Confidence calculation method and apparatus for state of charge estimation

Abstract

The application provides a confidence calculation method and device for state of charge estimation. The confidence calculation method is based on the open circuit voltage of a battery. The confidence calculation method comprises the following steps: calculating the overall error of the open circuit voltage of the battery; calculating the estimated difference of the state of charge by using the overall error of the open circuit voltage; and calculating the confidence of the state of charge estimation by using the estimated difference. According to some example embodiments of the application, the confidence of the state of charge estimation is calculated by using the overall error of the open circuit voltage, which quantifies the reliability criterion of the state of charge estimation method based on the open circuit voltage; and the uncontrollable pollution of the estimation error of the state of charge estimation method based on the open circuit voltage to the overall state of charge estimation result is avoided when the state of charge is estimated by using the multi-method fusion estimation.

Description

Technical Field

[0001] This application relates to the field of battery management, and more specifically, to a method and apparatus for calculating confidence levels for state of charge estimation. Background Technology

[0002] Because the slope of the Open Circuit Voltage-State of Charge (OCV-SOC) curve is very small in some voltage regions, even a small OCV prediction deviation in these regions can lead to a large SOC or State of Health (SOH) estimation error.

[0003] Since certain errors are inevitably generated during the battery OCV prediction process, it is necessary to analyze the SOC error generated during the battery OCV prediction process. Summary of the Invention

[0004] This application proposes a method and apparatus for calculating the confidence level of electrical state estimation, which is used to calculate the confidence level of electrical state.

[0005] According to one aspect of this application, a confidence calculation method for state of charge estimation is proposed. The confidence calculation method is based on the open-circuit voltage of the battery. The confidence calculation method includes: calculating the overall error of the open-circuit voltage of the battery; calculating the estimated difference of the state of charge using the overall error of the open-circuit voltage; and calculating the confidence of the state of charge estimation using the estimated difference.

[0006] According to some embodiments, the step of calculating the confidence level of the power state estimate using the estimated difference includes: determining whether the estimated difference is greater than a preset maximum permissible error; if it is greater, the confidence level of the power state estimate of the open circuit voltage is zero; otherwise, the confidence level of the power state estimate is calculated using the estimated difference and the maximum permissible error.

[0007] According to some embodiments, the confidence level of the state of charge estimate is calculated using the following formula:

[0008]

[0009] Where β is the confidence level, δ max SOC is the maximum permissible error, and δSOC is the estimated difference.

[0010] According to some embodiments, the overall error of the open-circuit voltage includes system error and prediction method error.

[0011] According to some embodiments, the overall error of the open-circuit voltage is calculated using the following formula:

[0012] δOCV=δU m +δU p

[0013] Where δOCV is the overall error of the battery's open-circuit voltage, and δU m For systematic error, δU m The error of the prediction method is denoted as .

[0014] According to some embodiments, the step of calculating the estimated difference of the state of charge using the overall error of the open-circuit voltage includes: obtaining the relationship between the open-circuit voltage of the battery and the change in the state of charge, and using the relationship and the overall error of the open-circuit voltage to calculate the estimated difference of the state of charge.

[0015] According to some embodiments, the estimated difference in state of charge is calculated using the following formula:

[0016] δSOC=F OCV-SOC (OCV+δOCV)-F OCV-SOC (OCV-δOCV)

[0017] Where δSOC is the estimated difference, F OCV-SOC Let be the function relating the open-circuit voltage of the battery to its state of charge.

[0018] According to some embodiments, the confidence level varies continuously between 0 and 1.

[0019] According to one aspect of this application, a confidence calculation device for state of charge estimation is proposed. The confidence calculation device is based on the open-circuit voltage of a battery. The confidence calculation device includes: an overall error calculation unit for calculating the overall error of the open-circuit voltage of the battery; an estimation difference calculation unit for calculating the estimation difference of the state of charge using the overall error of the open-circuit voltage; and a confidence calculation unit for calculating the confidence level of the state of charge estimation using the estimation difference.

[0020] According to one aspect of this application, a confidence calculation device for power state estimation is proposed, the confidence calculation device comprising: one or more processors; a storage device for storing one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0021] According to one aspect of this application, a computer program product is proposed, comprising a computer program or instructions, characterized in that the computer program or instructions, when executed by a processor, implement the method as described in any of the preceding methods.

[0022] According to some example embodiments of this application, the confidence level of the state of charge estimation is calculated using the overall error of the open-circuit voltage, which quantifies the reliability criterion of the state of charge estimation method based on open-circuit voltage; thus avoiding the uncontrollable contamination of the overall state of charge estimation result by the estimation error of the state of charge estimation method based on open-circuit voltage when multiple methods are fused to estimate the state of charge. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0024] Figure 1 A flowchart illustrating a confidence calculation method for energy state estimation according to an example embodiment of this application is shown.

[0025] Figure 2 An OCV-SOC relationship curve is shown according to an example embodiment of this application.

[0026] Figure 3 The confidence curves for different OCV points are shown in an example embodiment of this application.

[0027] Figure 4 A block diagram of a confidence calculation device for electrical state estimation according to an embodiment of this application is shown.

[0028] Figure 5 A block diagram of another confidence calculation apparatus for energy state estimation according to an exemplary embodiment of this application is shown. Detailed Implementation

[0029] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0030] The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of these specific details, or other methods, components, materials, apparatus, or operations may be employed. In these cases, well-known structures, methods, apparatuses, implementations, materials, or operations will not be shown or described in detail.

[0031] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0032] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0033] Because the slope of the Open Circuit Voltage-State of Charge (OCV-SOC) curve is very small in some voltage regions, even a small OCV prediction deviation in these regions can lead to a large SOC or State of Health (SOH) estimation error.

[0034] Since certain errors are inevitably generated during the battery OCV prediction process, it is necessary to analyze the SOC error generated during the battery OCV prediction process.

[0035] According to some example embodiments of this application, the SOC error that may be caused by the OCV-SOC curve is analyzed using the OCV-SOC curve, and the SOC estimation confidence level of different OCV points is calculated.

[0036] Figure 1 A flowchart illustrating a confidence calculation method for state of charge estimation according to an example embodiment of this application is shown. The following is in conjunction with... Figure 1 This application provides a detailed description of a confidence calculation method for energy state estimation based on an example embodiment.

[0037] According to some example embodiments of this application, Figure 1 The confidence level calculation method shown is based on the open-circuit voltage of the battery to calculate the confidence level of the state of charge estimate.

[0038] In step S101, the overall error of the battery's open-circuit voltage is calculated.

[0039] According to some example embodiments of this application, the overall open-circuit voltage error includes systematic error and prediction method error.

[0040] According to some embodiments, the overall error of open-circuit voltage is represented by formula (1).

[0041] δOCV=δU m +δU p (1)

[0042] Where δOCV is the overall error of the battery's open-circuit voltage, and δU m For systematic error, δU m This represents the error in the prediction method.

[0043] According to some embodiments, the open-circuit voltage of the battery is obtained by measurement.

[0044] For example, after a battery has been left to rest for an extended period, such as 20 hours, it reaches an equilibrium state. At this point, the battery voltage is its open-circuit voltage. The open-circuit voltage of a battery obtained through measurement only contains systematic errors.

[0045] According to other embodiments, the open-circuit voltage of the battery is obtained by a combination of measurement and prediction.

[0046] For example, when the battery has not fully reached equilibrium, the final equilibrium voltage can be predicted using the already acquired voltage to obtain the battery's open-circuit voltage. This can be achieved, for instance, by using a first-order equivalent circuit model to predict the battery's open-circuit voltage. In this case, both systematic and prediction errors exist.

[0047] According to some embodiments, the system error is determined by the accuracy of the measurement sensor hardware and can be obtained by querying the parameters of the sensor that measures the battery status.

[0048] According to other embodiments, the prediction method error is determined by the accuracy of the model used to predict the open-circuit voltage of the battery. Different models result in different levels of accuracy.

[0049] According to some embodiments, the prediction method error of open-circuit voltage is obtained using the predicted value-true value method. For example, OCV prediction is performed on multiple sets of battery release curves, and the predicted OCV values ​​are recorded; the obtained predicted OCV values ​​are compared with the measured OCV values ​​obtained by a high-precision voltmeter after a long period of rest, and the maximum prediction method error is calibrated.

[0050] In step S103, the estimated difference in electrical state is calculated using the overall error of the open-circuit voltage.

[0051] According to some example embodiments of this application, the estimated difference of the open-circuit voltage is calculated by the following method:

[0052] First, obtain the relationship between the battery's open-circuit voltage and changes in its state of charge;

[0053] Then, the estimated difference in open-circuit voltage is calculated using the relationship between the battery's open-circuit voltage and state of charge change, as well as the overall error of the open-circuit voltage.

[0054] According to some embodiments, before step S103, it is also necessary to obtain the relationship curve between the battery's open-circuit voltage and state of charge (SOC). This relationship is represented using the battery's open-circuit voltage and SOC curve. According to some embodiments, the relationship curve is represented using a function relating the battery's open-circuit voltage and SOC, and the relationship between the battery's open-circuit voltage and SOC is also represented using this function.

[0055] According to some embodiments, the estimated difference of the electrical state between two points OCV+δOCV and OCV-δOCV is calculated using formula (2), and the influence of the overall error of any open circuit voltage on the estimated value of the electrical state is obtained.

[0056] δSOC=F OCV-SOC (OCV+δOCV)-F OCV-SOC (OCV-δOCV) (2)

[0057] Where δSOC is the estimated difference caused by the overall error of the open-circuit voltage, F OCV-SOC This is a function relating the battery's open-circuit voltage to its state of charge.

[0058] In step S105, the confidence level of the power state estimate is calculated using the estimated difference in power state.

[0059] According to some example embodiments of this application, the confidence level of the state of charge estimation is calculated by the following method:

[0060] First, it is determined whether the estimated difference in battery status exceeds a preset maximum permissible error. According to some embodiments, the preset maximum permissible error is 0.05.

[0061] If the difference in the estimated state of power exceeds the preset maximum permissible error, the confidence level of the estimated state of power is considered to be zero.

[0062] If the estimated difference in the state of charge is less than or equal to the preset maximum permissible error, the confidence level of the state of charge estimate is calculated using the estimated difference in the state of charge and the maximum permissible error.

[0063] According to some embodiments, the confidence level for calculating the state of charge estimate is calculated using formula (3):

[0064]

[0065] Where β is the confidence level, δmax SOC is the preset maximum permissible error, and δSOC is the estimated difference in battery status.

[0066] According to some embodiments, the confidence level of the state of charge estimate calculated in step S105 varies continuously between 0 and 1.

[0067] according to Figure 1 The embodiment shown uses the overall error of the open-circuit voltage to calculate the confidence level of the state of charge estimation, quantifying the reliability criterion of the state of charge estimation method based on open-circuit voltage; it avoids the uncontrollable contamination of the overall state of charge estimation result by the estimation error of the state of charge estimation method based on open-circuit voltage when multiple methods are fused to estimate the state of charge.

[0068] Figure 2 An OCV-SOC relationship curve is shown according to an example embodiment of this application. Figure 3 The confidence curves for different OCV points are shown in an example embodiment of this application.

[0069] like Figure 2 As shown, the object of measurement is a lithium iron phosphate battery. According to... Figure 2 The OCV-SOC relationship curve, Figure 3 The confidence curves for different OCV points are shown in an example embodiment of this application.

[0070] The following is combined Figure 2 and Figure 3 Please describe in detail according to Figure 2 The OCV-SOC relationship curve shown is calculated. Figure 3 The confidence levels of different OCV points are shown.

[0071] Step S201: Calculate the overall error of the open-circuit voltage of the lithium iron phosphate battery.

[0072] Using according to Figure 1 The example shown calculates the sum of the system error and the system prediction method error. Figure 2 The overall error of the open-circuit voltage of the lithium iron phosphate battery shown is 2mV.

[0073] In step S203, the estimated difference in electrical state is calculated using the overall error of the open-circuit voltage.

[0074] Using formula (2), calculate Figure 2 The SOC estimation difference between OCV+δOCV and OCV-δOCV points on the OCV-SOC relationship curve is used to obtain the possible impact of the OCV system measurement and prediction method error on the SOC estimation value at each OCV point.

[0075] In step S205, the SOC estimation confidence level at each OCV point is calculated.

[0076] If the δSOC at a certain OCV point is greater than the actual specified maximum allowable error for SOC estimation, for example, 0.05, then the confidence level at that point is 0; otherwise, the SOC estimation confidence level at each OCV point is calculated using formula (3).

[0077] By executing steps S201 to S205, using Figure 2 The OCV-SOC relationship curve shown can be used to obtain the following: Figure 3 The confidence curves for different OCV points are shown.

[0078] Figure 4 A block diagram of a confidence calculation device for electrical state estimation according to an embodiment of this application is shown.

[0079] According to some embodiments of this application, Figure 4 The confidence calculation device shown calculates the confidence level of the state of charge estimate based on the battery's open-circuit voltage.

[0080] like Figure 4 The confidence calculation device for battery state estimation shown includes an overall error calculation unit 401, an estimation difference calculation unit 403, and a confidence calculation unit 405. The overall error calculation unit 401 is used to calculate the overall error of the open-circuit voltage of the battery, the estimation difference calculation unit 403 is used to calculate the estimation difference of the battery state using the overall error of the open-circuit voltage, and the confidence calculation unit 405 is used to calculate the confidence of the battery state estimation using the estimation difference.

[0081] Figure 5 A block diagram of another confidence calculation apparatus for energy state estimation according to an exemplary embodiment of this application is shown.

[0082] The following reference Figure 5 This application describes a confidence calculation device 200 according to this embodiment. Figure 5 The confidence calculation device 200 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0083] like Figure 5 As shown, the confidence calculation device 200 is presented in the form of a general-purpose computing device. The components of the confidence calculation device 200 may include, but are not limited to: at least one processing unit 210, at least one storage unit 220, a bus 230 connecting different system components (including storage unit 220 and processing unit 210), a display unit 240, etc.

[0084] The storage unit stores program code, which can be executed by the processing unit 210 to perform the methods described in this specification according to various exemplary embodiments of this application. For example, the processing unit 210 can perform, for example... Figure 1 The method shown.

[0085] Storage unit 220 may include readable media in the form of volatile storage units, such as random access memory (RAM) 2201 and / or cache memory 2202, and may further include read-only memory (ROM) 2203.

[0086] Storage unit 220 may also include a program / utility 2204 having a set (at least one) program module 2205, such program module 2205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0087] Bus 230 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0088] The confidence computing device 200 can also communicate with one or more external devices 300 (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with the confidence computing device 200, and / or any device that enables the confidence computing device 200 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 250. Furthermore, the confidence computing device 200 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of the confidence computing device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the confidence computing device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0089] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. The technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to the embodiments of this application.

[0090] Software products may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0091] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0092] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0093] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the aforementioned functions.

[0094] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0095] According to some embodiments of this application, the confidence level of the state of charge estimation is calculated using the overall error of the open-circuit voltage, which quantifies the reliability criterion of the state of charge estimation method based on open-circuit voltage; thus avoiding the uncontrollable contamination of the overall state of charge estimation result by the estimation error of the state of charge estimation method based on open-circuit voltage when multiple methods are fused to estimate the state of charge.

[0096] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. Furthermore, any changes or modifications made by those skilled in the art based on the ideas of this application, and on the specific implementation methods and application scope of this application, are all within the scope of protection of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A confidence calculation method for state of charge estimation, the confidence calculation method being based on the open-circuit voltage of a battery, characterized in that, The confidence level calculation method includes: Calculate the overall error of the open-circuit voltage of the battery; The estimated difference in electrical state is calculated using the overall error of the open-circuit voltage; The confidence level of the energy state estimate is calculated using the estimated difference. in, The overall error of the open-circuit voltage is calculated using the following formula: in, δ OCV stands for Overall Open Circuit Voltage Error of the Battery. δU m For systematic error, δU m This refers to the error in the prediction method. The estimated difference in electrical state is calculated using the following formula: in, δ SOC is the estimated difference. F OCV-SOC Let be the function relating the open-circuit voltage of the battery to its state of charge.

2. The confidence calculation method according to claim 1, characterized in that, The step of calculating the confidence level of the energy state estimate using the estimated difference includes: Determine whether the estimated difference is greater than the preset maximum permissible error; If it is greater than 0, then the confidence level of the state of charge estimation of the open circuit voltage is zero. Otherwise, the confidence level of the state of charge estimate is calculated using the estimated difference and the maximum permissible error.

3. The confidence calculation method according to claim 2, characterized in that, The confidence level of the state of charge estimate is calculated using the following formula: in, β For confidence level, δ max SOC is the maximum permissible error. δ SOC is the estimated difference.

4. The confidence calculation method according to claim 1, characterized in that, The overall error of the open-circuit voltage includes system error and prediction method error.

5. The confidence calculation method according to claim 1, characterized in that, The method of calculating the estimated difference of the electrical state using the overall error of the open-circuit voltage includes: Obtain the relationship between the open-circuit voltage and the state of charge change of the battery. The estimated difference in electrical state is calculated using the aforementioned relationship and the overall error of the open-circuit voltage.

6. The confidence calculation method according to claim 1, characterized in that, The confidence level varies continuously between 0 and 1.

7. A confidence calculation device for state of charge estimation, the confidence calculation device being based on the open-circuit voltage of a battery, characterized in that, The confidence calculation device is used to perform the confidence calculation method as described in any one of claims 1-6, and the confidence calculation device includes: The overall error calculation unit is used to calculate the overall error of the open-circuit voltage of the battery; An estimation difference calculation unit is used to calculate the estimated difference of the electrical state using the overall error of the open-circuit voltage; A confidence calculation unit is used to calculate the confidence level of the electrical state estimate using the estimated difference.

8. A confidence calculation device for electrical state estimation, characterized in that, The confidence calculation device includes: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the method as described in any one of claims 1-6.

Images