Lithium ion battery SOH state estimation method and device, terminal, medium and product

Abstract

The invention provides a lithium ion battery SOH state estimation method and device, a terminal, a medium and a product, and the method comprises the steps: obtaining original charging data collected by a BMS, and carrying out the multi-dimensional cleaning and screening, and obtaining a plurality of effective charging segments; a PSO-ECM identification model of the lithium ion battery is constructed; based on each effective charging segment, performing ECM parameter identification on the PSO-ECM identification model by using a PSO algorithm to obtain a corresponding capacity identification result and a final objective function value; and performing variable weight Kalman filtering based on the final objective function value on each capacity identification result to obtain the actual capacity of the lithium ion battery, and calculating the SOH of the lithium ion battery based on the actual capacity of the lithium ion battery. According to the method, independent identification can be achieved for any charging segment meeting the SOC span requirement, the problem that an existing method depends on cycle data is solved, and meanwhile high-precision lithium ion battery SOH estimation is achieved.

Description

Technical Field

[0001] This application relates to the field of intelligent battery management technology, and in particular to a method, device, terminal, medium and product for estimating the state of harmonics (SOH) of a lithium-ion battery. Background Technology

[0002] With the large-scale application of power batteries and energy storage batteries in new energy vehicles, energy storage power stations, and ultra-large mining equipment, the safety, health status assessment, and residual value management throughout the battery's entire life cycle have become core industry requirements. Battery state of health (SOH), as a key indicator characterizing battery capacity decay and performance degradation, directly determines the reliability of battery safety management, lifespan prediction, and asset valuation through its estimation accuracy.

[0003] In existing technologies, battery SOH estimation mainly relies on SOC and SOH estimation methods at the BMS end, which are achieved through traditional methods such as ampere-hour integration and open-circuit voltage method. However, in practical applications, the BMS end is limited by computing power and cannot deploy complex models. Furthermore, it is affected by factors such as the diversity of operating conditions, sampling noise, and cumulative sensor errors, making it difficult to maintain high accuracy and high consistency of SOH estimation during long-term battery operation.

[0004] Meanwhile, special application scenarios such as ultra-large mining equipment present challenges such as harsh battery operating environments, frequent charging fragmentation, and rapid battery aging. Traditional estimation methods are prone to parameter identification divergence and capacity estimation jumps when faced with these non-ideal conditions, failing to meet the needs of actual business operations for accurate battery SOH assessment, aging mechanism analysis, and power capability monitoring. While some estimation methods based on black-box neural networks can achieve a certain level of accuracy, the model parameters lack clear physical meaning, failing to provide effective data support for battery aging mechanism analysis and fault early warning. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a method, apparatus, terminal, medium and product for estimating the state of harmonics (SOH) of lithium-ion batteries, in order to solve the problems of low accuracy and poor adaptability of existing battery SOH estimation.

[0006] To achieve the above and other related objectives, the first aspect of this application provides a method for estimating the state of equilibrium (SOH) of a lithium-ion battery, comprising: acquiring raw charging data collected by a battery management system (BMS) and performing multi-dimensional cleaning and filtering to obtain multiple effective charging segments; constructing a PSO-ECM identification model for the lithium-ion battery; based on each effective charging segment, using the PSO algorithm to identify the ECM parameters of the PSO-ECM identification model to obtain the corresponding capacity identification result and the final objective function value; performing a variable-weight Kalman filter on each capacity identification result based on the final objective function value to obtain the actual capacity of the lithium-ion battery, and calculating the SOH of the lithium-ion battery based on the actual capacity of the lithium-ion battery.

[0007] In some embodiments of the first aspect of this application, the method of constructing the PSO-ECM identification model of a lithium-ion battery includes: constructing a lithium-ion battery ECM model and constructing an ampere-hour integral model; combining the ampere-hour integral model with the OCV-SOC mapping function to obtain the relationship function between the ECM model and the capacity; and improving the lithium-ion battery ECM model based on the relationship function between the ECM model and the capacity to obtain the PSO-ECM identification model of the lithium-ion battery.

[0008] In some embodiments of the first aspect of this application, the PSO-ECM identification model is: ;in, Here, SOC is the initial value, I is the current, t is the time, and Cap is the capacitance. Open circuit voltage, For ohmic internal resistance, Polarization voltage, This refers to the battery terminal voltage. This is the OCV-SOC mapping function.

[0009] In some embodiments of the first aspect of this application, multi-dimensional cleaning and filtering includes: format cleaning, invalid data removal, and SOC range filtering.

[0010] In some embodiments of the first aspect of this application, based on each effective charging segment, the PSO algorithm is used to identify the ECM parameters of the PSO-ECM identification model to obtain the corresponding capacity identification result and the final fitness value, including:

[0011] The PSO algorithm is employed, with the objective function set as the mean square error between the actual battery terminal voltage at multiple times and the simulated battery terminal voltage at the corresponding multiple times for each effective charging segment. By iteratively identifying the EMC parameters of the PSO-ECM model, the difference between the actual battery terminal voltage at multiple times and the simulated battery terminal voltage at the corresponding multiple times for each effective charging segment is minimized, thereby obtaining the optimal values ​​of the corresponding EMC parameters and the final objective function value. The EMC parameters include: initial SOC, capacity, ohmic internal resistance, polarization resistance, and time constant. The capacity identification result for each effective charging segment is obtained from the optimal values ​​of the EMC parameters for each effective charging segment.

[0012] The formula for the objective function is: Where n is the number of moments in the charging segment. Let be the actual battery terminal voltage at time i. The model simulates the battery terminal voltage at time i.

[0013] In some embodiments of the first aspect of this application, performing variable-weight Kalman filtering based on the final objective function value on each capacity identification result includes: determining the observation noise covariance corresponding to each capacity identification result based on the final objective function value corresponding to each capacity identification result; and performing Kalman filtering on each capacity identification result using the observation noise covariance corresponding to each capacity identification result to obtain the corresponding filtered capacity identification result.

[0014] To achieve the above and other related objectives, a second aspect of this application provides a lithium-ion battery SOH state estimation device, comprising: an acquisition module for acquiring raw charging data collected by a BMS and performing multi-dimensional cleaning and filtering to obtain multiple effective charging segments; a model building module for constructing a PSO-ECM identification model for the lithium-ion battery; a parameter identification module for identifying ECM parameters of the PSO-ECM identification model based on each effective charging segment using the PSO algorithm to obtain the corresponding capacity identification result and the final objective function value; and an SOH calculation module for performing variable-weight Kalman filtering on each capacity identification result based on the final objective function value to obtain the actual capacity of the lithium-ion battery, and calculating the lithium-ion battery SOH based on the actual capacity of the lithium-ion battery.

[0015] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lithium-ion battery SOH state estimation method.

[0016] To achieve the above and other related objectives, a fourth aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to implement the lithium-ion battery SOH state estimation method.

[0017] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the lithium-ion battery SOH state estimation method.

[0018] As described above, the lithium-ion battery SOH state estimation method, apparatus, terminal, medium, and product of this application have the following beneficial effects: This application can independently identify any charging segment that meets the SOC span requirement, solves the dependence of existing methods on complete cycle data, and achieves high-precision lithium-ion battery SOH estimation. Attached Figure Description

[0019] Figure 1 The diagram shown is a flowchart illustrating a lithium-ion battery SOH state estimation method in one embodiment of this application.

[0020] Figure 2 The diagram shown is a schematic of a first-order RC equivalent circuit in one embodiment of this application.

[0021] Figure 3 The diagram shown is a schematic representation of the ECM parameter identification process in one embodiment of this application.

[0022] Figure 4 The diagram shown is a schematic representation of the capacity identification results before and after Kalman filtering in one embodiment of this application.

[0023] Figure 5 The diagram shown is a schematic block diagram of a lithium-ion battery SOH state estimation device according to an embodiment of this application.

[0024] Figure 6 The diagram shown is a structural schematic of an electronic terminal according to an embodiment of this application. Detailed Implementation

[0025] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0026] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0027] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0028] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0029] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0030] <1> OCV (Open Circuit Voltage): OCV is the terminal voltage between the positive and negative terminals of a battery when it is in a static equilibrium state with no load and no current.

[0031] <2> SOC (State of Charge): SOC is the percentage of remaining battery capacity.

[0032] <3> BMS (Battery Management System): A BMS is an electronic system specifically designed for managing and maintaining batteries (especially rechargeable lithium-ion battery packs). The main functions of a BMS include, but are not limited to, real-time monitoring, state estimation, and safety protection.

[0033] <4> Charging Segment: A charging segment is a continuous, localized segment of charging data captured during the full charging process of a battery.

[0034] To facilitate understanding of the embodiments of this application, firstly, in conjunction with Figure 1 Detailed explanation. Figure 1 This document illustrates a flowchart of a lithium-ion battery SOH state estimation method according to an embodiment of the present invention. The lithium-ion battery SOH state estimation method in this embodiment mainly includes the following steps:

[0035] Step S101: Obtain the raw charging data collected by the BMS and perform multi-dimensional cleaning and filtering to obtain multiple effective charging segments.

[0036] In one embodiment, the raw charging data collected by the BMS is the charging segment CSV data collected by the BMS, including: timestamp, voltage, current, temperature, SOC, and cumulative discharge amount.

[0037] In one embodiment, multi-dimensional cleaning and filtering are used to ensure the validity of input data. Multi-dimensional cleaning and filtering includes:

[0038] Format cleaning: Numeric fields in the original charging data are forcibly converted to numeric format, the string-based voltage and temperature lists are parsed into numeric arrays, and non-numeric content is filled with default values. It should be understood that the default values ​​can be set according to actual needs; this embodiment does not impose any limitations on this.

[0039] Time series alignment: The original timestamps are converted into a relative time series at the second level, correcting time jumps and sampling interval anomalies, and avoiding integration errors. After time series alignment, the charging data is divided into charging segments, resulting in multiple charging segments. It should be understood that the division of charging segments can refer to existing methods, which will not be elaborated here.

[0040] Invalid data removal: Remove abnormal data points where the absolute value of the current is not greater than a preset current threshold or the voltage is not greater than a preset voltage threshold, and discard charging segments with fewer data points than a preset data point threshold. It should be understood that data at one moment represents one data point.

[0041] SOC range filtering: Only long charging segments with a starting SOC not greater than a preset first SOC threshold and an ending SOC not less than a preset second SOC threshold are retained to ensure the accuracy of OCV-SOC curve fitting.

[0042] For example, the preset current threshold is 0.8A, the preset voltage threshold is 2.0V, and the data point threshold is 50. The first SOC threshold is 30%, and the second SOC threshold is 80%. Specifically, the absolute value of the current is discarded. ,Voltage For abnormal data points, discard charging segments with fewer than 50 data points. Only retain long charging segments with an initial SOC ≤ 30% and an ending SOC ≥ 80%.

[0043] In one embodiment, the multiple charging segments obtained after the above-mentioned multi-dimensional cleaning and screening process are all valid charging segments.

[0044] Step S102: Construct the PSO-ECM identification model for lithium-ion batteries.

[0045] In one embodiment, a lithium-ion battery refers to a battery managed by a BMS.

[0046] In one embodiment, the method for constructing the PSO-ECM identification model of a lithium-ion battery includes: constructing a lithium-ion battery ECM model and constructing an ampere-hour integral model; combining the ampere-hour integral model with the OCV-SOC mapping function to obtain the relationship function between the ECM model and the capacity; and improving the lithium-ion battery ECM model based on the relationship function between the ECM model and the capacity to obtain the PSO-ECM identification model of the lithium-ion battery.

[0047] Specifically, the ECM (Equivalent Circuit Model) is a model that uses circuit units such as resistors, capacitors, and inductors to simulate the dynamic charging and discharging behavior of a lithium-ion battery. It is widely used in the field of lithium-ion battery state estimation, such as battery power state estimation and state of charge estimation. The first-order RC equivalent circuit model is the most commonly used ECM model, offering higher accuracy than the Rint model while requiring less computation compared to multi-order RC models. Therefore, the first-order RC equivalent circuit model is chosen as the lithium-ion battery ECM model to meet the capacity identification requirements of this invention.

[0048] First-order RC equivalent circuit as follows Figure 2 As shown, it includes multiple modules such as circuitry, internal resistance, and capacitors. The equations of the first-order RC equivalent circuit model are as follows:

[0049] (1)

[0050] in, This is the battery open-circuit voltage. For Ohm internal resistance The resulting polarization voltage, The polarization voltage is caused by the polarization internal resistance. This refers to the battery terminal voltage. It represents electric current.

[0051] polarization voltage The calculation formula is:

[0052] (2)

[0053] (3)

[0054] in, For polarization internal resistance, Polarized capacitor, The time constant is given. When all parameters of the ECM are known, the terminal voltage of the model can be calculated using equation (1). Equation (3) is The standard formula for represents resistance R × capacitance C. Treating equation (2) as a linear time-invariant system, we can derive equation (4):

[0055] (4)

[0056] in, The polarization voltage at time k+1 , For the interval sampling time, The polarization voltage at time k , Let be the current value at time k.

[0057] Since the ECM model does not contain capacity values, it is necessary to establish a relationship between capacity and ECM. Firstly, as... Figure 3 As shown, an ampere-hour integral model is introduced, which describes the relationship between SOC and capacity. The equation of the ampere-hour integral model is as follows:

[0058] (5)

[0059] in, Where SOC is the initial value, I is the current, t is the time, and Cap is the capacitance.

[0060] And because of open circuit voltage Given a stable mapping relationship with SOC, and assuming the current I and time t are known, substituting equation (5) into the OCV-SOC mapping function... From this, we can obtain:

[0061] (6)

[0062] Equation (6) is the relationship function between the ECM model and capacity.

[0063] Substituting equation (6) into equation (1) above, we get:

[0064] (7)

[0065] Equation (7) is the constructed ECM-PSO identification model for lithium-ion batteries. In Equation (7) The calculation method is the same as that in equations (2), (3) and (4) above.

[0066] It should be noted that the OCV-SOC mapping function is the OCV-SOC curve. A lithium-ion battery has one OCV-SOC curve, and the construction method of the OCV-SOC curve can refer to existing technologies, which will not be elaborated here.

[0067] Step S103: Based on each effective charging segment, use the PSO algorithm to identify the ECM parameters of the PSO-ECM identification model to obtain the corresponding capacity identification results and the final objective function value.

[0068] Since there are generally multiple effective charging segments, the PSO algorithm is used to perform ECM parameter identification on the PSO-ECM identification model for each effective charging segment to obtain the capacity identification result corresponding to each effective charging segment. Each effective charging segment includes the actual battery terminal voltage and actual current at multiple times. Referring to the ECM-PSO identification model of lithium-ion battery in the above equation (7), the ECM parameters include: initial SOC ( ), Capacitance (Cap), Internal resistance (ohms) ), polarization resistor ( ), and time constant ( ).

[0069] For any valid charging segment, the following will be combined with the appendix. Figure 3 This describes the specific process of using the PSO algorithm to perform ECM parameter identification on the PSO-ECM identification model based on the effective charging segment, and obtaining the capacity identification result corresponding to the charging segment:

[0070] First, define the objective function and parameter space:

[0071] Objective function selection RMSE function :

[0072] (8)

[0073] in, Let be the actual battery terminal voltage at time i, and be the value acquired by the BMS sensor. Let n be the simulated battery terminal voltage at time i. The accuracy of ECM parameter identification can be measured by calculating the RMSE between the actual battery terminal voltage and the simulated battery terminal voltage, where n represents the number of time segments in the charging process. When the RMSE is sufficiently small, the two voltage curves almost overlap, indicating that the ECM parameters can simulate a real battery. Conversely, when the RMSE is large, the ECM fitting effect is poor.

[0074] Initialize multiple particles (typically 30 to 50 particles). Each particle's information includes its position and velocity. The particle position is essentially a multi-dimensional vector, including at least one dimension. The position of each particle is represented as [...]. Cap, , , That is, the position of each particle represents a set of ESM parameters. The parameter space defines the range of parameter values ​​for each dimension of the particle position, and each dimension value must be within the corresponding parameter value range.

[0075] The position of each particle and the actual current at each moment of the effective charging segment are input into the PSO-ECM identification model to obtain the model simulation battery terminal voltage at each moment of the effective charging segment. The model simulation battery terminal voltage and the actual battery terminal voltage at each moment of the effective charging segment are input into the RMSE function of equation (8) to obtain the corresponding objective function value (RMSE function value). At this time, the local optimal solution of each particle is the position of the particle with the smallest objective function value, and the global optimal solution is the local optimal solution of the particle with the smallest objective function value. The particle needs to be iterated based on the objective function value. If iteration is required, the particle is iterated using the following particle velocity update formula (Equation 9) and particle position update formula (Equation 10):

[0076] (9)

[0077] (10)

[0078] in, Let be the particle position at time l. As weight, It is a random number. This is a locally optimal solution. Let k be the particle position at time k. This is the globally optimal solution.

[0079] The particle is iterated multiple times until the convergence condition is met (reaching the preset maximum number of iterations or less than the objective function threshold set by the objective function). The final output global optimal solution is the optimal value of the EMC parameters corresponding to this charging segment. The capacity extracted from the optimal value of the EMC parameters corresponding to this charging segment is the capacity identification result corresponding to this charging segment. The objective function value corresponding to the global optimal solution is the final objective function value.

[0080] In a preferred embodiment, core features are extracted / calculated from each valid charging segment to provide prior information for parameter identification:

[0081] The cumulative discharge amount of each valid charging segment is read as a reference benchmark for battery aging and drives the aging adaptive boundary logic. The cumulative discharge amount is different at each moment in the charging segment. During the particle iteration of the PSO algorithm, the value range of the particle position dimension representing capacity can be dynamically adjusted according to the change of cumulative discharge amount to obtain more accurate identification results.

[0082] Simultaneously, the current of each effective charging segment is integrated over time to obtain the corresponding charge (unit: Ah), which is then compared with the result of the traditional two-point method capacity calculation. The charge is used as the initial value of the particle position dimension representing the capacity of each particle.

[0083] Step S104: Perform variable weighted Kalman filtering on each capacity identification result based on the final objective function value to obtain the actual capacity of the lithium-ion battery, and calculate the SOH of the lithium-ion battery based on the actual capacity of the lithium-ion battery.

[0084] If the capacity of a lithium-ion battery is considered to be almost constant between the previous and next time steps, then the state matrix is:

[0085] (11)

[0086] in, Let J be the capacity estimated by the Kalman filter at time j. This is system noise; due to the small capacity variation over a relatively short time range, A smaller value can be taken.

[0087] The observation equation is:

[0088] (12)

[0089] in, The capacity identification result at time j (the identification value of the PSO-ECM identification model). To observe noise, one must consider factors such as data and parameters.

[0090] The completed Kalman filtering process is as follows:

[0091] Prediction process:

[0092] (13)

[0093] (14)

[0094] in, The optimal capacity identification result at time j is given. This represents system noise. Let be the error at time j, and q be a constant of 0.01. Because the capacity changes little over a relatively small time range, A smaller value can be chosen. q is generally set to a small value.

[0095] Update process:

[0096] (15)

[0097] (16)

[0098] (17)

[0099] in, Let r be the Kalman gain, and r be the observation noise covariance. The capacity identification result is at time j.

[0100] In the above embodiments, the capacity identification result corresponding to each effective charging segment corresponds to a time point. In engineering, this time point is generally the median time of the effective charging segment. Kalman filtering is performed on each capacity identification result in chronological order. Before performing Kalman filtering on each capacity identification result, the value of r (observation noise covariance) in equation (15) corresponding to each capacity identification result is determined according to the final objective function value corresponding to each capacity identification result. When r is larger, the Kalman gain is smaller, and the influence of the observation value during state update is smaller; when r is smaller, the Kalman gain is larger, and it is closer to 1, so the influence of the observation value during state update is greater. Then, based on the value of r corresponding to each capacity identification result, Kalman filtering is performed on each capacity identification result with reference to the above equations (13) to (17).

[0101] In one specific embodiment, the capacity identification results before and after filtering are as follows: Figure 4 As shown.

[0102] In one embodiment, the capacity smoothed by Kalman filtering is used as the actual battery capacity, and the State of Health (SOH) is calculated in conjunction with the battery's nominal capacity, achieving a continuous and smooth estimation of SOH. It should be understood that Battery Health SOH = Actual Battery Capacity Battery nominal capacity. Battery nominal capacity refers to the minimum amount of electricity that the battery can theoretically discharge under standard conditions, as indicated by the manufacturer. The unit is usually mAh (milliampere-hour) or Ah (ampere-hour).

[0103] The advantages of this invention are:

[0104] (1) High estimation accuracy and good consistency throughout the entire life cycle: This invention uses a first-order RC physical model combined with the PSO algorithm to achieve global and accurate identification of the core battery parameters. Simultaneously, by combining variable-weight Kalman filtering and an aging adaptive mechanism, the average relative error of capacity estimation is less than 4% under stable operating conditions, and the average RMSE after temperature correction is as low as 1.2386%. The SOH estimation accuracy is stably controlled within [specific range missing]. Within 3%, and can achieve continuous and smooth tracking of the capacity decay curve, without physical jumps.

[0105] (2) Breaking through the computing power limitation of the end side: The method of this application can be applied to the cloud or host computer environment, freeing it from the computing power limitation of the BMS end side. At the same time, the first-order RC model reduces the amount of computation by 40% while ensuring accuracy. The PSO algorithm takes only 17.4 seconds per instance for single-inference, which meets the real-time requirements of large-scale batch processing of battery data in the cloud. It can also be adapted to embedded deployment by trimming hyperparameters.

[0106] (3) Strong global optimization capability and avoidance of local optima: Compared with traditional gradient descent algorithms, the improved adaptive PSO algorithm does not require derivative information. It achieves global search of parameter space through particle swarm information sharing. Combined with dynamic inertia weight and variable acceleration factor strategy, it effectively solves the non-convex optimization problem of battery parameter identification and avoids getting trapped in local minima.

[0107] (4) Strong adaptability and support for fragmented charging conditions: No complete charge and discharge cycle data is required. It can independently identify any charging segment that meets the SOC span requirement. Combined with a multi-dimensional data preprocessing mechanism, it solves the problem of dependence on complete cycle data in traditional methods.

[0108] (5) High robustness, with noise immunity and low-quality data shielding capabilities: A variable weight Kalman filter mechanism based on RMSE is constructed, which can automatically identify low-quality data caused by sampling noise, electromagnetic interference, sensor misalignment, etc. The low-quality data is shielded by dynamically adjusting the measurement noise covariance. At the same time, hard constraints on physical parameters are set to ensure that the identification results conform to electrochemical laws, thereby improving the model's anti-interference capability under complex actual working conditions.

[0109] (6) Strong physical interpretability and support for multiple application scenarios: All parameters of the model have clear physical meaning.

[0110] Figure 5 This is a schematic block diagram of the lithium-ion battery SOH state estimation device provided in the embodiments of this application. Figure 5 As shown, the lithium-ion battery SOH state estimation device 500 includes:

[0111] The acquisition module 501 is used to acquire the raw charging data collected by the BMS and perform multi-dimensional cleaning and filtering to obtain multiple effective charging segments.

[0112] Model building module 502 is used to build a PSO-ECM identification model for lithium-ion batteries;

[0113] The parameter identification module 503 is used to identify the ECM parameters of the PSO-ECM identification model based on each effective charging segment using the PSO algorithm, and obtain the corresponding capacity identification result and the final objective function value.

[0114] SOH calculation module 504 is used to perform variable weight Kalman filtering on each capacity identification result based on the final objective function value to obtain the actual capacity of the lithium-ion battery, and calculate the SOH of the lithium-ion battery based on the actual capacity of the lithium-ion battery.

[0115] It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0116] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0117] Figure 6 This is a schematic block diagram of the electronic terminal provided in an embodiment of this application. Figure 6 As shown, the electronic terminal 600 includes at least one processor 601, a memory 602, at least one network interface 603, and a user interface 605. The various components in the electronic terminal 600 are coupled together via a bus system 604. It is understood that the bus system 604 is used to implement communication between these components. In addition to a data bus, the bus system 604 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 6 The general will label all buses as bus systems.

[0118] The user interface 605 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0119] It is understood that memory 602 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0120] In this embodiment of the invention, the memory 602 is used to store various types of data to support the operation of the electronic terminal 600. Examples of this data include: any executable program for operation on the electronic terminal 600, such as the operating system 6021 and application program 6022; the operating system 6021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 6022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The implementation of the lithium-ion battery SOH state estimation method provided in this embodiment of the invention can be included in the application program 6022.

[0121] The methods disclosed in the above embodiments of the present invention can be applied to processor 601, or implemented by processor 601. Processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 601 or by instructions in the form of software. The processor 601 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 601 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 601 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0122] In an exemplary embodiment, the electronic terminal 600 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to perform the aforementioned method.

[0123] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the lithium-ion battery SOH state estimation method in the above embodiments.

[0124] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when run on a computer, causes the computer to execute the lithium-ion battery SOH state estimation method in the above embodiments.

[0125] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0126] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0127] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0131] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0132] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0133] In summary, this application provides a method, apparatus, terminal, medium, and product for estimating the State of Health (SOH) of a lithium-ion battery. The method includes: acquiring raw charging data collected by a Battery Management System (BMS) and performing multi-dimensional cleaning and filtering to obtain multiple effective charging segments; constructing a PSO-ECM identification model for the lithium-ion battery; based on each effective charging segment, using the PSO algorithm to identify the ECM parameters of the PSO-ECM identification model, obtaining the corresponding capacity identification result and the final objective function value; performing a variable-weight Kalman filter on each capacity identification result based on the final objective function value to obtain the actual capacity of the lithium-ion battery, and calculating the SOH of the lithium-ion battery based on the actual capacity. This application can independently identify any charging segment that meets the SOC span requirement, solving the dependence of existing methods on completed cycle data, and achieving high-precision lithium-ion battery SOH estimation. Therefore, this application effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0134] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for estimating the state of equilibrium (SOH) of a lithium-ion battery, characterized in that, include: The raw charging data collected by the BMS is acquired and cleaned and filtered in multiple dimensions to obtain multiple effective charging segments; Constructing a PSO-ECM identification model for lithium-ion batteries; Based on each effective charging segment, the PSO algorithm is used to identify the ECM parameters of the PSO-ECM identification model to obtain the corresponding capacity identification results and the final objective function value. Each capacity identification result is subjected to a variable-weight Kalman filter based on the final objective function value to obtain the actual capacity of the lithium-ion battery, and the SOH of the lithium-ion battery is calculated based on the actual capacity of the lithium-ion battery.

2. The method for estimating the state of harm (SOH) of a lithium-ion battery according to claim 1, characterized in that, Methods for constructing PSO-ECM identification models for lithium-ion batteries include: Construct an ECM model for lithium-ion batteries and an ampere-hour integral model; By combining the ampere-hour integral model with the OCV-SOC mapping function, the relationship function between the ECM model and capacity is obtained; The lithium-ion battery ECM model is improved based on the relationship function between the ECM model and capacity to obtain the PSO-ECM identification model of the lithium-ion battery.

3. The method for estimating the state of harm (SOH) of a lithium-ion battery according to claim 2, characterized in that, The PSO-ECM identification model is as follows: ； in, Here, SOC is the initial value, I is the current, t is the time, and Cap is the capacitance. Open circuit voltage, For ohmic internal resistance, Polarization voltage, This refers to the battery terminal voltage. This is the OCV-SOC mapping function.

4. The method for estimating the state of harm (SOH) of a lithium-ion battery according to claim 1, characterized in that, Multi-dimensional cleaning and filtering includes: format cleaning, invalid data removal, and SOC range filtering.

5. The method for estimating the state of harm (SOH) of a lithium-ion battery according to claim 1, characterized in that, Based on each effective charging segment, the PSO algorithm is used to identify the ECM parameters of the PSO-ECM identification model, obtaining the corresponding capacity identification result and the final fitness value, including: The PSO algorithm is employed, with the objective function set as the mean square error between the actual battery terminal voltage at multiple times and the simulated battery terminal voltage at the corresponding multiple times for each effective charging segment. By iteratively identifying the EMC parameters of the PSO-ECM model, the difference between the actual battery terminal voltage at multiple times and the simulated battery terminal voltage at the corresponding multiple times for each effective charging segment is minimized, thereby obtaining the optimal values ​​of the corresponding EMC parameters and the final objective function value. The EMC parameters include: initial SOC, capacity, ohmic internal resistance, polarization resistance, and time constant. The capacity identification result for each effective charging segment is obtained from the optimal values ​​of the EMC parameters for each effective charging segment. The formula for the objective function is: ； Where n is the number of moments in the charging segment. Let be the actual battery terminal voltage at time i. The model simulates the battery terminal voltage at time i.

6. The lithium-ion battery SOH state estimation method according to claim 5, characterized in that, For each capacity identification result, a variable-weight Kalman filter based on the final objective function value is applied, including: Based on the final objective function value corresponding to each capacity identification result, determine the observation noise covariance corresponding to each capacity identification result; Kalman filtering is applied to each capacity identification result using the observation noise covariance corresponding to each capacity identification result to obtain the corresponding filtered capacity identification result.

7. A lithium-ion battery SOH state estimation device, characterized in that, include: The acquisition module is used to acquire the raw charging data collected by the BMS and perform multi-dimensional cleaning and filtering to obtain multiple effective charging segments; The model building module is used to build a PSO-ECM identification model for lithium-ion batteries. The parameter identification module is used to identify the ECM parameters of the PSO-ECM identification model based on each effective charging segment using the PSO algorithm, and obtain the corresponding capacity identification result and the final objective function value. The SOH calculation module is used to perform variable-weight Kalman filtering on each capacity identification result based on the final objective function value to obtain the actual capacity of the lithium-ion battery, and calculate the SOH of the lithium-ion battery based on the actual capacity of the lithium-ion battery.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to implement the method as described in any one of claims 1 to 6.

10. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method as described in any one of claims 1 to 6.

Images