Apparatus and method for SOH estimation of battery, method for forming 2d-LUT thereof

Abstract

An apparatus for battery state of health (SOH) estimation, comprising: a DV calculation unit (200) configured to calculate a charge derivative voltage dV / dQ from an operating parameter of a battery, and to filter the charge derivative voltage dV / dQ; a feature detection unit (300) configured to perform peak and threshold detection based on the filtered charge derivative voltage dV / dQ to detect a feature satisfying a requirement defined by a typical SOC range and a height interval of a center graphite peak, and to calculate an average temperature of the battery during a time interval corresponding to the detected feature; and an estimation unit (400) configured to perform SOH estimation by a 2D lookup table (2D-LUT) based on the detected feature and the average temperature.

Description

[0001] Related Applications

[0002] The present invention claims priority to patent application number 17 / 749,853, filed with the U.S. Patent and Trademark Office on May 20, 2022, entitled “DEVICE AND METHOD FOR BATTERY SOH ESTIMATION, METHOD FOR FORMING 2D-LUT THEREOF,” the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] Embodiments of the present invention generally relate to the field of batteries, and more particularly, to an apparatus and method for battery state of health (SOH) estimation, and a method for forming a 2D-LUT thereof. Background Art

[0004] This section introduces aspects that may help to better understand the present invention. Therefore, the statements in this section should be understood from this perspective, and should not be understood as an admission of the contents in the prior art or the contents outside the prior art.

[0005] Secondary (rechargeable) batteries, such as lithium-ion batteries, are used to provide high power and high energy density in portable applications including, for example, mobile devices, computing devices, and powertrains for electric vehicles.

[0006] Power and energy management of lithium-ion batteries relies on accurate determination of battery parameters including state of health (SOH).

[0007] The health status of secondary batteries can be monitored mainly through three parameters: internal resistance, impedance and capacity.

[0008] The present invention describes SOH estimation with respect to capacity fade due to degradation mechanisms.

[0009] SOH can be defined as the maximum dischargeable capacity of a fully charged battery divided by the nominal capacity.

[0010] SOH estimation methods can be mainly divided into three categories: direct measurement, i.e., coulomb counting of full discharge, etc.; model-based measurement, i.e., SOC-OCV relationship, DVA-based methods, etc.; adaptive battery model, i.e., neural network, Kalman filter, observer, etc.

[0011] Regarding coulomb counting, this method describes the direct measurement of capacity by discharging a fully charged battery at a specific temperature and current rate (ie, T = 25°C, I = 1 / 3*CN) until the cut-off voltage is reached. The advantage of this method is that there is no error due to estimation.

[0012] Regarding the SOC-OCV relationship, this method estimates SOH by relating SOC to open circuit voltage (OCV). The SOC and OCV of a balanced battery are first measured, and then the battery cells are discharged or charged indefinitely. After the discharge or charge process is completed, the battery cells are allowed to return to a balanced state, and then a second SOC and OCV measurement is taken. Then, the relationship between the changes in SOC and OCV due to the discharge or charge process can be related to SOH. The advantage of this method is that it is simple to implement.

[0013] Regarding DVA-based SOH estimation, this method uses differential voltage analysis, i.e., the relationship between dV / dQ and Q to determine the source of capacity decay of lithium-ion batteries, or uses differential charge analysis (called incremental capacity analysis, ICA), i.e., the relationship between dQ / dV and V to determine the capacity decay of lithium-ion batteries and quantify the composition changes in the material. For example, the distance between peaks or between peaks and thresholds or between peaks and the end of charge can be used for estimation. The advantage of this method is high accuracy and makes it possible to directly measure the electrode capacity. Summary of the invention

[0014] The inventors of the present invention have discovered that there are some limitations in the existing battery SOH estimation methods.

[0015] For example: the coulomb counting method may take a lot of time; the SOC-OCV relationship method is not applicable to all battery chemistries, i.e., LFP, and in addition, the method is temperature dependent and requires battery rest time; the DVA-based SOH estimation method requires a lot of computational work and filtering, and is temperature dependent.

[0016] In general, the embodiments of the present invention provide an apparatus for battery SOH estimation, a method for battery SOH estimation, and a method for forming a 2D-LUT for battery SOH estimation. The present invention aims to accurately estimate battery SOH using a simple and fast method.

[0017] In a first aspect, there is provided an apparatus for battery state of health (SOH) estimation, comprising:

[0018] a DV calculation unit configured to calculate a charge discrete charge derivative voltage dV / dQ according to an operating parameter of the battery and filter the charge discrete charge derivative voltage dV / dQ;

[0019] a feature detection unit configured to perform peak and threshold detection based on the filtered charge discrete charge derivative voltage dV / dQ to detect a feature that meets the requirements defined by the typical SOC range and height interval of the central graphite peak, and calculate an average temperature of the battery during a time interval corresponding to the detected feature;

[0020] An estimation unit is configured to perform SOH estimation through a 2D lookup table (2D-LUT) based on the detected features and the average temperature.

[0021] In a second aspect, a method for battery state of health (SOH) estimation is provided, comprising:

[0022] Calculating a charge discrete charge derivative voltage dV / dQ according to an operating parameter of the battery, and filtering the charge discrete charge derivative voltage dV / dQ;

[0023] performing peak and threshold detection based on the filtered charge discrete charge derivative voltage dV / dQ to detect a feature that meets requirements defined by a typical SOC range and a height interval of a central graphite peak, and calculating an average temperature of the battery during a time interval corresponding to the detected feature;

[0024] SOH estimation is performed through a 2D lookup table (2D-LUT) based on the detected features and the average temperature.

[0025] In a third aspect, there is provided a method for forming a 2D lookup table for battery health state estimation, comprising:

[0026] extracting current, voltage and temperature data from experimental results of battery tests designed to simulate aging of battery applications;

[0027] The charge discrete charge derivative voltage dV / dQ is calculated and filtered using a low-pass filter to extract features that meet the requirements defined by the typical SOC range and height interval of the central graphite peak, and the true SOH of the tested battery is calculated using coulomb counting;

[0028] Calculate a linear regression model for each temperature by relating the features to the true SOH; and

[0029] The 2D-LUT was formed at different temperatures based on the linear regression model.

[0030] According to various embodiments of the present invention, a feature is detected from the charge discrete charge derivative voltage dV / dQ, and the SOH can be estimated through a 2D lookup table (2D-LUT) based on the feature and the average temperature. Therefore, a simple and fast method can be used to accurately estimate the battery SOH. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] By way of example, the above and other aspects, features and benefits of various embodiments of the present invention will become more fully apparent from the following detailed description with reference to the accompanying drawings, in which the same reference numerals or letters are used to represent the same or equivalent elements. The accompanying drawings are shown to facilitate a better understanding of the embodiments of the present invention and are not necessarily drawn to scale, wherein:

[0032] Figure 1 is a block diagram schematically illustrating an apparatus for estimating SOH of a lithium-ion battery according to one embodiment of the present invention;

[0033] Figure 2 is a diagram schematically showing a DV calculation unit according to one embodiment of the present invention;

[0034] Figure 3 is a diagram schematically showing a feature detection unit according to one embodiment of the present invention;

[0035] Figure 4 is a diagram schematically illustrating an estimation unit according to one embodiment of the present invention;

[0036] Figure 5 is a graph of data associated with charge / discharge characteristics of an exemplary battery including full cell discharge, cathode discharge response curve versus lithium, and anode charge response curve versus lithium in accordance with one embodiment of the present invention;

[0037] Figure 6 is a graph of anode / cathode potential derivatives 640 / 650 calculated from a measured discharge voltage profile of a battery and a measured potential derivative according to one embodiment of the present invention;

[0038] Figure 7 is a block diagram schematically showing an apparatus for estimating SOH of a lithium-ion battery according to another embodiment of the present invention;

[0039] Figure 8 is a flow chart sequentially illustrating a method for forming a 2D-LUT at different temperatures according to one embodiment of the present invention;

[0040] Fig. 9 is a flowchart sequentially illustrating a method for estimating a health status according to one embodiment of the present invention;

[0041] Fig.10 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The present invention will now be discussed with reference to several exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and thus implement the present invention, rather than implying any limitation on the scope of the present invention.

[0043] As used herein, the terms "first" and "second" refer to different elements. Unless the context clearly indicates otherwise, the singular forms "one" and "an" are also intended to include plural forms. The terms "include", "comprise", "have", "have", "include" and / or "include" used herein specify the presence of the features, elements and / or parts, etc., but do not exclude the presence or addition of one or more other features, elements, parts and / or combinations thereof. The term "based on" should be understood as "based at least in part on". The terms "one embodiment" and "embodiment" should be understood as "at least one embodiment". The term "another embodiment" should be understood as "at least one other embodiment". Other explicit and implicit definitions may be included below.

[0044] First aspect of the embodiment

[0045] In a first aspect of the embodiments, an apparatus for battery state of health (SOH) estimation is provided.

[0046] Figure 1 1 is a block diagram schematically showing an apparatus 10 for estimating the SOH of a battery according to an embodiment of the present invention. Figure 1 As shown in FIG. 1 , the apparatus 10 includes a differential voltage (DV) calculation unit 200, a feature detection unit 300, and an estimation unit 400. The module 100 is a battery including one or more battery modules connected in parallel.

[0047] In at least one embodiment, the DV calculation unit 200 is configured to calculate the charge discrete charge derivative voltage dV / dQ according to the operating parameters of the battery 100 and filter the charge discrete charge derivative voltage dV / dQ. For example, the operating parameters may include the current, voltage, and temperature of the battery 100.

[0048] The feature detection unit 300 is configured to perform peak and threshold detection based on the filtered charge discrete charge derivative voltage dV / dQ to detect a feature that meets the requirements defined by the typical SOC range and height interval of the central graphite peak, and calculate the average temperature of the battery 100 during the time interval corresponding to the detected feature.

[0049] The estimation unit 400 is configured to perform SOH estimation through a 2D lookup table (2D-LUT) based on the detected features and the average temperature.

[0050] According to the first aspect of the present invention, the feature is detected from the charge discrete charge derivative voltage dV / dQ, and the SOH can be estimated based on the feature and the average temperature through a 2D lookup table (2D-LUT). Therefore, a simple and fast method can be used to accurately estimate the battery SOH.

[0051] Figure 2 FIG. 1 is a block diagram schematically showing a DV calculation unit according to an embodiment of the present invention. Figure 2 As shown in , the DV calculation unit 200 includes a charge discretization unit 240 , a dV / dQ calculation unit 250 , and a low-pass filter unit 260 .

[0052] The charge discretization unit 240 may convert a time-discrete signal of the operating parameter into a charge-discrete signal.

[0053] Charge discrete signals mean measurements based on Lebesgue sampling or event-based sampling. Lebesgue sampling or event-based sampling is an alternative to Riemann sampling. It means that the signal is sampled only when the measurement exceeds certain limits. For more description of Lebesgue sampling or event-based sampling, please refer to the document https: / / ieeexplore.ieee.org / document / 1184824.

[0054] Measuring a time discrete signal at a constant time interval is described as Riemann sampling. In contrast, Lebesgue sampling or event-based sampling measures when the observed signal exceeds a defined limit. In the present invention, the charge discretization unit 240 can extract and / or process a signal from the time discrete signal when the accumulated charge exceeds a defined limit in order to generate a charge discrete signal. The defined limit can be: dQ Lebesgue =Q(t0)-Q(t1)≥1mAh, where t1>t0.

[0055] The dV / dQ calculation unit 250 may calculate the charge discrete charge derivative voltage dV / dQ based on the charge discrete signal (eg, the charge discrete current and the charge discrete voltage). For example, the charge discrete charge derivative voltage dV / dQ may be calculated by the following equation:

[0056]

[0057] In at least one embodiment, the charge discrete signal used by the dV / dQ calculation unit 250 may correspond to a low charge current I (eg, 0 <I<C N / 3) period, where CN represents the capacity of the battery. If the charging current becomes too high, peak detection becomes more difficult due to the higher overvoltage. The low-pass filter unit 260 may be an IIR (infinite impulse response) low-pass filter unit. In at least one embodiment, the low-pass filter unit 260 may be implemented as a three-order cascaded IIR low-pass filter based on the following discretized first-order filter calculation formula:

[0058]

[0059] Among them, T seconds is the time constant, Ts seconds is the calculation cycle time, k is the calculation cycle, u k is the input signal, x k is the filtered signal, x k+1 is the filtered signal of the next calculation cycle. For the charge discrete charge derivative voltage dV / dQ filtering, the input signal u k is dV / dQ, the filtered signal x k is the dV / dQ after filtering.

[0060] like Figure 2 As shown in , the DV calculation unit 200 may further include a current detection unit 210 for detecting the current of the battery 100, a voltage detection unit 220 for detecting the voltage of the battery 100, and a temperature detection unit 230 for detecting the temperature of the battery 100. In at least one embodiment, the method for detecting the current, voltage, and temperature of the battery 100 may refer to the related art. However, the embodiment is not limited thereto, and the current detection unit 210, the voltage detection unit 220, and the temperature detection unit 230 may not be part of the DV calculation unit 200.

[0061] Figure 3 FIG. 3 is a block diagram schematically showing a feature detection unit 300 according to an embodiment of the present invention. Figure 3 As shown in , the feature detection unit 300 includes a peak detection unit 310 , a threshold detection unit 320 , a feature calculation unit 330 , a rationality check unit 340 and an average temperature calculation unit 350 .

[0062] The peak detection unit 310 can detect a first position of a central graphite peak about the accumulated charge based on the filtered charge discrete charge derivative voltage dV / dQ. Wherein the accumulated charge can correspond to the SOC of the battery. For example, the peak detection unit 310 detects a typical central graphite peak of the graphite-anode in the differential voltage curve, which represents the relationship between dV / dQ and the accumulated charge (SOC). In at least one embodiment, the peak detection unit 310 detects the peak by executing a zero-crossing detection algorithm based on the secondary charge discrete charge derivative voltage dV2 / d2Q.

[0063] The threshold detection unit 320 detects a second position about the accumulated charge when the filtered charge discrete charge derivative voltage dV / dQ reaches a threshold. The value of the threshold may be greater than the height of the detected peak, but less than (the height of the detected peak) / {(nominal capacity)*2}, where the nominal capacity refers to the nominal capacity of the battery 100.

[0064] The feature calculation unit 330 calculates the distance of the accumulated charges between the first position and the second position. For example, the feature calculation unit 330 may perform subtraction of the accumulated charges of the first position and the accumulated charges of the second position to calculate the distance as a feature.

[0065] The rationality check unit 340 may perform a rationality check on the calculated signature based on the SOC and the detected current, thereby outputting a signature that meets the requirements defined by the typical SOC range and height interval of the central graphite peak at a life cycle and a predetermined current rate.

[0066] For example, the rationality check unit 340 checks whether the detected peak meets all requirements defined by the central graphite peak and whether the charging current is constant during peak and threshold detection. The requirements for the detected peak define a specific SOC range and height interval within which the central graphite peak can be detected throughout the life at different temperatures and current rates. The rationality check unit 340 only outputs features that meet the requirements. For example, the requirements may include at least one of a minimum peak height in V / Ah, a maximum peak height in V / Ah, a minimum SOC in %, and a maximum SOC in %. It should be noted that if the charging current is not constant, other peaks and valleys that are not related to the electrode material composition will be introduced in the differential curve. If the current fluctuates within a certain range, it is considered a constant current.

[0067] The average temperature calculation unit 350 calculates the average temperature measured between the time when the central graphite peak is detected and the time when the filtered charge discrete charge derivative voltage dV / dQ reaches the threshold value.

[0068] Figure 4 4 is a block diagram schematically illustrating an estimation unit 400 according to one embodiment of the present invention. Module 410 is a 2D lookup table (2D-LUT). The estimation unit 400 can use the detected features and the calculated average temperature to interpolate the SOH based on the 2D lookup table, the data of which comes from battery experiments of different batteries of the same type at different temperatures.

[0069] Figure 5 The figure shows the half-cell potential of the cathode and anode and the battery voltage V during the charging process of a typical lithium-ion battery at a low current rate (<0.5C). cellThe curve diagram of the change of the full battery SOC. cell It can also be calculated based on Equation 2.

[0070] V cell =V cathode -V anode (Equation 2)

[0071] exist Figure 5 510 is the x-axis and describes the measured SOC of the full cell. 520 is the y-axis and describes the potential values ​​of the half cell and the full cell. 530 is the potential of the full cell at the SOC. 540 is the anode potential at the SOC. 550 is the cathode potential at the SOC.

[0072] Figure 6 2 is a graph showing the charge discrete charge differential voltage dV / dQ of the cathode and anode and the full battery calculated on the SOC of the full battery based on the superposition principle of Equation 2.

[0073] exist Figure 6 , 610 is the x-axis and describes the SOC of the full battery. 620 is the y-axis and describes the value of the charge discrete charge derivative voltage dV / dQ normalized by the nominal capacity of the battery. 630 is the charge discrete charge derivative voltage dV / dQ of the full battery at the SOC of the full battery. 640 is the charge discrete charge derivative voltage dV / dQ of the anode at the SOC of the full battery. 650 is the charge discrete charge derivative voltage dV / dQ of the cathode at the SOC of the full battery. 660 is a typical central graphite peak of a lithium-ion battery based on a graphite negative electrode. 661 is the first position of the central graphite peak with respect to the SOC (corresponding to the accumulated charge). 670 is a threshold. 680 is a second position when the charge discrete charge derivative voltage dV / dQ exceeds the threshold. 690 is the distance between the first position 661 and the second position 680 with respect to the SOC. 690 can be used as a feature for estimating SOH. For example, the estimation unit 400 (in Figure 1 ) uses feature 690 and the calculated average temperature (during the time corresponding to the first position 671 and the time corresponding to the second position 680) to interpolate the SOH based on a 2D lookup table to estimate the SOH.

[0074] exist Figure 1 In the embodiment, the 2D lookup table used in the device 10 can be preset so that the device 10 can directly use the 2D lookup table.

[0075] like Figure 7As shown, in at least another embodiment, the device 10a includes a differential voltage (DV) calculation unit 200, a feature detection unit 300, and an estimation unit 400. The device may further include a table formation unit 500, which may form a 2D look-up table used in the device 10. The same parts of the device 10a and the device 10 will not be described again. The table formation unit 500 will be described below.

[0076] Figure 8 is a flowchart sequentially showing the blocks for forming a 2D look-up table for SOH estimation by the table formation unit 500 according to an embodiment of the present invention.

[0077] Block S010 describes the cycling aging process of multiple batteries at different temperatures (e.g., T = [-20 0 20 45] °C). Block S020 describes the battery dormancy process, during which the batteries will dorm until they reach an equilibrium state, but for no more than 1 hour. Block S030 describes the inspection process, during which the batteries are cycled at a low current rate (0 < I < CN / 3) for at least 3 times to determine the true capacity of the batteries and the above-mentioned features (i.e., the distance between the central graphite peak of the cumulative charge and the threshold, also see Figure 6 690). This is repeated until the SOH of the battery reaches <80%.

[0078] In block S020, the table formation unit 500 may perform the following operations:

[0079] Operation 1. The table formation unit 500 extracts current, voltage, and temperature data from the experimental results of battery tests designed to simulate the aging of battery applications;

[0080] Operation 2. The table formation unit 500 calculates and filters the charge discrete charge derivative voltage dV / dQ using a low-pass filter to extract features, and calculates the true SOH of the tested battery using Coulomb counting. The working principle of extracting features can refer to the description of the feature detection unit 300. For example, the table formation unit 500 can control the feature detection unit 300 to extract features, or the table formation unit 500 can extract features by itself;

[0081] Operation 3. The table formation unit 500 calculates the linear regression model for each temperature by correlating the features with the true SOH; and

[0082] Operation 4. The table formation unit 500 forms a 2D-LUT at different temperatures based on the linear regression model.

[0083] From the above embodiments, it can be seen that the feature is detected from the charge discrete charge derivative voltage dV / dQ, and the SOH can be estimated through a 2D lookup table (2D-LUT) based on the feature and the average temperature. Therefore, a simple and fast method can be used to accurately estimate the battery SOH.

[0084] The device for battery SOH estimation of the first aspect of the embodiment has the following advantages:

[0085] Simple and accurate SOH estimation (maximum absolute error <5%, RMSE <2.5%);

[0086] Based on the electrochemical composition of the electrode material;

[0087] Can be used to distinguish between cathode aging and anode aging;

[0088] Compared with full capacity measurement @C / 3, the time consumption is reduced by 60%;

[0089] Compared to other methods, it takes lower computational effort due to Lebesgue sampling or event-based sampling;

[0090] Can be used in lithium-ion battery chemistries with graphite anodes (NMC, LFP, NCA, etc.);

[0091] Can be applied at different temperatures;

[0092] Can be easily implemented in any battery application;

[0093] Can be adjusted online;

[0094] Compared to other methods such as neural networks (= black box), it can be described as a white box;

[0095] A full charge is not required, a partial charge from SOC < 50% to SOC > 90% is sufficient.

[0096] Second aspect of the embodiment

[0097] In a second aspect of the embodiment, a method for battery state of health (SOH) estimation is provided. The method corresponds to the apparatus for battery state of health (SOH) estimation provided in the first aspect of the embodiment. The same contents as the first embodiment are omitted.

[0098] Fig. 9 is a flow chart of a method for battery health state estimation according to one embodiment.

[0099] like Fig. 9 As shown in , the method includes:

[0100] S901. Calculate the charge discrete charge derivative voltage dV / dQ according to the operating parameters of the battery, and filter the charge discrete charge derivative voltage dV / dQ;

[0101] S902. Perform peak and threshold detection based on the filtered charge discrete charge derivative voltage dV / dQ to detect a feature that meets the requirements defined by the typical SOC range and height interval of the central graphite peak, and calculate the average temperature of the battery during the time interval corresponding to the detected feature;

[0102] S903. Perform SOH estimation through a 2D lookup table (2D-LUT) based on the detected features and the average temperature.

[0103] For details of each block, reference may be made to the corresponding description in the first aspect of the embodiment.

[0104] like Fig. 9 As shown in , the method may further include:

[0105] S904. Form a 2D lookup table (2D-LUT).

[0106] For details of block S904, reference may be made to the first aspect of the embodiment. Figure 7 Flowchart of the process.

[0107] From the above embodiments, it can be seen that the characteristic is detected from the charge discrete charge derivative voltage dV / dQ, and the SOH can be estimated based on the characteristic and the average temperature through a 2D lookup table (2D-LUT). Therefore, a simple and fast method can be used to accurately estimate the battery SOH.

[0108] The third aspect of the embodiment

[0109] A third aspect of the embodiments of the present invention provides an electronic device, which includes the apparatus 10, 10a or the table forming unit 500 described in the first aspect of the embodiments.

[0110] Fig.10 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Fig.10 As shown in FIG. 1 , the electronic device 1000 may include a processor 1010 and a memory 1020, and the memory 1020 is coupled to the processor 1010. The memory 1020 may store various data, and may also store a program 1030 for data processing, and execute the program 1030 under the control of the processor 1010.

[0111] In one embodiment, the functions of the apparatus 10, 10a or the parameter calculator 600 may be integrated into the processor 1010. The processor 1010 may be configured to execute the method in the second aspect of the embodiment.

[0112] In another implementation, the device 10, 10a or the table forming unit 500 and the processor 1010 may be configured separately; for example, the device 10 or the table forming unit 500 may be configured as a chip connected to the processor 1010 and perform the functions of the device 10 or the table forming unit 500 under the control of the processor 1010.

[0113] An embodiment of the present invention may also provide a computer-readable program, which, when executed in a device or electronic device, enables the device or electronic device to perform the method as described in the second aspect of the embodiment of the present invention.

[0114] An embodiment of the present invention may also provide a computer storage medium, including a computer readable program, which enables a device or electronic device to execute the method described in the second aspect of the embodiment of the present invention.

[0115] The above-mentioned apparatus and method of the present invention can be realized by hardware, or by a combination of hardware and software. The present invention relates to such a computer-readable program, when the program is executed by a logic device, the logic device can execute the above-mentioned apparatus or component, or execute the above-mentioned method or step. The present invention also relates to a storage medium for storing the above-mentioned program, such as a hard disk, a floppy disk, a CD, a DVD and a flash memory.

[0116] The method / device described with reference to the embodiments of the present invention may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. For example, one or more functional block diagrams and / or one or more combinations of functional block diagrams shown in the accompanying drawings may correspond to software modules of a computer program process, or to hardware modules. Such software modules may correspond to the steps shown in the accompanying drawings, respectively. And the hardware module may be implemented, for example, by using a field programmable gate array (FPGA) to fix the soft module.

[0117] The soft module may be located in RAM, flash memory, ROM, EPROM and EEPROM, registers, hard disk, floppy disk, CD-ROM or any other form of storage medium known in the art. The storage medium may be coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium; or the storage medium may be a component of the processor. The processor and the storage medium may be located in an ASIC. The soft module may be stored in the memory of the mobile terminal or in a memory card of a pluggable mobile terminal. For example, if the device (such as a mobile terminal) adopts a relatively large-capacity MEGA-SIM card or a large-capacity flash memory device, the soft module may be stored in the MEGA-SIM card or the large-capacity flash memory device.

[0118] One or more functional blocks and / or one or more combinations of functional blocks in the accompanying drawings may be implemented as a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or any suitable combination thereof for performing the functions described in the present application. And one or more functional block diagrams and / or one or more combinations of functional block diagrams in the accompanying drawings may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple processors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0119] The present invention has been described above with reference to specific embodiments. However, it will be appreciated by those skilled in the art that such description is merely illustrative and is not intended to limit the scope of protection of the present invention. According to the spirit and principles of the present invention, those skilled in the art may make various modifications and variations, and these modifications and variations fall within the scope of the present invention.

Claims

1. A device for battery state of health (SOH) estimation, characterized in that: include: A DV calculation unit (200) is configured to calculate a charge discrete charge derivative voltage dV / dQ according to an operating parameter of the battery, and filter the charge discrete charge derivative voltage dV / dQ; A feature detection unit (300) configured to perform peak and threshold detection based on the filtered discrete charge derivative voltage dV / dQ to detect a feature that meets the requirements defined by the typical SOC range and height interval of the central graphite peak, and calculate the average temperature of the battery during a time interval corresponding to the detected feature; The estimation unit (400) is configured to perform SOH estimation through a 2D lookup table (2D-LUT) based on the detected characteristics and the average temperature.

2. The device for estimating the battery health state according to claim 1, characterized in that: The operating parameters include current, voltage and temperature.

3. The device for estimating the battery health state according to claim 1, characterized in that: The DV calculation unit (200) comprises: A charge discretization unit (240) is configured to convert the time discrete signal of the operating parameter into a charge discrete signal; a dV / dQ calculation unit (250) is configured to calculate the charge discrete charge derivative voltage dV / dQ based on the charge discrete signal; A low-pass filter unit (260) is configured to filter the charge discrete charge derivative voltage dV / dQ and output the filtered charge discrete charge derivative voltage dV / dQ.

4. The device for estimating the battery health state according to claim 1, characterized in that: The feature detection unit (300) comprises: A peak detection unit (310) configured to detect a first position of a central graphite peak with respect to accumulated charges based on the filtered discrete charge derivative voltage dV / dQ of the charges; A threshold detection unit (320) configured to detect a second position of the accumulated charge at a moment when the filtered discrete charge derivative voltage dV / dQ reaches a threshold; a feature calculation unit (330) configured to calculate a distance of accumulated charges between the first position and the second position, and output the distance as the feature; a rationality check unit (340) configured to perform a rationality check on the calculated characteristics based on the SOC and the detected current, thereby outputting characteristics that meet the requirements defined by the typical SOC range and height interval of the central graphite peak at a life cycle and a predetermined current rate; and An average temperature calculation unit (350) is configured to calculate an average temperature measured between the time when the central graphite peak is detected and the time when the filtered discrete charge derivative voltage dV / dQ reaches the threshold.

5. The device for estimating the battery health state according to claim 4, characterized in that: The peak detection unit (310) detects the peak by executing a zero-crossing detection algorithm based on the secondary charge derivative voltage dV2 / d2Q.

6. The device for estimating the battery health state according to claim 1, characterized in that: The estimation unit (400) uses the detected features and the calculated average temperature to interpolate the SOH based on the 2D lookup table, the data of which comes from battery experiments of different batteries of the same type at different temperatures.

7. The device for estimating the battery health status according to claim 1, characterized in that: The apparatus further comprises a table forming unit configured to: Extracting current, voltage and temperature data from experimental results of battery tests designed to simulate aging of battery applications; calculating and filtering the charge derivative voltage dV / dQ using a low-pass filter to extract the features, and calculating the true SOH of the tested battery using coulomb counting; calculating a linear regression model for each temperature by correlating the features with the true SOH; and A 2D-LUT was formed at different temperatures based on the linear regression model.

8. A method for battery state of health (SOH) estimation, comprising: Calculating a charge derivative voltage dV / dQ according to an operating parameter of the battery, and filtering the charge derivative voltage dV / dQ; performing peak and threshold detection based on the filtered charge derivative voltage dV / dQ to detect a feature that meets requirements defined by a typical SOC range and a height interval of a central graphite peak, and calculating an average temperature of the battery during a time interval corresponding to the detected feature; SOH estimation is performed through a 2D lookup table (2D-LUT) based on the detected features and the average temperature.

9. The method for estimating the battery health status according to claim 8, characterized in that: The operating parameters include current, voltage and temperature.

10. The method for estimating the battery health status according to claim 8, characterized in that: Calculating the filtered charge derivative voltage dV / dQ includes: Converting the time discrete signal of the operating parameter into a charge discrete signal; Calculating the charge derivative voltage dV / dQ based on the charge discrete signal; and The charge derivative voltage dV / dQ is filtered, and the filtered charge derivative voltage dV / dQ is output.

11. The method for estimating the battery health status according to claim 8, characterized in that: Detecting the feature and calculating the average temperature includes: detecting a first position of a central graphitic peak with respect to accumulated charges based on the filtered charge derivative voltage dV / dQ; At the moment when the filtered discrete charge derivative voltage dV / dQ reaches a threshold, detecting a second position of the accumulated charge; calculating a distance of accumulated charges between the first position and the second position, and outputting the distance as the feature; performing a plausibility check on the calculated signature based on the SOC and the detected current, thereby outputting a signature that meets requirements defined by a typical SOC range and height interval of the central graphite peak over a life cycle and a predetermined current rate; and The average temperature measured between the time when the central graphite peak is detected and the time when the filtered charge discrete charge derivative voltage dV / dQ reaches the threshold is calculated.

12. The method for estimating the battery health status according to claim 11, characterized in that: The peak is detected by executing a zero-crossing detection algorithm based on the secondary charge derivative voltage dV2 / d2Q.

13. The method for estimating the battery health status according to claim 8, characterized in that: Performing a SOH estimate involves: The detected features and the calculated average temperature are used to interpolate the SOH based on the 2D lookup table, wherein data of the 2D lookup table is from battery cell experiments of different battery cells of the same type at different temperatures.

14. The method for estimating the battery health status according to claim 8, characterized in that: The method further comprises: Extracting current, voltage and temperature data from experimental results of battery tests designed to simulate aging of battery applications; calculating and filtering the charge derivative voltage dV / dQ using a low-pass filter to extract the features, and calculating the true SOH of the tested battery using coulomb counting; calculating a linear regression model for each temperature by correlating the features with the true SOH; and A 2D-LUT was formed at different temperatures based on the linear regression model.

15. A method for forming a 2D lookup table for battery health state estimation, comprising: extracting current, voltage and temperature data from experimental results of battery tests designed to simulate aging of battery applications; The charge derivative voltage dV / dQ is calculated and filtered using a low-pass filter to extract features that meet the requirements defined by the typical SOC range and height interval of the central graphitic peak, and the true SOH of the tested battery is calculated using coulomb counting; calculating a linear regression model for each temperature by correlating the features with the true SOH; and A 2D-LUT was formed at different temperatures based on the linear regression model.

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