Lithium battery charge state estimation method, apparatus and device, and storage medium
By correcting the test parameters of lithium batteries at different temperatures, the deviation problem of lithium battery state of charge estimation method under temperature changes is solved, and high-accuracy SOC estimation is achieved at different temperatures.
Patent Information
- Application Number
- CN202511220077.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-28
- Publication Date
- 2025-11-18
AI Technical Summary
Existing methods for estimating the state of charge (SOC) of lithium batteries exhibit excessive parameter deviations at different temperatures, leading to inaccurate SOC estimation by the extended Kalman filter algorithm.
By conducting hybrid pulse power characteristic tests at different target temperatures, test parameters are obtained and compared with calibration parameters. The difference is calculated to obtain a correction value, which is then used as input to the extended Kalman filter algorithm to eliminate temperature deviation.
It improves the accuracy of lithium battery state of charge estimation, especially under high or low temperature conditions, the SOC estimation error is less than 2%.
Smart Images
Figure CN120971981A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to lithium battery technology, and in particular to a lithium battery state of charge estimation method, device, equipment and storage medium. BACKGROUND
[0002] Lithium batteries are widely used in electric vehicles, portable electronic devices, energy storage systems and other fields as an efficient, safe and environmentally friendly energy storage device. The state of charge (SOC) is a key indicator of the remaining battery capacity. Accurate estimation of the battery SOC is crucial for the optimal control of the battery management system, battery life prediction, safety warning, etc.
[0003] The SOC of a lithium battery is a state variable that cannot be directly measured and needs to be estimated by other measurable variables. Currently, the common estimation method is the Extended Kalman Filter (EKF), which linearizes the nonlinear system locally to provide accurate state estimation for the dynamic model of the battery.
[0004] The parameters required by the current EKF algorithm need to be obtained through the Hybird Pulse Power Characterization (HPPC) test. However, the parameters obtained by HPPC testing at different temperatures will be offset. Only when the parameters measured at room temperature are used for EKF, the estimated SOC will be consistent with the actual value. However, in actual high temperature or low temperature working conditions, the parameter deviation is too large, resulting in a large deviation between the estimated SOC by the EKF algorithm and the actual value. SUMMARY
[0005] The present application provides a lithium battery state of charge estimation method, device, equipment and storage medium, which corrects the test parameters obtained by HPPC testing, eliminates the deviation caused by temperature on the test parameters, and further improves the accuracy of the SOC estimated by the EKF algorithm.
[0006] In a first aspect, the present application provides a lithium battery state of charge estimation method, comprising:
[0007] For a plurality of different target temperatures, obtain test parameters obtained by performing a hybrid pulse power characterization test on a lithium battery at the target temperature;
[0008] Compare the test parameters with calibration parameters calibrated in advance at the target temperature through simulation;
[0009] When the test parameter and the calibration parameter do not match, an actual dynamic parameter obtained by continuously discharging the lithium battery at the target temperature is acquired, and a simulation dynamic parameter obtained by continuously discharging the lithium battery at the target temperature based on the test parameter is acquired;
[0010] A difference between the actual dynamic parameter and the simulation dynamic parameter is calculated to obtain a correction value;
[0011] The test parameter is corrected by using the correction value to obtain a corrected test parameter;
[0012] The corrected test parameter is used as an input of an extended Kalman filtering algorithm to estimate the state of charge of the lithium battery.
[0013] Optionally, the test parameter is compared with a calibration parameter calibrated in advance at the target temperature through simulation, including:
[0014] The plurality of test parameters are sorted in time sequence to obtain a vector expression of the test parameter;
[0015] The plurality of calibration parameters are sorted in time sequence to obtain a vector expression of the calibration parameter;
[0016] Similarity between the vector expression of the test parameter and the vector expression of the calibration parameter is calculated;
[0017] It is judged whether the similarity is greater than a preset value;
[0018] If yes, it is determined that the test parameter and the calibration parameter match;
[0019] If no, it is determined that the test parameter and the calibration parameter do not match.
[0020] Optionally, when the test parameter and the calibration parameter match, further including:
[0021] The test parameter is used as an input of an extended Kalman filtering algorithm to estimate the state of charge of the lithium battery.
[0022] Optionally, the test parameter and the calibration parameter include an open circuit voltage of the lithium battery.
[0023] Optionally, the test parameter is corrected by using the correction value to obtain a corrected test parameter, including:
[0024] The test parameter and the correction value are added to obtain the corrected test parameter.
[0025] Optionally, the modified test parameter is taken as an input of an extended Kalman filtering algorithm to estimate the state of charge of the lithium battery, including:
[0026] establishing state equations and observation equations based on an equivalent circuit model of the lithium battery;
[0027] calculating a Jacobian matrix of the state equations and a Jacobian matrix of the observation equations;
[0028] taking the modified test parameter and a current state as inputs, inputting the state equations, and predicting a state at a next time point, the state including the state of charge of the lithium battery;
[0029] calculating an error covariance matrix based on the Jacobian matrix of the state equations;
[0030] calculating a Kalman gain based on the error covariance matrix and the Jacobian matrix of the observation equations;
[0031] modifying the state at the next time point based on the Kalman gain and a state measurement value, and updating the error covariance matrix.
[0032] Optionally, the equivalent circuit model of the lithium battery is a second-order RC model.
[0033] In a second aspect, the present application further provides a lithium battery state of charge estimation device, including:
[0034] a test parameter acquisition module configured to acquire, for a plurality of different target temperatures, test parameters obtained by performing a hybrid pulse power characteristic test on a lithium battery at the target temperature;
[0035] a comparison module configured to compare the test parameters with calibration parameters calibrated in advance at the target temperature through simulation;
[0036] a dynamic parameter acquisition module configured to, when the test parameters and the calibration parameters do not match, acquire actual dynamic parameters obtained by performing a continuous discharge test on the lithium battery at the target temperature, and acquire simulation dynamic parameters obtained by performing a continuous discharge simulation test on the lithium battery at the target temperature based on the test parameters;
[0037] a correction value calculation module configured to calculate a difference between the actual dynamic parameters and the simulation dynamic parameters to obtain a correction value;
[0038] a parameter correction module configured to correct the test parameters by using the correction value to obtain modified test parameters;
[0039] a state estimation module configured to take the modified test parameters as an input of an extended Kalman filtering algorithm to estimate the state of charge of the lithium battery.
[0040] Thirdly, the present invention also provides an electronic device, comprising:
[0041] One or more processors;
[0042] Storage device for storing one or more programs;
[0043] When the one or more programs are executed by the one or more processors, the one or more processors implement the lithium battery state of charge estimation method provided in the first aspect of the present invention.
[0044] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lithium battery state-of-charge estimation method as provided in the first aspect of the present invention.
[0045] The lithium battery state-of-charge (SOC) estimation method provided by this invention involves obtaining test parameters from a hybrid pulse power characteristic test of the lithium battery at multiple different target temperatures. These test parameters are then compared with calibration parameters pre-calibrated through simulation at the target temperatures. If the test parameters and calibration parameters do not match, the method acquires the actual dynamic parameters obtained from a continuous discharge test of the lithium battery at the target temperatures, as well as the simulated dynamic parameters obtained from a continuous discharge simulation test of the lithium battery at the target temperatures based on the test parameters. The difference between the actual and simulated dynamic parameters is calculated to obtain a correction value. This correction value is then used to correct the test parameters, resulting in corrected test parameters. These corrected test parameters are then used as input to an extended Kalman filter (EKF) algorithm to predict the SOC of the lithium battery. This invention corrects the test parameters obtained from the HPPC test, eliminating the temperature-induced deviation in the test parameters and thus improving the accuracy of the SOC predicted by the EKF algorithm.
[0046] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description
[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0048] Figure 1 The diagram shows the actual SOC curve and the SOC curve predicted by EKF at 25℃.
[0049] Figure 2 The diagram shows the actual SOC curve and the SOC curve predicted by EKF at -10℃.
[0050] Figure 3 The diagram shows the actual SOC curve and the SOC curve predicted by EKF at -30℃.
[0051] Figure 4 A flowchart of a method for estimating the state of charge of a lithium battery provided by the present invention;
[0052] Figure 5 This is a schematic diagram of the SOC curve predicted by the method of the present invention and the actual SOC curve at 25°C.
[0053] Figure 6 This is a schematic diagram showing the SOC curve predicted and the actual SOC curve at -10℃ using the method of this invention.
[0054] Figure 7 This is a schematic diagram of the SOC curve predicted and the actual SOC curve using the method of this invention at -20℃.
[0055] Figure 8 A schematic diagram of the structure of a lithium battery state of charge estimation device provided by the present invention;
[0056] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.
[0057] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation
[0058] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0059] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0060] Figure 1 This is a schematic diagram showing the actual SOC curve and the EKF-predicted SOC curve at 25℃. Figure 2 This is a schematic diagram showing the actual SOC curve and the EKF-predicted SOC curve at -10℃. Figure 3 The diagram shows the actual SOC curve and the EKF-predicted SOC curve at -30℃. (For reference) Figures 1-3 The parameters obtained by HPPC testing will deviate at different temperatures. Only when the parameters measured at room temperature are used for EKF can the estimated SOC be more in line with reality. The lower the temperature, the greater the deviation between the actual SOC curve (RealSOC) and the SOC curve predicted by EKF (EKFSOC).
[0061] Figure 4 This is a flowchart of a lithium battery state-of-charge estimation method provided by the present invention. This embodiment can be applied to correct the input parameters of the extended Kalman filter, thereby improving the accuracy of the state of charge estimated by the extended Kalman filter. This method can be executed by the lithium battery state-of-charge estimation device provided by the present invention. This device can be implemented by software and / or hardware, and is usually configured in electronic devices, such as... Figure 4 As shown, the method for estimating the state of charge of a lithium battery includes the following steps:
[0062] S101. For multiple different target temperatures, obtain the test parameters obtained by performing a mixed pulse power characteristic test on the lithium battery at the target temperature.
[0063] In this embodiment of the invention, test parameters obtained by performing hybrid pulse power characteristic testing (HPPC) on a lithium battery are acquired at multiple different target temperatures. Multiple test parameters can be collected at equal time intervals at each target temperature. Exemplarily, the test parameters may include the open-circuit voltage, ohmic internal resistance, concentration polarization resistance, tolerance polarization capacitance, electrochemical polarization resistance, and electrochemical polarization capacitance of the lithium battery, etc., and are not limited thereto in this invention.
[0064] S102. Compare the test parameters with the calibration parameters that have been calibrated in advance at the target temperature through simulation.
[0065] In this embodiment of the invention, the test parameters obtained in the above steps are compared with the calibration parameters calibrated in advance at the target temperature through simulation. For example, the HPPC test can be simulated in advance to obtain the calibration parameters calibrated at the target temperature. Then, the test parameters are compared with the calibration parameters.
[0066] For example, multiple test parameters can be fitted to obtain a curve of the test parameters as a function of time, and multiple calibration parameters can be fitted to obtain a curve of the calibration parameters as a function of time. Then, the two curves can be compared to see if they are consistent.
[0067] In some embodiments of the present invention, multiple test parameters are sorted in chronological order to obtain vector representations of the test parameters, and multiple calibration parameters are sorted in chronological order to obtain vector representations of the calibration parameters. Then, the similarity between the vector representations of the test parameters and the vector representations of the calibration parameters is calculated, and it is determined whether the similarity between the two is greater than a preset value. If so, the test parameters and calibration parameters are determined to match; if not, the test parameters and calibration parameters are determined to be mismatched.
[0068] S103. When the test parameters and calibration parameters do not match, obtain the actual dynamic parameters obtained by conducting a continuous discharge test on the lithium battery at the target temperature, and obtain the simulated dynamic parameters obtained by conducting a continuous discharge simulation test on the lithium battery at the target temperature based on the test parameters.
[0069] In this embodiment of the invention, when the test parameters and calibration parameters do not match, the lithium battery is continuously discharged at a target temperature with a set discharge current (e.g., 1 / 3C), and the discharge parameters (e.g., discharge voltage) of the lithium battery are tested as actual dynamic parameters. Furthermore, the simulated dynamic parameters (e.g., discharge voltage) are obtained by simulating the continuous discharge of the lithium battery at the target temperature with a set discharge current (e.g., 1 / 3C).
[0070] When the test parameters and calibration parameters match, it means that the obtained test parameters have no deviation or a small deviation. In this case, the test parameters are directly used as the input of the extended Kalman filter algorithm to predict the state of charge of the lithium battery.
[0071] S104. Calculate the difference between the actual dynamic parameters and the simulated dynamic parameters to obtain the correction value.
[0072] In this embodiment of the invention, the difference between the actual dynamic parameters and the simulated dynamic parameters is calculated to obtain the correction value.
[0073] S105. Correct the test parameters using correction values to obtain the corrected test parameters.
[0074] In this embodiment of the invention, the test parameters are corrected using a correction value to obtain the corrected test parameters. The correction method can be direct summation or weighted summation; this embodiment of the invention does not limit the method. For example, in one embodiment of the invention, the test parameters are added to the correction value to obtain the corrected test parameters.
[0075] S106. Use the corrected test parameters as input to the extended Kalman filter algorithm to predict the state of charge of the lithium battery.
[0076] In this embodiment of the invention, the corrected test parameters are used as input to the Extended Kalman Filter (EKF) algorithm to predict the state of charge (SOC) of the lithium battery.
[0077] For example, state equations and observation equations are pre-established based on an equivalent circuit model of a lithium battery, and the Jacobian matrices of the state equations and observation equations are calculated. The corrected test parameters and the current state are used as inputs to the state equations to predict the state at the next time step. The state includes the state of charge of the lithium battery, and the error covariance matrix is calculated based on the Jacobian matrix of the state equations. Then, the Kalman gain is calculated based on the error covariance matrix and the Jacobian matrix of the observation equations. Finally, the state at the next time step is corrected based on the Kalman gain and the state measurement, and the error covariance matrix is updated. This process is repeated iteratively.
[0078] In this embodiment of the invention, the equivalent circuit model of the lithium battery is a second-order RC model, which can further improve the accuracy of SOC prediction.
[0079] State equations are used to describe the dynamic changes of SOC and their relationship with other state variables (such as open-circuit voltage). The state equations are as follows:
[0080]
[0081] in, Estimate the prior state (including SOC) for the current moment. The state of u at the previous moment k This is the input to EKF at the current time.
[0082] The observation equation is used to describe the relationship between battery terminal voltage and SOC, current, internal resistance, etc., and the observation equation is as follows:
[0083] V k =g(SOC) k ,I k ,R)
[0084] Among them, V k The current battery terminal voltage, SOC kLet I be the SOC at the current moment. k R is the current at the current moment, and R is the internal resistance of the battery.
[0085] The calculation process for the error covariance matrix is as follows:
[0086]
[0087] Among them, F k Let be the Jacobian matrix of the state equation at the current moment. Let P be the prior error covariance matrix calculated at the current time. k-1 Let F be the error covariance matrix of the previous time step. k T For F k The transpose matrix, Q k Let be the process noise covariance matrix.
[0088] The calculation process for Kalman gain is as follows:
[0089]
[0090] Among them, K k H is the Kalman gain at the current moment. k Let R be the Jacobian matrix of the observation equation. k To observe the noise covariance matrix.
[0091] The state correction process is as follows:
[0092]
[0093] in, Z represents the corrected state at the current moment (including SOC). k The input to EKF is the corrected test parameters. These are the prediction parameters.
[0094] The error covariance matrix update process is as follows:
[0095]
[0096] Among them, P k This is the updated error covariance matrix at the current time.
[0097] The lithium battery state-of-charge (SOC) estimation method provided by this invention involves obtaining test parameters from a hybrid pulse power characteristic test of the lithium battery at multiple different target temperatures. These test parameters are then compared with calibration parameters pre-calibrated through simulation at the target temperatures. If the test parameters and calibration parameters do not match, the method acquires the actual dynamic parameters obtained from a continuous discharge test of the lithium battery at the target temperatures, as well as the simulated dynamic parameters obtained from a continuous discharge simulation test of the lithium battery at the target temperatures based on the test parameters. The difference between the actual and simulated dynamic parameters is calculated to obtain a correction value. This correction value is then used to correct the test parameters, resulting in corrected test parameters. These corrected test parameters are then used as input to an extended Kalman filter (EKF) algorithm to predict the SOC of the lithium battery. This invention corrects the test parameters obtained from the HPPC test, eliminating the temperature-induced deviation in the test parameters and thus improving the accuracy of the SOC predicted by the EKF algorithm.
[0098] Figure 5 This is a schematic diagram showing the SOC curve predicted and the actual SOC curve using the method of this invention at 25°C. Figure 6 This is a schematic diagram showing the SOC curve predicted and the actual SOC curve at -10℃ using the method of this invention. Figure 7 The diagram shows the SOC curves predicted and the actual SOC curves at -20°C using the method of this invention. (Refer to...) Figures 5-7 At different temperatures, the SOC curve (P-thermal OCV-EKFSOC) predicted by the method of this invention and the actual SOC curve (RealSOC) are highly consistent, with the maximum estimation error of SOC being only 2%.
[0099] Figure 8 This is a schematic diagram of the structure of a lithium battery state of charge estimation device provided by the present invention, as shown below. Figure 8 As shown, the lithium battery state of charge estimation device includes:
[0100] The test parameter acquisition module 201 is used to acquire test parameters obtained by performing a mixed pulse power characteristic test on a lithium battery at multiple different target temperatures.
[0101] The comparison module 202 is used to compare the test parameters with calibration parameters that have been pre-calibrated by simulation at the target temperature;
[0102] The dynamic parameter acquisition module 203 is used to acquire the actual dynamic parameters obtained by performing a continuous discharge test on the lithium battery at the target temperature when the test parameters and the calibration parameters do not match, and to acquire the simulated dynamic parameters obtained by performing a continuous discharge simulation test on the lithium battery at the target temperature based on the test parameters.
[0103] The correction value calculation module 204 is used to calculate the difference between the actual dynamic parameters and the simulated dynamic parameters to obtain the correction value;
[0104] The parameter correction module 205 is used to correct the test parameters using the correction value to obtain the corrected test parameters;
[0105] The state prediction module 206 is used to use the corrected test parameters as input to the extended Kalman filter algorithm to predict the state of charge of the lithium battery.
[0106] In some embodiments of the present invention, the comparison module 202 includes:
[0107] The first vector representation unit is used to sort the multiple test parameters in chronological order to obtain a vector representation of the test parameters;
[0108] The second vector representation unit is used to sort the multiple calibration parameters in chronological order to obtain a vector representation of the calibration parameters;
[0109] A similarity calculation unit is used to calculate the similarity between the vector representation of the test parameter and the vector representation of the calibration parameter;
[0110] A judgment unit is used to determine whether the similarity is greater than a preset value;
[0111] The first determination unit is used to determine that the test parameters and the calibration parameters match when the similarity is greater than a preset value.
[0112] The second determination unit is used to determine that the test parameters and the calibration parameters do not match when the similarity is not greater than a preset value.
[0113] In some embodiments of the present invention, the state prediction module 206 is further configured to:
[0114] When the test parameters and the calibration parameters match, the test parameters are used as input to the extended Kalman filter algorithm to predict the state of charge of the lithium battery.
[0115] In some embodiments of the present invention, the test parameters and the calibration parameters include the open-circuit voltage of the lithium battery.
[0116] In some embodiments of the present invention, the parameter correction module 205 includes:
[0117] The correction unit is used to add the test parameters to the correction value to obtain the corrected test parameters.
[0118] In some embodiments of the present invention, the state prediction module 206 includes:
[0119] The equation-establishing unit is used to establish state equations and observation equations based on the equivalent circuit model of the lithium battery.
[0120] A Jacobian matrix calculation unit is used to calculate the Jacobian matrix of the state equation and the Jacobian matrix of the observation equation;
[0121] A state prediction unit is used to take the corrected test parameters and the current state as inputs to the state equation and predict the state at the next moment, wherein the state includes the state of charge of the lithium battery.
[0122] The error covariance matrix calculation unit is used to calculate the error covariance matrix based on the Jacobian matrix of the state equation.
[0123] The Kalman gain calculation unit is used to calculate the Kalman gain based on the error covariance matrix and the Jacobian matrix of the observation equation.
[0124] The state correction unit is used to correct the state at the next moment based on the Kalman gain and the state measurement value, and to update the error covariance matrix.
[0125] In some embodiments of the present invention, the equivalent circuit model of the lithium battery is a second-order RC model.
[0126] The aforementioned lithium battery state of charge estimation device can execute the lithium battery state of charge estimation method provided in the foregoing embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the lithium battery state of charge estimation method.
[0127] Figure 9 This is a schematic diagram of an electronic device provided for an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0128] like Figure 9As shown, the electronic device includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0129] Multiple components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0130] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as lithium battery state of charge estimation methods.
[0131] In some embodiments, the lithium battery state-of-charge estimation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the lithium battery state-of-charge estimation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the lithium battery state-of-charge estimation method by any other suitable means (e.g., by means of firmware).
[0132] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0133] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0134] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0135] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0136] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0137] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0138] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the lithium battery state-of-charge estimation method as provided in any embodiment of this application.
[0139] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages or a combination thereof. Programming languages include object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0140] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.
[0141] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A method for estimating the state of charge of a lithium battery, characterized in that, include: For multiple different target temperatures, obtain the test parameters obtained by performing a mixed pulse power characteristic test on the lithium battery at the target temperature; The test parameters are compared with the calibration parameters that were previously calibrated by simulation at the target temperature; When the test parameters and the calibration parameters do not match, the actual dynamic parameters obtained by performing a continuous discharge test on the lithium battery at the target temperature are acquired, and the simulated dynamic parameters obtained by performing a continuous discharge simulation test on the lithium battery at the target temperature based on the test parameters are acquired. Calculate the difference between the actual dynamic parameters and the simulated dynamic parameters to obtain the correction value; The test parameters are corrected using the correction value to obtain the corrected test parameters; The corrected test parameters are used as input to the extended Kalman filter algorithm to predict the state of charge of the lithium battery.
2. The method for estimating the state of charge of a lithium battery according to claim 1, characterized in that, The test parameters are compared with calibration parameters that have been pre-calibrated through simulation at the target temperature, including: The test parameters are sorted in chronological order to obtain a vector representation of the test parameters; The calibration parameters are sorted in chronological order to obtain a vector representation of the calibration parameters; Calculate the similarity between the vector representation of the test parameters and the vector representation of the calibration parameters; Determine whether the similarity is greater than a preset value; If so, then the test parameters and the calibration parameters are determined to match; If not, then the test parameters and the calibration parameters are determined to be mismatched.
3. The method for estimating the state of charge of a lithium battery according to claim 1, characterized in that, Also includes: When the test parameters and the calibration parameters match, the test parameters are used as input to the extended Kalman filter algorithm to predict the state of charge of the lithium battery.
4. The method for estimating the state of charge of a lithium battery according to claim 1, characterized in that, The test parameters and calibration parameters include the open-circuit voltage of the lithium battery.
5. The method for estimating the state of charge of a lithium battery according to claim 1, characterized in that, The test parameters are corrected using the correction value to obtain the corrected test parameters, including: The corrected test parameters are obtained by adding the test parameters to the correction value.
6. The method for estimating the state of charge of a lithium battery according to claim 1, characterized in that, The corrected test parameters are used as input to the extended Kalman filter algorithm to predict the state of charge of the lithium battery, including: State equations and observation equations are established based on the equivalent circuit model of the lithium battery. Calculate the Jacobian matrix of the state equation and the Jacobian matrix of the observation equation; The corrected test parameters and the current state are used as inputs to the state equation to predict the state at the next moment, where the state includes the state of charge of the lithium battery. The error covariance matrix is calculated based on the Jacobian matrix of the state equation. The Kalman gain is calculated based on the error covariance matrix and the Jacobian matrix of the observation equation. The state at the next moment is corrected based on the Kalman gain and the state measurement, and the error covariance matrix is updated.
7. The method for estimating the state of charge of a lithium battery according to claim 6, characterized in that, The equivalent circuit model of the lithium battery is a second-order RC model.
8. A lithium battery state of charge estimation device, characterized in that, include: The test parameter acquisition module is used to acquire test parameters obtained by performing a mixed pulse power characteristic test on a lithium battery at multiple different target temperatures. The comparison module is used to compare the test parameters with calibration parameters that have been pre-calibrated by simulation at the target temperature; The dynamic parameter acquisition module is used to acquire the actual dynamic parameters obtained by performing a continuous discharge test on the lithium battery at the target temperature when the test parameters and the calibration parameters do not match, and to acquire the simulated dynamic parameters obtained by performing a continuous discharge simulation test on the lithium battery at the target temperature based on the test parameters. The correction value calculation module is used to calculate the difference between the actual dynamic parameters and the simulated dynamic parameters to obtain the correction value; The parameter correction module is used to correct the test parameters using the correction value to obtain the corrected test parameters; The state prediction module is used to predict the state of charge of the lithium battery by taking the corrected test parameters as input to the extended Kalman filter algorithm.
9. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the lithium battery state of charge estimation method as described in any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the lithium battery state-of-charge estimation method as described in any one of claims 1-7.