A method and system for evaluating state of charge of a lithium-ion battery

Abstract

The embodiment of the present invention provides a method and system for evaluating the state of charge of a lithium-ion battery. The method includes: obtaining the original IC curve during the charging process of the lithium-ion battery; filtering the original IC curve to obtain a smooth IC curve; dividing the IC curve to obtain a preset number of sections, and determining the fitted SOC-IC function of each section; updating the fitted SOC-IC function based on the adjustment coefficient associated with the size to obtain a reference SOC-IC function; and obtaining the variation law of the SOC-IC function coefficient of the lithium-ion battery with the charging rate, updating the reference SOC-IC function according to the variation law and the current charging rate to obtain the SOC-IC function, and evaluating the state of charge of the lithium-ion battery based on the SOC-IC function. The present invention constructs a SOC-IC function, uses the IC value measured during the charging process of the lithium battery to evaluate the battery SOC, and can achieve real-time and accurate evaluation of the SOC during the entire charging process.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery evaluation, and in particular to a method and system for evaluating the state of charge of a lithium-ion battery. Background Art

[0002] Due to its high power density and multiple charge and discharge characteristics, lithium-ion batteries are widely used in portable devices, grid energy storage systems, and electric vehicles. However, during operation, harsh environments and complex operating conditions may pose safety hazards to lithium-ion batteries and may cause serious consequences such as thermal runaway. Therefore, in order to ensure the safe operation of lithium-ion batteries, it is necessary to accurately evaluate their status.

[0003] As an important indicator to characterize the operating status of lithium batteries, the state of charge (SOC) is defined as the ratio of the current charge of the battery to its maximum capacity. Through accurate SOC evaluation, overcharging and over-discharging of the battery, as well as the safety problems caused by them, can be effectively avoided. At present, the evaluation methods of SOC mainly include time integration, open circuit voltage (OCV), circuit model and data-driven methods. Among them, time integration is to calculate the battery SOC by integrating the current, which is easy to implement in embedded management systems, but the current measurement error accumulated over a long period of time will eventually cause a large deviation in the SOC evaluation. To solve this problem, the SOC-OCV correspondence table can be used to correct the SOC evaluation error. However, the measurement of OCV requires the battery to stand for tens of minutes, and for lithium iron phosphate batteries, when the SOC is in the range of 35% to 70%, the OCV curve enters a plateau period (that is, the SOC change has little effect on the OCV), such as Figure 1 As shown in the figure, this brings great difficulties to SOC evaluation. Compared with time-based integration, the accuracy of SOC evaluation using the constructed circuit model is higher, but the model parameters need to be updated in real time with the battery's charge and discharge status. Parameter update requires the introduction of Kalman filtering, particle filtering and other computationally intensive algorithms, which increases the complexity of SOC evaluation. Data-driven methods such as support vector machines, fuzzy logic and neural networks require a large amount of battery operation data for analysis, which makes such methods difficult to be applied to embedded battery management systems. To date, in embedded battery management systems, time-based integration is still the most widely used SOC evaluation method.

[0004] In order to reduce the evaluation error of time-based integration, the incremental capacity analysis (ICA) technology can be used to correct the SOC and improve the evaluation accuracy of the SOC. During the charging process of lithium batteries, the formation of internal compounds can be divided into different stages, and these stages correspond to different peaks of the incremental capacity (IC) curve, so these peaks and the corresponding valleys can be used as characteristic points for SOC calibration. However, due to the limited number of peaks on the IC curve, the battery SOC can only be calibrated at limited characteristic points and cannot be used to evaluate the SOC during the entire charging process. In addition, since the existing ICA is only applicable to SOC estimation during low-rate charging (less than 1C), and with the development of high-rate charging lithium-ion battery technology and its application in fast charging stations, etc., there is still a lack of accurate SOC estimation methods in high-rate charging scenarios. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a lithium-ion battery state of charge assessment method and system. The lithium-ion battery state of charge assessment method and system construct a SOC-IC function, use the IC value measured during the lithium battery charging process to assess the battery SOC, and achieve real-time and accurate SOC assessment throughout the charging process.

[0006] In order to achieve the above object, an embodiment of the present invention provides a method for evaluating the state of charge of a lithium-ion battery, the method comprising:

[0007] Get the original IC curve of lithium-ion battery during charging;

[0008] Filtering the original IC curve to obtain a smooth IC curve;

[0009] Based on a longitudinal parameter value on the longitudinal axis of the IC curve and a preset number of transverse parameter values ​​on the transverse axis of the IC curve, the IC curve is divided to obtain a preset number of sections, and a fitting SOC-IC function of each section is determined;

[0010] acquiring an adjustment coefficient associated with a size of the lithium-ion battery, and updating the fitted SOC-IC function based on the adjustment coefficient to obtain a reference SOC-IC function; and

[0011] The variation law of the SOC-IC function coefficient of the lithium-ion battery with the charging rate is obtained, the reference SOC-IC function is updated according to the variation law and the current charging rate to obtain the SOC-IC function, and the state of charge of the lithium-ion battery is evaluated based on the SOC-IC function.

[0012] Preferably, obtaining the original IC curve during the charging process of the lithium-ion battery includes:

[0013] The original IC curve during the charging process of the lithium-ion battery is calculated by the following formula:

[0014]

[0015] Wherein, ΔQ and ΔV are the changes in charging capacity and voltage between two sampling points during the charging process of the lithium-ion battery, Δt is the time interval between the two sampling points, and I is the charging current.

[0016] Preferably, filtering the original IC curve to obtain a smooth IC curve comprises:

[0017] Filtering the original IC curve by a sliding mean filter to obtain a profile of the IC curve;

[0018] The profile of the IC curve was filtered by a Butterworth low-pass filter to obtain a smooth IC curve.

[0019] Preferably, filtering the profile of the IC curve by a Butterworth low-pass filter to obtain a smooth IC curve comprises:

[0020] Acquire a boundary value, and separate the contour of the IC curve based on the boundary value to obtain a first part and a second part;

[0021] The first part and the second part are filtered according to respective cutoff frequencies corresponding to the first part and the second part to obtain a smooth IC curve.

[0022] Preferably, the obtaining of the adjustment coefficient associated with the size of the lithium-ion battery comprises:

[0023] The adjustment coefficient is calculated by the following formula:

[0024]

[0025] Wherein, p is the adjustment coefficient, N A is the reference battery number of the lithium-ion battery, and N is the size of the lithium-ion battery to be evaluated.

[0026] Preferably, updating the reference SOC-IC function according to the change rule and the current charging rate includes:

[0027] Acquiring historical experimental parameters, wherein the historical experimental parameters include the law of the SOC-IC function coefficient changing with different charging rates, and the error between the SOC-IC function and experimental data; and

[0028] When the error is less than a preset error threshold, the reference SOC-IC function is updated according to the variation rule and the current charging rate.

[0029] In addition, the present invention also provides a lithium-ion battery state of charge assessment system, the lithium-ion battery state of charge assessment system comprising:

[0030] A curve acquisition unit, used to acquire the original IC curve during the charging process of the lithium-ion battery;

[0031] A filtering unit, used for filtering the original IC curve to obtain a smooth IC curve;

[0032] a fitting SOC-IC function determining unit, configured to divide the IC curve to obtain a preset number of sections based on a vertical axis parameter value on the vertical axis of the IC curve and a preset number of horizontal axis parameter values ​​on the horizontal axis of the IC curve, and determine a fitting SOC-IC function for each section;

[0033] a reference SOC-IC function obtaining unit, configured to obtain an adjustment coefficient associated with the size of the lithium-ion battery, and update the fitted SOC-IC function based on the adjustment coefficient to obtain a reference SOC-IC function; and

[0034] The battery state of charge evaluation unit is used to obtain the change law of the SOC-IC function coefficient of the lithium-ion battery with the charging rate, update the reference SOC-IC function according to the change law and the current charging rate to obtain the SOC-IC function, and evaluate the state of charge of the lithium-ion battery based on the SOC-IC function.

[0035] Preferably, the curve acquisition unit calculates the original IC curve during the charging process of the lithium-ion battery by the following formula:

[0036]

[0037] Wherein, ΔQ and ΔV are the changes in charging capacity and voltage between two sampling points during the charging process of the lithium-ion battery, Δt is the time interval between the two sampling points, and I is the charging current.

[0038] In addition, the present invention also provides a machine-readable storage medium, on which instructions are stored, characterized in that the instructions are used to enable a machine to execute the above-mentioned lithium-ion battery charge state assessment method.

[0039] In addition, the present invention further provides a processor for running a program, wherein the program, when being run, is used to execute: the method for assessing the state of charge of a lithium-ion battery as described above.

[0040] Through the above technical solution, a lithium battery SOC estimation method in a high-rate charging scenario based on the IC curve is developed. By constructing a SOC-IC function and using the IC value measured during the lithium battery charging process to evaluate the battery SOC, real-time and accurate SOC evaluation can be achieved throughout the charging process. In addition, by adjusting the SOC-IC function for different charging rates and battery pack sizes (the number of batteries contained in the battery pack), a common method for estimating the SOC of lithium batteries at high charging rates can be achieved. This method has low computational complexity and is easy to implement in an embedded battery management system, providing a new method for SOC evaluation in high-rate charging scenarios such as energy storage power stations and fast charging stations.

[0041] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0043] Figure 1 is a schematic diagram illustrating the relationship between the charging voltage and the SOC of the present invention;

[0044] Figure 2 This is a schematic diagram of the IC curve obtained by the MA filter with a width of 1500;

[0045] Figure 3a This is the IC curve obtained by the Butterworth filter with a cutoff frequency of 0.001Hz;

[0046] Figure 3b This is the IC curve obtained by the Butterworth filter with a cutoff frequency of 0.01Hz;

[0047] Figure 4a It is the IC curve obtained by the segment boundary with IC value of 10;

[0048] Figure 4b It is the IC curve obtained by the segment boundary with IC value of 8;

[0049] Figure 4c It is the IC curve obtained by the segment boundary with IC value of 12;

[0050] Figure 5a Schematic diagram of the segmentation of the IC curve (a) Part A is divided into three sections (4, 5, 6);

[0051] Figure 5b Schematic diagram of the segmentation of the IC curve (a) Part B is divided into four sections (1, 2, 3, 7);

[0052] Figure 6 is the derivative of the 5th IC curve;

[0053] Figure 7 is a schematic diagram of the battery test platform;

[0054] Figure 8a is the SOC estimation error of battery pack A at 1.5C charging rate;

[0055] Figure 8b is the SOC estimation error of battery pack A at 1.6C charging rate;

[0056] Figure 8c is the SOC estimation error of battery pack A at 1.7C charging rate;

[0057] Figure 9a is the SOC estimation error of battery pack A at 1.8C charging rate;

[0058] Figure 9b is the SOC estimation error of battery pack A at 1.9C charging rate;

[0059] Fig.9c is the SOC estimation error of battery pack A at 2C charging rate;

[0060] Fig.10a is the SOC estimation error of battery pack B at 1.5C charging rate;

[0061] Fig.10b is the SOC estimation error of battery pack B at 1.6C charging rate;

[0062] Fig.10c is the SOC estimation error of battery pack B at 1.7C charging rate;

[0063] Fig.10d is the SOC estimation error of battery pack B at 1.8C charging rate;

[0064] Fig.10e is the SOC estimation error of battery pack B at a charging rate of 1.9C;

[0065] Fig.10f is the SOC estimation error of battery pack B at 2.0C charging rate;

[0066] Fig.11a is the SOC estimation error of battery pack C at 1.5C charging rate;

[0067] Fig.11b is the SOC estimation error of battery pack C at a charging rate of 1.6C;

[0068] Fig.11c is the SOC estimation error of battery pack C at a charging rate of 1.7C;

[0069] Fig.11d is the SOC estimation error of battery pack C at a charging rate of 1.8C;

[0070] Fig.11e is the SOC estimation error of battery pack C at a charging rate of 1.9C;

[0071] Fig.11f is the SOC estimation error of battery pack C at a 2.0C charging rate;

[0072] Fig.12 It is a flow chart of the method for evaluating the state of charge of a lithium-ion battery of the present invention. DETAILED DESCRIPTION

[0073] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0074] Fig.12 The present invention provides a method for evaluating the state of charge of a lithium-ion battery. Fig.12 As shown, the lithium-ion battery charge state assessment method includes:

[0075] S121, obtaining an original IC curve during the charging process of the lithium-ion battery;

[0076] S122, filtering the original IC curve to obtain a smooth IC curve;

[0077] S123, dividing the IC curve to obtain a preset number of sections based on a vertical axis parameter value on the vertical axis of the IC curve and a preset number of horizontal axis parameter values ​​on the horizontal axis of the IC curve, and determining a fitting SOC-IC function for each section;

[0078] S124, acquiring an adjustment coefficient associated with the size of the lithium-ion battery, and updating the fitted SOC-IC function based on the adjustment coefficient to obtain a reference SOC-IC function; and

[0079] S125, obtaining a variation pattern of the SOC-IC function coefficient of the lithium-ion battery with the charging rate, updating the reference SOC-IC function according to the variation pattern and the current charging rate to obtain a SOC-IC function, and evaluating the state of charge of the lithium-ion battery based on the SOC-IC function.

[0080] Among them, the SOC-IC function finally obtained by the present invention can more accurately evaluate the state of charge of the lithium-ion battery. In addition to time integration, the existing technologies have high computational complexity. For example, the circuit model method requires a complex model and relies on a complex algorithm to update the model parameters in real time to ensure prediction accuracy; the data-driven algorithm requires a large number of models to ensure prediction accuracy. This method only needs to calculate the IC value and after simple filtering, the SOC at the current moment can be calculated through the SOC-IC function, and the computational complexity is low. In addition, under high-current charging conditions, this method can guarantee an SOC evaluation accuracy of about 2%, and its prediction accuracy can reach the same level as the complex algorithm of the circuit model. With the rapid development of charging technology and battery technology, the charging speed is constantly improving, and the traditional ICA method is no longer suitable for high-current charging scenarios. This method can be used in high-current charging scenarios, which is consistent with the development trend of new energy batteries and has certain promotion value in the fields of energy storage peak regulation and frequency modulation.

[0081] The present invention will be described in detail below with reference to the accompanying drawings.

[0082] Preferably, the step S121 of obtaining an original IC curve during the charging process of the lithium-ion battery includes:

[0083] The original IC curve during the charging process of the lithium-ion battery is calculated by the following formula:

[0084]

[0085] Wherein, ΔQ and ΔV are the changes in charging capacity and voltage between two sampling points during the charging process of the lithium-ion battery, Δt is the time interval between the two sampling points, and I is the charging current.

[0086] Preferably, the step S122 of filtering the original IC curve to obtain a smooth IC curve includes:

[0087] The original IC curve is filtered by a sliding mean filter to obtain the profile of the IC curve; and the profile of the IC curve is filtered by a Butterworth low-pass filter to obtain a smooth IC curve.

[0088] Preferably, filtering the profile of the IC curve by a Butterworth low-pass filter to obtain a smooth IC curve comprises:

[0089] Acquire a boundary value, and separate the contour of the IC curve based on the boundary value to obtain a first part and a second part;

[0090] The first part and the second part are filtered according to respective cutoff frequencies corresponding to the first part and the second part to obtain a smooth IC curve.

[0091] Among them, the original IC value calculated according to formula (1) has obvious noise. In order to obtain a smooth SOC-IC curve, a denoising method, such as a filtering method, is required. Among different filtering methods, the sliding mean (MA) filter is the most widely used, as shown in formula (2):

[0092]

[0093] Where m is the filter width. MA filters are highly dependent on their width. Increasing the width results in better smoothing, but the curve is deformed and the time lag is obvious. In this patent, in order to filter out noise and avoid excessive time lag, the filter width is set to 1500. The results are as follows: Figure 2 shown.

[0094] from Figure 2 It can be seen that the outline of the IC curve has been obtained through MA filtering, but there is still significant noise near the peak of the IC curve, so the Butterworth low-pass filter is used to further remove the noise. Within the passband, the frequency response of the Butterworth filter is smoothest to the greatest extent, and the relationship between its amplitude and frequency is shown in formula (3)

[0095]

[0096] Where n is the filter order, ω c is the cut-off frequency, that is, the frequency of the filter when the amplitude is reduced by 3dB.

[0097] According to the principle of Butterworth filter, the lower the cutoff frequency, the smoother the filtering result, but the data features may be lost. Figure 3a As shown in Figure 2, at a cutoff frequency of 0.001Hz, the noise near the peak of the curve is filtered out. However, near 15% SOC, compared with Figure 1 , the curve shape changes, and the original curve characteristics are lost. When the cutoff frequency increases to 0.01Hz, Figure 3b As shown, the curve features near 15% SOC are retained, but the noise near the peak of the curve still exists.

[0098] Therefore, in order to improve the smoothness of the IC curve (which is beneficial to the derivation of the SOC-IC function) and retain the characteristics of the IC curve, the IC curve is divided into two parts with a certain IC value as the boundary, and different cutoff frequencies are used for each part. Figure 4a-4c The curve segmentation under different boundary values ​​is given, where the IC curve is divided into two parts. The dotted line and the solid line are the results of filtering with cutoff frequencies of 0.001Hz and 0.01Hz respectively. Figure 4b , 4cIt can be seen that when the boundary value is large, a gap will appear between the two parts, while a small boundary value will cause the two parts to overlap, which hinders the evaluation of SOC. Therefore, in the analysis, the boundary value is set to 10, and a continuous IC curve can be obtained, such as Figure 4a shown.

[0099] Preferably, in S123, after the above filtering, the IC curve is smooth enough to be used for fitting the SOC-IC function. In order to improve the versatility of the proposed method, the method uses a simple function to express the relationship between SOC and IC. For this purpose, the IC curve is divided into three sections in part A and four sections in part B, such as Figure 5a , Figure 5b The node boundaries are determined as SOC 10%, 17%, 40%, and 55%, as shown in Figure 5a , Figure 5b After this segmentation, the IC curves in each section are monotonic except for the fifth section, where one IC corresponds to two SOCs, which interferes with the evaluation of SOC. To solve this problem, the derivative of the IC curve is used to establish the SOC-IC function to ensure its monotonicity, as shown in Figure 6 shown.

[0100] According to the results of the above curve filtering and segmentation, formula (4) can be used to fit the SOC-IC function of the first and third sections, formula (5) can be used to fit the SOC-IC function of the fifth section, and the SOC-IC functions of the remaining sections can be fitted by formula (6).

[0101] IC i =a i (SOC) 2 +b i (SOC)+c i (4)

[0102] (IC i )′ = a i (SOC)+b i (5)

[0103] IC i =a i (SOC)+b i (6)

[0104] Where a, b, c are the coefficients of the function, and i is the section number.

[0105] In addition, the coefficients in formula (4-6) will change with the charging rate and battery pack size. The law of change of the function coefficient with the charging rate is related to the battery properties, such as electrode materials, etc. This change law can be obtained through experiments under different charging rates, and the relationship between the function coefficient and the battery pack size can be derived as follows.

[0106] As the number of batteries in a series battery pack increases, its capacity remains unchanged and the cut-off voltage doubles. From formula (1), it can be seen that the IC value of a battery pack is related to its capacity and voltage. When the capacity remains unchanged, the IC value decreases as the voltage increases.

[0107] Preferably, the obtaining of the adjustment coefficient associated with the size of the lithium-ion battery comprises:

[0108] The adjustment coefficient is calculated by the following formula (7):

[0109]

[0110] Wherein, p is the adjustment coefficient, N A is the reference number of cells in the lithium-ion battery, and N is the size of the lithium-ion battery to be evaluated. As long as the SOC-IC function of any battery pack is determined, the number of cells in the battery pack can be set to N A .

[0111] By adjusting the coefficients and the SOC-IC function determined on the reference battery pack, the SOC-IC function can be updated according to equation (8) to evaluate the SOC of battery packs of different sizes.

[0112]

[0113] Preferably, updating the reference SOC-IC function according to the change rule and the current charging rate includes:

[0114] Acquiring historical experimental parameters, wherein the historical experimental parameters include the law of the SOC-IC function coefficient changing with different charging rates, and the error between the SOC-IC function and experimental data; and

[0115] When the error is less than a preset error threshold, the reference SOC-IC function is updated according to the variation rule and the current charging rate.

[0116] Specifically, the final function coefficients are obtained through the following experiments.

[0117] Battery test platform (such as Figure 7The device includes the following components: (1) a battery charging and discharging device, with a voltage and current range of 20 V and 10 A respectively, and a voltage and current measurement accuracy of 0.1% FS; (2) a host computer for collecting data and controlling the battery charging and discharging device; (3) a lithium iron phosphate battery for testing, with specific parameters as shown in Table 1.

[0118] Table 1. Test battery parameters

[0119]

[0120] The IC curves at different charging rates (1.5C, 1.6C, 1.7C, 1.8C, 1.9C and 2C) were measured experimentally. In the analysis, the SOC-IC function and its coefficients were derived using the IC curves at charging rates of 1.5C, 1.6C and 1.7C, respectively, and the derived function was verified using data at other charging rates. In addition, in order to study the effectiveness of the proposed method for SOC identification of battery packs of different sizes, the above-mentioned different charging current experiments were carried out on three different battery packs. In the test, battery pack A contains 2 batteries in series, battery pack B contains 3 batteries in series, and battery pack C contains 4 batteries in series. The SOC-IC function will be derived from battery pack A and then verified with the other two battery packs. The specific experimental plan is shown in Table 2.

[0121] Table 2. Battery charging and discharging experimental plan

[0122]

[0123]

[0124] The original experimental data were filtered and segmented and then fitted according to equation (4-6). The fitting results are shown in Tables 3-6. The coefficient of determination R was selected in the fitting process. 2 Evaluation of fitting effect, R 2 It is a symbol that reflects the fitting effect of the SOC-IC function. Its value does not exceed 1. R 2 The larger the value, the better the fitting effect.

[0125] Table 3. Fitting results of Sections 1 and 2

[0126]

[0127] Table 4. Fitting results of Sections 3 and 4

[0128]

[0129] Table 5. Fitting results of Sections 5 and 6

[0130]

[0131] Table 6. Fitting results of Section 7

[0132]

[0133] It should be pointed out that since the results in the above table are determined using battery pack A at a charge rate of 1.5C-1.7C, we first study its SOC estimation performance under this condition. Figure 8a-8c The estimated results are given. At each rate, three charging experiments are carried out to obtain three estimated results. It can be seen that according to the SOC-IC function, the estimation error can be controlled within 2%.

[0134] In addition, in order to extend the effectiveness of the SOC-IC function in estimating SOC to a higher charging rate, it is necessary to establish the relationship between the function coefficient and the charging rate. By further studying the variation of the function coefficient with the charging rate (here 1.5C, 1.6C, 1.7C), it can be determined that the linear relationship between the function coefficient and the charging current is shown in formula (9-15).

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142] Where I is the charging current.

[0143] According to Tables 3-6 and equations (9-15), the SOC of battery pack A at a higher charging rate can be estimated. Figure 8a-8c The SOC estimation of battery pack A at 1.8C, 1.9C and 2C charging rates is given.

[0144] like Figure 9a-9c As shown, the obtained SOC-IC function can be used to accurately estimate the SOC of battery pack A, and the maximum estimation error does not exceed 2.1%.

[0145] In addition, in practical applications, battery packs containing different numbers of cells can be used to provide the required power output. Therefore, this paper further studies the versatility of the proposed method in estimating the SOC of battery packs containing different numbers of cells.

[0146] First, determine the adjustment factor. Battery pack A is selected as the benchmark because its SOC-IC function has been determined. Therefore, N A is 2, and for battery packs B and C, N is 3 and 4 respectively.

[0147] According to the updated SOC-IC function in formula (8), the SOC of battery pack B and battery pack C are estimated respectively, as follows: Figure 10a-10f , Figure 11a-Figure 11f As shown. Figure 10a-10f , Figure 11a-Figure 11f It can be seen that according to the proposed SOC-IC function, the SOC of different battery packs can be well estimated. The estimation error can be controlled within 2%, with only a few exceptions, where the maximum error in SOC estimation occurs when battery pack B is charged at a 2C rate, about 2.3%, but still within an acceptable level. This confirms that the proposed method can be applied to battery packs containing different numbers of batteries and SOC at different charging rates.

[0148] Based on the above discussion, this method can accurately estimate the SOC of battery packs of different sizes at different charging rates.

[0149] In addition, the present invention also provides a lithium-ion battery state of charge assessment system, the lithium-ion battery state of charge assessment system comprising:

[0150] A curve acquisition unit, used to acquire the original IC curve during the charging process of the lithium-ion battery;

[0151] A filtering unit, used for filtering the original IC curve to obtain a smooth IC curve;

[0152] a fitting SOC-IC function determining unit, configured to divide the IC curve to obtain a preset number of sections based on a vertical axis parameter value on the vertical axis of the IC curve and a preset number of horizontal axis parameter values ​​on the horizontal axis of the IC curve, and determine a fitting SOC-IC function for each section;

[0153] a reference SOC-IC function obtaining unit, configured to obtain an adjustment coefficient associated with the size of the lithium-ion battery, and update the fitted SOC-IC function based on the adjustment coefficient to obtain a reference SOC-IC function; and

[0154] The battery state of charge evaluation unit is used to obtain the change law of the SOC-IC function coefficient of the lithium-ion battery with the charging rate, update the reference SOC-IC function according to the change law and the current charging rate to obtain the SOC-IC function, and evaluate the state of charge of the lithium-ion battery based on the SOC-IC function.

[0155] Preferably, the curve acquisition unit calculates the original IC curve during the charging process of the lithium-ion battery by the following formula:

[0156]

[0157] Wherein, ΔQ and ΔV are the changes in charging capacity and voltage between two sampling points during the charging process of the lithium-ion battery, Δt is the time interval between the two sampling points, and I is the charging current.

[0158] In addition, the present invention also provides a machine-readable storage medium, on which instructions are stored, characterized in that the instructions are used to enable a machine to execute the above-mentioned lithium-ion battery charge state assessment method.

[0159] In addition, the present invention further provides a processor for running a program, wherein the program, when being run, is used to execute: the method for assessing the state of charge of a lithium-ion battery as described above.

[0160] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0161] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0165] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0166] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0167] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0168] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0169] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for assessing the state of charge of a lithium-ion battery, characterized in that: The lithium-ion battery state of charge assessment method comprises: Get the original IC curve of lithium-ion battery during charging; Filtering the original IC curve to obtain a smooth IC curve; Based on a vertical axis parameter value on the vertical axis of the IC curve and a preset number of horizontal axis parameter values ​​on the horizontal axis of the IC curve, the smooth IC curve is separated to obtain a preset number of sections, and the fitting SOC-IC function of each section is determined; wherein the fitting SOC-IC function of the first and third sections is IC i =a i (SOC) 2 +b i (SOC)+c i ; The fitting SOC-IC function in Section 5 is (IC i )′=a i (SOC)+b i ; The fitting SOC-IC function of the remaining sections except the first, third, and fifth sections is IC i =a i (SOC)+b i ; Where a, b, c are the coefficients of the function, and i is the section number; Acquire an adjustment coefficient associated with the size of the lithium-ion battery, and update the fitted SOC-IC function based on the adjustment coefficient to obtain a reference SOC-IC function, wherein the SOC-IC function update formula is: as well as Obtain the variation law of the SOC-IC function coefficient of the lithium-ion battery with the charging rate, that is, Wherein I is the charging current; the reference SOC-IC function is updated according to the change rule and the current charging rate to obtain the SOC-IC function, and the state of charge of the lithium-ion battery is evaluated based on the SOC-IC function; The obtaining of the adjustment coefficient associated with the size of the lithium-ion battery comprises: The adjustment coefficient is calculated by the following formula: Wherein, p is the adjustment coefficient, N A is the reference battery number of the lithium-ion battery, and N is the size of the lithium-ion battery to be evaluated.

2. The method for assessing the state of charge of a lithium-ion battery according to claim 1, wherein: The obtaining of the original IC curve during the charging process of the lithium-ion battery comprises: The original IC curve during the charging process of the lithium-ion battery is calculated by the following formula: Wherein, ΔQ and ΔV are the changes in charging capacity and voltage between two sampling points during the charging process of the lithium-ion battery, Δt is the time interval between the two sampling points, and I is the charging current.

3. The method for assessing the state of charge of a lithium-ion battery according to claim 1, wherein: The filtering the original IC curve to obtain a smooth IC curve comprises: Filtering the original IC curve by a sliding mean filter to obtain a profile of the IC curve; The profile of the IC curve was filtered by a Butterworth low-pass filter to obtain a smooth IC curve.

4. The method for assessing the state of charge of a lithium-ion battery according to claim 3, wherein: The step of filtering the profile of the IC curve by a Butterworth low-pass filter to obtain a smooth IC curve comprises: Acquire a boundary value, and separate the contour of the IC curve based on the boundary value to obtain a first part and a second part; The first part and the second part are filtered according to respective cutoff frequencies corresponding to the first part and the second part to obtain a smooth IC curve.

5. The method for assessing the state of charge of a lithium-ion battery according to claim 3, wherein: The updating of the reference SOC-IC function according to the change rule and the current charging rate comprises: Acquiring historical experimental parameters, wherein the historical experimental parameters include the law of the SOC-IC function coefficient changing with different charging rates, and the error between the SOC-IC function and experimental data; and When the error is less than a preset error threshold, the reference SOC-IC function is updated according to the variation rule and the current charging rate.

6. A lithium-ion battery state of charge assessment system, characterized in that: The lithium-ion battery charge state assessment system comprises: A curve acquisition unit, used to acquire the original IC curve during the charging process of the lithium-ion battery; A filtering unit, used for filtering the original IC curve to obtain a smooth IC curve; A fitting SOC-IC function determination unit is used to divide the IC curve into a preset number of sections based on a vertical axis parameter value on the vertical axis of the IC curve and a preset number of horizontal axis parameter values ​​on the horizontal axis of the IC curve, and determine the fitting SOC-IC function of each section; wherein the fitting SOC-IC function of the first and third sections is IC i =a i (SOC) 2 +b i (SOC)+c i ; The fitting SOC-IC function in Section 5 is (IC i )′=a i (SOC)+b i ; The fitting SOC-IC function of the remaining sections except the first, third, and fifth sections is IC i =a i (SOC)+b i ; Where a, b, c are the coefficients of the function, and i is the section number; A reference SOC-IC function obtaining unit is used to obtain an adjustment coefficient associated with the size of the lithium-ion battery, and to update the fitted SOC-IC function based on the adjustment coefficient to obtain a reference SOC-IC function, wherein the SOC-IC function update formula is: as well as The battery state of charge evaluation unit is used to obtain the variation law of the SOC-IC function coefficient of the lithium-ion battery with the charging rate, that is, Wherein I is the charging current; the reference SOC-IC function is updated according to the change rule and the current charging rate to obtain the SOC-IC function, and the state of charge of the lithium-ion battery is evaluated based on the SOC-IC function; The obtaining of the adjustment coefficient associated with the size of the lithium-ion battery comprises: The adjustment coefficient is calculated by the following formula: Wherein, p is the adjustment coefficient, N A is the reference battery number of the lithium-ion battery, and N is the size of the lithium-ion battery to be evaluated.

7. The lithium-ion battery state of charge assessment system according to claim 6, characterized in that: The curve acquisition unit calculates the original IC curve during the charging process of the lithium-ion battery by the following formula: Wherein, ΔQ and ΔV are the changes in charging capacity and voltage between two sampling points during the charging process of the lithium-ion battery, Δt is the time interval between the two sampling points, and I is the charging current.

8. A machine-readable storage medium having instructions stored thereon, characterized in that: The instruction is used to enable the machine to execute the lithium-ion battery state of charge assessment method described in any one of claims 1-5.

9. A processor, characterized in that: Used to run a program, wherein the program, when run, is used to execute: the method for evaluating the state of charge of a lithium-ion battery as described in any one of claims 1 to 5.

Images