Battery power state estimation method and device

Abstract

The present invention provides a method and device for estimating a battery power state, comprising: obtaining real-time operating parameters of a battery; determining a battery power state corresponding to the real-time operating parameters of the battery according to the real-time operating parameters of the battery and a pre-trained battery power estimation model, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification, and a battery SOP estimation method based on parameter model and neural network identification is used to comprehensively analyze limiting constraint factors such as voltage, SOC limit, and battery factory setting limit, thereby avoiding excessive calculation requirements while ensuring the accuracy of battery SOP estimation.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and in particular relates to a method and a device for estimating a battery power state. Background Art

[0002] The battery power state SOP is the power released or absorbed by the power battery in a predetermined time interval under the current state. In the existing literature research. SOP can evaluate the power capacity of the power battery charging in the current state and determine whether the current state of the battery meets the power requirements of the power system acceleration without damaging the battery life.

[0003] The battery SOP describes the battery power state, usually expressed as a short-term peak power value. Lithium-ion batteries mostly work in the vehicle starting, accelerating or braking state, providing or absorbing instantaneous high power for the vehicle. The peak power of the battery output and input directly affects the vehicle's ability to start quickly, accelerate and brake urgently, and thus affects the safety and reliability of the vehicle's operation. Therefore, the BMS must have the ability to estimate the peak power of the lithium-ion battery, namely the SOP, online. Accurate SOP estimation results can provide support and reference for the power allocation and energy control of the vehicle system, which can not only ensure the vehicle's operating performance, but also ensure the vehicle's fuel economy. The battery SOP is an implicit state quantity, and it is necessary to achieve online estimation by modeling the SOP. SOP is affected by temperature, SOC, aging degree, internal resistance, etc., and it is difficult to directly quantify it through certain specific parameters. At present, the estimation methods of lithium-ion battery SOP can be divided into three categories: interpolation method, parameter model method, and data-driven non-parametric model method, but these algorithms can only be applied to different scenarios. How to estimate the battery power in different scenarios and improve the accuracy of the estimation has become a technical problem that needs to be solved in this field. Summary of the invention

[0004] The object of the present invention is to provide a method and device for estimating a battery power state.

[0005] According to a first aspect of the present invention, there is provided a method for estimating a battery power state, comprising:

[0006] Get the real-time operating parameters of the battery;

[0007] According to the real-time operating parameters of the battery and a pre-trained battery power estimation model, a battery power state corresponding to the real-time operating parameters of the battery is determined, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification.

[0008] Optionally, the battery power estimation model includes at least three layers, the first layer is an input layer, the second layer is a hidden layer, and the third layer is an output layer;

[0009] The input layer is used to input the historical data and the instance data obtained based on parameter identification, the historical data at least including current, voltage and temperature, and convert the historical data and the instance data into corresponding feature vectors;

[0010] The hidden layer is used to set corresponding weights and biases for the feature vectors, and perform a sum operation based on the weights and the feature vectors to obtain a power vector;

[0011] The output layer is used to output the power vector.

[0012] Optionally, the method further comprises:

[0013] During the training process, the weights and biases are adjusted by calculating the loss function.

[0014] Optionally, the method further comprises:

[0015] Construct an equivalent circuit model of the battery;

[0016] According to the equivalent circuit model, an OCV curve and component parameters are determined; wherein the OCV curve is obtained through experiments, and the component parameters are obtained through a hybrid power pulse characteristic algorithm.

[0017] Optionally, the instance data obtained based on parameter identification is obtained in the following manner:

[0018] Calculate the current and voltage curve of a single pulse through the hybrid power pulse characteristic algorithm;

[0019] Determine curve characteristics according to the single pulse current-voltage curve, the curve characteristics at least including: a vertical drop value of the voltage at the time of discharge, a voltage before the discharge pulse being higher than a voltage reaching a stable state at the end of discharge, a curve segment of a slow rise in the battery terminal voltage after the end of discharge, and a curve segment of a slow drop in the battery terminal voltage during discharge;

[0020] According to the curve characteristics, the ohmic internal resistance of the capacitor and the battery is determined, and the capacitor is used to describe the change in open circuit voltage generated by current accumulation.

[0021] According to a second aspect of the present invention, there is provided a device for estimating a battery power state, comprising:

[0022] An acquisition module is used to obtain real-time operating parameters of the battery;

[0023] An estimation module is used to determine a battery power state corresponding to the real-time operating parameters of the battery according to the real-time operating parameters of the battery and a pre-trained battery power estimation model, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification.

[0024] Optionally, the battery power estimation model includes at least three layers, the first layer is an input layer, the second layer is a hidden layer, and the third layer is an output layer;

[0025] The input layer is used to input the historical data and the instance data obtained based on parameter identification, the historical data at least including current, voltage and temperature, and convert the historical data and the instance data into corresponding feature vectors;

[0026] The hidden layer is used to set corresponding weights and biases for the feature vectors, and perform a sum operation based on the weights and the feature vectors to obtain a power vector;

[0027] The output layer is used to output the power vector.

[0028] Optionally, the estimation module is further used for:

[0029] During the training process, the weights and biases are adjusted by calculating the loss function.

[0030] Optionally, the acquisition module is used to:

[0031] Construct an equivalent circuit model of the battery;

[0032] According to the equivalent circuit model, an OCV curve and component parameters are determined; wherein the OCV curve is obtained through experiments, and the component parameters are obtained through a hybrid power pulse characteristic algorithm.

[0033] Optionally, the instance data obtained based on parameter identification is obtained in the following manner:

[0034] Calculate the current and voltage curve of a single pulse through the hybrid power pulse characteristic algorithm;

[0035] Determine curve characteristics according to the single pulse current-voltage curve, the curve characteristics at least including: a vertical drop value of the voltage at the time of discharge, a voltage before the discharge pulse being higher than a voltage reaching a stable state at the end of discharge, a curve segment of a slow rise in the battery terminal voltage after the end of discharge, and a curve segment of a slow drop in the battery terminal voltage during discharge;

[0036] According to the curve characteristics, the ohmic internal resistance of the capacitor and the battery is determined, and the capacitor is used to describe the change in open circuit voltage generated by current accumulation.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] The beneficial effects brought by the present invention are as follows:

[0041] It can be seen from the above scheme that an embodiment of the present invention provides a method and device for estimating a battery power state, including: obtaining real-time operating parameters of a battery; determining a battery power state corresponding to the real-time operating parameters of the battery according to the real-time operating parameters of the battery and a pre-trained battery power estimation model, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification, and a battery SOP estimation method based on parameter model and neural network identification is used to comprehensively analyze limiting constraints such as voltage, SOC limit, and battery factory setting limit, thereby avoiding excessive calculation requirements while ensuring the accuracy of battery SOP estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of a method for estimating a battery power state according to an embodiment;

[0043] Figure 2 A flowchart of another method for estimating a battery power state according to an embodiment is shown.

[0044] Figure 3 A schematic flow chart of another method for estimating a battery power state according to an embodiment;

[0045] Figure 4 A schematic diagram of an improved equivalent circuit model provided according to an embodiment;

[0046] Figure 5 is an open circuit voltage curve of a battery provided according to an embodiment;

[0047] Figure 6 A schematic diagram of a HPPC test current and voltage curve provided according to an embodiment;

[0048] Figure 7 A parameter identification flow chart according to an embodiment;

[0049] Figure 8 A schematic diagram of training a power prediction model based on a neural network provided according to an embodiment;

[0050] Fig. 9 It is a structural block diagram of a battery power state estimation device of the present application.

[0051] Fig.10 It is a block diagram of an electronic device of the present application.

[0052] Fig.11 It is a block diagram of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0053] 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.

[0054] Reference Figure 1 , shows a flowchart of a method for estimating a battery power state of the present application, which can be applied to an electronic device, wherein the method can specifically include the following steps:

[0055] S101, obtaining real-time operating parameters of the battery;

[0056] S102. Determine a battery power state corresponding to the real-time operating parameters of the battery according to the real-time operating parameters of the battery and a pre-trained battery power estimation model, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification.

[0057] Battery SOP estimation based on multiple constraints analyzes the limiting factors of SOP during battery operation, and uses an iterative method to calculate the peak current of the battery under the charge and discharge cut-off voltage and SOC constraints. Combined with the limitation of the battery's design current, the battery voltage is estimated, and the peak power under the limitation of SOE is calculated, and finally the battery's SOP is obtained.

[0058] like Figure 2As shown, the embodiment of the present application constructs an equivalent circuit model based on an equivalent circuit and HPPC experiment, and uses historical data and the equivalent circuit model to construct a power prediction model based on a neural network. The prediction results are obtained by inputting real-time battery characteristics and historical models, and finally the SOP estimation value under multiple constraints is obtained.

[0059] like Figure 3 As shown, the first step is to build an equivalent circuit model;

[0060] Step 2: Get the battery parameters through the HPPC (Hybrid Pulse Power Characteristic) experiment; it is a characteristic used to reflect the pulse charge and discharge performance of the power battery. HPPC testing is generally completed using dedicated battery testing equipment. HPPC testing can complete the test of the battery's DC internal resistance.

[0061] Step 3: Instantiate the equivalent circuit model according to the parameter values;

[0062] Step 4: Train and optimize the battery power model based on the neural network according to historical data;

[0063] Step 5: Feed the trained model with real-time battery data and obtain prediction results.

[0064] Battery power state estimation is difficult to obtain due to the coupling of multiple influencing factors. The parameters of the traditional parameter identification method are used as fixed constants after identification. However, with the change of operating conditions and life, the parameters actually change in real time, so this method is difficult to cope with real-time use; on the other hand, if only the neural network-based method is used, the construction of the model requires a large amount of data, which is often difficult to obtain due to the life limit of the battery. Compared with the traditional method, this scheme adopts a method based on parameter identification and neural network, which overcomes the data acquisition of the neural network method and ensures the real-time applicability of the method.

[0065] Another embodiment of the present application further supplements the battery power state estimation method provided in the above embodiment.

[0066] Optionally, the battery power estimation model includes at least three layers, the first layer is an input layer, the second layer is a hidden layer, and the third layer is an output layer;

[0067] The input layer is used to input historical data and instance data obtained based on parameter identification, where the historical data includes at least current, voltage and temperature, and convert the historical data and instance data into corresponding feature vectors;

[0068] The hidden layer is used to set the corresponding weights and biases for the feature vectors, and perform sum operations based on the weights and feature vectors to obtain the power vector;

[0069] The output layer is used to output the power vector.

[0070] like Figure 8 As shown in the figure, the neural network is constructed with three layers. In the training phase, the network model is trained using historical data (current, voltage, temperature and other values ​​as input, power as output). In real-time prediction, the real-time battery data value is applied to the hybrid model of the instance and the network to obtain the real-time predicted SOP value.

[0071] Optionally, the method further comprises:

[0072] During the training process, the weights and biases are adjusted by calculating the loss function.

[0073] Optionally, the method further comprises:

[0074] Construct an equivalent circuit model of the battery;

[0075] According to the equivalent circuit model, the OCV curve and component parameters are determined; wherein the OCV curve is obtained through experiments, and the component parameters are obtained through a hybrid power pulse characteristic algorithm.

[0076] Specifically, let the peak discharge power be Xp (to be determined), the constant discharge power be Xc (preset value), and the current actual discharge power of the vehicle be Xf (current value); then the integral value of the actual power exceeding the constant power up to the current moment is calculated (used to calculate the integral value y1 of the actual power exceeding the constant power up to the moment t0):

[0077]

[0078] The integral value y2 of the peak power exceeding the constant power:

[0079]

[0080] Define a coefficient Factor to represent the degree of closeness f between y1 and y2.

[0081]

[0082] It can be seen that power calculation is analyzed from the perspective of energy (integral of power over time). The closer the value of f is to 1, the more energy is available, that is, the more power can be used in the next second; the closer f is to 0, the less energy is available. When f = 0, it is generally considered that the available power of the battery cell is the constant power. Combined with f, we can use the following formula as the real-time power budget for the next second:

[0083] X next =Xc+(Cp-Xc)*f

[0084] According to the above formula, Xnext must be a value between Xc and Xp, and it is consistent with the intuitive perception of power usage. The larger f is, the closer the available power at the next moment is to Xp. Of course, other formulas can also be used for estimation.

[0085] The battery model is the basis of battery state estimation and energy management. As a typical battery model, the equivalent circuit model has a clear physical meaning. It is composed of components such as resistors, capacitors and voltage sources to form a circuit network to simulate the charging and discharging characteristics of the battery. The equivalent circuit model takes into account the impact of battery state and working conditions on the model parameters, and can more accurately describe the battery characteristics. The equivalent circuit model parameters are functions of battery state, temperature and current, and different batteries have different corresponding functions. In view of the fact that lithium iron phosphate batteries have two battery constants, in order to more accurately describe the polarization phenomenon of lithium iron phosphate batteries, a parallel RC link is added on the basis of the equivalent circuit model, such as Figure 4 shown.

[0086] In the improved equivalent circuit model, E is an ideal voltage source, representing the open circuit voltage of the battery, and capacitor C b Describes the change in open circuit voltage caused by current accumulation, E and C b Together they represent the open circuit voltage U ocv The change of R0 is the ohmic internal resistance of the battery; R p1 and R p2 are the two polarization internal resistances of the battery, C p1 and C p2 The RC parallel-series link composed of two polarized capacitors of the battery simulates 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.

[0087] U L =U ocv -I L R0-U p1 -U p2

[0088] Therefore, constructing an instantiated equivalent circuit model requires identifying the above parameter values ​​such as OCV (Uocv), capacitance (C), and resistance (R).

[0089] Optionally, the instance data obtained based on parameter identification is obtained by:

[0090] Calculate the current and voltage curve of a single pulse through the hybrid power pulse characteristic algorithm;

[0091] Determine the curve characteristics according to the single pulse current-voltage curve, the curve characteristics at least including: the vertical drop value of the voltage at the discharge moment, the voltage before the discharge pulse is higher than the voltage reaching a stable state at the end of discharge, the curve segment of the battery terminal voltage slowly rising after the end of discharge, and the curve segment of the battery terminal voltage slowly falling during discharge;

[0092] According to the curve characteristics, the ohmic internal resistance of the capacitor and battery is determined. The capacitor is used to describe the change in open circuit voltage caused by current accumulation.

[0093] Specifically, the equivalent circuit model parameters can be divided into OCV curve and component parameters. The OCV curve is directly obtained through standard tests, and the component parameters are obtained by identifying and calculating the current pulse test data. Both the OCV curve and the component parameters are in the form of functions of the SOC curve.

[0094] The OCV curve can be obtained through standard experiments. The experimental process is as follows: (1) set the experimental temperature; (2) standard charge and discharge experiment; (3) low current charge and discharge experiment; (4) draw the OCV curve, such as Figure 5 shown.

[0095] The parameters of the model components cannot be measured directly and need to be obtained through parameter identification. The actual capacity of the battery is obtained through the capacity test experiment. The battery is subjected to a standard HPPC test and the voltage and current data are recorded.

[0096] like Figure 6 The figure shows a schematic diagram of the HPPC (hybrid power pulse characteristic) single pulse current-voltage curve. The following features can be extracted from the voltage response curve:

[0097] (1) The voltage drops vertically at the discharge time t1, which is the instantaneous voltage drop caused by the ohmic internal resistance of the battery;

[0098] (2) The voltage U1 before the discharge pulse is higher than the voltage U5 at the end of the discharge to reach a stable state. This is due to the voltage difference caused by the integration of the discharge current on the polarization capacitor.

[0099] (3) After the discharge is completed, the battery terminal voltage rises slowly. This is the process of the polarized capacitor discharging the polarized resistor, which is the zero input response of the RC parallel circuit.

[0100] (4) During discharge, the battery terminal voltage drops slowly. This is the process of the discharge current charging the polarized capacitor, which is the zero-state response of the RC parallel circuit.

[0101] Identify model parameters, R0 and C b The identification calculation is the same as follows:

[0102] R0=ΔU / I (2)

[0103] Cb =ΔQ / ΔU ocv (3)

[0104] 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).

[0105] The zero input response corresponding to feature (3) is:

[0106]

[0107] Where U p is the sum of the series voltages, U 01 and U 02 are the initial polarization voltages, τ1 and τ2 are the time constants of the two RC links, 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 , U 02 , τ1, τ2 are taken as unknown parameters for parameter identification to obtain time constants τ1 and τ2.

[0108] The zero-state response corresponding to feature (4) is:

[0109]

[0110] In the formula, U′ is the starting point value of the voltage after the vertical drop at the beginning of discharge. Substitute τ1 and τ2 into it, and similarly replace R p1 and R p2 As the unknown parameters, the polarization resistance parameters are obtained through curve fitting, and the polarization capacitance parameters are obtained from the time constant formula.

[0111] The whole parameter identification process is as follows Figure 7 As shown. This method is used to obtain the SOC R at each point p1 , R p2 , C p1 , C p2 and R0 data.

[0112] After identifying the data discrete points of the five parameters at multiple SOC points, a quartic polynomial is selected as the fitting target formula as shown in formula (6), where x is SOC, f(x) is the parameter to be fitted (that is, the C and R values ​​mentioned above, and a wide range of values ​​are obtained through fitting here), and the least squares method is used to implement it. After obtaining the fitting curve, take data points on the five parameter curves at intervals of 0.001 SOC, and establish a query table of parameter values ​​and SOC corresponding curves.

[0113] f(x)=p1x 4 +p2x 3 +p3x 2 +p4x+p5 (6)

[0114] Table 1 shows an example of the result of a table lookup construction. When using it, use p(1-5) under R0 to obtain the R0 value, and other values ​​can be deduced in the same way.

[0115] Table 1 Polynomial fitting coefficients

[0116]

[0117] An embodiment of the present invention provides a method for estimating a battery power state, comprising: obtaining real-time operating parameters of a battery; determining a battery power state corresponding to the real-time operating parameters of the battery according to the real-time operating parameters of the battery and a pre-trained battery power estimation model, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification, and a battery SOP estimation method based on parameter model and neural network identification is used to comprehensively analyze limiting constraints such as voltage, SOC limit, and battery factory setting limit, thereby avoiding excessive calculation requirements while ensuring the accuracy of battery SOP estimation.

[0118] 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.

[0119] Another embodiment of the present application provides a battery power state estimation device, which is used to execute the battery power state estimation method provided in the above embodiment.

[0120] like Fig. 9 FIG. 9 is a schematic diagram of a battery power state estimation device according to an embodiment of the present application. The device includes an acquisition module 901 and an estimation module 902, wherein:

[0121] The acquisition module 901 is used to acquire the real-time operating parameters of the battery;

[0122] The estimation module 902 is used to determine the battery power state corresponding to the real-time operating parameters of the battery based on the real-time operating parameters of the battery and a pre-trained battery power estimation model, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification.

[0123] 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.

[0124] Another embodiment of the present application further supplements the battery power state estimation device provided in the above embodiment.

[0125] Optionally, the battery power estimation model includes at least three layers, the first layer is an input layer, the second layer is a hidden layer, and the third layer is an output layer;

[0126] The input layer is used to input historical data and instance data obtained based on parameter identification, where the historical data includes at least current, voltage and temperature, and convert the historical data and instance data into corresponding feature vectors;

[0127] The hidden layer is used to set the corresponding weights and biases for the feature vectors, and perform sum operations based on the weights and feature vectors to obtain the power vector;

[0128] The output layer is used to output the power vector.

[0129] Optionally, the estimation module is further configured to:

[0130] During the training process, the weights and biases are adjusted by calculating the loss function.

[0131] Optionally, obtain a module for:

[0132] Construct an equivalent circuit model of the battery;

[0133] According to the equivalent circuit model, the OCV curve and component parameters are determined; wherein the OCV curve is obtained through experiments, and the component parameters are obtained through a hybrid power pulse characteristic algorithm.

[0134] Optionally, the instance data obtained based on parameter identification is obtained by:

[0135] Calculate the current and voltage curve of a single pulse through the hybrid power pulse characteristic algorithm;

[0136] Determine the curve characteristics according to the single pulse current-voltage curve, the curve characteristics at least including: the vertical drop value of the voltage at the discharge moment, the voltage before the discharge pulse is higher than the voltage reaching a stable state at the end of discharge, the curve segment of the battery terminal voltage slowly rising after the end of discharge, and the curve segment of the battery terminal voltage slowly falling during discharge;

[0137] According to the curve characteristics, the ohmic internal resistance of the capacitor and battery is determined. The capacitor is used to describe the change in open circuit voltage caused by current accumulation.

[0138] 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.

[0139] An embodiment of the present invention provides a battery power state estimation device, comprising: obtaining real-time operating parameters of a battery; determining a battery power state corresponding to the real-time operating parameters of the battery according to the real-time operating parameters of the battery and a pre-trained battery power estimation model, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification, and a battery SOP estimation method based on parameter model and neural network identification is used to comprehensively analyze limiting constraint factors such as voltage, SOC limit, and battery factory setting limit, thereby avoiding excessive calculation requirements while ensuring the accuracy of battery SOP estimation.

[0140] 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.

[0141] 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.

[0142] Fig.10 800 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.

[0143] Reference Fig.10, 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 .

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Fig.11 19 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.

[0155] Reference Fig.11 , 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 method for estimating a battery power state, characterized in that: include: Get the real-time operating parameters of the battery; According to the real-time operating parameters of the battery and a pre-trained battery power estimation model, a battery power state corresponding to the real-time operating parameters of the battery is determined, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification.

2. The method for estimating the battery power state according to claim 1, characterized in that: The battery power estimation model comprises at least three layers, the first layer is an input layer, the second layer is a hidden layer, and the third layer is an output layer; The input layer is used to input the historical data and the instance data obtained based on parameter identification, the historical data at least including current, voltage and temperature, and convert the historical data and the instance data into corresponding feature vectors; The hidden layer is used to set corresponding weights and biases for the feature vectors, and perform a sum operation based on the weights and the feature vectors to obtain a power vector; The output layer is used to output the power vector.

3. The method for estimating the battery power state according to claim 2, characterized in that: The method further comprises: During the training process, the weights and biases are adjusted by calculating the loss function.

4. The method for estimating the battery power state according to claim 2, characterized in that: The method further comprises: Construct an equivalent circuit model of the battery; According to the equivalent circuit model, an OCV curve and component parameters are determined; wherein the OCV curve is obtained through experiments, and the component parameters are obtained through a hybrid power pulse characteristic algorithm.

5. The method for estimating the battery power state according to claim 4, characterized in that: The example data obtained based on parameter identification is obtained in the following manner: Calculate the current and voltage curve of a single pulse through the hybrid power pulse characteristic algorithm; Determine curve characteristics according to the single pulse current-voltage curve, the curve characteristics at least including: a vertical drop value of the voltage at the time of discharge, a voltage before the discharge pulse being higher than a voltage reaching a stable state at the end of discharge, a curve segment of a slow rise in the battery terminal voltage after the end of discharge, and a curve segment of a slow drop in the battery terminal voltage during discharge; According to the curve characteristics, the ohmic internal resistance of the capacitor and the battery is determined, and the capacitor is used to describe the change in open circuit voltage generated by current accumulation.

6. A battery power state estimation device, characterized in that: include: An acquisition module is used to obtain real-time operating parameters of the battery; An estimation module is used to determine a battery power state corresponding to the real-time operating parameters of the battery according to the real-time operating parameters of the battery and a pre-trained battery power estimation model, wherein the pre-trained battery power estimation model is obtained by training a neural network model using historical data and instance data obtained based on parameter identification.

7. The battery power state estimation device according to claim 6, characterized in that: The battery power estimation model comprises at least three layers, the first layer is an input layer, the second layer is a hidden layer, and the third layer is an output layer; The input layer is used to input the historical data and the instance data obtained based on parameter identification, the historical data at least including current, voltage and temperature, and convert the historical data and the instance data into corresponding feature vectors; The hidden layer is used to set corresponding weights and biases for the feature vectors, and perform a sum operation based on the weights and the feature vectors to obtain a power vector; The output layer is used to output the power vector.

8. The battery power state estimation device according to claim 7, characterized in that: The estimation module is also used for: During the training process, the weights and biases are adjusted by calculating the loss function.

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 5 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 5 is implemented.

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