Obtaining state of health and state of charge of battery

Abstract

The invention provides obtaining a state of health (SOH) and a state of charge (SOC) of a battery, and particularly provides a method for obtaining a state of health (SOH) and a state of charge (SOC) of a battery. The method comprises the following steps: acquiring real-time data of a battery, wherein the real-time data comprises voltage, current, temperature, initial SOC and initial SOH; obtaining battery parameters from a lookup table LUT based on the real-time current, the temperature, the initial SOC and the initial SOH; and obtaining a new SOC and a new SOH based on the battery parameters, the real-time current, the real-time voltage, the initial SOC and the initial SOH. The disclosure further relates to corresponding devices, systems, and computer readable storage media.

Description

Technical Field

[0001] This disclosure relates to a method, storage medium, apparatus, and system for obtaining the state of health (SOH) and state of charge (SOC) of a battery. Background Technology

[0002] For battery systems (e.g., lithium-ion battery systems), State of Hypothesis (SOH) is a crucial parameter indicating the battery's remaining capacity for continued use. Accurate SOH values ​​allow for full utilization of the battery's available capacity, saving costs and preventing battery damage. Generally, SOH cannot be directly measured during operation. Regular capacity checks are necessary to confirm its accuracy. max Furthermore, the rated capacity of the battery (Q) can be used on this basis. r The SOH was calculated as described in equation (1):

[0003]

[0004] Among them, Q r It is the battery's rated capacity, and Q max (t) is the maximum amount of electricity stored in the battery at time t.

[0005] To estimate the State of Emergency (SOH) in real time under uncertain noise statistics, a Kalman filter (KF) can be used. The KF is a series of mathematical equations that provides an efficient computational method for estimating the process state while minimizing the mean of the squared error in real time. In battery energy storage systems (BESS), since it is a nonlinear stochastic system, partial derivatives and Taylor series expansions can be used to linearize the nonlinear function based on the KF. This can be called an extended Kalman filter (EKF).

[0006] For SOH, in lithium-ion battery systems, capacity decay over time is inevitable due to the growth and decomposition of the solid electrolyte interfacial film, or irreversible lithium plating during cycling or even storage.

[0007] Furthermore, values ​​from the test platform (e.g., current) may be affected by noise in the BESS. Additionally, external interference may occur during operation. Therefore, these deviations can affect the test results. In the absence of precise statistical knowledge about the noise covariance matrix, and considering the uncertainty of the Q-noise covariance, it is difficult to generate accurate SOH using EKF.

[0008] In other words, accurately estimating SOH in real time and reducing the impact of noise in SOH estimation is difficult. Therefore, it is necessary to estimate the current SOH in real time during operation without waiting for capacity checks. This could offer significant advantages compared to existing technologies.

[0009] These and other objectives can all be achieved through this disclosure. Summary of the Invention

[0010] This invention is defined by the independent claims. Preferred embodiments are defined in the dependent claims. In the following description, while many features may be specified as optional, all features included in the independent claims should not be construed as optional.

[0011] This disclosure relates to a method for obtaining the state of health (SOH) and state of charge (SOC) of a battery. The method includes: obtaining (step 1) real-time voltage, real-time current, real-time temperature, initial SOC, and initial SOH; obtaining (step 2) battery parameters from a lookup table (LUT) based on the real-time current, real-time temperature, initial SOC, and initial SOH; and obtaining (step 3) a new SOC and a new SOH based on the battery parameters, real-time current, real-time voltage, initial SOC, and initial SOH.

[0012] Various embodiments may preferably implement the following features.

[0013] Preferably, the method further includes: using real-time battery parameters obtained from the LUT, as well as real-time current, real-time voltage, new SOC, and new SOH, iteratively performing the above steps to obtain an updated new SOC and an updated new SOH.

[0014] Preferably, the method further includes continuously performing the above steps to obtain a new SOC and a new SOH.

[0015] Preferably, the method further includes: before step 1, generating battery parameters of the LUT through battery testing based on different SOH, SOC, temperature, and current.

[0016] Preferably, battery testing involves hybrid pulse power characterization (HPPC) and / or galvanostatic intermittent titration (GITT) techniques.

[0017] Preferably, the battery parameters are ECM parameters (equivalent circuit model parameters).

[0018] Preferably, the ECM parameters include open-circuit voltage (OCV), resistance, and capacitance, which depend on the real-time data.

[0019] Preferably, obtaining the new SOC and new SOH (step 3) is based on dual Kalman filtering, especially dual extended Kalman filtering.

[0020] Preferably, the initial SOC is obtained from the battery management system (BMS) connected to the battery or from a capacity check of the battery.

[0021] Preferably, the initial state of harm (SOH) is obtained based on a battery capacity check.

[0022] Preferably, the battery is connected to a battery energy storage system (BESS).

[0023] Preferably, the method is performed on BESS or an external server.

[0024] This disclosure further relates to an apparatus for obtaining the state of health (SOH) and state of charge (SOC) of a battery, the apparatus being connected to the battery and including a processor configured to: obtain real-time voltage, real-time current, real-time temperature, initial SOC, and initial SOH; obtain battery parameters from a lookup table (LUT) based on the real-time current, real-time temperature, initial SOC, and initial SOH; and obtain a new SOC and a new SOH based on the battery parameters, real-time current, real-time voltage, initial SOC, and initial SOH.

[0025] Preferably, the processor is further configured to perform the method as described above.

[0026] This disclosure also relates to a battery energy storage system (BESS) comprising at least one battery cell and the device described above.

[0027] This disclosure further relates to a computer-readable storage medium including instructions that, when executed by a processor, instruct the processor to perform the method described above.

[0028] Other aspects, features, and advantages will be apparent from the foregoing summary of the invention and the following description (including the drawings and claims). Attached Figure Description

[0029] Embodiments of this disclosure will now be described by way of example only with reference to the following accompanying drawings. In the drawings, the same reference numerals denote the same or similar elements.

[0030] Figure 1 This is a flowchart of an embodiment according to the present disclosure.

[0031] Figure 2 This is a flowchart of an embodiment according to the present disclosure. Detailed Implementation

[0032] refer to Figure 1This disclosure relates to a method for obtaining the state of health (SOH) and state of charge (SOC) of a battery. The method includes: (S1) obtaining (S1) real-time data of the battery, the real-time data including voltage, current, temperature, initial SOC, and initial SOH; (S2) obtaining (S2) battery parameters from a lookup table (LUT) based on the real-time current, temperature, initial SOC, and initial SOH; and (S3) obtaining (S3) a new SOC and a new SOH based on the battery parameters, real-time current, real-time voltage, initial SOC, and initial SOH.

[0033] As used herein, the term “obtain” (particularly in the context of step S3) may be equivalent to “estimate” or “determine”. Therefore, these terms may be used as synonyms in this disclosure.

[0034] In an embodiment, the method further includes: using real-time battery parameters obtained from the LUT, as well as real-time current, real-time voltage, new SOC, and new SOH, iteratively performing the above steps S1 to S3 to obtain an updated new SOC and an updated new SOH.

[0035] In an embodiment, the method further includes continuously performing the above steps to obtain an updated new SOC and an updated new SOH.

[0036] In other words, in the steps described above, the new SOC / new SOH replaces the initial SOC / initial SOH, respectively, for obtaining battery parameters from the LUT and for the next iteration to obtain the updated SOC / updated SOH. Furthermore, real-time data of the current voltage, current, and temperature replace previous current, voltage, and temperature data. In other words, the method uses the latest dataset to provide updated SOC / SOH estimates in real time. Each data point for measured current, voltage, temperature, SOC, and SOH can only be used once until it is replaced by the latest version.

[0037] In an embodiment, the method further includes: prior to S1, generating battery parameters of the LUT through battery testing based on different SOH, SOC, temperature and current.

[0038] In the embodiments, battery testing involves hybrid pulse power characterization (HPPC) and / or galvanostatic intermittent titration (GITT). However, this disclosure is not limited to these methods, and any approach may be used to obtain the battery parameters.

[0039] The LUT can remain unchanged during the execution of the method. In an embodiment, the LUT can remain unchanged throughout the lifespan of the battery or battery cell.

[0040] In this embodiment, the battery parameters are equivalent circuit model (ECM) parameters.

[0041] In this embodiment, the ECM parameters include open-circuit voltage (OCV), resistance, and capacitance, which depend on real-time data.

[0042] GITT and HPPC can be used to identify ECM parameters (e.g., OCV, R0, R1, R2, C1, C2) stored in the LUT. For routine charge / discharge tests, real-time data of voltage, current, and temperature are obtained for SOC and SOH estimation.

[0043] In the embodiment, the estimation (S3) of the new SOC and the new SOH is based on dual Kalman filtering, particularly dual extended Kalman filtering.

[0044] In this embodiment, the initial SOC is obtained from the battery management system (BMS) connected to the battery or from a capacity check of the battery.

[0045] In this embodiment, the initial SOH is obtained based on a battery capacity check.

[0046] In one embodiment, the battery is connected to a battery energy storage system (BESS). In another embodiment, the method is performed on a BESS or an external server.

[0047] This disclosure further relates to a corresponding device for estimating the SOH and SOC of a battery, the device being connected to the battery and including a processor configured to: acquire real-time data of the battery, the real-time data including voltage, current, temperature, initial SOC, and initial SOH; acquire battery parameters from a LUT based on the real-time data current, temperature, initial SOC, and initial SOH; and estimate a new SOC and a new SOH based on the battery parameters, real-time current, real-time voltage, initial SOC, and initial SOH.

[0048] The processor can be further configured to perform the methods described above.

[0049] The device may be a battery management system (BMS) or may be included within a BMS. Alternatively, the device may be a server or computing device external to the BMS (located near the BMS or at a remote location).

[0050] This disclosure also relates to a BESS comprising at least one battery cell and the device described above.

[0051] This disclosure further relates to a computer-readable storage medium including instructions that, when executed by a processor, instruct the processor to perform the method described above.

[0052] This disclosure applies to battery systems at the cell level, module level, and / or rack level. That is, this disclosure can be used for isolated battery cells or for multiple interconnected battery cells.

[0053] This disclosure will now be described in more detail. Unless otherwise stated, all embodiments disclosed herein are fully compatible with each other.

[0054] To achieve real-time and accurate SOH estimation, an SOH estimator is proposed. In particular, this disclosure may relate to an EKF-based SOH estimator. Based on an exemplary ECM with two resistor-capacitor (RC) branches for battery systems (such as lithium-ion battery systems), the state variables and measurement variables can be calculated using equations (2) and (3), respectively.

[0055] In these equations, some ECM parameters These parameters can be obtained from hybrid pulse power characterization (HPPC) for the discharge process and galvanostatic intermittent titration (GITT) for the charging process. As outlined above, these battery parameters can be stored in a LUT. Therefore, a LUT with ECM parameters from HPPC and GITT test results can be used as input to the EKF of the SOH estimator.

[0056]

[0057] V 0,k =V OCV,k -V 1,k -V 2,k -R0I k-1 (3)

[0058] In the above formula, C N This indicates the maximum amount of electricity stored in the battery. R1 and C1 represent the polarization resistor and capacitor, R2 and C2 represent the diffusion resistor and capacitor, and V1 and V2 indicate the voltage across capacitors C1 and C2. OCV V is the open-circuit voltage, V0 is the terminal voltage, I is the current through the circuit system, R0 is the internal resistance, k is the exponent (current iteration), and t is the sampling time.

[0059] The output of the above equation (step S3) is the new SOH. k This can be used for subsequent estimations.

[0060] Specifically, the maximum amount of electricity C stored in the battery NThe output is based on estimations using equations (2) and (3). The battery's rated capacity (assuming it is constant) is used to calculate the SOH as in equation (1). Then, the SOH (and the SOC output, real-time current, and real-time temperature in equations (4) and (5) below) are used to obtain the battery parameters from the LUT.

[0061] To obtain the ECM parameters from the LUT in real time (step S2), in addition to current and temperature, the (current) SOC and SOH inputs can also be provided in real time (step S1). To improve the accuracy of the SOH estimator and reduce the impact of noise, another EKF SOC estimator, together with the SOH estimator, constitutes a dual extended Kalman filter (DEKF) system. In this SOC estimator, the same ECM with two RC branches used for the battery system can be used as the SOH estimator, with its state variables and measurement variables as described in equations (4) and (5), respectively. It should be noted that these ECM parameters are obtained from the same LUT used for the SOH estimator as described above.

[0062]

[0063] V 0,k =V OCV,k -V 1,k -V 2,k -R0I k-1 (5)

[0064] The above formula can be used to estimate or obtain the current SOC. k .

[0065] Specifically, the SOC values ​​obtained through equations (4) and (5) and the SOH values ​​obtained using equations (2) and (3) (combined with real-time current and real-time temperature) are used to obtain the battery parameters (OCV, R0, R1, R2, C1, C2) from the LUT. These parameters can then be used to calculate V1 and V2, as described above. Taking into account the measured voltage value V0 (which can be obtained from the real-time data of the BMS), this voltage value is compared with V1, V2, and V... OCV Use them together.

[0066] The calculation aimed at using the EKF algorithm to make the best estimate of SOC can minimize the mean square error.

[0067] The DEKF described in the embodiment includes a SOH estimator and a SOC estimator (see above) for real-time synchronous estimation of the battery's state during operation. In this case, the real-time values ​​of SOC and SOH are simultaneously input into the LUT to obtain the ECM parameters as described above. These ECM parameters are then input into the state equation and measurement equation of the DEKF. This improves both the accuracy and performance of the two estimators. Due to the presence of state noise and measurement noise, Q can be optimized before running the DEKF to further improve its accuracy and reliability.

[0068] Reference has been made to embodiments of this disclosure. Figure 2 To further illustrate the above content.

[0069] As described above, real-time data is acquired in block 10 (i.e., step S1). The real-time data includes real-time voltage, real-time current, real-time temperature, (initial) SOC, and (initial) SOH. This real-time data can be acquired or measured. That is, at least some of the data can be provided or stored externally.

[0070] In block 20, real-time temperature, real-time current, initial SOC, and initial SOH are fed into the LUT to obtain relevant battery parameters (i.e., step S2).

[0071] These obtained battery parameters, along with the initial SOC, initial SOH, real-time current, and real-time voltage data, are provided to the SOC estimator 31 and the SOH estimator 32. As described above, the new SOC and the new SOH (updated SOC / SOH) are estimated respectively (i.e., step S3).

[0072] SOC estimator 31 uses equations (4) and (5) above, while SOH estimator 32 uses equations (2) and (3) above to estimate the new SOC and the new SOH respectively.

[0073] The method is then repeated using the new SOC and new SOH, along with real-time temperature and real-time current, to obtain updated battery parameters from LUT20. These battery parameters, along with real-time voltage, real-time current, new SOC, and new SOH, are input into SOC estimator 31 and SOH estimator 32 to estimate the updated SOC and updated SOH.

[0074] In the next iteration, the updated SOC and SOH replace the previously used new SOC and new SOH, and the real-time current, voltage, and temperature replace the corresponding data used previously.

[0075] The method can be executed iteratively, continuously, or until a certain termination condition is met. For example, the method can be executed until a certain threshold of voltage, SOC, or SOH is reached.

[0076] Current can be used to determine whether a battery is being used in charging or discharging mode. That is, a positive current indicates that the battery is in discharging mode, and a negative current indicates that the battery is in charging mode (from the perspective of a battery tester).

[0077] By using a combination of SOC estimator and SOH estimator, the SOC estimator can reduce the noise in the system and thus achieve an accurate estimate of the current SOH.

[0078] In summary, this disclosure may include generating or obtaining parameters such as OCV, resistance, capacitance, and so on based on different temperatures, currents, SOC, and SOH values. The LUT (Low-Unit Underlying Device) is generated. This generation can be performed in a laboratory or during the battery manufacturing process. Real-time data on I, T, V, and initial SOC from the Battery Management System (BMS), along with the battery parameters from the LUT, can then be provided to DEKF to perform the aforementioned method. Both cell-level and rack-level data can be used as input. This method (i.e., DEKF) is used to obtain a real-time SOH estimate.

[0079] Note that the state noise covariance matrix (Q) and the measurement noise covariance matrix (R) can be related to the EKF algorithm. In the embodiment, the state noise covariance matrix Q can be used for the state equations (equations (2) and (4)), while the measurement noise covariance matrix R can be used for the measurement equations (equations (3) and (5)). The accurate values ​​of these noise covariance matrices can improve the accuracy and performance of real-time SOH estimation in BESS.

[0080] According to this disclosure, an accurate and reliable method for estimating current SOH and SOC is provided. With this data, the state of the battery or battery system can be monitored, and the battery system can be used at full capacity. Furthermore, by monitoring battery health, the battery can be used efficiently and its lifespan can be extended.

[0081] While various embodiments of this disclosure have been described above, it should be understood that these embodiments are presented by way of example only and not by way of limitation. Similarly, various figures may depict exemplary architectures or configurations provided to enable those skilled in the art to understand the exemplary features and functionality of this disclosure. However, those skilled in the art should understand that this disclosure is not limited to the illustrated exemplary architectures or configurations, but can be implemented using various alternative architectures and configurations. Furthermore, as those skilled in the art will understand, one or more features of one embodiment may be combined with one or more features of another embodiment described herein. Therefore, the breadth and scope of this disclosure should not be limited to any of the foregoing exemplary embodiments.

[0082] It should also be understood that any reference to elements in this document using names such as "first," "second," etc., generally does not restrict the number or order of those elements. Rather, these names may be used in this document as a convenient way to distinguish two or more elements or instances of elements. Therefore, referring to the first and second elements does not imply that only two elements can be used, or that the first element must somehow precede the second element.

[0083] Furthermore, those skilled in the art will understand that information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, and symbols (e.g., possibly mentioned in the above description) can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0084] Those skilled in the art will further understand that any of the various illustrative logic blocks, units, processors, devices, circuits, methods, and functions described in conjunction with the aspects disclosed herein can be implemented by electronic hardware (e.g., digital implementation, analog implementation, or a combination of both), firmware, various forms of program or design code containing instructions (which may be referred to herein as "software" or "software unit" for convenience), or any combination of these techniques.

[0085] To clearly illustrate this interchangeability of hardware, firmware, and software, various illustrative components, blocks, units, circuits, and steps have been described above generally according to their functions. Whether such functionality is implemented as hardware, firmware, or software, or a combination of these technologies, depends on the specific application and design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in various ways for each specific application, but such implementation decisions will not lead to a departure from the scope of this disclosure. According to various embodiments, processors, devices, components, circuits, structures, machines, units, etc., can be configured to perform one or more functions described herein. The terms "configured to" or "configured for" as used herein with respect to a specified operation or function refer to processors, devices, components, circuits, structures, machines, units, etc., physically constructed, programmed, and / or arranged to perform the specified operation or function.

[0086] Furthermore, those skilled in the art will understand that the various illustrative logic blocks, units, devices, components, and circuits described herein may be implemented within or executed by an integrated circuit (IC) that may include 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 devices or any combination thereof. Logic blocks, units, and circuits may further include antennas and / or transceivers for communication with various components within a network or device. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other suitable configuration to perform the functions described herein. If implemented in software, these functions may be stored as one or more instructions or code on a computer-readable medium. Therefore, the steps of the methods or algorithms disclosed herein may be implemented as software stored on a computer-readable medium.

[0087] Computer-readable media include computer storage media and communication media, including any medium capable of transferring computer programs or code from one place to another. Storage media can be any available medium that is accessible to a computer. By way of example and not limitation, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and is accessible to a computer.

[0088] In this document, the term "unit" as used herein refers to software, firmware, hardware, and any combination of such elements for performing the relevant functions described herein. Furthermore, for purposes of discussion, individual units are described as discrete units; however, it will be apparent to those skilled in the art that two or more units may be combined to form a single unit performing the relevant functions according to embodiments of this disclosure.

[0089] Additionally, memory or other storage devices and communication components may be employed in the embodiments of this disclosure. It should be understood that, for clarity, the above description has referenced various functional units and processors in the embodiments of this disclosure. However, it will be apparent that any suitable functional distribution among different functional units, processing logic elements, or domains may be used without departing from this disclosure. For example, functions illustrated to be performed by a separate processing logic element or controller may be performed by the same processing logic element or controller. Therefore, references to specific functional units are merely references to suitable means for providing the described functions and do not represent a strict logical or physical structure or organization.

[0090] Various modifications to the embodiments described in this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the embodiments shown, but is intended to be accorded the maximum scope consistent with the novel features and principles disclosed in the appended claims.

Claims

1. A method for obtaining a state of health, SOH, and a state of charge, SOC, of a battery, the method comprising: obtaining (SI) a real-time voltage, a real-time current, a real-time temperature, an initial SOC and an initial SOH; obtaining (S2) a battery parameter from a look-up table, LUT, based on the real-time current, real-time temperature, initial SOC and initial SOH; and obtaining (S3) a new SOC and a new SOH based on the battery parameter, real-time current, real-time voltage, initial SOC and initial SOH.

2. The method according to claim 1, further comprising iteratively performing steps (SI) to (S3) using the real-time battery parameter obtained from the LUT, and the real-time current, real-time voltage, new SOC and new SOH, to obtain an updated new SOC and an updated new SOH.

3. The method according to claim 1 or 2, further comprising continuously performing steps (SI) to (S3) to obtain an updated new SOC and an updated new SOH.

4. The method according to any one of claims 1 to 3, further comprising, prior to step (SI), generating the battery parameter of the LUT by a battery test based on different SOH, SOC and temperature, current.

5. The method of claim 4, wherein, The battery test involves hybrid pulse power characterization, HPPC, and / or galvanostatic intermittent titration technique, GITT.

6. The method of any one of claims 1 to 5, wherein, The battery parameter is an equivalent circuit model, ECM, parameter, wherein the ECM parameter preferably comprises an open circuit voltage, OCV, a resistance and a capacitance dependent on the real-time data.

7. The method of any one of claims 1 to 6, wherein, Obtaining (S3) the new SOC and the new SOH is based on a dual Kalman filter, in particular a dual extended Kalman filter.

8. The method of any one of claims 1 to 7, wherein, The initial SOC is obtained from a battery management system, BMS, connected to the battery or a capacity check of the battery.

9. The method of any one of claims 1 to 7, wherein, The initial SOH is obtained based on a capacity check of the battery.

10. The method of any one of claims 1 to 9, wherein, The battery is connected to a battery energy storage system, BESS.

11. The method of claim 10, wherein, The method is performed on the BESS or an external server.

12. An apparatus for obtaining a state of health, SOH, and a state of charge, SOC, of a battery, the apparatus being connected to a battery and comprising a processor configured to: obtain a real-time voltage, a real-time current, a real-time temperature, an initial SOC and an initial SOH; obtain a battery parameter from a look-up table, LUT, based on the real-time current, real-time temperature, initial SOC and initial SOH; and obtain a new SOC and a new SOH based on the battery parameter, real-time current, real-time voltage, initial SOC and initial SOH.

13. The apparatus of claim 12, wherein, The processor is further configured to perform the method according to any one of claims 2 to 11.

14. A battery energy storage system, BESS, comprising: at least one battery cell; the apparatus according to claim 12 or 13.

15. A computer readable storage medium comprising instructions which, when executed by a processor, instruct the processor to perform the method according to any one of claims 1 to 11.

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