Lithium ion battery SOC and SOH estimation method and device, and computer storage medium

Abstract

The invention relates to the technical field of batteries, and discloses a lithium ion battery SOC and SOH estimation method and device, and a computer storage medium, and the method comprises the steps: collecting battery output information at preset time intervals in a process of carrying out a life cycle experiment on a lithium ion battery, and calculating a unit time voltage drop corresponding to the lithium ion battery, and based on the battery output information and the unit time voltage drop, constructing an SOC calculation model of the lithium ion battery, defining a correction factor according to the SOC calculation model, and according to the battery output information, the unit time voltage drop and the correction factor, constructing an SOH calculation model of the lithium ion battery. Therefore, by implementing the method, the SOC and SOH models can be defined based on the unit time voltage drop, the instantaneous SOC and SOH of the battery can be estimated on line under the condition that the battery is not subjected to full-period testing, the efficiency and accuracy of determining the SOC and SOH of the battery are improved, the model precision and robustness are high, and the accuracy of estimating the SOC and SOH of the lithium ion battery on line can be improved.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a method and device for estimating SOC and SOH of a lithium-ion battery, and a computer storage medium. Background Art

[0002] With the continuous development and progress of battery technology, batteries are being used more and more widely in people's production and life. Lithium-ion batteries, as a rechargeable battery with high energy density and long cycle life, are widely used in many fields such as electric vehicles and smart electrical equipment. Therefore, the estimation method of the state of charge (SOC) and state of health (SOH) of lithium batteries has become a hot research topic.

[0003] At present, the methods for estimating the SOC of lithium batteries in the industry mainly include data-driven methods and model-based methods. However, the data-driven methods need to rely on the accuracy of the current sensor, or need to give the battery enough rest time, and the measurement accuracy and efficiency are low. The model-based methods are only applicable to new batteries. The methods for estimating the SOH of lithium batteries in the industry mainly include data-driven methods and adaptive system-based methods. However, the data-driven methods need to evaluate large data sets through physical analysis to determine the correlation between battery operation and degradation, and the adaptive system-based methods need to determine the battery's SOH by calculating parameters that are sensitive to battery degradation. The computational load and real-time performance of these two methods are poor. 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 batteries. Summary of the invention

[0004] The present invention provides a method and device for estimating the SOC and SOH of a lithium-ion battery, and a computer storage medium, which can help improve the accuracy of online estimation of the SOC and SOH of a lithium-ion battery.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses a method for estimating SOC and SOH of a lithium-ion battery, the method comprising:

[0006] In the process of conducting a life cycle experiment on a lithium-ion battery, collecting battery output information of the lithium-ion battery at preset time intervals, wherein the battery output information includes an output current and an output voltage at each collection moment;

[0007] Calculating a voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information, and constructing a SOC calculation model for the lithium-ion battery based on the battery output information and the voltage drop per unit time;

[0008] Defining a correction factor according to the SOC calculation model, and constructing 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;

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

[0010] As an optional implementation manner, in the first aspect of the present invention, calculating the voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information includes:

[0011] In each of the output voltages at the acquisition moment, based on two adjacent output voltages, the voltage drop per unit time corresponding to the lithium-ion battery is calculated; or,

[0012] Determining the initial voltage of the lithium-ion battery, and calculating the overpotential of the lithium-ion battery after aging based on the output voltage at each acquisition moment and the initial voltage;

[0013] Calculating the resistance change of the lithium-ion battery after aging according to the output current and the overpotential at each acquisition moment;

[0014] 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 acquisition moment.

[0015] As an optional implementation, in the first aspect of the present invention, in the output voltage at each acquisition moment, the calculation formula for calculating the voltage drop per unit time corresponding to the lithium-ion battery based on two adjacent output voltages includes:

[0016] ΔV=V1-V2

[0017] Wherein, V′ represents the voltage drop per unit time, V1 and V2 represent two adjacent output voltages, and Δt represents the unit time;

[0018] The calculation formula for 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 moment includes:

[0019]

[0020] Wherein, Δη represents the overpotential, I represents the output current at each acquisition moment, and ΔR represents the resistance change.

[0021] As an optional implementation, in the first aspect of the present invention, constructing the SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time includes:

[0022] Performing data regression fitting based on the battery output information to obtain the SOC model coefficient of the lithium-ion battery;

[0023] Constructing a SOC calculation model of the lithium-ion battery based on the voltage drop per unit time and the SOC model coefficient;

[0024] The SOC calculation model includes:

[0025]

[0026] Wherein, V represents the output voltage at each acquisition moment, and a, b and c represent SOC model coefficients.

[0027] As an optional implementation, in the first aspect of the present invention, 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 includes:

[0028] Constructing a 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;

[0029] Based on the correction factor, a SOH calculation model of the lithium-ion battery is constructed on the basis of the SOH regression model of the preset SOC level.

[0030] As an optional implementation, in the first aspect of the present invention, constructing a 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 includes:

[0031] Performing data regression fitting based on the battery output information to obtain the SOH model coefficient of the lithium-ion battery;

[0032] Based on the voltage drop per unit time and the SOH model coefficient, constructing a SOH regression model of the lithium-ion battery at a preset SOC level;

[0033] The SOH regression model includes:

[0034]

[0035] The SOH calculation model includes:

[0036]

[0037] Wherein, A and B represent the SOH model coefficients, and α(SOC) represents the correction factor.

[0038] As an optional implementation manner, in the first aspect of the present invention, defining a correction factor according to the SOC calculation model includes:

[0039] Performing data regression fitting based on the SOC calculation model and the battery output information to obtain a correction factor coefficient of the lithium-ion battery;

[0040] defining a correction factor based on the correction factor coefficient and the SOC calculation model;

[0041] The calculation formula of the correction factor includes:

[0042] α(SOC)=C1(SOC) 3 +C2(SOC) 2 +C3(SOC) 1 +C0

[0043] Wherein, C1, C2, C3 and C0 represent the correction factor coefficients.

[0044] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0045] Calculating a battery test value of the lithium-ion battery according to the SOC calculation model and the SOH calculation model respectively, wherein the battery test value includes an SOC test value and an SOH test value;

[0046] Obtaining a battery verification value of the lithium-ion battery, and calculating model indicators corresponding to the SOC calculation model and the SOH calculation model respectively according to the battery test value and the battery verification value, wherein the battery verification value includes an SOC verification value and an SOH verification value, and the model indicators include an average correlation coefficient and a root mean square error;

[0047] The SOC calculation model and the SOH calculation model are respectively verified based on model indicators corresponding to the SOC calculation model and the SOH calculation model to obtain verification results, and the SOC calculation model and the SOH calculation model are optimized based on the verification results.

[0048] A second aspect of the present invention discloses a device for estimating SOC and SOH of a lithium-ion battery, the device comprising:

[0049] The device comprises:

[0050] A collection module, used to collect battery output information of the lithium-ion battery at preset intervals during a life cycle experiment of the lithium-ion battery, wherein the battery output information includes an output current and an output voltage at each collection moment;

[0051] A calculation module, used to calculate the voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information, and to construct a SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time;

[0052] A construction module is used to define a correction factor according to the SOC calculation model, and to 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; wherein the SOC calculation model is used to calculate the SOC value of the lithium-ion battery, and the SOH calculation model is used to calculate the SOH value of the lithium-ion battery.

[0053] As an optional implementation, in the second aspect of the present invention, the calculation module calculates the voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information, specifically including:

[0054] In each of the output voltages at the acquisition moment, based on two adjacent output voltages, the voltage drop per unit time corresponding to the lithium-ion battery is calculated; or,

[0055] Determining the initial voltage of the lithium-ion battery, and calculating the overpotential of the lithium-ion battery after aging based on the output voltage at each acquisition moment and the initial voltage;

[0056] Calculating the resistance change of the lithium-ion battery after aging according to the output current and the overpotential at each acquisition moment;

[0057] 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 acquisition moment.

[0058] As an optional implementation, in the second aspect of the present invention, the calculation module calculates the voltage drop per unit time corresponding to the lithium-ion battery based on two adjacent output voltages in the output voltage at each acquisition moment, and the calculation formula includes:

[0059] ΔV=V1-V2

[0060] Wherein, V′ represents the voltage drop per unit time, V1 and V2 represent two adjacent output voltages, and Δt represents the unit time;

[0061] The calculation module calculates the voltage drop per unit time corresponding to the lithium-ion battery according to the resistance change and the output current at each acquisition moment, and the calculation formula includes:

[0062]

[0063] Wherein, Δη represents the overpotential, I represents the output current at each acquisition moment, and ΔR represents the resistance change.

[0064] As an optional implementation, in the second aspect of the present invention, the calculation module constructs the SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time in a manner that specifically includes:

[0065] Performing data regression fitting based on the battery output information to obtain the SOC model coefficient of the lithium-ion battery;

[0066] Constructing a SOC calculation model of the lithium-ion battery based on the voltage drop per unit time and the SOC model coefficient;

[0067] The SOC calculation model includes:

[0068]

[0069] Wherein, V represents the output voltage at each acquisition moment, and a, b and c represent SOC model coefficients.

[0070] As an optional implementation, in the second aspect of the present invention, the construction module 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, specifically including:

[0071] Constructing a 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;

[0072] Based on the correction factor, a SOH calculation model of the lithium-ion battery is constructed on the basis of the SOH regression model of the preset SOC level.

[0073] As an optional implementation, in the second aspect of the present invention, the construction module constructs 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, specifically including:

[0074] Performing data regression fitting based on the battery output information to obtain the SOH model coefficient of the lithium-ion battery;

[0075] Based on the voltage drop per unit time and the SOH model coefficient, constructing a SOH regression model of the lithium-ion battery at a preset SOC level;

[0076] The SOH regression model includes:

[0077]

[0078] The SOH calculation model includes:

[0079]

[0080] Wherein, A and B represent the SOH model coefficients, and α(SOC) represents the correction factor.

[0081] As an optional implementation, in the second aspect of the present invention, the construction module defines the correction factor according to the SOC calculation model in a manner that specifically includes:

[0082] Performing data regression fitting based on the SOC calculation model and the battery output information to obtain a correction factor coefficient of the lithium-ion battery;

[0083] defining a correction factor based on the correction factor coefficient and the SOC calculation model;

[0084] The calculation formula of the correction factor includes:

[0085] α(SOC)=C1(SOC) 3 +C2(SOC) 2 +C3(SOC) 1 +C0

[0086] Wherein, C1, C2, C3 and C0 represent the correction factor coefficients.

[0087] As an optional implementation, in the second aspect of the present invention, the calculation module is further used to calculate the battery test value of the lithium-ion battery according to the SOC calculation model and the SOH calculation model respectively, and the battery test value includes the SOC test value and the SOH test value;

[0088] The device also includes:

[0089] An acquisition module, used to acquire a battery verification value of the lithium-ion battery, and calculate model indicators corresponding to the SOC calculation model and the SOH calculation model respectively according to the battery test value and the battery verification value, wherein the battery verification value includes an SOC verification value and an SOH verification value, and the model indicators include an average correlation coefficient and a root mean square error;

[0090] A verification module is used to perform model verification on the SOC calculation model and the SOH calculation model based on model indicators corresponding to the SOC calculation model and the SOH calculation model, respectively, to obtain verification results, and to perform model optimization on the SOC calculation model and the SOH calculation model based on the verification results.

[0091] The third aspect of the present invention discloses another device for estimating SOC and SOH of a lithium-ion battery, the device comprising:

[0092] A memory storing executable program code;

[0093] a processor coupled to the memory;

[0094] The processor calls the executable program code stored in the memory to execute the method for estimating the SOC and SOH of a lithium-ion battery disclosed in the first aspect of the present invention.

[0095] The fourth aspect of the present invention discloses a computer storage medium, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the method for estimating the SOC and SOH of a lithium-ion battery disclosed in the first aspect of the present invention.

[0096] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0097] In the embodiment of the present invention, during the life cycle experiment of the lithium-ion battery, the battery output information of the lithium-ion battery is collected at preset intervals, the voltage drop per unit time corresponding to 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, and 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 voltage drop per unit time and the correction factor. It can be seen that the implementation of the present invention can define the model of SOC and SOH based on the new variable voltage drop per unit time, and estimate the instantaneous SOC and SOH of the battery online without performing a full cycle test on the battery, thereby improving the efficiency and accuracy of determining the SOC and SOH of the battery, and can model any one of the same batch of batteries, and then estimate the SOC and SOH of all batteries, the model has high accuracy and robustness, and can improve the accuracy of online estimation of the SOC and SOH of the lithium-ion battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0099] Figure 1 It is a flow chart of a method for estimating SOC and SOH of a lithium-ion battery disclosed in an embodiment of the present invention;

[0100] Figure 2 It is a flow chart of another method for estimating SOC and SOH of a lithium-ion battery disclosed in an embodiment of the present invention;

[0101] Figure 3 It is a structural schematic diagram of a device for estimating SOC and SOH of a lithium-ion battery disclosed in an embodiment of the present invention;

[0102] Figure 4 It is a structural schematic diagram of another device for estimating SOC and SOH of a lithium-ion battery disclosed in an embodiment of the present invention;

[0103] Figure 5 It is a structural schematic diagram of another device for estimating SOC and SOH of a lithium-ion battery disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0104] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0105] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0106] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0107] The present invention discloses a method and device for estimating the SOC and SOH of a lithium-ion battery, and a computer storage medium. The method can define a model of the SOC and SOH based on a new variable, the voltage drop per unit time, and estimate the instantaneous SOC and SOH of the battery online without performing a full cycle test on the battery, thereby improving the efficiency of determining the SOC and SOH of the battery, and having high accuracy. The method can be modeled based on any one of the batteries in the same batch, and then the SOC and SOH of all batteries can be estimated. The model has high accuracy and robustness, and can improve the accuracy of online estimation of the SOC and SOH of the lithium-ion battery. The following are detailed descriptions.

[0108] Embodiment 1

[0109] See also Figure 1 , Figure 1 1 is a flow chart of a method for estimating SOC and SOH of a lithium-ion battery disclosed in an embodiment of the present invention. Figure 1 The described method for estimating the SOC and SOH of a lithium-ion battery may be applied to a device for estimating the SOC and SOH of a lithium-ion battery, wherein the device for estimating the SOC and SOH of a lithium-ion battery may include an intelligent server or intelligent platform for estimating the SOC value and SOH value of a lithium-ion battery, and the intelligent server includes a local server or a cloud server, which is not limited in the embodiments of the present invention. Figure 1 As shown, the method for estimating the SOC and SOH of a lithium-ion battery may include the following operations:

[0110] 101. During the life cycle experiment of lithium-ion batteries, the battery output information of the lithium-ion batteries is collected at preset time intervals.

[0111] In an embodiment of the present invention, optionally, during a life cycle experiment on a lithium-ion battery, a preset time interval for collecting battery output information of the lithium-ion battery may be set by a tester according to actual needs. For example, the information may be collected every 30 seconds. The battery output information of the lithium-ion battery may include the output current and output voltage of the lithium-ion battery at each collection moment, and the lithium-ion battery may be multiple batteries in the same batch of batteries, which is not limited by the present invention.

[0112] 102. According to the battery output information, the voltage drop per unit time corresponding to the lithium-ion battery is calculated, and based on the battery output information and the voltage drop per unit time, a SOC calculation model of the lithium-ion battery is constructed.

[0113] In the embodiment of the present invention, optionally, the voltage drop per unit time can represent the voltage drop per unit time of lithium ions during the discharge process. SOC (State of Charge) represents the state of charge of the battery, and is often referred to as the remaining power or the charge and discharge state. The most convenient and direct output of the battery in use is voltage, current and time. A single voltage value is never sufficient to identify the SOC of the battery, because the voltage will change as the battery ages. Therefore, the parameter of the voltage drop per unit time corresponding to the lithium-ion battery is proposed to establish the SOC and SOH model. The test data shows that the average correlation between the battery SOC and the battery voltage is 0.988, and the average correlation between the battery SOC and the inverse of the battery voltage drop per unit time is 0.984. Therefore, the relationship between the battery SOC and the voltage and the inverse of the voltage drop per unit time is approximately linear, and the present invention does not limit it.

[0114] 103. 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.

[0115] In an embodiment of the present invention, optionally, the SOH (State of Health) of the battery represents the health state of the battery, which is affected by the capacity decay and impedance increase of the battery. The correction factor defined according to the SOC calculation model is used 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 of the battery on the SOC changes with the change of SOH, which is not limited by the present invention.

[0116] It can be seen that implementation Figure 1The described method for estimating the SOC and SOH of a lithium-ion battery can collect the battery output information of the lithium-ion battery at preset intervals during a life cycle experiment on the lithium-ion battery, calculate the corresponding voltage drop per unit time of the lithium-ion battery based on the battery output information, and 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 based on the SOC calculation model, and construct a SOH calculation model of the lithium-ion battery based on the battery output information, the voltage drop per unit time and the correction factor, and can define the model of SOC and SOH based on the new variable voltage drop per unit time, and estimate the instantaneous SOC and SOH of the battery online without performing a full cycle test on the battery, thereby improving the efficiency and accuracy of determining the SOC and SOH of the battery, and can model any one of the batteries in the same batch to estimate the SOC and SOH of all batteries, and the model has high accuracy and robustness, and can improve the accuracy of online estimation of the SOC and SOH of the lithium-ion battery.

[0117] In an optional embodiment, constructing a SOH calculation model for a lithium-ion battery according to the battery output information, the voltage drop per unit time, and the correction factor may include the following operations:

[0118] Based on the battery output information and the voltage drop per unit time, a SOH regression model of the lithium-ion battery at a preset SOC level is constructed;

[0119] Based on the correction factor, the SOH calculation model of lithium-ion batteries is constructed on the basis of the SOH regression model of the preset SOC level.

[0120] In this optional embodiment, optionally, according to the battery output information and the voltage drop per unit time, the SOH regression model of the lithium-ion battery at a certain SOC level is set as: Where A and B are unknown coefficients. Based on the correction factor, the SOH calculation model of lithium-ion batteries is constructed on the basis of the SOH regression model of the preset SOC level, namely: That is, the SOH of the battery can be calculated based on the SOC of the battery, and the influence of the SOH of the battery on the SOC changes with the change of the SOH.

[0121] It can be seen that the implementation of this optional embodiment can construct a SOH regression model of a lithium-ion battery at a preset SOC level based on the battery output information and the voltage drop per unit time, and based on the correction factor, construct a SOH calculation model for the lithium-ion battery on the basis of the SOH regression model at the preset SOC level. The SOH calculation model can be constructed based on the correction factor newly defined in the SOC calculation model, thereby improving the accuracy of the SOH model and, at the same time, improving the accuracy and efficiency of calculating the battery SOH.

[0122] In another optional embodiment, constructing a SOH regression model of a lithium-ion battery at a preset SOC level according to the battery output information and the voltage drop per unit time may include the following operations:

[0123] The SOH model coefficient of the lithium-ion battery is obtained by performing data regression fitting based on the battery output information;

[0124] Based on the voltage drop per unit time and the SOH model coefficient, a SOH regression model for lithium-ion batteries at a preset SOC level is constructed;

[0125] The SOH regression model includes:

[0126]

[0127] The SOH calculation model includes:

[0128]

[0129] Among them, A and B represent the SOH model coefficients, and α(SOC) represents the correction factor.

[0130] In this optional embodiment, optionally, data regression fitting is performed based on the battery output information to obtain SOH model coefficients A and B of the lithium-ion battery, and α(SOC) represents a correction factor.

[0131] It can be seen that the implementation of this optional embodiment can construct a SOH regression model for a lithium-ion battery at a preset SOC level based on the voltage drop per unit time and the SOH model coefficient, and then construct a SOH calculation model based on the newly defined correction factor of the SOC calculation model, which can improve the accuracy of the SOH model while improving the accuracy and efficiency of calculating the battery SOH.

[0132] In yet another optional embodiment, defining the correction factor according to the SOC calculation model may include the following operations:

[0133] Based on the SOC calculation model and battery output information, data regression fitting is performed to obtain the correction factor coefficient of the lithium-ion battery;

[0134] Define a correction factor based on the correction factor coefficient and the SOC calculation model;

[0135] The calculation formula of the correction factor includes:

[0136] α(SOC)=C1(SOC) 3 +C2(SOC) 2 +C3(SOC) 1 +C0

[0137] Wherein, C1, C2, C3 and C0 represent correction factor coefficients.

[0138] In this optional embodiment, α(SOC) is a newly defined correction factor based on SOC, which is a function of SOC. α does not change monotonically with SOC, so interpolation is required, and the cubic polynomial is accurate enough. Therefore, the calculation formula of the correction factor is α(SOC)=C1(SOC) 3 +C2(SOC) 2 +C3(SOC) 1 +C0, wherein data regression fitting can be performed based on the SOC calculation model and the battery output information to obtain the correction factor coefficient of the lithium-ion battery, and C1, C2, C3 and C0 represent the correction factor coefficient.

[0139] It can be seen that the implementation of this optional embodiment can perform data regression fitting based on the SOC calculation model and battery output information to obtain the correction factor coefficient of the lithium-ion battery. The correction factor is defined based on the correction factor coefficient and the SOC calculation model, which can improve the accuracy of the correction factor. In addition, the correction factor is introduced into the SOH calculation model without the need to regress and calculate the model coefficient again, thereby improving the model accuracy and calculation efficiency.

[0140] In yet another optional embodiment, the method for estimating the SOC and SOH of a lithium-ion battery may further include the following operations:

[0141] Calculating battery test values ​​of the lithium-ion battery according to the SOC calculation model and the SOH calculation model respectively, the battery test values ​​including the SOC test value and the SOH test value;

[0142] Obtaining a battery verification value of the lithium-ion battery, and calculating model indicators corresponding to the SOC calculation model and the SOH calculation model respectively according to the battery test value and the battery verification value, wherein the battery verification value includes an SOC verification value and an SOH verification value, and the model indicators include an average correlation coefficient and a root mean square error;

[0143] The SOC calculation model and the SOH calculation model are respectively verified based on model indicators corresponding to the SOC calculation model and the SOH calculation model to obtain verification results, and the SOC calculation model and the SOH calculation model are optimized based on the verification results.

[0144] In this optional embodiment, the battery test value includes an SOC test value and an SOH test value, and the battery verification value of the lithium-ion battery includes an SOC verification value and an SOH verification value, which are used to verify the accuracy of the model. The model indicators include an average correlation coefficient and a root mean square error. The calculation of the average correlation coefficient

[0145] The square root of the n ratio 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. The model is optimized based on the average correlation coefficient and the root mean square error. Four lithium-ion batteries A, B, C, and D from the same batch are used as experimental objects to conduct battery life cycle tests and obtain battery output information. When the SOC calculation model and the SOH calculation model are constructed based on the battery output information of lithium-ion battery A, and the SOC calculation model and the SOH calculation model are used to estimate the SOC values ​​and SOH values ​​of the four batteries, the average correlation coefficients and root mean square errors of all battery SOC estimates are shown in Table 1 below:

[0146] Table 1:

[0147]

[0148] 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 calculation model and the SOH calculation model are used to estimate the SOC values ​​and SOH values ​​of the four batteries, 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 below:

[0149] Table 2:

[0150] Statistical parameters Battery A Battery B Battery C Battery D Data Points 42 36 39 41 <![CDATA[R 2 ]]> 0.990 0.980 0.954 0.946 RMSE 0.0262 0.0382 0.0352 0.0428

[0151] It can be seen that the SOC calculation model and the SOH calculation model show excellent accuracy when used to estimate the same batch of 4 test batteries, and using any one of the same batch of batteries for model building produces similar accuracy for all four batteries. Optionally, the SOC calculation model can be used for online SOC estimation within the voltage range of 3.55V-3.95V, which is not limited in this embodiment.

[0152] It can be seen that the implementation of this 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 model prediction value and the true value, and after modeling with any one of the batteries in the same batch, the accuracy and precision of calculating the SOC and SOH of other batteries are similar, thereby improving the accuracy and precision of the model.

[0153] Embodiment 2

[0154] See also Figure 2 , Figure 2 1 is a flow chart of a method for estimating SOC and SOH of a lithium-ion battery disclosed in an embodiment of the present invention. Figure 2The described method for estimating the SOC and SOH of a lithium-ion battery may be applied to a device for estimating the SOC and SOH of a lithium-ion battery, wherein the device for estimating the SOC and SOH of a lithium-ion battery may include an intelligent server or intelligent platform for estimating the SOC value and SOH value of a lithium-ion battery, and the intelligent server includes a local server or a cloud server, which is not limited in the embodiments of the present invention. Figure 2 As shown, the method for estimating the SOC and SOH of a lithium-ion battery may include the following operations:

[0155] 201. During the life cycle experiment of lithium-ion batteries, battery output information of the lithium-ion batteries is collected at preset time intervals.

[0156] 202. In each output voltage collected at a time, based on two adjacent output voltages, calculate a voltage drop per unit time corresponding to the lithium-ion battery.

[0157] In the embodiment of the present invention, optionally, based on two adjacent output voltages, the voltage drop per unit time corresponding to the lithium-ion battery is calculated, that is, the voltage drop per unit time corresponding to the lithium-ion battery is Wherein, ΔV represents the difference between two consecutive output voltages during the discharge process of the lithium-ion battery, that is, ΔV=V1-V2.

[0158] 203. Determine the initial voltage of the lithium-ion battery, and calculate the overpotential of the lithium-ion battery after aging based on the output voltage and the initial voltage at each acquisition moment.

[0159] In the embodiment of the present invention, optionally, the initial voltage of the lithium-ion battery may represent the initial voltage of the lithium-ion battery when it leaves the factory, that is, the voltage of the lithium-ion battery before aging, and the overpotential of the lithium-ion battery after aging may 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 may be:

[0160] V(terminal)=V eg +η

[0161] 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, indicating the degree of deviation from the equilibrium voltage.

[0162] 204. Calculate the resistance change of the lithium-ion battery after aging based on the output current and overpotential at each acquisition moment.

[0163] In the embodiment of the present invention, optionally, the voltage drop corresponding to the discharge process of the lithium-ion battery can be approximately replaced by the overpotential of the lithium-ion battery, that is, Then, the resistance change of the lithium-ion battery after aging is calculated based on the output current at each acquisition moment.

[0164] 205. Calculate the voltage drop per unit time of the lithium-ion battery according to the resistance change and the output current at each acquisition moment.

[0165] In the embodiment of the present invention, optionally, 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 acquisition moment, that is, At this time, the voltage drop per unit time corresponding to the lithium-ion battery is used to associate the battery SOC with battery aging. Most existing models use the cycle life N as the main aging parameter to express the battery SOH. However, the battery rarely undergoes a complete charge / discharge cycle during operation, and the cycle life is difficult to track. Therefore, the voltage drop per unit time corresponding to the lithium-ion battery is used to associate the battery SOC with battery aging, which can remove the cycle life N and improve the accuracy of the model.

[0166] 206. Based on the battery output information and the voltage drop per unit time, a SOC calculation model for lithium-ion batteries is constructed.

[0167] 207. 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.

[0168] In the embodiment of the present invention, for other descriptions of step 201, step 206 and step 207, please refer to the detailed description of step 101 to step 103 in the first embodiment, and the embodiment of the present invention will not be repeated.

[0169] It can be seen that implementation Figure 2The described method for estimating the SOC and SOH of a lithium-ion battery can collect the battery output information of the lithium-ion battery at preset intervals during a life cycle experiment on the lithium-ion battery. In the output voltage at each collection moment, the voltage drop per unit time corresponding to the lithium-ion battery is calculated based on two adjacent output voltages to determine the initial voltage of the lithium-ion battery. The overpotential of the lithium-ion battery after aging is calculated based on the output voltage and the initial voltage at each collection moment. The resistance change of the lithium-ion battery after aging is calculated based on the output current and the overpotential at each collection moment. The voltage drop per unit time corresponding to the lithium-ion battery is calculated based on the resistance change and the output current at each collection moment. The SOC of the battery can be associated with the battery aging based on the voltage drop per unit time corresponding to the lithium-ion battery, thereby avoiding the use of difficult-to-trace battery cycle life parameters to express the battery. SOH, reduces the difficulty of determining the battery SOC and SOH, while improving the accuracy and efficiency of determination, constructs the SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time, defines the correction factor according to the SOC calculation model, and constructs the SOH calculation model of the lithium-ion battery based on the battery output information, the voltage drop per unit time and the correction factor, can define the SOC and SOH model based on the new variable unit time voltage drop, and estimate the instantaneous SOC and SOH of the battery online without performing a full cycle test on the battery, which improves the efficiency of determining the battery SOC and SOH, and has high accuracy, and can be modeled based on any one of the same batch of batteries, and then estimate the SOC and SOH of all batteries, the model has high accuracy and robustness, and can improve the accuracy of online estimation of the SOC and SOH of lithium-ion batteries.

[0170] In an optional embodiment, in the output voltage at each acquisition moment, based on two adjacent output voltages, a calculation formula for calculating the voltage drop per unit time corresponding to the lithium-ion battery includes:

[0171] ΔV=V1-V2

[0172] Wherein, V′ represents the voltage drop per unit time, V1 and V2 represent two adjacent output voltages, and Δt represents the unit time;

[0173] According to the resistance change and the output current at each acquisition moment, the calculation formula for calculating the voltage drop per unit time corresponding to the lithium-ion battery includes:

[0174]

[0175] Among them, Δη represents the overpotential, I represents the output current at each acquisition moment, and ΔR represents the resistance change.

[0176] In this optional embodiment, ΔV represents the difference between two consecutive output voltages during the discharge 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 ages, the overpotential of the lithium-ion battery after aging can represent the aging voltage of the lithium-ion battery, and the relationship between the initial voltage, output voltage and overpotential of the lithium-ion battery can be: V(terminal)=V eg +η, where V(terminal) represents the initial voltage of the lithium-ion battery, V eg represents the output voltage of the lithium-ion battery, η represents the overpotential of the lithium-ion battery, and the corresponding voltage drop during the discharge process of the lithium-ion battery can be approximately replaced by the overpotential of the lithium-ion battery, that is, Substitute the output current of the lithium-ion battery at each sampling moment into the formula to obtain Transform the formula to get Among them, Δη represents the overpotential, I represents the output current at each acquisition moment, and ΔR represents the resistance change. At this time, the voltage drop per unit time corresponding to the lithium-ion battery is used to associate the battery SOC with the battery aging, and the number of cycles can be removed.

[0177] It can be seen that the implementation of this optional embodiment can calculate the voltage drop per unit time based on 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 voltage drop per unit time corresponding to the lithium-ion battery according to the resistance change and the output current at each acquisition moment, and use the overpotential to calculate the resistance change to reflect the battery aging, and then associate the battery SOC with the battery aging based on the voltage drop per unit time corresponding to the lithium-ion battery, avoid using difficult-to-track battery cycle life parameters to express the battery SOH, reduce the difficulty of determining the battery SOC and SOH, and improve the determination accuracy and efficiency.

[0178] In another optional embodiment, based on the battery output information and the voltage drop per unit time, constructing the SOC calculation model of the lithium-ion battery may include the following operations:

[0179] The SOC model coefficient of the lithium-ion battery is obtained by performing data regression fitting based on the battery output information;

[0180] Based on the voltage drop per unit time and the SOC model coefficient, the SOC calculation model of the lithium-ion battery is constructed;

[0181] The SOC calculation model includes:

[0182]

[0183] Wherein, V represents the output voltage at each acquisition moment, and a, b, and c represent SOC model coefficients.

[0184] In this optional embodiment, optionally, the relationship between the SOC of the battery and the inverse of the voltage and the voltage drop per unit time is approximately linear, so the SOC calculation model of the lithium-ion battery is set to a linear equation, that is, 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.

[0185] It can be seen that the implementation of this optional embodiment can construct a SOC calculation model for a lithium-ion battery based on the voltage drop per unit time and the SOC model coefficient, thereby improving the accuracy of the SOC model and improving the accuracy and efficiency of calculating the battery SOC.

[0186] Embodiment 3

[0187] See also Figure 3 , Figure 3 1 is a schematic diagram of a structure of a device for estimating SOC and SOH of a lithium-ion battery disclosed in an embodiment of the present invention. Figure 3 The described device for estimating the SOC and SOH of a lithium-ion battery may include an intelligent server or intelligent platform for estimating the SOC value and SOH value of a lithium-ion battery, and the intelligent server includes a local server or a cloud server, which is not limited in the embodiment of the present invention. Figure 3 As shown, the estimation device of the SOC and SOH of the lithium-ion battery may include:

[0188] The acquisition module 301 is used to acquire the battery output information of the lithium-ion battery at preset intervals during the life cycle experiment of the lithium-ion battery, where the battery output information includes the output current and output voltage at each acquisition moment;

[0189] A calculation module 302 is used to calculate the voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information, and to construct a SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time;

[0190] The construction module 303 is used to define the correction factor according to the SOC calculation model, and to 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 used to calculate the SOC value of the lithium-ion battery, and the SOH calculation model is used to calculate the SOH value of the lithium-ion battery.

[0191] It can be seen that implementation Figure 3The described device for estimating the SOC and SOH of a lithium-ion battery can collect the battery output information of the lithium-ion battery at preset intervals during a life cycle experiment on the lithium-ion battery, calculate the corresponding voltage drop per unit time of the lithium-ion battery based on the battery output information, and construct a SOC calculation model for the lithium-ion battery based on the battery output information and the voltage drop per unit time, define a correction factor based on the SOC calculation model, and construct a SOH calculation model for the lithium-ion battery based on the battery output information, the voltage drop per unit time and the correction factor, and can define the model of SOC and SOH based on the new variable voltage drop per unit time, and estimate the instantaneous SOC and SOH of the battery online without performing a full-cycle test on the battery, thereby improving the efficiency of determining the SOC and SOH of the battery, and having high accuracy, and can model any one of the batteries in the same batch, and then estimate the SOC and SOH of all batteries, and the model has high accuracy and robustness, and can improve the accuracy of online estimation of the SOC and SOH of the lithium-ion battery.

[0192] In an optional embodiment, if Figure 4 As shown, the specific method 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:

[0193] In the output voltage at each acquisition moment, based on two adjacent output voltages, the voltage drop per unit time corresponding to the lithium-ion battery is calculated; or,

[0194] Determine the initial voltage of the lithium-ion battery, and calculate the overpotential of the lithium-ion battery after aging based on the output voltage and the initial voltage at each acquisition moment;

[0195] Calculate the resistance change of lithium-ion battery after aging based on the output current and overpotential at each acquisition moment;

[0196] According to the resistance change and the output current at each acquisition moment, the voltage drop per unit time corresponding to the lithium-ion battery is calculated.

[0197] It can be seen that implementation Figure 4The described device for estimating the SOC and SOH of a lithium-ion battery can collect the battery output information of the lithium-ion battery at preset intervals during a life cycle experiment on the lithium-ion battery, calculate the voltage drop per unit time corresponding to the lithium-ion battery based on two adjacent output voltages in the output voltage at each collection moment, determine the initial voltage of the lithium-ion battery, and calculate the overpotential of the lithium-ion battery after aging based on the output voltage and the initial voltage at each collection moment, calculate the resistance change of the lithium-ion battery after aging based on the output current and the overpotential at each collection moment, calculate the voltage drop per unit time corresponding to the lithium-ion battery based on the resistance change and the output current at each collection moment, and can associate the SOC of the battery with the battery aging based on the voltage drop per unit time corresponding to the lithium-ion battery, thereby avoiding the use of difficult-to-trace battery cycle life parameters to express the battery SOH, reduces the difficulty of determining the battery SOC and SOH, while improving the accuracy and efficiency of determination, constructs the SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time, defines the correction factor according to the SOC calculation model, and constructs the SOH calculation model of the lithium-ion battery based on the battery output information, the voltage drop per unit time and the correction factor, can define the SOC and SOH model based on the new variable unit time voltage drop, and estimate the instantaneous SOC and SOH of the battery online without performing a full cycle test on the battery, which improves the efficiency of determining the battery SOC and SOH, and has high accuracy, and can be modeled based on any one of the same batch of batteries, and then estimate the SOC and SOH of all batteries, the model has high accuracy and robustness, and can improve the accuracy of online estimation of the SOC and SOH of lithium-ion batteries.

[0198] In another optional embodiment, Figure 4 As shown, the calculation module 302 calculates the voltage drop per unit time corresponding to the lithium-ion battery based on two adjacent output voltages in the output voltage at each acquisition moment, and the calculation formula includes:

[0199] ΔV=V1-V2

[0200] Wherein, V′ represents the voltage drop per unit time, V1 and V2 represent two adjacent output voltages, and Δt represents the unit time;

[0201] The calculation module 302 calculates the voltage drop per unit time of the lithium-ion battery according to the resistance change and the output current at each acquisition moment, and the calculation formula includes:

[0202]

[0203] Among them, Δη represents the overpotential, I represents the output current at each acquisition moment, and ΔR represents the resistance change.

[0204] It can be seen that implementation Figure 4 The described device for estimating the SOC and SOH of a lithium-ion battery can calculate the voltage drop per unit time based on 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 acquisition moment, and use the overpotential to calculate the resistance change to reflect the battery aging, and then associate the battery SOC with the battery aging based on the corresponding voltage drop per unit time of the lithium-ion battery, avoid using difficult-to-track battery cycle life parameters to express the battery SOH, reduce the difficulty of determining the battery SOC and SOH, and improve the determination accuracy and efficiency.

[0205] In yet another optional embodiment, Figure 4 As shown, the specific manner 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:

[0206] The SOC model coefficient of the lithium-ion battery is obtained by performing data regression fitting based on the battery output information;

[0207] Based on the voltage drop per unit time and the SOC model coefficient, the SOC calculation model of the lithium-ion battery is constructed;

[0208] The SOC calculation model includes:

[0209]

[0210] Wherein, V represents the output voltage at each acquisition moment, and a, b, and c represent SOC model coefficients.

[0211] It can be seen that implementation Figure 4 The described lithium-ion battery SOC and SOH estimation device can construct a SOC calculation model for the lithium-ion battery based on the voltage drop per unit time and the SOC model coefficient, thereby improving the accuracy of the SOC model and improving the accuracy and efficiency of calculating the battery SOC.

[0212] In yet another optional embodiment, Figure 4 As shown, 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 in a specific manner including:

[0213] Based on the battery output information and the voltage drop per unit time, a SOH regression model of the lithium-ion battery at a preset SOC level is constructed;

[0214] Based on the correction factor, the SOH calculation model of lithium-ion batteries is constructed on the basis of the SOH regression model of the preset SOC level.

[0215] It can be seen that implementation Figure 4 The described lithium-ion battery SOC and SOH estimation device can construct a SOH regression model for the lithium-ion battery at a preset SOC level based on the battery output information and the voltage drop per unit time, and based on the correction factor, construct a SOH calculation model for the lithium-ion battery on the basis of the SOH regression model at the preset SOC level. The SOH calculation model can be constructed based on the correction factor newly defined in the SOC calculation model, thereby improving the accuracy of the SOH model and improving the accuracy and efficiency of calculating the battery SOH.

[0216] In yet another optional embodiment, Figure 4 As shown, the construction module 303 constructs 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 in a specific manner including:

[0217] The SOH model coefficient of the lithium-ion battery is obtained by performing data regression fitting based on the battery output information;

[0218] Based on the voltage drop per unit time and the SOH model coefficient, a SOH regression model for lithium-ion batteries at a preset SOC level is constructed;

[0219] The SOH regression model includes:

[0220]

[0221] The SOH calculation model includes:

[0222]

[0223] Among them, A and B represent the SOH model coefficients, and α(SOC) represents the correction factor.

[0224] It can be seen that implementation Figure 4 The described device for estimating the SOC and SOH of a lithium-ion battery can construct a SOH regression model of a lithium-ion battery at a preset SOC level based on the voltage drop per unit time and the SOH model coefficient, and then construct a SOH calculation model based on the correction factor newly defined in the SOC calculation model, which can improve the accuracy of the SOH model and improve the accuracy and efficiency of calculating the battery SOH.

[0225] In yet another optional embodiment, Figure 4 As shown, the specific manner in which the construction module 303 defines the correction factor according to the SOC calculation model includes:

[0226] Based on the SOC calculation model and battery output information, data regression fitting is performed to obtain the correction factor coefficient of the lithium-ion battery;

[0227] Define a correction factor based on the correction factor coefficient and the SOC calculation model;

[0228] The calculation formula of the correction factor includes:

[0229] α(SOC)=C1(SOC) 3 +C2(SOC) 2 +C3(SOC) 1 +C0

[0230] Wherein, C1, C2, C3 and C0 represent correction factor coefficients.

[0231] It can be seen that implementation Figure 4 The described lithium-ion battery SOC and SOH estimation device can perform data regression fitting based on the SOC calculation model and battery output information to obtain the correction factor coefficient of the lithium-ion battery. The correction factor is defined based on the correction factor coefficient and the SOC calculation model, which can improve the accuracy of the correction factor. The correction factor is introduced into the SOH calculation model without the need to regress and calculate the model coefficient, thereby improving the model accuracy and calculation efficiency.

[0232] In yet another optional embodiment, Figure 4 As shown, the calculation module 302 is further used to calculate the battery test value of the lithium-ion battery according to the SOC calculation model and the SOH calculation model respectively, and the battery test value includes the SOC test value and the SOH test value;

[0233] The lithium-ion battery SOC and SOH estimation device may also include:

[0234] An acquisition module 304 is used to acquire a battery verification value of the lithium-ion battery, and calculate model indicators corresponding to the SOC calculation model and the SOH calculation model respectively according to the battery test value and the battery verification value, wherein the battery verification value includes an SOC verification value and an SOH verification value, and the model indicators include an average correlation coefficient and a root mean square error;

[0235] The verification module 305 is used 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, to obtain verification results, and to perform model optimization on the SOC calculation model and the SOH calculation model based on the verification results.

[0236] It can be seen that implementation Figure 4The described lithium-ion battery SOC and SOH estimation device 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 model prediction value and the true value, and after using any one of the batteries in the same batch for modeling, the accuracy and precision of calculating the SOC and SOH of other batteries are similar, thereby improving the accuracy and precision of the model.

[0237] Embodiment 4

[0238] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another device for estimating SOC and SOH of a lithium-ion battery disclosed in an embodiment of the present invention. Figure 5 As shown, the estimation device of the SOC and SOH of the lithium-ion battery may include:

[0239] A memory 401 storing executable program codes;

[0240] a processor 402 coupled to the memory 401;

[0241] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the method for estimating the SOC and SOH of a lithium-ion battery described in the first embodiment of the present invention or the second embodiment of the present invention.

[0242] Embodiment 5

[0243] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the method for estimating the SOC and SOH of a lithium-ion battery described in Embodiment 1 or Embodiment 2 of the present invention.

[0244] Embodiment 6

[0245] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the method for estimating the SOC and SOH of a lithium-ion battery described in Embodiment 1 or Embodiment 2.

[0246] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0247] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and 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 electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0248] Finally, it should be noted that the method and device for estimating SOC and SOH of a lithium-ion battery, and the computer storage medium disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for estimating SOC and SOH of a lithium-ion battery, characterized in that: The method comprises: In the process of conducting a life cycle experiment on a lithium-ion battery, collecting battery output information of the lithium-ion battery at preset time intervals, wherein the battery output information includes an output current and an output voltage at each collection moment; Calculating a voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information, and constructing a SOC calculation model for 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 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 SOC calculation model is used to calculate the SOC value of the lithium-ion battery, and the SOH calculation model is used to calculate the SOH value of the lithium-ion battery.

2. The method for estimating SOC and SOH of a lithium-ion battery according to claim 1, characterized in that: The step of calculating the voltage drop per unit time corresponding to the lithium-ion battery according to the battery output information includes: In each of the output voltages at the acquisition moment, based on two adjacent output voltages, the voltage drop per unit time corresponding to the lithium-ion battery is calculated; or, Determining the initial voltage of the lithium-ion battery, and calculating the overpotential of the lithium-ion battery after aging based on the output voltage at each acquisition moment and the initial voltage; Calculating the resistance change of the lithium-ion battery after aging according to the output current and the overpotential at each acquisition moment; 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 acquisition moment.

3. The method for estimating SOC and SOH of a lithium-ion battery according to claim 2, characterized in that: The calculation formula for calculating the voltage drop per unit time corresponding to the lithium-ion battery based on two adjacent output voltages in each of the output voltages at the acquisition moment includes: 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 voltage drop per unit time corresponding to the lithium-ion battery according to the resistance change and the output current at each acquisition moment includes: Wherein, Δη represents the overpotential, I represents the output current at each acquisition moment, and ΔR represents the resistance change.

4. The method for estimating SOC and SOH of a lithium-ion battery according to claim 3, characterized in that: The step of constructing a SOC calculation model of the lithium-ion battery based on the battery output information and the voltage drop per unit time includes: Performing data regression fitting based on the battery output information to obtain the SOC model coefficient of the lithium-ion battery; Constructing a SOC calculation model of the lithium-ion battery based on the voltage drop per unit time and the SOC model coefficient; The SOC calculation model includes: Wherein, V represents the output voltage at each acquisition moment, and a, b and c represent SOC model coefficients.

5. The method for estimating SOC and SOH of a lithium-ion battery according to any one of claims 1 to 4, characterized in that: The step of constructing 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 includes: Constructing a 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; Based on the correction factor, a SOH calculation model of the lithium-ion battery is constructed on the basis of the SOH regression model of the preset SOC level.

6. The method for estimating SOC and SOH of a lithium-ion battery according to claim 5, characterized in that: The constructing a 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 includes: Performing data regression fitting based on the battery output information to obtain the SOH model coefficient of the lithium-ion battery; Based on the voltage drop per unit time and the SOH model coefficient, constructing a SOH regression model of the lithium-ion battery at a preset SOC level; The SOH regression model includes: The SOH calculation model includes: Wherein, A and B represent the SOH model coefficients, and α(SOC) represents the correction factor.

7. The method for estimating SOC and SOH of a lithium-ion battery according to claim 6, characterized in that: Defining a correction factor according to the SOC calculation model includes: Performing data regression fitting based on the SOC calculation model and the battery output information to obtain a correction factor coefficient of the lithium-ion battery; defining a correction factor based on the correction factor coefficient and the SOC calculation model; The calculation formula of the correction factor includes: α(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 for estimating SOC and SOH of a lithium-ion battery according to any one of claims 1 to 4, characterized in that: The method further comprises: Calculating a battery test value of the lithium-ion battery according to the SOC calculation model and the SOH calculation model respectively, wherein the battery test value includes an SOC test value and an SOH test value; Obtaining a battery verification value of the lithium-ion battery, and calculating model indicators corresponding to the SOC calculation model and the SOH calculation model respectively according to the battery test value and the battery verification value, wherein the battery verification value includes an SOC verification value and an SOH verification value, and the model indicators include an average correlation coefficient and a root mean square error; The SOC calculation model and the SOH calculation model are respectively verified based on model indicators corresponding to the SOC calculation model and the SOH calculation model to obtain verification results, and the SOC calculation model and the SOH calculation model are optimized based on the verification results.

9. A device for estimating SOC and SOH of a lithium-ion battery, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method for estimating the SOC and SOH of a lithium-ion battery as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the method for estimating the SOC and SOH of a lithium-ion battery as described in any one of claims 1 to 8.

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