Power battery pack charge state and available capacity estimation method and related equipment

Abstract

The invention discloses a method for estimating the state of charge and the available capacity of a power battery pack and related equipment, and the method comprises the steps: carrying out the online decomposition and decoupling of a voltage potential of a selected representative battery, carrying out the feature extraction of the decoupled voltage potential, and introducing the output state of charge and the available capacity into MCSCKF for optimization. And substituting the charge state and the available capacity of the representative battery into a corresponding formula to complete the estimation of the charge state and the available capacity of the battery pack. According to the invention, the charge state of the representative battery is estimated in a closed-loop mode based on the machine learning of the battery physical information; and calculating the residual charging capacity and the residual discharging capacity of the battery pack according to the state of the representative battery, and finally, accurately and stably estimating the charging state and the capacity of the battery pack on line.

Description

Background Art

[0002] Since the battery health management system is difficult to fully and accurately evaluate the battery pack state of charge (SOC) and available capacity (capacity), problems such as range anxiety and battery life are becoming increasingly prominent. This defect inhibits consumers' subjective willingness to purchase new energy electric vehicles and increases the possibility of major safety accidents. However, due to the complex vehicle operating conditions, variable ambient temperature, and obvious inconsistency of single cells, it is difficult to quickly and accurately estimate the battery pack SOC and capacity online.

[0003] Current solutions are divided into two categories: large-battery-based methods and representative-battery-based methods. The former regards the lithium-ion power battery pack as a whole (i.e., a single large battery) to roughly estimate its SOC and capacity values, ignoring the inconsistency of temperature, vibration, humidity and other conditions between single cells. It has low accuracy and reliability, and is easily affected by the measurement errors of battery pack sensors. The latter selects multiple representative single cells from the group based on certain principles to characterize the overall SOC and capacity status. The amount of calculation is slightly increased, but the estimation accuracy is higher, and it has great research and application potential.

[0004] The bottlenecks of the previous representative battery-based methods are: first, the selection of representative batteries fails to consider the ease of implementation, the inconsistency of battery cells, and the robustness of field operation; second, the battery model used to estimate the state of representative battery cells is difficult to achieve an effective balance between complexity, accuracy, and robustness; finally, the experimental verification results are usually obtained from small lithium-ion battery packs under laboratory conditions, which limits the reliability of the SOC and capacity joint estimation method when applied in the field. In summary, accurate SOC and capacity estimation of lithium-ion power battery packs is a technical problem that needs to be solved urgently. Existing research methods have made some progress but still need to be improved. An estimation method with a simple and effective model, comprehensive and balanced performance, and good performance in real vehicles will be beneficial for accurately and robustly estimating the battery pack SOC and capacity state under different driving conditions and ambient temperatures. Summary of the invention

[0005] The purpose of the present invention is to provide a method and related equipment for estimating the state of charge and available capacity of a power battery pack, so as to solve the technical problems in the prior art that the method for estimating the state of charge and available capacity of a power battery pack is complex, has low accuracy and poor reliability.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: A method for estimating the state of charge and available capacity of a power battery pack comprises the following steps: Select the battery with the maximum state of charge and the battery with the minimum state of charge as the representative battery of the battery pack; Online decomposition and decoupling of voltage potential of representative batteries; The neural network is used to extract the features of the decomposed voltage potential and estimate the state of charge and available capacity of the representative battery. The state of charge and available capacity of the representative battery output by the neural network are introduced into MCSCKF for optimization. The state of charge and available capacity of the battery pack are obtained by optimizing the state of charge and available capacity of the representative battery.

[0007] Furthermore, the specific method of performing online voltage and potential decomposition and decoupling on the representative battery is to perform online voltage and potential decomposition and decoupling through the Thevenin model.

[0008] Furthermore, the voltage potential of the representative battery is decomposed and decoupled online to obtain the open circuit potential, ohmic overpotential and polarization overpotential.

[0009] Furthermore, the neural network adopts LSTM-DNN neural network.

[0010] Furthermore, the measurement equation of the MCSCKF is:

[0011] Among them, f PINN represents the state estimation of the representative unit based on PINN, t is the sampling time, U t and I t is the sampling voltage and current corresponding to the representative unit, R t To measure noise, the state function of the MCSCKF is a variation of the ampere-hour integral equation:

[0012] Among them, SOC state,t is the predicted SOC value at time t, µ is the Coulomb efficiency, usually taken as 100%, C is the battery capacity, ∆t is the sampling period, Q k is the process noise.

[0013] Furthermore, the specific steps of obtaining the state of charge and available capacity of the battery pack through the optimized state of charge and available capacity of the representative battery are: The remaining charge capacity (RCC) and remaining discharge capacity (RDC) of a battery are obtained using the state of charge and available capacity of the optimized representative battery; The capacity and state of charge of the battery pack are calculated based on the remaining charge capacity (RCC) and remaining discharge capacity (RDC) of all cells in the battery pack.

[0014] Furthermore, the capacity expression of the battery pack is:

[0015] The state of charge of the battery pack is:

[0016] in:

[0017]

[0018] Among them, C i , C l They are the battery cell capacity at the maximum state of charge and the battery cell capacity at the minimum state of charge, SOC j It is the state of charge of the battery cell with the smallest capacity in the battery pack.

[0019] In a second aspect, the present invention provides a power battery pack state of charge and available capacity estimation system, comprising a selection module, a decoupling module, an optimization module and an output module, wherein: Selection module: used to select the battery with the maximum state of charge and the minimum state of charge as the representative battery of the battery pack; Decoupling module: used to perform online voltage potential decomposition and decoupling of representative batteries; Optimization module: used to extract features of the decomposed voltage potential using a neural network, estimate the state of charge and available capacity of a representative battery, and introduce the state of charge and available capacity of the representative battery output by the neural network into MCSCKF for optimization; Output module: used to obtain the state of charge and available capacity of the battery pack through the optimized state of charge and available capacity of the representative battery.

[0020] According to a third aspect, a terminal device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0021] According to a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0022] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention provides a method for estimating the state of charge and available capacity of a power battery pack. The method comprises the following steps: online decomposition and decoupling of the voltage potential of a selected representative battery is performed, feature extraction of the decoupled voltage potential is performed, and the output state of charge and available capacity are introduced into MCSCKF for optimization. The state of charge and available capacity of the battery pack can be estimated by substituting the state of charge and available capacity of the representative battery into corresponding formulas. The present invention estimates the state of charge of a representative battery in a closed-loop manner based on machine learning of battery physical information, and calculates the remaining charge capacity and remaining discharge capacity of the battery pack according to the state of the representative battery, thereby finally realizing online accurate and robust estimation of the state of charge and capacity of the battery pack.

[0023] Furthermore, representative cell selection is performed based on the relaxed terminal voltage of the battery, and by analyzing the battery data using a charge-capacity diagram, representative cells within the battery pack can be identified online without increasing the computational burden.

[0024] Furthermore, the voltage dynamics can be decomposed online using the Thevenin model while adding a small computational burden.

[0025] Furthermore, based on the Sigmoid function, hyperbolic function and complex gate unit, the LSTM unit can effectively increase or decrease the sequence information in the short and long term; the rectified linear unit is used as an active function to enhance the nonlinear capability of the developed PINN.

[0026] Furthermore, the RMSE metric is used to evaluate the state estimation results of the battery pack under different driving cycles and temperatures while considering severe drift measurement noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention is a flowchart of a method for estimating the state of charge and available capacity of a power battery pack in an embodiment of the present invention.

[0028] Figure 2 Framework diagram of the joint estimation method for battery pack charge state and capacity.

[0029] Figure 3 It is a representative battery screening method (taking the charge-capacity analysis of the battery pack under WLTP standard conditions at room temperature as an example).

[0030] Figure 4 This is the Thevenin model diagram for online decomposition of voltage potential.

[0031] Figure 5 Schematic diagram of the PINN architecture for representative battery state estimation.

[0032] Figure 6 Schematic diagram of the closed-loop MCSCKF estimation architecture.

[0033] Figure 7 A schematic diagram of a computer device.

[0034] Figure 8 A block diagram of an electronic device. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0037] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0038] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0039] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0040] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0041] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0042] Glossary MCSCKF: Maximum Entropy Square Root Cubic Kalman Filter.

[0043] The present invention is further described in detail below in conjunction with the accompanying drawings: like Figure 1 As shown, a method for estimating the state of charge and available capacity of a power battery pack comprises the following steps: Step 1: Select the batteries with the maximum state of charge and the minimum state of charge as the representative batteries of the battery pack: Specifically, the state of charge and available capacity of the battery pack are represented by the battery with the maximum SOC and the battery with the minimum SOC. The representative battery selection is based on the relaxation terminal voltage of the battery. By analyzing the battery data using a charge-capacity diagram, the representative batteries in the battery pack can be identified online without increasing the computational burden.

[0044] Step 2: Online decomposition and decoupling of voltage potential of representative batteries: Specifically, the Thevenin physical model is used to perform online decoupling of the voltage potential of a representative battery, decomposing its voltage potential into three main parts: open circuit potential, ohmic overpotential, and polarization overpotential, in order to reduce the computational burden.

[0045] Step 3: Use the neural network to extract the features of the decomposed voltage potential and estimate the state of charge and available capacity of the representative battery. The state of charge and available capacity of the representative battery output by the neural network are introduced into MCSCKF for optimization: Specifically, the neural network adopts LSTM-DNN neural network, and the measurement equation of MCSCKF is:

[0046] Among them, f PINN represents the state estimation of the representative unit based on PINN, t is the sampling time, U t and I t is the sampling voltage and current corresponding to the representative unit, R t To measure noise. The state function of MCSCKF is a variant of the ampere-hour integral equation:

[0047] Among them, SOC state,t is the predicted SOC value at time t, µ is the Coulomb efficiency, usually taken as 100%, C is the battery capacity, ∆t is the sampling period, Q k is the process noise.

[0048] Step 4: Obtain the state of charge and available capacity of the battery pack through the optimized state of charge and available capacity of the representative battery.

[0049] Specifically, the specific steps of obtaining the state of charge and available capacity of the battery pack through the optimized state of charge and available capacity of the representative battery are: The remaining charge capacity (RCC) and remaining discharge capacity (RDC) of a battery are obtained using the state of charge and available capacity of the optimized representative battery; The capacity and state of charge of the battery pack are calculated based on the remaining charge capacity (RCC) and remaining discharge capacity (RDC) of all cells in the battery pack.

[0050] The capacity expression of the battery pack is:

[0051] The state of charge of the battery pack is:

[0052] in:

[0053]

[0054] Among them, C i , C l They are the battery cell capacity at the maximum state of charge and the battery cell capacity at the minimum state of charge, SOC j It is the state of charge of the battery cell with the smallest capacity in the battery pack.

[0055] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0056] Example A method for estimating the state of charge and available capacity of a power battery pack includes a representative battery selection link, a potential decoupling link with error compensation, a long short-term memory neural network (LSTM-DNN) estimation link, a maximum entropy square root cubature Kalman filter (MCSCKF) closed-loop system link, a battery pack state estimation link with a small number of representative cells, and an online state of charge and available capacity evaluation link.

[0057] like Figure 2 As shown, the method specifically includes the following steps: Step 1, selecting the battery with the maximum state of charge and the battery with the minimum state of charge as the representative battery of the battery pack; Specifically, in step 1, Figure 3 As shown in the figure, the battery pack dataset comes from an experimental test of a real electric vehicle (Volkswagen ID.3) battery pack, with a positive electrode material of NMC and a negative electrode material of graphite, and a nominal capacity of 78Ah. The lithium-ion power battery pack consists of nine battery modules connected in series, and each battery module has 24 lithium-ion batteries connected in 2p12s mode.

[0058] The state of charge and available capacity of the battery pack are represented by the battery with the maximum SOC and the battery with the minimum SOC. The representative battery selection is based on the relaxed terminal voltage of the battery. By analyzing the battery data using a charge-capacity diagram, the representative battery in the battery pack can be identified online without increasing the computational burden.

[0059] Specifically, please combine Figure 3 The representative battery screening method (taking the battery pack power-capacity analysis under the WLTP standard working condition at room temperature as an example) verifies the effectiveness of the representative battery cell selection under the three traffic environments of highway, intercity and city and different temperatures under the WLTP standard working condition. The blue dot is the battery cell status in the battery pack, the orange dot is the representative battery cell status, and the red dot is the battery pack status. It can be seen that the RCC of the representative battery with the maximum state of charge is the same as the RCC of the battery pack, and the RDC of the representative battery with the minimum state of charge is the same as the RDC of the battery pack.

[0060] Step 2, such as Figure 4As shown, the Thevenin physical model is used to decouple the voltage potential of the representative battery online, and its voltage potential is decomposed into three main parts: open circuit potential, ohmic overpotential and polarization overpotential, in order to reduce the calculation burden; The mathematical modeling of the Thevenin model can be expressed as:

[0061]

[0062]

[0063] in, U t Indicates the terminal voltage, U ocv represents the open circuit potential, U o represents the ohmic overpotential, U p represents the polarization overpotential of the battery, such as Figure 4 The Thevenin model of the online decomposition of voltage potential is shown in the figure. Based on the above equation, the voltage dynamics can be decomposed online while adding a small computational burden. The online decomposition of battery dynamics can be described as:

[0064]

[0065] Among them, f FFRLS represents the online parameterization of the Thevenin model based on the Forgetting Factor Recursive Least Squares (FFRLS) algorithm. error,t Describes the voltage error of the Thevenin model.

[0066] Step 3, using LSTM-DNN neural network to extract features of the decomposed voltage potential and estimate the state of charge and available capacity of the representative battery; Combination Figure 5 PINN architecture diagram for representative battery state estimation. In step 3, the physics-based machine learning (PINN) for representative battery state estimation consists of 1 input layer, 1 decoupling layer, 3 long short-term memory (LSTM) layers, 1 fully connected layer, and 1 regression layer. Each LSTM layer consists of 128 LSTM units. The calculation of the LSTM unit can be expressed in mathematical form as:

[0067]

[0068]

[0069]

[0070]

[0071] Among them, W and U are weights, b is bias, i, f, o and c are units of input, forgetting, output and historical information, and σ is the Sigmoid function.

[0072] Specifically, in step 3, the calculation of the residual part is based on a hyperbolic function, whose second-order derivative can remain non-zero for a long time to achieve long-term memory of historical information. Based on the Sigmoid function, hyperbolic function and complex gate unit, the LSTM unit can effectively increase or decrease sequence information in the short and long term. The LSTM layer is followed by a fully connected layer for weighted regression. In addition, the rectified linear unit is used as an active function to enhance the nonlinear ability of the developed PINN.

[0073] Step 4: Based on the estimated value of the neural network, the maximum entropy square root cubic Kalman filter (MCSCKF) is introduced in a closed-loop form to improve the robustness of the estimated result; Combination Figure 6 Closed-loop MCSCKF estimation architecture, in step 4, the maximum entropy square root cubic Kalman filter, can be expressed as:

[0074] Among them, f PINN represents the state estimation of the representative unit based on PINN, t is the sampling time, U t and I t is the sampling voltage and current corresponding to the representative unit, R t To measure noise, the state function of MCSCKF is a variation of the ampere-hour counting equation:

[0075] Among them, SOC state,t is the predicted SOC value at time t, µ is the Coulomb efficiency, usually taken as 100%, C is the battery capacity, ∆t is the sampling period, Q k is the process noise.

[0076] Specifically, in step 4, MCSCKF is an improved version of the traditional SCKF, which uses the maximum entropy criterion to replace the traditional minimum mean square error criterion to enhance the robustness under non-Gaussian noise. Therefore, MCSCKF not only maintains the robustness under Gaussian noise, but also performs better than SCKF under non-Gaussian noise.

[0077] Step 5: Substitute the state of charge and available capacity of the representative battery into the formula to calculate the state of charge and available capacity of the battery pack; In step 5, assume that the SOC and corresponding capacity of the kth battery are represented as SOC k and C k Then, the RCC and RDC of the kth battery are respectively expressed as RCC k and RDC k The mathematical calculation is:

[0078]

[0079] According to the RCC and RDC of each monomer, the capacity of the battery pack can be calculated:

[0080] By simplifying the calculation, it is an effective method to select a small number of representative battery cells to calculate the state of charge and battery pack capacity. The state of the battery pack can usually be represented by a maximum of three cells in the battery pack, namely the cell with the maximum state of charge, the cell with the minimum state of charge and the cell with the minimum capacity. The mathematical expression is:

[0081]

[0082] Among them, C i , C l They are the battery cell capacity at the maximum state of charge and the battery cell capacity at the minimum state of charge, SOC j It is the state of charge of the battery cell with the smallest capacity in the battery pack.

[0083] The capacity expression of the battery pack is:

[0084] The state of charge of the battery pack during discharge is:

[0085] Step 6: Evaluate the validity of the battery pack online state of charge and available capacity estimation results.

[0086] Specifically, in step 6, the root mean square error (RMSE) indicator is used to evaluate the state estimation of the battery pack, and its mathematical expression is:

[0087] in, s e and s r Represent the estimated state and actual state of the battery pack, respectively. nis the number of estimation points. To comprehensively evaluate the performance, the RMSE metric is used to evaluate the state estimation results of the battery pack under different driving cycles and temperatures, taking into account the severe drift measurement noise.

[0088] In yet another embodiment of the present invention, a power battery pack state of charge and available capacity estimation system is provided, comprising a selection module, a decoupling module, an optimization module and an output module, wherein: Selection module: used to select the battery with the maximum state of charge and the minimum state of charge as the representative battery of the battery pack; Decoupling module: used to perform online voltage potential decomposition and decoupling of representative batteries; Optimization module: used to extract features of the decomposed voltage potential using a neural network, estimate the state of charge and available capacity of a representative battery, and introduce the state of charge and available capacity of the representative battery output by the neural network into MCSCKF for optimization; Output module: used to obtain the state of charge and available capacity of the battery pack through the optimized state of charge and available capacity of the representative battery.

[0089] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the power battery pack charge state and available capacity estimation method, including: Select the battery with the maximum state of charge and the battery with the minimum state of charge as the representative battery of the battery pack; Online decomposition and decoupling of voltage potential of representative batteries; The neural network is used to extract the features of the decomposed voltage potential and estimate the state of charge and available capacity of the representative battery. The state of charge and available capacity of the representative battery output by the neural network are introduced into MCSCKF for optimization. The state of charge and available capacity of the battery pack are obtained by optimizing the state of charge and available capacity of the representative battery.

[0090] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0091] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0092] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0093] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for estimating the state of charge and available capacity of the residual power battery pack in the above embodiment; the processor may load and execute the following steps: Select the battery with the maximum state of charge and the battery with the minimum state of charge as the representative battery of the battery pack; Online decomposition and decoupling of voltage potential of representative batteries; The neural network is used to extract the features of the decomposed voltage potential and estimate the state of charge and available capacity of the representative battery. The state of charge and available capacity of the representative battery output by the neural network are introduced into MCSCKF for optimization. The state of charge and available capacity of the battery pack are obtained by optimizing the state of charge and available capacity of the representative battery.

[0094] Figure 7 A schematic diagram of a computer device provided by an embodiment of the present invention.

[0095] See also Figure 7 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the composition of fluid in the reservoir transformation wellbore in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the calculation system for the composition of fluid in the reservoir transformation wellbore in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0096] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 7 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0097] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0098] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0099] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0100] Any reference to a memory, database or other medium used in the embodiments provided in the present application may include at least one of a non-volatile and a volatile memory. Non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetic random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. Volatile memory may include a random access memory (RAM) or an external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0101] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0102] Figure 8 The present invention is a block diagram of an electronic device provided according to an embodiment.

[0103] See also Figure 8 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0104] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0105] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0106] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0107] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0108] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0109] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0110] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0111] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0113] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0117] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0120] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for estimating the state of charge and available capacity of a power battery pack, characterized in that: The following steps are involved: Select the battery with the maximum state of charge and the battery with the minimum state of charge as the representative battery of the battery pack; Online decomposition and decoupling of voltage potential of representative batteries; The neural network is used to extract the features of the decomposed voltage potential and estimate the state of charge and available capacity of the representative battery. The state of charge and available capacity of the representative battery output by the neural network are introduced into MCSCKF for optimization. The state of charge and available capacity of the battery pack are obtained by optimizing the state of charge and available capacity of the representative battery.

2. A method for estimating state of charge and available capacity of a power battery pack according to claim 1, characterized in that: The specific method of performing online voltage and potential decomposition and decoupling on the representative battery is to perform online voltage and potential decomposition and decoupling through the Thevenin model.

3. A method for estimating state of charge and available capacity of a power battery pack according to claim 2, characterized in that: The open circuit potential, ohmic overpotential and polarization overpotential are obtained after online decomposition and decoupling of the voltage potential of the representative battery.

4. A method for estimating state of charge and available capacity of a power battery pack according to claim 1, characterized in that: The neural network adopts LSTM-DNN neural network.

5. A method for estimating state of charge and available capacity of a power battery pack according to claim 1, characterized in that: The measurement equation of the MCSCKF is: Among them, f PINN represents the state estimation of the representative unit based on PINN, t is the sampling time, U t and I t represents the sampling voltage and current corresponding to the unit, R t To measure noise, the state function of the MCSCKF is a variation of the ampere-hour integral equation: Among them, SOC state,t is the predicted SOC value at time t, µ is the Coulomb efficiency, usually taken as 100%, C is the battery capacity, ∆t is the sampling period, Q k is the process noise.

6. A method for estimating state of charge and available capacity of a power battery pack according to claim 1, characterized in that: The specific steps of obtaining the state of charge and available capacity of the battery pack through the optimized state of charge and available capacity of the representative battery are: The remaining charge capacity (RCC) and remaining discharge capacity (RDC) of a battery are obtained using the state of charge and available capacity of the optimized representative battery; The capacity and state of charge of the battery pack are calculated based on the remaining charge capacity (RCC) and remaining discharge capacity (RDC) of all cells in the battery pack.

7. A method for estimating state of charge and available capacity of a power battery pack according to claim 6, characterized in that: The capacity expression of the battery pack is: The state of charge of the battery pack is: in: Among them, C i , C l They are the battery cell capacity at the maximum state of charge and the battery cell capacity at the minimum state of charge, SOC j It is the state of charge of the battery cell with the smallest capacity in the battery pack.

8. A power battery pack state of charge and available capacity estimation system, characterized in that: It includes selection module, decoupling module, optimization module and output module, among which: Selection module: used to select the battery with the maximum state of charge and the minimum state of charge as the representative battery of the battery pack; Decoupling module: used to perform online voltage potential decomposition and decoupling of representative batteries; Optimization module: used to extract features of the decomposed and decoupled voltage potential using a neural network, estimate the state of charge and available capacity of a representative battery, and introduce the state of charge and available capacity of the representative battery output by the neural network into MCSCKF for optimization; Output module: used to obtain the state of charge and available capacity of the battery pack through the optimized state of charge and available capacity of the representative battery.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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