Method and apparatus for estimating SOC and SOH of lithium-ion battery, and computer storage medium

Abstract

Disclosed in the present application are a method and apparatus for estimating the SOC and SOH of a lithium-ion battery, and a computer storage medium. The method comprises: during a life cycle experiment of a lithium-ion battery, collecting battery output information at intervals of a preset duration; then, calculating a voltage drop per unit time corresponding to the lithium-ion battery, and on the basis of the battery output information and the voltage drop per unit time, constructing an SOC calculation model for the lithium-ion battery; and on the basis of the SOC calculation model, defining a correction factor, and on the basis of the battery output information, the voltage drop per unit time and the correction factor, constructing an SOH calculation model for the lithium-ion battery.

Description

Method and device for estimating state of charge and state of health of lithium ion battery, and computer storage medium

[0001] The present application claims priority to the Chinese patent application No. 202411390872.3, filed on September 30, 2024, to the Chinese Patent Office, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of battery, in particular to a method and device for estimating state of charge (SOC) and state of health (SOH) of lithium ion battery, and computer storage medium. BACKGROUND

[0003] With the continuous development and progress of battery technology, batteries are increasingly widely used in people's production and life. As a rechargeable battery with high energy density and long cycle life, lithium ion battery is widely used in electric vehicles, intelligent electrical equipment and many other fields. Therefore, the estimation method of state of charge (SOC) and state of health (SOH) of lithium battery has become a research hotspot. TECHNICAL PROBLEM

[0004] At present, the methods for estimating the SOC of lithium battery in the industry mainly include data-driven methods and model-based methods. However, the data-driven method needs to rely on the accuracy of the current sensor, or needs to give the battery enough rest time, and the measurement accuracy and efficiency are low. The model-based method is only suitable for new batteries. The methods for estimating the SOH of lithium battery in the industry mainly include data-driven methods and adaptive system-based methods. However, the data-driven method needs to evaluate a large data set through physical analysis to determine the correlation between battery operation and degradation, and the adaptive system-based method needs to calculate parameters sensitive to battery degradation to determine the SOH of the battery. Both methods have poor computational load and real-time performance. Therefore, it is particularly important to propose a technical solution that can improve the accuracy of online estimation of the SOC and SOH of lithium ion battery. TECHNICAL SOLUTION

[0005] In a first aspect, the present application provides a method for estimating the SOC and SOH of a lithium ion battery, the method comprising:

[0006] During the life cycle experiment of the lithium ion battery, the battery output information of the lithium ion battery is collected at intervals for a preset time length, the battery output information including the output current and output voltage at each collection time;

[0007] According to the battery output information, the unit time voltage drop corresponding to the lithium ion battery is calculated, and based on the battery output information and the unit time voltage drop, a SOC calculation model of the lithium ion battery is constructed.

[0008] define a correction factor according to the SOC calculation model, and construct the SOH calculation model of the lithium ion battery according to the battery output information, the voltage drop per unit time, and the correction factor;

[0009] The SOC calculation model is configured to calculate the SOC value of the lithium ion battery, and the SOH calculation model is configured to calculate the SOH value of the lithium ion battery.

[0010] In a second aspect, the present application provides an estimation device for SOC and SOH of a lithium ion battery, which comprises:

[0011] a memory storing executable program codes;

[0012] a processor coupled with the memory;

[0013] The processor invokes the executable program codes stored in the memory to execute the estimation method for SOC and SOH of a lithium ion battery disclosed in the first aspect of the present application.

[0014] In a third aspect, the present application provides a computer storage medium storing computer instructions, which are configured to execute the estimation method for SOC and SOH of a lithium ion battery disclosed in the first aspect of the present application when invoked. Advantages

[0015] The present application provides the following advantages: in the embodiments of the present application, during the life cycle experiment of the lithium ion battery, the battery output information of the lithium ion battery is collected at intervals for a preset time length, the corresponding voltage drop per unit time of the lithium ion battery is calculated according to the battery output information, and the SOC calculation model of the lithium ion battery is constructed based on the battery output information and the voltage drop per unit time. A correction factor is defined according to the SOC calculation model, and the SOH calculation model of the lithium ion battery is constructed according to the battery output information, the voltage drop per unit time, and the correction factor. It can be seen that the present application can define the models of SOC and SOH based on the new variable voltage drop per unit time, estimate the instantaneous SOC and SOH of the battery online without full cycle test of the battery, improve the efficiency and accuracy of determining the SOC and SOH of the battery, and estimate the SOC and SOH of all batteries based on any one in the same batch of batteries, thereby improving the accuracy of online estimation of the SOC and SOH of the lithium ion battery. BRIEF DESCRIPTION OF DRAWINGS

[0016] FIG. 1 is a flow diagram of an estimation method for SOC and SOH of a lithium ion battery disclosed in an embodiment of the present application;

[0017] FIG. 2 is a flowchart of another method for estimating SOC and SOH of a lithium ion battery according to an embodiment of the present application;

[0018] FIG. 3 is a structural diagram of an apparatus for estimating SOC and SOH of a lithium ion battery according to an embodiment of the present application;

[0019] FIG. 4 is a structural diagram of another apparatus for estimating SOC and SOH of a lithium ion battery according to an embodiment of the present application;

[0020] FIG. 5 is a structural diagram of still another apparatus for estimating SOC and SOH of a lithium ion battery according to an embodiment of the present application.

[0021] Embodiments of the present application

[0022] The present application discloses a method and apparatus for estimating SOC and SOH of a lithium ion battery, and a computer storage medium. The method can define a model of SOC and SOH based on a new variable, i.e., voltage drop per unit time, and estimate the instantaneous SOC and SOH of the battery online without full-cycle testing of the battery. The method improves the efficiency of determining the SOC and SOH of the battery, has high accuracy, and can model any one of the same batch of batteries to estimate the SOC and SOH of all the batteries. The model has high precision and robustness, and can improve the accuracy of online estimation of the SOC and SOH of the lithium ion battery. The following will be described in detail.

[0023] Embodiment one

[0024] Referring to FIG. 1, which is a flowchart of a method for estimating SOC and SOH of a lithium ion battery according to an embodiment of the present application. The method for estimating SOC and SOH of a lithium ion battery described in FIG. 1 can be applied to an apparatus for estimating SOC and SOH of a lithium ion battery, which can include an intelligent server or an intelligent platform configured to estimate the SOC and SOH values of the lithium ion battery. The intelligent server can include a local server or a cloud server, which is not limited in the embodiments of the present application. As shown in FIG. 1, the method for estimating SOC and SOH of a lithium ion battery can include the following operations:

[0025] 101. During a life cycle experiment of a lithium ion battery, battery output information of the lithium ion battery is collected at intervals of a preset time length.

[0026] In the embodiments of the present application, optionally, during the life cycle experiment of the lithium ion battery, the preset time length of the battery output information time interval of the lithium ion battery can be set by the tester according to actual needs, for example, it can be collected every 30 seconds, the battery output information of the lithium ion battery can include the output current and output voltage of the lithium ion battery at each collection time, and the lithium ion battery can be a plurality of batteries in the same batch, which is not limited in the present application.

[0027] 102. According to the battery output information, the unit time voltage drop corresponding to the lithium ion battery is calculated, and based on the battery output information and the unit time voltage drop, the SOC calculation model of the lithium ion battery is constructed.

[0028] In the embodiments of the present application, optionally, the unit time voltage drop can represent the voltage drop of lithium ion per unit time in the discharging process, SOC (State of Charge) represents the state of charge of the battery, which is also commonly known as the remaining capacity or charging and discharging state. The most convenient and direct output of the battery in use is voltage, current and time. A single voltage value is never enough to identify the SOC of the battery, because the voltage will change with the aging of the battery, therefore, the parameter of the unit time voltage drop corresponding to the lithium ion battery is set to establish the SOC and SOH model. The average correlation between the battery SOC and the battery voltage is 0.988, and the average correlation between the battery SOC and the reciprocal of the unit time voltage drop of the battery is 0.984, so the relationship between the SOC of the battery and the voltage and the reciprocal of the unit time voltage drop is approximately linear, which is not limited in the present application.

[0029] 103. According to the SOC calculation model, a correction factor is defined, and according to the battery output information, the unit time voltage drop and the correction factor, the SOH calculation model of the lithium ion battery is constructed.

[0030] In the embodiments of the present application, optionally, the SOH (State of Health) of the battery represents the health state of the battery, which is affected by the capacity attenuation and impedance increase of the battery. According to the correction factor defined by the SOC calculation model, the SOH calculation model of the battery is set to enable the SOH calculation model of the battery to calculate the SOH of the battery based on the SOC of the battery. The influence of the SOH on the SOC changes with the change of the SOH, which is not limited in the present application.

[0031] It can be seen that the method for estimating the SOC and SOH of the lithium ion battery described in FIG. 1 can collect the battery output information of the lithium ion battery at a preset interval during the life cycle experiment of the lithium ion battery, calculate the voltage drop per unit time of the lithium ion battery according to the battery output information, construct the SOC calculation model of the lithium ion battery based on the battery output information and the voltage drop per unit time, define the correction factor according to the SOC calculation model, and construct the SOH calculation model of the lithium ion battery according to the battery output information, the voltage drop per unit time, and the correction factor. The model of the SOC and SOH can be defined based on the new variable voltage drop per unit time, the instantaneous SOC and SOH of the battery can be estimated online without full cycle test of the battery, the efficiency and accuracy of determining the SOC and SOH of the battery are improved, and the SOC and SOH of all batteries can be estimated based on any one battery in the same batch, the model has high precision and robustness, and the accuracy of online estimation of the SOC and SOH of the lithium ion battery is improved.

[0032] In an optional embodiment, constructing the SOH calculation model of the lithium ion battery according to the battery output information, the voltage drop per unit time, and the correction factor can include the following operations:

[0033] Constructing the SOH regression model of the lithium ion battery at a preset SOC level according to the battery output information and the voltage drop per unit time;

[0034] Constructing the SOH calculation model of the lithium ion battery based on the SOH regression model at the preset SOC level based on the correction factor.

[0035] In the optional embodiment, optionally, the SOH regression model of the lithium ion battery at a certain SOC level is set according to the battery output information and the voltage drop per unit time as follows: where A and B are undetermined coefficients, the SOH calculation model of the lithium ion battery is constructed based on the SOH regression model at the preset SOC level based on the correction factor, that is: That is, the SOH of the battery can be calculated based on the SOC of the battery, and the influence of the SOH on the SOC changes with the change of the SOH.

[0036] It can be seen that the optional embodiment can construct the SOH regression model of the lithium ion battery at a preset SOC level according to the battery output information and the voltage drop per unit time, construct the SOH calculation model of the lithium ion battery based on the SOH regression model at the preset SOC level based on the correction factor, construct the SOH calculation model based on the correction factor newly defined by the SOC calculation model, improve the accuracy of the SOH model, and improve the accuracy and efficiency of calculating the SOH of the battery.

[0037] In another optional embodiment, constructing the SOH regression model of the lithium ion battery at the preset SOC level according to the battery output information and the voltage drop per unit time can include the following operations:

[0038] Based on the battery output information, data regression fitting is performed to obtain SOH model coefficients of the lithium ion battery;

[0039] Based on the voltage drop per unit time and the SOH model coefficients, the SOH regression model of the lithium ion battery at the preset SOC level is constructed;

[0040] The SOH regression model includes:

[0041] The SOH calculation model includes:

[0042] Wherein, A and B represent SOH model coefficients, and a(SOC) represents a correction factor.

[0043] In this optional embodiment, optionally, based on the battery output information, data regression fitting is performed to obtain SOH model coefficients A and B of the lithium ion battery, and a(SOC) represents a correction factor.

[0044] It can be seen that by implementing this optional embodiment, the SOH regression model of the lithium ion battery at the preset SOC level can be constructed based on the voltage drop per unit time and the SOH model coefficients, and the SOH calculation model can be constructed based on the newly defined correction factor of the SOC calculation model, thereby improving the accuracy of the SOH model and improving the accuracy and efficiency of calculating the SOH of the battery.

[0045] In yet another optional embodiment, defining the correction factor according to the SOC calculation model can include the following operations:

[0046] Based on the SOC calculation model and the battery output information, data regression fitting is performed to obtain correction factor coefficients of the lithium ion battery;

[0047] Based on the correction factor coefficients and the SOC calculation model, the correction factor is defined;

[0048] The calculation formula of the correction factor includes: a(SOC)=C1(SOC) 3 +C2(SOC) 2 +C3(SOC) 1 +C0

[0049] Wherein, C1, C2, C3 and C0 represent correction factor coefficients.

[0050] In the optional embodiment, the optional a(SOC) is a correction factor newly defined based on the SOC, which is a function of the SOC, a is not monotonically changed with the SOC, thus interpolation is needed, and a cubic polynomial is accurate enough, thus the calculation formula of the correction factor is a(SOC) = C1(SOC) + C2(SOC) + C3(SOC) + C0, wherein the correction factor coefficients of the lithium ion battery can be obtained through data regression fitting based on the SOC calculation model and the battery output information, and C1, C2, C3 and C0 represent the correction factor coefficients. 3 2 1

[0051] It can be seen that the optional embodiment can obtain the correction factor coefficients of the lithium ion battery through data regression fitting based on the SOC calculation model and the battery output information, define the correction factor based on the correction factor coefficients and the SOC calculation model, improve the accuracy of the correction factor, and introduce the correction factor into the SOH calculation model, without the need to re-regress the calculation model coefficients, thus improving the model accuracy and the calculation efficiency.

[0052] In yet another optional embodiment, the method for estimating the SOC and SOH of the lithium ion battery can further include the following operations:

[0053] The battery test values of the lithium ion battery are calculated according to the SOC calculation model and the SOH calculation model respectively, and the battery test values include the SOC test value and the SOH test value;

[0054] The battery verification values of the lithium ion battery are obtained, and the model indexes corresponding to the SOC calculation model and the SOH calculation model are calculated according to the battery test values and the battery verification values respectively, the battery verification values include the SOC verification value and the SOH verification value, and the model indexes include the average correlation coefficient and the root mean square error;

[0055] The SOC calculation model and the SOH calculation model are verified based on the model indexes corresponding to the SOC calculation model and the SOH calculation model respectively, verification results are obtained, and the SOC calculation model and the SOH calculation model are optimized based on the verification results.

[0056] In the optional embodiment, the battery test values include the SOC test value and the SOH test value, the battery verification values of the lithium ion battery include the SOC verification value and the SOH verification value, and the model accuracy is verified, the model indexes include the average correlation coefficient and the root mean square error. The calculation formula of the average correlation coefficient is: The calculation formula of the root mean square error is: ​​​The root mean square error is the square root of the square of the deviation of the predicted value from the true value and the number of observations n, which measures the deviation between the predicted value and the true value. The average correlation coefficient is high, and the root mean square error is small. Based on the average correlation coefficient and the root mean square error, the model is optimized. The same batch of four lithium ion batteries A, B, C and D are used as experimental objects to perform battery life cycle tests, and the battery output information is obtained. When the SOC calculation model and the SOH calculation model are constructed based on the battery output information of the lithium ion battery A, and the SOC value and the SOH value of the four batteries are estimated by the SOC calculation model and the SOH calculation model respectively, the average correlation coefficient and the root mean square error of all battery SOC estimates are shown in Table 1 as follows:

[0057] Table 1:

[0058] When the SOC calculation model and the SOH calculation model are constructed based on the battery output information of the lithium ion battery A, and the SOC value and the SOH value of the four batteries are estimated by the SOC calculation model and the SOH calculation model respectively, the average correlation coefficient and the root mean square error of the batteries A, B, C and D at a certain data point are shown in Table 2 as follows:

[0059] Table 2:

[0060] It can be seen that the SOC calculation model and the SOH calculation model show excellent accuracy when set to estimate the same batch of four test batteries, and using any one of the same batch of batteries for model construction produces similar accuracy for all four batteries. Optionally, the SOC calculation model can be set to online SOC estimation in the voltage range of 3.55V-3.95V, which is not limited in this embodiment.

[0061] It can be seen that the implementation of the optional embodiment can verify and optimize the SOC calculation model and the SOH calculation model based on the average correlation coefficient and the root mean square error, reduce the deviation between the predicted value and the true value of the model, and after modeling with any one of the same batch of batteries, the accuracy and precision of calculating the SOC and SOH of other batteries are similar, improving the accuracy and precision of the model.

[0062] Embodiment Two

[0063] Please refer to FIG. 2, which is a flowchart of a method for estimating the SOC and SOH of a lithium ion battery according to an embodiment of the present application. The method for estimating the SOC and SOH of a lithium ion battery described in FIG. 2 can be applied to a device for estimating the SOC and SOH of a lithium ion battery, which can include an intelligent server or an intelligent platform configured to estimate the SOC and SOH of a lithium ion battery. The intelligent server can include a local server or a cloud server, which is not limited in the embodiments of the present application. As shown in FIG. 2, the method for estimating the SOC and SOH of a lithium ion battery can include the following operations:

[0064] 201. During the life cycle experiment of the lithium ion battery, the battery output information of the lithium ion battery is collected at intervals of a preset time length.

[0065] 202. In the output voltage at each collection time, the voltage drop per unit time corresponding to the lithium ion battery is calculated based on the adjacent two output voltages.

[0066] In the embodiments of the present application, the voltage drop per unit time corresponding to the lithium ion battery can be calculated based on the adjacent two output voltages, i.e. where ΔV represents the difference between the two consecutive output voltages during the discharge process of the lithium ion battery, i.e. ΔV = V1-V2.

[0067] 203. The initial voltage of the lithium ion battery is determined, and the overpotential of the lithium ion battery after aging is calculated based on the output voltage at each collection time and the initial voltage.

[0068] In the embodiments of the present application, the initial voltage of the lithium ion battery can represent the initial voltage of the lithium ion battery when it leaves the factory, i.e. the voltage before the lithium ion battery ages. The overpotential of the lithium ion battery after aging can represent the aging voltage of the lithium ion battery. The relationship between the initial voltage, the output voltage and the overpotential of the lithium ion battery can be: V(terminal) = V eg +η

[0069] where V(terminal) represents the initial voltage of the lithium ion battery, V eg represents the output voltage of the lithium ion battery, and η represents the overpotential of the lithium ion battery, including reversible and irreversible parts, representing the degree of deviation from the equilibrium voltage.

[0070] 204. The resistance change of the lithium ion battery after aging is calculated based on the output current at each collection time and the overpotential.

[0071] In the embodiments of the present application, the voltage drop corresponding to the discharge process of the lithium ion battery can be approximated by the overpotential of the lithium ion battery, i.e. Then, the resistance change of the lithium ion battery after aging is calculated based on the output current of each collection time.

[0072] 205、According to the resistance change and the output current of each collection time, the corresponding unit time voltage drop of the lithium ion battery is calculated.

[0073] In the embodiment of the application, optionally, according to the resistance change and the output current of each collection time, the corresponding unit time voltage drop of the lithium ion battery is calculated, that is, At this time, the SOC of the battery is associated with the battery aging using the corresponding unit time voltage drop of the lithium ion battery. Most of the related models express the SOH of the battery with the cycle life N as the main aging parameter. However, the battery rarely experiences a complete charge / discharge cycle process during operation, and the cycle life is difficult to track. Therefore, the SOC of the battery is associated with the battery aging using the corresponding unit time voltage drop of the lithium ion battery, which can remove the cycle life N and improve the accuracy of the model.

[0074] 206、Based on the battery output information and the unit time voltage drop, a SOC calculation model of the lithium ion battery is constructed.

[0075] 207、According to the SOC calculation model, a correction factor is defined, and according to the battery output information, the unit time voltage drop and the correction factor, an SOH calculation model of the lithium ion battery is constructed.

[0076] In the embodiment of the application, for other descriptions of steps 201, 206 and 207, please refer to the detailed description of steps 101-103 in embodiment one. The embodiment of the application will not be repeated here.

[0077] It can be seen that the lithium ion battery SOC and SOH estimation method described in FIG. 2 can collect the battery output information of the lithium ion battery at a preset interval during the life cycle experiment of the lithium ion battery, calculate the unit time voltage drop corresponding to the lithium ion battery based on the adjacent two output voltages in the output voltage at each collection time, determine the initial voltage of the lithium ion battery, and calculate the overpotential of the lithium ion battery after aging according to the output voltage at each collection time and the initial voltage. According to the output current and the overpotential at each collection time, the resistance change of the lithium ion battery after aging is calculated, and the unit time voltage drop corresponding to the lithium ion battery is calculated according to the resistance change and the output current at each collection time. The SOC of the battery can be associated with the battery aging based on the unit time voltage drop corresponding to the lithium ion battery, avoiding using the difficult-to-track battery cycle life parameters to express the SOH of the battery, reducing the difficulty of determining the battery SOC and SOH, and improving the determination accuracy and efficiency. Based on the battery output information and the unit time voltage drop, the SOC calculation model of the lithium ion battery is constructed, the correction factor is defined according to the SOC calculation model, and the SOH calculation model of the lithium ion battery is constructed according to the battery output information, the unit time voltage drop and the correction factor. The model of SOC and SOH can be defined based on the new variable unit time voltage drop, the instantaneous SOC and SOH of the battery can be estimated online without full cycle test of the battery, the efficiency of determining the battery SOC and SOH is improved, the accuracy is higher, and the SOC and SOH of all batteries can be estimated based on any one in the same batch of batteries, the model accuracy and robustness are higher, and the accuracy of online estimation of the SOC and SOH of the lithium ion battery is improved.

[0078] In an optional embodiment, in the output voltage at each collection time, the calculation formula of the unit time voltage drop corresponding to the lithium ion battery based on the adjacent two output voltages includes:

[0079] Wherein, V' represents the unit time voltage drop, V1 and V2 represent the adjacent two output voltages, and Δt represents the unit time.

[0080] The calculation formula of the unit time voltage drop corresponding to the lithium ion battery based on the resistance change and the output current at each collection time includes:

[0081] Wherein, Δη represents the overpotential, I represents the output current at each collection time, and ΔR represents the resistance change.

[0082] In the optional embodiment, optionally, ΔV represents the difference between two consecutive output voltages during the discharging process of the lithium ion battery, that is, ΔV = V1-V2, the initial voltage of the lithium ion battery can represent the initial voltage of the lithium ion battery when it leaves the factory, that is, the voltage before the lithium ion battery is aged, the overpotential after the lithium ion battery is aged can represent the aged voltage of the lithium ion battery, and the relationship among the initial voltage of the lithium ion battery, the output voltage and the overpotential can be: V(terminal) = V eg +η, wherein V(terminal) represents the initial voltage of the lithium ion battery, V eg represents the output voltage of the lithium ion battery, and η represents the overpotential of the lithium ion battery. The corresponding voltage drop during the discharging process of the lithium ion battery can be approximately replaced by the overpotential of the lithium ion battery, that is, The output current of the lithium ion battery at each collection time is brought into the formula to obtain The formula is transformed to obtain wherein Δη represents the overpotential, I represents the output current at each collection time, and ΔR represents the resistance change. At this time, the SOC of the battery is associated with the battery aging by using the corresponding voltage drop per unit time of the lithium ion battery, and the cycle number can be removed.

[0083] It can be seen that by implementing the optional embodiment, the voltage drop per unit time can be calculated based on two adjacent output voltages, or the resistance change after the lithium ion battery is aged can be calculated based on the overpotential after the lithium ion battery is aged. According to the resistance change and the output current at each collection time, the corresponding voltage drop per unit time of the lithium ion battery is calculated, the resistance change is calculated by using the overpotential to reflect the battery aging, and then the SOC of the battery is associated with the battery aging based on the corresponding voltage drop per unit time of the lithium ion battery, so as to avoid using the difficult-to-track battery cycle life parameter to express the SOH of the battery, reduce the difficulty of determining the battery SOC and SOH, and improve the determination accuracy and efficiency.

[0084] In another optional embodiment, based on the battery output information and the voltage drop per unit time, the SOC calculation model of the lithium ion battery can include the following operations:

[0085] Data regression fitting is performed based on the battery output information to obtain the SOC model coefficients of the lithium ion battery;

[0086] Based on the voltage drop per unit time and the SOC model coefficients, the SOC calculation model of the lithium ion battery is constructed;

[0087] The SOC calculation model includes:

[0088] wherein V represents the output voltage at each collection time, and a, b and c represent the SOC model coefficients.

[0089] In the optional embodiment, optionally, the SOC of the battery is approximately linearly related to the voltage and the reciprocal of the voltage drop per unit time, and therefore the SOC calculation model of the lithium ion battery is set as a linear equation, i.e. The SOC model coefficients a, b and c of the lithium ion battery are obtained by data regression fitting based on the battery output information.

[0090] It can be seen that the SOC calculation model of the lithium ion battery can be constructed based on the voltage drop per unit time and the SOC model coefficients by implementing the optional embodiment, thereby improving the accuracy of the SOC model and the accuracy and efficiency of the calculation of the battery SOC.

[0091] Embodiment Three

[0092] Please refer to FIG. 3, which is a structural schematic diagram of a lithium ion battery SOC and SOH estimation device disclosed by the embodiments of the present application. The lithium ion battery SOC and SOH estimation device described in FIG. 3 can include an intelligent server or an intelligent platform configured to estimate the SOC value and the SOH value of the lithium ion battery, and the intelligent server includes a local server or a cloud server, which is not limited by the embodiments of the present application. As shown in FIG. 3, the lithium ion battery SOC and SOH estimation device can include:

[0093] The acquisition module 301 is configured to acquire the battery output information of the lithium ion battery at intervals of a preset time length during the life cycle experiment of the lithium ion battery, and the battery output information includes the output current and the output voltage at each acquisition time;

[0094] The calculation module 302 is configured to calculate the voltage drop per unit time of the lithium ion battery according to the battery output information, and construct the SOC calculation model of the lithium ion battery based on the battery output information and the voltage drop per unit time.

[0095] The construction module 303 is configured to define a correction factor according to the SOC calculation model, and construct the SOH calculation model of the lithium ion battery according to the battery output information, the voltage drop per unit time and the correction factor; wherein the SOC calculation model is configured to calculate the SOC value of the lithium ion battery, and the SOH calculation model is configured to calculate the SOH value of the lithium ion battery.

[0096] It can be seen that the SOC and SOH estimation device for lithium-ion batteries described in FIG. 3 can collect battery output information of the lithium-ion battery at a preset interval during the life cycle experiment of the lithium-ion battery, calculate the corresponding voltage drop per unit time of the lithium-ion battery according to the battery output information, construct a SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time, define a correction factor according to the SOC calculation model, and construct a SOH calculation model of the lithium-ion battery according to the battery output information, the voltage drop per unit time, and the correction factor. The model of SOC and SOH can be defined based on the new variable voltage drop per unit time, and the instantaneous SOC and SOH of the battery can be estimated online without full cycle testing of the battery, which improves the efficiency of determining the SOC and SOH of the battery, has high accuracy, and can model any one battery in the same batch to estimate the SOC and SOH of all batteries, which has high model accuracy and robustness, and can improve the accuracy of online estimation of the SOC and SOH of the lithium-ion battery.

[0097] In an optional embodiment, as shown in FIG. 4, the specific manner in which the calculation module 302 calculates the voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information includes:

[0098] In the output voltage at each collection time, the voltage drop per unit time corresponding to the lithium-ion battery is calculated based on the adjacent two output voltages; or,

[0099] The initial voltage of the lithium-ion battery is determined, and the overpotential of the lithium-ion battery after aging is calculated according to the output voltage at each collection time and the initial voltage;

[0100] The resistance change of the lithium-ion battery after aging is calculated according to the output current at each collection time and the overpotential;

[0101] The voltage drop per unit time corresponding to the lithium-ion battery is calculated according to the resistance change and the output current at each collection time.

[0102] It can be seen that the SOC and SOH estimation device of the lithium ion battery described in FIG. 4 can collect the battery output information of the lithium ion battery at a preset interval during the life cycle experiment of the lithium ion battery, calculate the corresponding unit time voltage drop of the lithium ion battery based on the adjacent two output voltages in the output voltage at each collection time, determine the initial voltage of the lithium ion battery, and calculate the overpotential of the aged lithium ion battery according to the output voltage at each collection time and the initial voltage. According to the output current and the overpotential at each collection time, the resistance change of the aged lithium ion battery is calculated, and the corresponding unit time voltage drop of the lithium ion battery is calculated according to the resistance change and the output current at each collection time. The SOC of the battery can be associated with the aging of the battery based on the corresponding unit time voltage drop of the lithium ion battery, avoiding using the difficult-to-track battery cycle life parameters to express the SOH of the battery, reducing the difficulty of determining the battery SOC and SOH, and improving the determination accuracy and efficiency. Based on the battery output information and the unit time voltage drop, the SOC calculation model of the lithium ion battery is constructed, the correction factor is defined according to the SOC calculation model, and the SOH calculation model of the lithium ion battery is constructed according to the battery output information, the unit time voltage drop and the correction factor. The model of SOC and SOH can be defined based on the new variable unit time voltage drop, the instantaneous SOC and SOH of the battery can be estimated online without full cycle test of the battery, the efficiency of determining the battery SOC and SOH is improved, the accuracy is high, and the SOC and SOH of all batteries can be estimated based on any one in the same batch of batteries, the model accuracy and robustness are high, and the accuracy of online estimation of the SOC and SOH of the lithium ion battery is improved.

[0103] In another optional embodiment, as shown in FIG. 4, the calculation formula of the calculation module 302 for calculating the corresponding unit time voltage drop of the lithium ion battery in the output voltage at each collection time based on the adjacent two output voltages includes:

[0104] Wherein, V' represents the unit time voltage drop, V1 and V2 represent the adjacent two output voltages, and Δt represents the unit time.

[0105] The calculation formula of the calculation module 302 for calculating the corresponding unit time voltage drop of the lithium ion battery based on the resistance change and the output current at each collection time includes:

[0106] Wherein, Δη represents the overpotential, I represents the output current at each collection time, and ΔR represents the resistance change.

[0107] It can be seen that the SOC and SOH estimation device for lithium-ion batteries described in FIG. 4 can calculate the voltage drop per unit time based on the two adjacent output voltages, or calculate the resistance change of the lithium-ion battery after aging based on the overpotential of the lithium-ion battery after aging, and calculate the corresponding voltage drop per unit time of the lithium-ion battery according to the resistance change and the output current at each collection time, use the overpotential to calculate the resistance change to reflect the battery aging, and then associate the SOC of the battery with the battery aging based on the corresponding voltage drop per unit time of the lithium-ion battery, avoid using the difficult-to-track battery cycle life parameters to express the SOH of the battery, reduce the difficulty of determining the SOC and SOH of the battery, and improve the determination accuracy and efficiency.

[0108] In yet another optional embodiment, as shown in FIG. 4, the specific way in which the calculation module 302 constructs the SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time includes:

[0109] Based on the battery output information, data regression fitting is performed to obtain the SOC model coefficients of the lithium-ion battery;

[0110] Based on the voltage drop per unit time and the SOC model coefficients, the SOC calculation model of the lithium-ion battery is constructed;

[0111] The SOC calculation model includes:

[0112] Wherein, V represents the output voltage at each collection time, a, b and c represent the SOC model coefficients.

[0113] It can be seen that the SOC and SOH estimation device for lithium-ion batteries described in FIG. 4 can construct the SOC calculation model of the lithium-ion battery based on the voltage drop per unit time and the SOC model coefficients, improve the accuracy of the SOC model, and improve the accuracy and efficiency of calculating the SOC of the battery.

[0114] In yet another optional embodiment, as shown in FIG. 4, the specific way in which the construction module 303 constructs the SOH calculation model of the lithium-ion battery according to the battery output information, the voltage drop per unit time and the correction factor includes:

[0115] According to the battery output information and the voltage drop per unit time, the SOH regression model of the lithium-ion battery at the preset SOC level is constructed;

[0116] Based on the correction factor, the SOH calculation model of the lithium-ion battery is constructed based on the SOH regression model at the preset SOC level.

[0117] It can be seen that the SOC and SOH estimation device described in Figure 4 can construct the SOH regression model of the lithium ion battery at the preset SOC level based on the battery output information and the voltage drop per unit time, construct the SOH calculation model of the lithium ion battery based on the SOH regression model at the preset SOC level and the correction factor, and construct the SOH calculation model based on the newly defined correction factor of the SOC calculation model, thereby improving the accuracy of the SOH model and the accuracy and efficiency of calculating the SOH of the battery.

[0118] In another optional embodiment, as shown in Figure 4, the specific way in which the construction module 303 constructs the SOH regression model of the lithium ion battery at the preset SOC level based on the battery output information and the voltage drop per unit time includes:

[0119] Based on the battery output information, the SOH model coefficients of the lithium ion battery are obtained by data regression fitting;

[0120] Based on the voltage drop per unit time and the SOH model coefficients, the SOH regression model of the lithium ion battery at the preset SOC level is constructed;

[0121] The SOH regression model includes:

[0122] The SOH calculation model includes:

[0123] Wherein, A and B represent the SOH model coefficients, and a(SOC) represents the correction factor.

[0124] It can be seen that the SOC and SOH estimation device described in Figure 4 can construct the SOH regression model of the lithium ion battery at the preset SOC level based on the voltage drop per unit time and the SOH model coefficients, and further construct the SOH calculation model based on the newly defined correction factor of the SOC calculation model, thereby improving the accuracy of the SOH model and the accuracy and efficiency of calculating the SOH of the battery

[0125] In another optional embodiment, as shown in Figure 4, the specific way in which the construction module 303 defines the correction factor based on the SOC calculation model includes:

[0126] Based on the SOC calculation model and the battery output information, the correction factor coefficients of the lithium ion battery are obtained by data regression fitting;

[0127] Based on the correction factor coefficients and the SOC calculation model, the correction factor is defined;

[0128] The calculation formula of the correction factor includes: a(SOC)=C1(SOC) 3 +C2(SOC) 2 +C3(SOC)1 +C0

[0129] wherein, C1, C2, C3 and C0 represent correction factor coefficients.

[0130] It can be seen that the SOC and SOH estimation device for lithium ion battery described in Figure 4 can perform data regression fitting based on the SOC calculation model and the battery output information to obtain the correction factor coefficients of the lithium ion battery, define the correction factor based on the correction factor coefficients and the SOC calculation model, improve the accuracy of the correction factor, and introduce the correction factor in the SOH calculation model, without the need to re-regress the calculation model coefficients, thereby improving the model accuracy and calculation efficiency.

[0131] In another optional embodiment, as shown in Figure 4, the calculation module 302 is further configured to calculate the battery test values of the lithium ion battery according to the SOC calculation model and the SOH calculation model respectively, wherein the battery test values include the SOC test value and the SOH test value.

[0132] The SOC and SOH estimation device for lithium ion battery can further include:

[0133] The acquisition module 304 is configured to acquire the battery verification values of the lithium ion battery, and calculate the model indicators corresponding to the SOC calculation model and the SOH calculation model respectively according to the battery test values and the battery verification values, wherein the battery verification values include the SOC verification value and the SOH verification value, and the model indicators include the average correlation coefficient and the root mean square error.

[0134] The verification module 305 is configured to perform model verification on the SOC calculation model and the SOH calculation model based on the model indicators corresponding to the SOC calculation model and the SOH calculation model respectively, obtain a verification result, and perform model optimization on the SOC calculation model and the SOH calculation model based on the verification result.

[0135] It can be seen that the SOC and SOH estimation device for lithium ion battery described in Figure 4 can perform model verification and optimization on the SOC calculation model and the SOH calculation model based on the average correlation coefficient and the root mean square error, reduce the deviation between the model predicted value and the true value, and after modeling with any one of the same batch of batteries, the accuracy and precision of calculating the SOC and SOH of other batteries are similar, thereby improving the accuracy and precision of the model.

[0136] Embodiment Four

[0137] Referring to Figure 5, Figure 5 is a structural schematic diagram of another SOC and SOH estimation device for lithium ion battery disclosed in the embodiments of the present application. As shown in Figure 5, the SOC and SOH estimation device for lithium ion battery can include:

[0138] a memory 401 storing executable program code;

[0139] a processor 402 coupled with the memory 401;

[0140] The processor 402 invokes the executable program code stored in the memory 401 to perform the steps in the estimation method of the SOC and SOH of the lithium ion battery described in Embodiment One or Embodiment Two of the present application.

[0141] Embodiment Five

[0142] The embodiments of the present application disclose a computer storage medium storing computer instructions, which, when invoked, are configured to perform the steps in the estimation method of the SOC and SOH of the lithium ion battery described in Embodiment One or Embodiment Two of the present application.

[0143] Embodiment Six

[0144] The embodiments of the present application disclose a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the estimation method of the SOC and SOH of the lithium ion battery described in Embodiment One or Embodiment Two.

[0145] The apparatus embodiments described above are only schematic, wherein the modules illustrated as separate components can or can not be physically separate, and the components illustrated as modules can or can not be physical modules, i.e., can be located in one place, or can be distributed on multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments. Those skilled in the art can understand and implement without creative labor.

[0146] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and the necessary general hardware platform through the specific description of the above embodiments, and of course, the various embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions or the part that contributes to the related art can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

Claims

1. A method for estimating SOC and SOH of a lithium-ion battery, the method comprising: acquiring battery output information of the lithium-ion battery at intervals of a preset time length during a life cycle experiment of the lithium-ion battery, the battery output information comprising output current and output voltage at each acquisition time point; calculating a voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information, and constructing an SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time; defining a correction factor according to the SOC calculation model, and constructing an SOH calculation model of the lithium-ion battery according to the battery output information, the voltage drop per unit time, and the correction factor; wherein the SOC calculation model is configured to calculate an SOC value of the lithium-ion battery, and the SOH calculation model is configured to calculate an SOH value of the lithium-ion battery.

2. The method of estimating SOC and SOH of a lithium-ion battery according to claim 1, wherein, The calculation of the voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information comprises: calculating the voltage drop per unit time corresponding to the lithium-ion battery based on two adjacent output voltages among the output voltages at each acquisition time point; or determining an initial voltage of the lithium-ion battery, and calculating an overpotential of the lithium-ion battery after aging according to the output voltage at each acquisition time point and the initial voltage; calculating a resistance change of the lithium-ion battery after aging according to the output current at each acquisition time point and the overpotential; calculating the voltage drop per unit time corresponding to the lithium-ion battery according to the resistance change and the output current at each acquisition time point.

3. The method of estimating SOC and SOH of a lithium-ion battery according to claim 2, wherein, The calculation formula of the voltage drop per unit time of the lithium ion battery corresponding to each of the output voltages comprises: wherein V' represents the voltage drop per unit time, V1 and V2 represent two adjacent output voltages, and Δt represents the unit time; The calculation formula for calculating the corresponding voltage drop per unit time of the lithium ion battery according to the resistance change and the output current at each collection time comprises: wherein Δη represents the overpotential, I represents the output current at each acquisition time point, and ΔR represents the resistance change.

4. The method of estimating SOC and SOH of a lithium-ion battery according to claim 3, wherein, The construction of the SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time comprises: performing data regression fitting based on the battery output information to obtain SOC model coefficients of the lithium-ion battery; constructing the SOC calculation model of the lithium-ion battery based on the voltage drop per unit time and the SOC model coefficients; The SOC computation model includes: wherein V represents the output voltage at each acquisition time point, and a, b, and c represent SOC model coefficients.

5. The method of estimating SOC and SOH of a lithium-ion battery according to any one of claims 1 to 4, wherein, The construction of the SOH calculation model of the lithium-ion battery according to the battery output information, the voltage drop per unit time, and the correction factor comprises: constructing an SOH regression model of the lithium-ion battery at a preset SOC level according to the battery output information and the voltage drop per unit time; constructing the SOH calculation model of the lithium-ion battery based on the SOH regression model at the preset SOC level and the correction factor.

6. The method of estimating SOC and SOH of a lithium-ion battery according to claim 5, wherein, The construction of the SOH regression model of the lithium-ion battery at a preset SOC level according to the battery output information and the voltage drop per unit time comprises: perform data regression fitting based on the battery output information to obtain SOH model coefficients of the lithium ion battery; construct an SOH regression model of the lithium ion battery at a preset SOC level based on the unit time voltage drop and the SOH model coefficients; The SOH regression model includes: The SOH calculation model includes: wherein A and B represent the SOH model coefficients, and a(SOC) represents the correction factor.

7. The method of estimating SOC and SOH of a lithium-ion battery according to claim 6, wherein, The correction factor is defined according to the SOC calculation model, including: perform data regression fitting based on the SOC calculation model and the battery output information to obtain correction factor coefficients of the lithium ion battery; define the correction factor based on the correction factor coefficients and the SOC calculation model; The calculation formula of the correction factor includes: a(SOC) = C1(SOC) 3 + C2(SOC) 2 + C3(SOC) 1 + C0 wherein C1, C2, C3 and C0 represent the correction factor coefficients.

8. The method of estimating SOC and SOH of a lithium-ion battery according to any one of claims 1 to 4, wherein, The method further includes: respectively calculate battery test values of the lithium ion battery according to the SOC calculation model and the SOH calculation model, wherein the battery test values include SOC test values and SOH test values; obtain battery verification values of the lithium ion battery, and respectively calculate model indexes corresponding to the SOC calculation model and the SOH calculation model according to the battery test values and the battery verification values, wherein the battery verification values include SOC verification values and SOH verification values, and the model indexes include average correlation coefficients and root mean square errors; respectively perform model verification on the SOC calculation model and the SOH calculation model based on the model indexes corresponding to the SOC calculation model and the SOH calculation model, obtain verification results, and perform model optimization on the SOC calculation model and the SOH calculation model based on the verification results.

9. An estimation device of SOC and SOH of a lithium ion battery, the device comprising: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the estimation method of SOC and SOH of the lithium ion battery according to any one of claims 1-8.

10. A computer storage medium storing computer instructions, the computer instructions being invoked to set to execute the estimation method of SOC and SOH of the lithium ion battery according to any one of claims 1-8.

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