Battery capacity prediction method and device based on particle filtering

Abstract

The invention provides a battery capacity prediction method and device based on particle filtering. The method comprises the following steps: acquiring the current battery residual capacity of a battery; determining the residual capacity of the battery at the next moment according to a preset capacity prediction model; the preset capacity prediction model is obtained by synthesizing historical data of a battery and calculating according to an equivalent circuit model and particle filtering, a large number of battery working condition models can be constructed by utilizing limited experimental data and a data synthesis method, and the capacity of the battery is predicted by utilizing an example filtering method.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery detection, and in particular relates to a battery capacity prediction method and device based on particle filtering. Background Art

[0002] Battery capacity prediction based on particle filter (PF) belongs to the field of battery management system and data-driven predictive control technology, focusing on using particle filter algorithm to improve the accuracy of battery available capacity prediction. This research involves multiple technical fields such as signal processing, machine learning, control theory and energy management, aiming to predict battery capacity through advanced algorithms, which is of great significance for electric vehicles, renewable energy storage systems and other modern applications that rely on battery technology.

[0003] The existing battery capacity estimation methods mainly include parameter model method, composite pulse capacity characteristic method, and data-driven method. The parameter identification-based method and the composite pulse capacity characteristic method are greatly affected by battery conditions such as temperature and battery aging, and the difference in their results gradually increases with the change of battery conditions. The data-driven method has a high requirement for the amount of data in the early stage, but for batteries, the charging and discharging cycle is too long and does not support the rapid acquisition of large amounts of data. Therefore, how to improve the efficiency of battery capacity prediction based on particle filtering has become a technical problem that needs to be solved urgently in this field. Summary of the invention

[0004] The object of the present invention is to provide a battery capacity prediction method and device based on particle filtering.

[0005] According to a first aspect of the present invention, a battery capacity prediction based on particle filtering is provided, comprising:

[0006] Get the current remaining battery capacity of the battery;

[0007] The remaining capacity of the battery at the next moment is determined according to a preset capacity prediction model; the preset capacity prediction model is synthesized using battery historical data and is calculated according to an equivalent circuit model and a particle filter.

[0008] Optionally, the capacity prediction model is obtained by the following method:

[0009] Construct an equivalent circuit model of the battery;

[0010] Using historical battery data, determine synthetic data of the battery and its operating conditions;

[0011] Obtaining a preliminary capacity current value through a capacity prediction model and the synthetic data;

[0012] The capacity prediction model is obtained according to the particle filter and the preliminary capacity current value.

[0013] Optionally, the using of battery historical data to determine synthetic data of the battery and its operating conditions includes:

[0014] According to the battery historical data, voltage curves, current curves and capacity curves are obtained by group respectively;

[0015] According to the voltage curve, the current curve and the capacity curve, a deviation range constant is determined, and multiple sets of synthetic data are randomly generated within a preset range.

[0016] Optionally, obtaining a preliminary capacity current value through a capacity prediction model and the synthesized data includes:

[0017] generating a data model according to the voltage curve, the current curve and the capacity curve;

[0018] Using the synthetic model data, equivalent model parameters are obtained.

[0019] Optionally, obtaining the capacity prediction model according to the particle filter and the preliminary capacity current value includes:

[0020] Calculating the current value of the preliminary capacity according to the equivalent model parameters;

[0021] The capacity prediction model is obtained according to the preliminary capacity current value and the particle filtering algorithm.

[0022] Optionally, the particle filtering algorithm is:

[0023]

[0024] Among them, SOQ pred is the predicted value of SOQ, N is the number of particles, w i is the weight of particle i, and SOQi is the SOQ value of particle i.

[0025] According to a second aspect of the present invention, there is provided a battery capacity prediction device based on particle filtering, comprising:

[0026] An acquisition module is used to obtain the current remaining battery capacity of the battery;

[0027] The prediction module is used to determine the remaining capacity of the battery at the next moment according to a preset capacity prediction model; the preset capacity prediction model is synthesized using battery historical data and calculated according to an equivalent circuit model and particle filtering.

[0028] Optionally, the capacity prediction model is obtained by the following method:

[0029] Construct an equivalent circuit model of the battery;

[0030] Using historical battery data, determine synthetic data of the battery and its operating conditions;

[0031] Obtaining a preliminary capacity current value through a capacity prediction model and the synthetic data;

[0032] The capacity prediction model is obtained according to the particle filter and the preliminary capacity current value.

[0033] Optionally, the acquisition module is used to:

[0034] According to the battery historical data, voltage curves, current curves and capacity curves are obtained by group respectively;

[0035] According to the voltage curve, the current curve and the capacity curve, a deviation range constant is determined, and multiple sets of synthetic data are randomly generated within a preset range.

[0036] Optionally, the acquisition module is used to:

[0037] generating a data model according to the voltage curve, the current curve and the capacity curve;

[0038] Using the synthetic model data, equivalent model parameters are obtained.

[0039] Optionally, the acquisition module is used to:

[0040] Calculating the current value of the preliminary capacity according to the equivalent model parameters;

[0041] The capacity prediction model is obtained according to the preliminary capacity current value and the particle filtering algorithm.

[0042] Optionally, the particle filtering algorithm is:

[0043]

[0044] Among them, SOQpred is the predicted value of SOQ, N is the number of particles, and w is the i is the weight of particle i, and SOQi is the SOQ value of particle i.

[0045] In a third aspect, the present application shows an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in any of the above aspects.

[0046] In a fourth aspect, the present application illustrates a non-temporary computer-readable storage medium, which, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute a method as described in any of the above aspects.

[0047] In a fifth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method described in any of the above aspects.

[0048] The beneficial effects brought by the present invention are as follows:

[0049] It can be seen from the above scheme that the embodiment of the present invention provides a battery capacity prediction method and device based on particle filtering, including: obtaining the current battery remaining capacity of the battery; determining the battery remaining capacity at the next moment according to a preset capacity prediction model; the preset capacity prediction model is synthesized using battery historical data and obtained according to an equivalent circuit model and particle filtering calculation. A large number of battery operating condition models can be constructed using a limited amount of experimental data and a data synthesis method, and the battery capacity can be predicted using an example filtering method based on this. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of a flow chart of a battery capacity prediction method based on particle filtering provided according to an embodiment;

[0051] Figure 2 A schematic flow chart of another battery capacity prediction method based on particle filtering provided according to an embodiment;

[0052] Figure 3 A schematic diagram of a flow chart of another battery capacity prediction method based on particle filtering provided according to an embodiment;

[0053] Figure 4 A schematic diagram of an equivalent circuit model provided according to an embodiment;

[0054] Figure 5 It is a structural block diagram of a battery capacity prediction device based on particle filtering of the present application;

[0055] Figure 6 is a block diagram of an electronic device of the present application;

[0056] Figure 7 is a block diagram of a computer-readable storage medium of the present application;

[0057] Figure 8 is a block diagram of a computer-readable storage medium 1900 shown in the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all 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.

[0059] Reference Figure 1 , shows a flow chart of the steps of a battery capacity prediction method based on particle filtering of the present application, which can be applied to electronic devices, wherein the method can specifically include the following steps:

[0060] S101, obtaining the current remaining battery capacity of the battery;

[0061] S102, determining the remaining capacity of the battery at the next moment according to a preset capacity prediction model; the preset capacity prediction model is synthesized using battery historical data and is calculated according to an equivalent circuit model and a particle filter.

[0062] like Figure 2 As shown, the first step is to build an equivalent circuit model;

[0063] Step 2: Use historical data to build a synthetic model of the battery and its operating conditions;

[0064] Step 3: Obtain the current value of preliminary capacity through the capacity prediction model;

[0065] Step 4: Obtain capacity prediction value based on particle filtering;

[0066] Step 5: Output prediction results.

[0067] The parameter identification-based method and the composite pulse capacity characteristic method are greatly affected by the battery status such as temperature and battery aging, and the difference in the results gradually increases with the change of the battery status. The data-driven method has a large demand for the amount of data in the early stage, but for the battery, its charge and discharge cycle is too long and does not support the rapid acquisition of a large amount of data.

[0068] The embodiments of the present application can construct a large number of battery operating condition models by using a limited amount of experimental data and a data synthesis method, and then use the example filtering method to predict the capacity of the battery.

[0069] Another embodiment of the present application further supplements the battery capacity prediction method based on particle filtering provided in the above embodiment.

[0070] Specifically, Figure 3 As shown in Figure 1, the capacity prediction model is obtained by the following method:

[0071] Construct an equivalent circuit model of the battery;

[0072] Battery Capacity Equation Construction

[0073] The remaining capacity (SOQ) of a battery refers to the ratio of the battery's current fully charged capacity to the battery's rated capacity. SOQ is usually expressed as a percentage, ranging from 0% to 100%. The relationship between the battery's raw capacity and capacity consumption can be described by the battery state equation, which reflects the change in the battery's SOQ over time during the discharge process.

[0074]

[0075] SOQ(t) is the remaining capacity of the battery at cycle t. SOQ(t-1) is the remaining capacity of the battery at cycle t-1. The equation shows that the SOQ of the battery decreases over time.

[0076] Using historical battery data, determine synthetic data of the battery and its operating conditions;

[0077] The synthetic data is generated in the following way:

[0078] yi=y*(1+random(-0.05,0.05))

[0079] The above method is used to generate multiple data sets such as current, voltage, and full charge capacity value.

[0080] Obtain preliminary capacity current value through capacity prediction model and synthetic data;

[0081] According to the particle filter and the current value of the preliminary capacity, a capacity prediction model is obtained.

[0082] Optionally, using the battery historical data, synthetic data of the battery and its operating conditions are determined, including:

[0083] According to the battery historical data, obtain the voltage curve, current curve and capacity curve by group respectively;

[0084] According to the voltage curve, current curve and capacity curve, the deviation range constant is determined, and multiple sets of synthetic data are randomly generated within the preset range.

[0085] Optionally, a preliminary capacity current value is obtained through a capacity prediction model and synthetic data, including:

[0086] Generate a data model according to the voltage curve, current curve and capacity curve;

[0087] Using the synthetic model data, equivalent model parameters are obtained.

[0088] like Figure 4As shown, the synthetic data set is used to solve the values ​​of R and C in the above figure, and the equivalent circuit model is constructed.

[0089] In the equivalent circuit model, Usoc is an ideal voltage source, which represents the open circuit voltage of the battery, R0 is the ohmic internal resistance of the battery; R p1 is the polarization internal resistance of the battery, C p1 The RC parallel and series links composed of the polarization capacitors of the battery jointly simulate the polarization characteristics of the battery. L is the battery loop current, U L is the battery external voltage. According to the circuit model, the relationship between the battery terminal voltage and current is established as follows.

[0090] U L =U soc -I L R0-U1

[0091] Use the following method to step by step to get each parameter.

[0092] Identify model parameters, R0 and C b The identification calculation is the same as follows:

[0093] R0=ΔU / I (2)

[0094] C b =ΔQ / ΔU ocv (3)

[0095] U in the model ocv For E and U b The voltage source E is the terminal voltage of the battery after long-term static state. In formula 2, ΔU is the vertical drop voltage difference U1-U2 in characteristic (1), and I is the discharge current; in formula (3), ΔQ is the discharge quantity, which is recorded by the instrument, and ΔU ocv is the open circuit voltage difference U1-U5 in feature (4).

[0096] The zero input response corresponding to feature (3) is:

[0097]

[0098] Where U p is the sum of the series voltages, U 01 and U 02 are the initial polarization voltages, τ1 is the time constant of the RC link, τ = RC. The experimental data were used to fit the curve using the Matlab parameter fitting toolbox curve fitting with equation (4) as the target equation, U 01 , τ1 are taken as unknown parameters for parameter identification to obtain the time constant τ1.

[0099] The zero-state response corresponding to feature (4) is:

[0100]

[0101] In the formula, U′ is the starting point value of the voltage after the vertical drop at the beginning of discharge. Substitute τ1 into it and replace R p1 As the unknown parameters, the polarization resistance parameters are obtained by curve fitting, and the polarization capacitance parameters are obtained by the time constant formula, such as Figure 5 shown.

[0102] Optionally, a capacity prediction model is obtained according to the particle filter and the preliminary capacity current value, including:

[0103] Calculate the current value of preliminary capacity based on equivalent model parameters;

[0104] According to the preliminary capacity current value and the particle filter algorithm, a capacity prediction model is obtained.

[0105] Optionally, the particle filter algorithm is:

[0106]

[0107] Among them, SOQpred is the predicted value of SOQ, N is the number of particles, and w is the i is the weight of particle i, and SOQi is the SOQ value of particle i.

[0108] Specifically, the particle filter-based battery remaining capacity prediction particle filter formula is:

[0109]

[0110] Among them, SOQpred is the predicted value of SOQ, N is the number of particles, and w is the i is the weight of particle i, and SOQi is the SOQ value of particle i.

[0111] The state equation of the prediction model is as follows, where k is the number of cycles:

[0112]

[0113] The battery remaining energy state SOE is calculated based on the noisy power, and the state equation is:

[0114]

[0115] Assuming that the observed value follows a Gaussian distribution, randn()*noise_std is the observed error value, and the Gaussian likelihood is used to update the weights. The formulas for updating the weights are as follows:

[0116]

[0117] Among them, winew is the updated weight of particle i.

[0118] Substituting the updated weights into the original equation 4, we can obtain the updated predicted SOQ value.

[0119] The embodiment of the present invention provides a battery capacity prediction method based on particle filtering, including: obtaining the current remaining battery capacity of the battery; determining the remaining battery capacity at the next moment according to a preset capacity prediction model; the preset capacity prediction model is synthesized using battery historical data and obtained according to an equivalent circuit model and particle filtering calculation, and a large number of battery operating condition models can be constructed using a limited amount of experimental data and a data synthesis method, and the battery capacity is predicted using an example filtering method based on the model.

[0120] It should be noted that each implementable method in this embodiment may be implemented separately, or may be implemented in combination in any combination without conflict, and this application is not limited thereto.

[0121] Another embodiment of the present application provides a battery capacity prediction device based on particle filtering, which is used to execute the battery capacity prediction method based on particle filtering provided in the above embodiment.

[0122] like Figure 6 , which is a schematic diagram of the structure of a battery capacity prediction device based on particle filtering provided in an embodiment of the present application. The battery capacity prediction device based on particle filtering includes an acquisition module 501 and a prediction module 502, wherein:

[0123] The acquisition module 501 is used to acquire the current remaining battery capacity of the battery;

[0124] The prediction module 502 is used to determine the remaining capacity of the battery at the next moment according to a preset capacity prediction model; the preset capacity prediction model is synthesized using battery historical data and calculated according to an equivalent circuit model and particle filtering.

[0125] Regarding the device in this embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0126] Another embodiment of the present application further supplements the battery capacity prediction device based on particle filtering provided in the above embodiment.

[0127] Optionally, the capacity prediction model is obtained by:

[0128] Construct an equivalent circuit model of the battery;

[0129] Using historical battery data, determine synthetic data of the battery and its operating conditions;

[0130] Obtain preliminary capacity current value through capacity prediction model and synthetic data;

[0131] According to the particle filter and the current value of the preliminary capacity, a capacity prediction model is obtained.

[0132] Optionally, obtain a module for:

[0133] According to the battery historical data, obtain the voltage curve, current curve and capacity curve by group respectively;

[0134] According to the voltage curve, current curve and capacity curve, the deviation range constant is determined, and multiple sets of synthetic data are randomly generated within the preset range.

[0135] Optionally, obtain a module for:

[0136] Generate a data model according to the voltage curve, current curve and capacity curve;

[0137] Using the synthetic model data, equivalent model parameters are obtained.

[0138] Optionally, obtain a module for:

[0139] Calculate the current value of preliminary capacity based on equivalent model parameters;

[0140] According to the preliminary capacity current value and the particle filter algorithm, a capacity prediction model is obtained.

[0141] Optionally, the particle filter algorithm is:

[0142]

[0143] Among them, SOQpred is the predicted value of SOQ, N is the number of particles, and w is the i is the weight of particle i, and SOQi is the SOQ value of particle i.

[0144] The embodiment of the present invention provides a battery capacity prediction device based on particle filtering, including: obtaining the current remaining battery capacity of the battery; determining the remaining battery capacity at the next moment according to a preset capacity prediction model; the preset capacity prediction model is synthesized using battery historical data and obtained according to an equivalent circuit model and particle filtering calculation, and a large number of battery operating condition models can be constructed using a limited amount of experimental data and a data synthesis method, and the battery capacity is predicted using an example filtering method based on the model.

[0145] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0146] Optionally, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0147] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0148] Figure 7 800 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0149] Reference Figure 7 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0150] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0151] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0152] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0153] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0154] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0155] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0156] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0157] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0158] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0159] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by a processor 820 of an electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0160] Figure 819 is a block diagram of a computer-readable storage medium 1900 shown in the present application. For example, the computer-readable storage medium 1900 may be provided as a server.

[0161] Reference Figure 8 , the computer-readable storage medium 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions, such as an application, that can be executed by the processing component 1922. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0162] The computer readable storage medium 1900 may also include a power supply component 1926 configured to perform power management of the computer readable storage medium 1900, a wired or wireless network interface 1950 configured to connect the computer readable storage medium 1900 to a network, and an input / output (I / O) interface 1958. The computer readable storage medium 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0163] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0164] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0165] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0166] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present application 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 this application.

[0167] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0168] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the 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.

[0169] 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.

[0170] In addition, each functional unit in each embodiment of the present application 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.

[0171] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0172] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0173] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A battery capacity prediction method based on particle filtering, characterized in that: include: Get the current remaining battery capacity of the battery; Determine the remaining battery capacity at the next moment according to a preset capacity prediction model; The preset capacity prediction model is synthesized using battery historical data and is calculated based on an equivalent circuit model and particle filtering.

2. The battery capacity prediction method based on particle filtering according to claim 1, characterized in that: The capacity prediction model is obtained by the following method: Construct an equivalent circuit model of the battery; Using historical battery data, determine synthetic data of the battery and its operating conditions; Obtaining a preliminary capacity current value through a capacity prediction model and the synthetic data; The capacity prediction model is obtained according to the particle filter and the preliminary capacity current value.

3. The battery capacity prediction method based on particle filtering according to claim 2 is characterized in that: The method of using the battery historical data to determine the synthetic data of the battery and its operating conditions includes: According to the battery historical data, voltage curves, current curves and capacity curves are obtained by group respectively; According to the voltage curve, the current curve and the capacity curve, a deviation range constant is determined, and multiple sets of synthetic data are randomly generated within a preset range.

4. The battery capacity prediction method based on particle filtering according to claim 3 is characterized in that: The obtaining of a preliminary capacity current value by using the capacity prediction model and the synthesized data includes: generating a data model according to the voltage curve, the current curve and the capacity curve; Using the synthetic model data, equivalent model parameters are obtained.

5. The battery capacity prediction method based on particle filtering according to claim 4 is characterized in that: The obtaining of the capacity prediction model according to the particle filter and the preliminary capacity current value includes: Calculating the current value of the preliminary capacity according to the equivalent model parameters; The capacity prediction model is obtained according to the preliminary capacity current value and the particle filtering algorithm.

6. The battery capacity prediction method based on particle filtering according to claim 5, characterized in that: The particle filter algorithm is: Among them, SOQ pred is the predicted value of SOQ, N is the number of particles, w i is the weight of particle i, SOQ i is the SOQ value of particle i.

7. A battery capacity prediction device based on particle filtering, characterized in that: include: An acquisition module is used to obtain the current remaining battery capacity of the battery; The prediction module is used to determine the remaining capacity of the battery at the next moment according to a preset capacity prediction model; the preset capacity prediction model is synthesized using battery historical data and calculated according to an equivalent circuit model and particle filtering.

8. The battery capacity prediction device based on particle filtering according to claim 7, characterized in that: The capacity prediction model is obtained by the following method: Construct an equivalent circuit model of the battery; Using historical battery data, determine synthetic data of the battery and its operating conditions; Obtaining a preliminary capacity current value through a capacity prediction model and the synthetic data; The capacity prediction model is obtained according to the particle filter and the preliminary capacity current value.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 6 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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