein the second time constant is greater than the first time constant; calculating the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitance in the RC loop where the electrochemical polarization resistance is located based on the first time constant; calculating a battery concentration polarization resistance and a parameter value corresponding to a second capacitor in an RC loop where the battery concentration polarization resistance is located based on the second time constant, the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitor; and evaluating the battery state based on the parameter values corresponding to 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. Therefore, two time constants in the equivalent circuit model of the battery are respectively determined according to actual design parameters and process conditions of the battery, the electrochemical characteristics of the battery in a high-frequency response area and a low-frequency response area are respectively corresponding, namely an RC ring module area in a second-order equivalent circuit model, offline identification of circuit equivalent model parameters is respectively carried out in the two areas, and in the offline parameter identification process, all model parameters are correlated, so that the characteristics of ohmic polarization, electrochemical polarization, concentration polarization and the like of the battery are fully embodied.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a battery state evaluation method in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a second-order equivalent circuit model of a battery according to an embodiment of the present invention;
fig. 3 is a schematic diagram of nyquist in an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the relationship between the open-circuit voltage and the state of charge of the battery according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a detailed operation of battery status evaluation according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a battery state evaluation apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device in an embodiment of the present invention.
Detailed Description
In order to make the objects, 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 drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply 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 limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The technical features mentioned in the different embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.
The lithium ion battery has the characteristics of high-frequency response and low-frequency response in the using process due to the characteristics of the lithium ion battery, namely, the model needs to have two parts of short-period model parameters and long-period model parameters at the same time, in the conventional direct current pulse test, if the parameter identification adopts a short-period time scale, the battery characteristics of the part of the lithium ion battery diffusion control cannot be embodied, and if the long-period time scale is adopted, the phenomenon of supersaturation of model data can occur; in an alternating current impedance test, a second-order RC circuit presents an arc-shaped RC ring in a low-frequency region of an impedance spectrum, and the RC ring does not completely conform to the characteristics of a battery in diffusion control; in a word, no matter which method is adopted, the charging and discharging characteristics of the battery at a high frequency band and a low frequency band can not be completely reflected, so that the identification of parameters of a battery model is not accurate, and the accuracy of a final state evaluation result of the lithium ion battery is further influenced.
Based on the above problem, an embodiment of the present invention provides a battery state evaluation method, as shown in fig. 1, the battery state evaluation method specifically includes the following steps:
step S101: and obtaining design parameters and process conditions of the target battery, and establishing an equivalent circuit model of the target battery.
The equivalent circuit model of the battery is a battery parameter identification and basis, and an electronic element in the model corresponds to the internal reaction process of the battery in the charging and discharging process;
the first-order equivalent circuit model often does not have the capability of reflecting the battery characteristics, and the identification process of the multi-order equivalent circuit model is complex, so the second-order equivalent circuit model is applied in the engineering field, as shown in fig. 2. The above design parameters and process conditions include: in the production and manufacturing process of the battery, the physical properties of the used materials such as copper foil, aluminum foil, diaphragm and the like extend to the battery to cause certain ohmic resistance, the positive active material and the negative active material of the battery form an electric double layer capacitor in the charging and discharging process, and the electric double layer capacitor can hinder the electrochemical reaction to a certain extent to form electrochemical polarization resistance; furthermore, the propagation rate of lithium ions in the battery is affected by the temperature, viscosity, conductivity, etc. of the electrolyte, and the propagation rate also affects the charging and discharging of the battery within a certain range, resulting in a concentration polarization resistance.
Step S102: and determining ohmic resistance in the equivalent circuit model, a first time constant corresponding to the electrochemical polarization resistance and a second time constant corresponding to the battery concentration polarization resistance based on the design parameters and the process conditions.
Wherein the second time constant is greater than the first time constant. In practical application, according to the reaction rate of the electrochemical reaction, the corresponding first time constant of the ohmic resistance and the electrochemical polarization resistance is set to be tau 1, and the corresponding second time constant of the cell concentration polarization resistance is set to be tau 2; one in each of these two time domains. For example, the 51Ah NCM cell τ 1 can be set within a range of 1-5 s, and τ 2 can be set within a range of 100-500 s, which is only an example and not a limitation of the invention.
Step S103: and calculating the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitance in the RC loop in which the electrochemical polarization resistance is positioned based on the first time constant.
Specifically, in practical applications, specific values of the ohmic resistance R0, the electrochemical polarization resistance R1, and the first capacitance C1 in the equivalent circuit model shown in fig. 2 are calculated by an alternating current impedance method. For example, the ac impedance test is generally performed directly using an electrochemical workstation, since the upper limit of τ 1 is 5s, the scanning range of the device is set to 0.2 to 1000Hz in consideration of the device capability, and the resulting nyquist diagram is shown in fig. 3, where R0 and R1 can be directly obtained from fig. 3, where zr is R0+ R1/2, C1 can be obtained from ω of the semicircular vertex P, and C1 is 1/(ω R1).
Step S104: and calculating the battery concentration polarization resistance and a parameter value corresponding to a second capacitor in the RC loop where the battery concentration polarization resistance is located based on the second time constant, the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitor.
Specifically, the dc pulse method may be used to calculate specific values of the battery concentration polarization resistance R2 and the second capacitor C2 as shown in fig. 2 on the basis of the above parameter calculation results.
Illustratively, according to kirchhoff's law, under a certain SOC condition, when the battery input current is I, the output terminal voltage is:
U(t)＝Uoc-IR0-U1-U2
the open circuit voltage is:
Uoc＝U0
wherein the identification parameters R0, R1, C1 of the high frequency region associated with τ 1 have been acquired, and the identification parameters R2, C2 of the low frequency region associated with τ 2 can be acquired by using convolution coupling calculation, which is as follows:
let tau 1 take on value range [15], tau 2 take on value range [100500],
the equivalent circuit model RC loop has capacitance voltage, and the charge accumulation of the capacitance voltage on tau 1 and tau 2 can be expressed by a convolution model:
wherein f (τ) is the state formula:
f1(τ)＝IR1(1-e-t/(R1*C1))
f2(τ)＝IR2(1-e-t/(R2*C2))
wherein g (t- τ) is the attenuation:
g1(t-τ)＝e-t/(R1*C1)
g2(t-τ)＝e-t/(R2*C2)
model parameters R2, C2 within the time constant range τ 2 can be obtained by least squares fitting.
The off-line parameter identification of the battery equivalent circuit model is completed through the process, in practical application, the result based on the dual-time-constant coupling parameter identification is led into the equivalent circuit model through simulation software and is compared with a test result, and the test result shows that the model simulation voltage is very similar to the actually measured voltage.
Step S105: and evaluating the battery state based on the parameter values corresponding to 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.
By executing the above steps, the battery state evaluation method provided by the embodiment of the invention determines two time constants in the equivalent circuit model of the battery respectively according to the actual design parameters and the process conditions of the battery, respectively corresponds to the electrochemical characteristics of the battery in the high-frequency response region and the low-frequency response region, namely an RC ring module area in the second-order equivalent circuit model, the circuit equivalent model parameters are respectively identified in the two areas in an off-line way, in the off-line parameter identification process, the model parameters are correlated, the characteristics of ohmic polarization, electrochemical polarization, concentration polarization and the like of the battery are fully reflected, the identification result is accurate and stable through the parameter identification of two time constants, and then, the offline identification result is used as an initial parameter value to perform online parameter identification, so that the accuracy of the online parameter identification result, namely the battery state evaluation result, is further improved.
Specifically, in an embodiment, the step S105 specifically includes the following steps:
step S501: and initializing model parameters of the equivalent circuit model according to the parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor and the second capacitor.
Specifically, the off-line identification result data is used as an initialization parameter of the equivalent circuit model.
Step S502: taking the battery state and the model parameters of the target battery as the state quantity of the double-Kalman filtering algorithm, and carrying out online parameter identification on the initialized parameter identification model to obtain a first battery state evaluation result of the target battery, wherein the first battery state evaluation result comprises the following steps: and estimating the state of charge of the battery.
Specifically, the specific implementation process of performing online parameter identification by using the dual kalman filter algorithm in step S502 is implemented by the prior art, and may be implemented by referring to the online identification process based on the kalman filter algorithm in the prior art, which is not described herein again.
Specifically, in an embodiment, before executing the step S502, the method for estimating a battery state according to an embodiment of the present invention further includes the following steps:
step S503: and acquiring the current operation condition of the target battery.
Step S504: and judging whether the current operation working condition belongs to a preset working condition or not.
Wherein, preset operating mode includes: the fully charged working condition of the battery and the fully standing working condition of the battery.
Specifically, when the current operating condition does not belong to the preset operating condition, the step S502 is executed, otherwise, the step S505 is executed.
Step S505: and when the current operating condition belongs to the preset operating condition, acquiring the current open-circuit voltage of the target battery.
Step S506: and determining a battery charge state estimation result of the target battery based on the relation curve of the open-circuit voltage and the battery charge state of the target battery and the current open-circuit voltage.
Specifically, errors are caused by problems of structural accuracy of an equivalent circuit model and accuracy of an observation sensor, and the errors and the interference are ubiquitous in nature, whether Kalman filtering or other methods are adopted. Under a preset working condition, the SOC and the battery model parameters of the battery can be corrected by referring to a relation curve of the open-circuit voltage of the battery and the state of charge of the battery, namely an SOC-OCV curve, so that the accuracy of a final estimation result is improved. For example, fig. 4 is a schematic diagram of an SOC-OCV curve of a certain battery under a preset operating condition.
In practical application, because the dimension of the online parameter identification solution is very high, the R2 and the C2 can jump in a large range, and as a result, the convergence of the whole model algorithm is slow or the effective convergence cannot be carried out, and the chip computing power is wasted. In addition, the synchronous estimation of the battery SOH is calculated based on battery model parameters, and the abnormal change of the model parameters can also cause the difficulty in estimating the SOH.
Specifically, in an embodiment, the first battery state evaluation result further includes: the method for estimating the battery state provided by the embodiment of the invention further comprises the following steps:
step S106: and acquiring the current accumulated service time and the current accumulated charge and discharge capacity of the target battery.
Step S107: a first battery state of health of the target battery is determined based on the current accumulated usage time and the current accumulated charge-discharge capacity.
Step S108: calculating a second battery state of health of the target battery based on the current model parameters;
step S109: a battery state of health estimate for the target battery is determined based on the first battery state of health and the second battery state of health.
Specifically, in step S109, the battery state of health evaluation weight corresponding to the target battery is obtained; and calculating a battery state of health estimation result of the target battery based on the first battery state of health, the second battery state of health and the battery state of health estimation weight. In practical application, the battery type of the target battery can be obtained; and determining the battery state of health evaluation weight corresponding to the target battery based on the battery type.
Specifically, the SOH of a battery is mainly affected by two aspects: one is the capacity fade (SOHC) of the battery, and the SOHC is generally influenced by two indexes of the throughput cycle and the calendar cycle of the battery, and the SOHC value is obtained by recording the accumulated use time and the accumulated charge-discharge capacity of the battery. The other is the increase of internal resistance (SOHR) of the battery, and the SOHR needs to refer to internal resistance parameters output by double Kalman filtering. It can be set that the final state of health of the battery is SOH ═ k × SOHR + (1-k) × SOHC, the selected value of the battery state of health evaluation weight k follows the battery design characteristics, the values of the general energy type battery and the power type battery k are selected from 0 to 1, the value of the power type battery k is generally large, and the specific value can be selected according to the actual battery design characteristics, which is not limited by the invention.
Therefore, the SOH of the battery is estimated by comprehensively considering the two influence factors of the SOH of the battery and determining the influence degree of the two influence factors on the state of health of the battery according to the design characteristics of the battery, so that the accuracy of the SOH estimation result of the battery is improved. According to the method, the precision of the model parameters is improved by adopting a double-time constant coupling identification method, and the precision of the final battery state estimation result is improved by introducing working condition correction in double-Kalman filtering. The battery state evaluation method provided by the invention has the characteristics of high equivalent circuit model parameter identification precision, strong model parameter normalization effect under various working conditions and stable parameters, can synchronously estimate the SOC and the SOH of the battery, and is beneficial to the energy management meaning service life optimization of the power battery.
Fig. 5 is a schematic diagram of a specific working process of battery state estimation according to an embodiment of the present invention. As can be seen from fig. 6, the two time constants τ 1 and τ 2 are set artificially, and the electrochemical characteristics of the battery in the high-frequency response region and the low-frequency response region, that is, the RC ring module region in the second-order equivalent circuit model, are corresponding to each other, and the battery model parameters are identified in the two regions respectively; by considering the technical characteristics of a direct current pulse testing method and an alternating current impedance testing method, in the process of off-line parameter identification, convolution mathematical operation is used for correlating two kinds of testing data, so that the characteristics of ohmic polarization, electrochemical polarization, concentration polarization and the like of the battery are fully embodied, and the identification result is accurate and stable. In addition, on-line parameter identification of closed-loop control is carried out on the basis of off-line model parameters, battery SOC and model dynamic parameters are used as state quantities of double Kalman filtering, alternate recursion calculation is carried out, and result correction is carried out on the basis of the off-line model parameters under the condition that specific working conditions are met, so that high-precision and strong correlation of SOC and SOH is finally realized.
It should be noted that, in practical applications, parameter identification of more time constants can be realized according to the parameter identification mode of two time constants provided in the embodiment of the present invention, so as to further improve accuracy of the parameter identification result, specifically, the more the time constants are set, the more accurate the final parameter identification result is, the higher the calculation complexity is, and generally, the parameter identification accuracy in two time constants can meet actual requirements, which is not limited by the present invention.
By executing the above steps, the battery state evaluation method provided by the embodiment of the invention determines two time constants in the equivalent circuit model of the battery respectively according to the actual design parameters and the process conditions of the battery, respectively corresponds to the electrochemical characteristics of the battery in the high-frequency response region and the low-frequency response region, namely an RC ring module area in the second-order equivalent circuit model, the circuit equivalent model parameters are respectively identified in the two areas in an off-line way, in the off-line parameter identification process, the model parameters are correlated, the characteristics of ohmic polarization, electrochemical polarization, concentration polarization and the like of the battery are fully reflected, the identification result is accurate and stable through the parameter identification of two time constants, and then, the offline identification result is used as an initial parameter value to carry out online parameter identification, so that the accuracy of the online parameter identification result, namely the battery state evaluation result, is further improved.
An embodiment of the present invention further provides a battery state evaluation device, as shown in fig. 6, the battery state evaluation device specifically includes:
the obtaining module 101 is configured to obtain design parameters and process conditions of a target battery, and establish an equivalent circuit model of the target battery. For details, refer to the related description of step S101 in the above method embodiment, and no further description is provided here.
The first processing module 102 is configured to determine an ohmic resistance in the equivalent circuit model, a first time constant corresponding to the electrochemical polarization resistance, and a second time constant corresponding to the battery concentration polarization resistance based on the design parameters and the process conditions, where the second time constant is greater than the first time constant. For details, refer to the related description of step S102 in the above method embodiment, and no further description is provided here.
And the second processing module 103 is configured to calculate, based on the first time constant, a parameter value corresponding to the ohmic resistance, the electrochemical polarization resistance, and the first capacitance in the RC loop where the electrochemical polarization resistance is located. For details, refer to the related description of step S103 in the above method embodiment, and no further description is provided here.
And the third processing module 104 is configured to calculate a battery concentration polarization resistance and a parameter value corresponding to the second capacitor in the RC loop where the battery concentration polarization resistance is located, based on the second time constant, the ohmic resistance, the electrochemical polarization resistance, and the parameter value corresponding to the first capacitor. For details, refer to the related description of step S104 in the above method embodiment, and no further description is provided here.
And the fourth processing module 105 is configured to perform battery state evaluation based on parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor, and the second capacitor, so as to obtain a battery state evaluation result of the target battery. For details, refer to the related description of step S105 in the above method embodiment, and no further description is provided here.
Through the cooperation of the above components, the battery state evaluation device provided by the embodiment of the invention, by respectively determining two time constants in the equivalent circuit model of the battery according to the actual design parameters and the process conditions of the battery, the electrochemical characteristics of the battery in a high-frequency response region and a low-frequency response region are respectively corresponding, namely an RC ring module area in the second-order equivalent circuit model, the circuit equivalent model parameters are respectively identified in the two areas in an off-line way, in the off-line parameter identification process, the model parameters are correlated, the characteristics of ohmic polarization, electrochemical polarization, concentration polarization and the like of the battery are fully reflected, the identification result is accurate and stable through the parameter identification of two time constants, and then, the offline identification result is used as an initial parameter value to carry out online parameter identification, so that the accuracy of the online parameter identification result, namely the battery state evaluation result, is further improved.
There is also provided an electronic device according to an embodiment of the present invention, as shown in fig. 7, the electronic device may include a processor 901 and a memory 902, where the processor 901 and the memory 902 may be connected by a bus or in another manner, and fig. 7 illustrates an example of a connection by a bus.
The memory 902, which is a non-transitory computer readable storage medium, may be used for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as 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, i.e. implements the methods in the above-described method embodiments, by running non-transitory software programs, instructions and modules stored in the memory 902.
The memory 902 may include a storage program area and a storage data area, wherein the storage program area may store an application program required for operating the device, at least one function; the storage data area may store data created by the processor 901, and the like. Further, 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 device. In some embodiments, the memory 902 may optionally include memory located remotely from the processor 901, which may be connected to the processor 901 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 902, which when executed by the processor 901 performs the methods in the above-described method embodiments.
The specific details of the electronic device may be understood by referring to the corresponding related descriptions and effects in the above method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, and the implemented program can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A battery state evaluation method, comprising:
obtaining design parameters and process conditions of a target battery, and establishing an equivalent circuit model of the target battery;
determining 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, wherein the second time constant is greater than the first time constant;
calculating the ohmic resistance, the electrochemical polarization resistance and a parameter value corresponding to a first capacitance in an RC loop in which the electrochemical polarization resistance is positioned based on the first time constant;
calculating a parameter value corresponding to a second capacitor in an RC loop in which the battery concentration polarization resistance and the battery concentration polarization resistance are positioned based on the second time constant, the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitor;
and performing battery state evaluation based on the parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitance and the second capacitance to obtain a battery state evaluation result of the target battery.
2. The method according to claim 1, wherein the performing a battery state estimation based on the parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitance and the second capacitance to obtain a battery state estimation result of the target battery comprises:
initializing model parameters of the equivalent circuit model according to parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitance and the second capacitance;
taking the battery state and the model parameters of the target battery as state quantities of a double-Kalman filtering algorithm, and performing online parameter identification on the initialized parameter identification model to obtain a first battery state evaluation result of the target battery, wherein the first battery state evaluation result comprises: and estimating the state of charge of the battery.
3. The method of claim 2, wherein before performing online parameter identification on the initialized parameter identification model by using the battery state and model parameters of the target battery as state quantities of the dual kalman filter algorithm, the method further comprises:
acquiring the current operation condition of the target battery;
judging whether the current operation working condition belongs to a preset working condition or not;
when the current operating condition belongs to a preset condition, acquiring the current open-circuit voltage of the target battery;
and determining a battery state of charge estimation result of the target battery based on the relation curve of the open-circuit voltage and the battery state of charge of the target battery and the current open-circuit voltage.
4. The method according to claim 3, wherein when the current operating condition does not belong to a preset condition, the battery state and the model parameters of the target battery are used as state quantities of a double-Kalman filtering algorithm, and online parameter identification is performed on the initialized parameter identification model.
5. The method of claim 3, wherein the first battery state assessment result further comprises: the parameters identify current model parameters of the model, the method further comprising:
acquiring the current accumulated service time and the current accumulated charge-discharge capacity of the target battery;
determining a first battery health state of the target battery based on the current accumulated usage time and the current accumulated charge-discharge capacity;
calculating a second battery state of health of the target battery based on the current model parameters;
determining a battery state of health estimation of the target battery based on the first battery state of health and the second battery state of health.
6. The method of claim 5, wherein determining the battery state of health estimate for the target battery based on the first battery state of health and the second battery state of health comprises:
acquiring a battery health state evaluation weight corresponding to the target battery;
calculating a battery state of health estimation result of the target battery based on the first and second battery states of health and the battery state of health estimation weight.
7. The method of claim 6, wherein obtaining the battery state of health assessment weight corresponding to the target battery comprises:
acquiring the battery type of the target battery;
and determining a battery state of health evaluation weight corresponding to the target battery based on the battery type.
8. A battery state evaluation device, characterized by comprising:
the acquisition module is used for acquiring design parameters and process conditions of a target battery and establishing an equivalent circuit model of the target battery;
a first processing module, configured to determine, based on the design parameters and process conditions, an ohmic resistance in the equivalent circuit model, a first time constant corresponding to an electrochemical polarization resistance, and a second time constant corresponding to a battery concentration polarization resistance, where the second time constant is greater than the first time constant;
the second processing module is used for calculating parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance and the first capacitance in an RC loop where the electrochemical polarization resistance is located based on the first time constant;
the third processing module is used for calculating a battery concentration polarization resistance and a parameter value corresponding to a second capacitor in an RC loop where the battery concentration polarization resistance is located based on the second time constant, the ohmic resistance, the electrochemical polarization resistance and the parameter value corresponding to the first capacitor;
and the fourth processing module is used for performing battery state evaluation on the basis of the parameter values corresponding to the ohmic resistance, the electrochemical polarization resistance, the battery concentration polarization resistance, the first capacitor and the second capacitor to obtain a battery state evaluation result of the target battery.
9. An electronic device, comprising:
a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory having stored therein computer instructions, the processor performing the method of any of claims 1-7 by executing the computer instructions.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115561637A (en) * | 2022-10-12 | 2023-01-03 | 上海玫克生储能科技有限公司 | Parameter identification method and system based on equivalent circuit model and storage medium |
| CN117239182A (en) * | 2023-11-13 | 2023-12-15 | 中国科学院宁波材料技术与工程研究所 | Design method of corrosion-resistant metal fuel cell pile and pile structure |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115561637A (en) * | 2022-10-12 | 2023-01-03 | 上海玫克生储能科技有限公司 | Parameter identification method and system based on equivalent circuit model and storage medium |
| CN117239182A (en) * | 2023-11-13 | 2023-12-15 | 中国科学院宁波材料技术与工程研究所 | Design method of corrosion-resistant metal fuel cell pile and pile structure |
| CN117239182B (en) * | 2023-11-13 | 2024-03-05 | 中国科学院宁波材料技术与工程研究所 | Design method of corrosion-resistant metal fuel cell pile and pile structure |
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