Method, system, device, equipment and medium for determining battery health status

Abstract

The disclosed embodiments provide a method, system, apparatus, device, and medium for determining the health status of a battery, relating to the field of battery technology. These methods address at least the problems of determining the health status of a battery, which typically involves shutting down the equipment and performing a complete low-rate charge-discharge test, resulting in low efficiency, complex operation, low accuracy, and poor interpretability. The determination method includes: in response to receiving a test instruction, obtaining a first operating data set of the battery under actual operating conditions; determining a second operating data set based on a reference sensitivity vector corresponding to a previous test instruction and the first operating data set; identifying electrochemical parameters of a physical model based on the second operating data set to obtain a target physical model; and determining the current health status of the battery based on the target physical model.

Description

Technical Field

[0001] The present disclosure relates to, but is not limited to, the field of battery technology, and in particular to a method, system, device, equipment, and medium for determining the health status of a battery. Background Art

[0002] With the rapid development of new energy batteries, the state of health (SOH) of batteries has become an important factor affecting the endurance and operating performance of equipment (for example, vehicles, aircraft, ships, etc.). Real-time understanding of the battery's SOH is crucial for improving reliability and safety.

[0003] In related technologies, when determining the SOH of a battery, the equipment is usually shut down before a complete low-rate charge and discharge test is performed. This has problems such as low efficiency, complex operation, low accuracy, and poor interpretability. Summary of the Invention

[0004] The embodiments of the present disclosure provide a method, system, apparatus, device, medium, and program product for determining the health status of a battery, so as to at least solve the problems in the related art of determining the health status of a battery by usually shutting down the equipment and performing a complete low-rate charge and discharge test, which has low efficiency, complex operation, low accuracy, and poor interpretability.

[0005] The technical solution of the embodiment of the present disclosure is implemented as follows:

[0006] An embodiment of the present disclosure provides a method for determining a battery health status, including:

[0007] In response to receiving the detection instruction, obtaining a first operating data set of the battery under actual operating conditions;

[0008] determining a second operating data set based on a reference sensitivity vector corresponding to a previous detection instruction and the first operating data set;

[0009] Identifying electrochemical parameters of the physical model based on the second operating data set to obtain a target physical model;

[0010] Based on the target physical model, a current state of health of the battery is determined.

[0011] In the embodiment of the present disclosure, first, the operating data of the battery under real working conditions is screened by referring to the sensitivity vector to obtain a second operating data set that has a high influence on the electrochemical parameters of the physical model, providing high-quality data support for the subsequent parameter identification of the physical model, thereby improving the identification effect, identification efficiency and identification accuracy; secondly, by using the operating data of the battery under real working conditions to identify the electrochemical parameters of the physical model in real time, the influence of the real working conditions on the health state of the battery is fully considered, thereby improving the accuracy of the health state predicted by the subsequent physical model; finally, the battery's health is determined by the identified target physical model. Compared with using a trained neural network model to determine the health status, this physical model can more accurately simulate the migration behavior of lithium ions in electrodes and electrolytes, and can dynamically trace and analyze various electrochemical parameters, thereby improving the interpretability and accuracy of the health status. At the same time, since the health status of the battery is determined online directly through the target physical model, there is no need to power off the device, disassemble the battery, or perform a complete low-rate charge and discharge test on the device. Therefore, it not only improves efficiency and real-time performance while reducing operational complexity, but also improves deployability.

[0012] In some embodiments, the first operating data set includes operating data at multiple sampling moments; determining the second operating data set based on the reference sensitivity vector corresponding to the previous detection instruction and the first operating data set includes: for each operating data in the first operating data set, determining the detection result of the operating data based on the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction; if the detection result of the operating data is the first detection result, using the operating data as one of the operating data in the second operating data set.

[0013] In the disclosed embodiment, the operating data at each sampling moment is screened according to the reference sensitivity vector, which not only provides high-information and high-quality data support for the parameter identification of the subsequent physical model, but also reduces the possibility of data redundancy, invalid excitation, etc., and also achieves the purpose of improving the identification effect, identification efficiency and identification accuracy. Moreover, since battery aging is essentially a continuous attenuation process, by using the reference sensitivity vector corresponding to the previous detection instruction, the health status of the previous battery is fully utilized, and the current health status of the battery can be captured more accurately, thereby achieving the purpose of effective monitoring and precise control of battery aging.

[0014] In some embodiments, determining the detection result of the operating data based on the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction includes: performing sensitivity analysis on the operating data to obtain the sensitivity vector of the operating data; determining the similarity between the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction; determining the distance between the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction; and determining the detection result of the operating data based on the similarity and the distance.

[0015] In the disclosed embodiments, on the one hand, a sensitivity analysis is performed on the operating data to systematically evaluate the degree of influence of different variables on the health status of the battery, and the key variables affecting the health status can be accurately identified; on the other hand, the detection results of the operating data are determined based on the similarity and distance between the sensitivity vector of the operating data and the reference sensitivity vector, thereby improving the accuracy of the detection results and realizing the rapid positioning of the operating data closest to the target in the operating data set, thereby achieving the purpose of efficient screening and reducing computational complexity.

[0016] In some embodiments, determining the detection result of the operating data based on the similarity and the distance includes: when the similarity is greater than a set similarity threshold and the distance is less than a set distance threshold, using the first detection result as the detection result of the operating data; when the similarity is not greater than the similarity threshold and / or the distance is not less than the distance threshold, using the second detection result as the detection result of the operating data.

[0017] In the embodiment of the present disclosure, the detection result of the operating data is determined based on whether the similarity is greater than the similarity threshold and whether the distance is less than the distance threshold, thereby realizing comparison in multiple dimensions and improving the accuracy of the detection results, thereby achieving the purpose of efficient screening.

[0018] In some embodiments, the electrochemical parameters of the physical model are identified based on the second operating data set to obtain a target physical model, including: for each round of iteration in the target round iteration, determining the current parameter value of the electrochemical parameter in the current round iteration, using the physical model to generate a first voltage sequence based on the current parameter value of the electrochemical parameter, and determining the output value of the objective function based on the first voltage sequence, the second operating data set and the reference voltage sequence corresponding to the previous detection instruction; based on the output value of the objective function in each round of iteration, the electrochemical parameters of the physical model are identified to obtain the target physical model.

[0019] In the disclosed embodiments, first, the parameter values ​​of each electrochemical parameter in each round of iteration are determined in real time, which improves the rationality, flexibility and accuracy of the parameter values ​​compared to the set values; second, the output value of the objective function is determined based on the voltage sequence generated by the physical model, the screened operating data set and the reference voltage sequence, which improves the accuracy of the objective function value; finally, each electrochemical parameter is identified based on the output value of the objective function in each round of iteration, and the parameter sensitivity is reflected in real time through the objective function value, which improves the convergence speed and the parameter identification accuracy.

[0020] In some embodiments, determining the output value of the objective function based on the first voltage sequence, the second operating data set, and the reference voltage sequence corresponding to the previous detection instruction includes: determining a voltage set based on the first voltage sequence and the second operating data set; generating a second voltage sequence based on the voltage set; and determining the output value of the objective function based on the second voltage sequence and the reference voltage sequence.

[0021] In the embodiment of the present disclosure, on the one hand, the first voltage sequence is screened according to the second operating data set to obtain a voltage set, thereby combining the actual operating conditions with the output of the physical model, thereby improving the reliability of the output of the identified physical model; on the other hand, the output value of the objective function is determined according to the voltage set and the reference voltage sequence, and the simulated output voltage and the measured voltage are compared to improve the accuracy of the output value of the objective function.

[0022] In some embodiments, the second voltage sequence includes predicted voltages at multiple moments, and the reference voltage sequence includes actual voltages at the multiple moments; determining the output value of the objective function based on the second voltage sequence and the reference voltage sequence includes: for each of the multiple moments, determining a deviation value corresponding to the moment based on the predicted voltage at the moment and the actual voltage at the moment; and determining the output value of the objective function based on the average of the deviation values ​​corresponding to each of the moments.

[0023] In the disclosed embodiment, the objective function value is determined by the mean of the voltage deviation values ​​at each moment, thereby improving the accuracy of the objective function value, providing support for the objective function to implement a closed loop of error feedback-parameter correction-physical verification, and converting abstract mathematical optimization into a quantitative expression of the electrochemical mechanism, thereby achieving the purpose of improving the accuracy and efficiency of parameter identification.

[0024] In some embodiments, determining the deviation value corresponding to the moment based on the predicted voltage at the moment and the actual voltage at the moment includes: determining the difference between the predicted voltage at the moment and the actual voltage at the moment; determining the weight corresponding to the moment; and determining the deviation value corresponding to the moment based on the difference and the weight corresponding to the moment.

[0025] In the embodiment of the present disclosure, the deviation value corresponding to each moment is determined based on the voltage difference and the weight, thereby improving the accuracy of the deviation value, thereby facilitating further adjustment of subsequent electrochemical parameters.

[0026] In some embodiments, determining the current parameter value of the electrochemical parameter in the current round of iteration includes: when the current round of iteration is the first round of iteration, using the default parameter value or the optimal parameter value corresponding to the previous detection instruction as the current parameter value; when the current round of iteration is other rounds of iteration, determining the current parameter value based on the parameter value of the electrochemical parameter in the previous round of iteration and the value range of the electrochemical parameter.

[0027] In the disclosed embodiment, the current parameter value of the electrochemical parameter is dynamically determined according to the number of iteration rounds, which improves the rationality, accuracy and flexibility of the current parameter value. At the same time, since the output value of the objective function can reflect the parameter sensitivity in real time, the parameter values ​​of each electrochemical parameter in the previous iteration can be used to achieve priority fine-tuning of highly sensitive parameters and fixed boundaries of low-sensitivity parameters, thereby providing support for improving the efficiency and accuracy of parameter identification.

[0028] In some embodiments, the electrochemical parameters of the physical model are identified based on the output value of the objective function in each round of iteration to obtain the target physical model, including: determining a target round iteration from each round of iteration based on the output value of the objective function in each round of iteration; using the parameter value of the electrochemical parameter in the target round iteration as the optimal parameter value corresponding to the detection instruction; and inputting the optimal parameter value corresponding to the detection instruction into the physical model to obtain the target physical model.

[0029] In the disclosed embodiment, the optimal parameter value is determined according to the output value of the objective function in each round of iteration, thereby improving the accuracy of the optimal parameter value and thus improving the accuracy of the target physical model.

[0030] In some embodiments, determining the current health state of the battery based on the target physical model includes: using the target physical model to simulate the discharge of the battery at a target electrical rate to generate a third voltage sequence; and determining the current health state of the battery based on the third voltage sequence.

[0031] In the disclosed embodiment, a target physical model is used to simulate the low-rate discharge process of a battery to obtain the current health status of the battery. This not only enables a rapid and accurate prediction of the health status without disconnecting the power supply to the device, but also eliminates the need to charge and discharge the device, thereby reducing interference with the operation of the device and improving the deployability of dynamic prediction of the battery's health status.

[0032] In some embodiments, the physical model includes a pseudo two-dimensional model, and the electrochemical parameters include at least one of the following: solid-phase positive electrode reaction rate constant, solid-phase negative electrode reaction rate constant, solid-phase positive electrode diffusion coefficient, solid-phase negative electrode diffusion coefficient, liquid phase diffusion coefficient, ohmic internal resistance, first positive electrode stoichiometric number, second positive electrode stoichiometric number, first negative electrode stoichiometric number, and second negative electrode stoichiometric number.

[0033] In the disclosed embodiment, by identifying the positive and negative electrode reaction rates, positive and negative electrode diffusion coefficients, liquid phase diffusion coefficients, ohmic internal resistance and stoichiometric coefficients of P2D, the physical model is simplified, and the amount of calculation and complexity are significantly reduced while ensuring accuracy.

[0034] An embodiment of the present disclosure provides a system for determining the health status of a battery, including an edge terminal and a service terminal, wherein:

[0035] The edge terminal is configured to obtain a first operating data set of the battery under actual operating conditions in response to receiving a detection instruction; and determine a second operating data set based on a reference sensitivity vector corresponding to a previous detection instruction and the first operating data set;

[0036] The server is configured to identify the electrochemical parameters of the physical model based on the second operating data set to obtain a target physical model; and determine the current health status of the battery based on the target physical model.

[0037] In the embodiment of the present disclosure, first, the edge side screens the operating data of the battery under real working conditions by referring to the sensitivity vector, and obtains a second operating data set that has a high influence on the electrochemical parameters of the physical model, providing high-quality data support for the subsequent parameter identification of the physical model, thereby improving the identification effect, identification efficiency and identification accuracy; wherein, the server side uses the operating data of the battery under real working conditions to identify the electrochemical parameters of the physical model in real time, fully considering the impact of the real working conditions on the health status of the battery, thereby improving the accuracy of the health status predicted by the subsequent physical model; finally, the server side uses the identified target physical model Compared to using a trained neural network model to determine the battery's state of health, this physical model can more accurately simulate the migration behavior of lithium ions in the electrode and electrolyte, and can dynamically trace and analyze various electrochemical parameters, improving the interpretability and accuracy of the health state. Furthermore, since the battery's health state is determined online directly through the target physical model, there is no need to power off the device, disassemble the battery, or perform a complete low-rate charge and discharge test on the device. Therefore, it not only improves efficiency and real-time performance while reducing operational complexity and enhancing deployability. Furthermore, by building a cloud-edge collaborative architecture, operating data collection and data screening are performed at the edge, and the server with more abundant computing resources is used to perform physical model parameter identification and health state prediction. Compared to performing parameter identification and prediction at the edge, this improves the real-time nature of parameter identification and efficient perception of the battery's health state, and reduces the possibility of identification and prediction failures due to insufficient computing resources.

[0038] An embodiment of the present disclosure provides a device for determining a battery health status, including:

[0039] an acquisition module, configured to acquire, in response to receiving a detection instruction, a first operating data set of the battery under actual operating conditions;

[0040] a first determining module, configured to determine a second operating data set based on a reference sensitivity vector corresponding to a previous detection instruction and the first operating data set;

[0041] an identification module, configured to identify electrochemical parameters of the physical model based on the second operating data set to obtain a target physical model;

[0042] The second determination module is configured to determine the current health state of the battery based on the target physical model.

[0043] An embodiment of the present disclosure provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, any of the above methods is implemented.

[0044] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, any of the above methods is implemented.

[0045] An embodiment of the present disclosure provides a computer program product, including a computer program or instructions, which implement any of the above methods when executed by a processor.

[0046] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0048] Figure 1 A schematic diagram of a flow chart for implementing a method for determining a battery health status provided in an embodiment of the present disclosure;

[0049] Figure 2 A schematic diagram of a current-time curve under a real working condition provided by an embodiment of the present disclosure;

[0050] Figure 3 A schematic diagram of the structure of a P2D model provided in an embodiment of the present disclosure;

[0051] Figure 4 A schematic diagram of cosine similarity between a first running data set and a reference sensitivity vector provided by an embodiment of the present disclosure;

[0052] Figure 5 A schematic diagram of a Euclidean distance between a first operating data set and a reference sensitivity vector provided by an embodiment of the present disclosure;

[0053] Figure 6 A schematic diagram of the structure of a battery health status determination system provided by an embodiment of the present disclosure;

[0054] Figure 7 A schematic block diagram of an implementation process of a method for determining a battery health status provided by an embodiment of the present disclosure;

[0055] Figure 8 A schematic diagram of a voltage sequence predicted using different methods provided in an embodiment of the present disclosure;

[0056] Figure 9 A schematic diagram of the structure of a device for determining the health status of a battery provided in an embodiment of the present disclosure;

[0057] Figure 10 A schematic diagram of a hardware entity of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting the present disclosure. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0059] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0060] In the following description, the terms "first\second\third" are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understandable that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art of the present disclosure. The terms used herein are only for the purpose of describing the embodiments of the present disclosure and are not intended to limit the present disclosure.

[0062] New energy batteries are increasingly being used in everyday life and industry. They are not only used in energy storage systems such as hydropower, thermal, wind, and solar power plants, but are also widely used in electric vehicles such as electric bicycles, electric motorcycles, and electric vehicles, as well as in aerospace and other fields. As the application areas of power batteries continue to expand, the market demand is also growing. Batteries can be single cells. A single cell is a basic unit that can convert chemical energy into electrical energy and can be used to make battery modules or battery packs, which are then used to power electrical devices. A single cell can be a secondary battery, which is a cell that can be recharged to activate the active material after discharge, allowing for continued use. Cells can be lithium-ion batteries, sodium-ion batteries, sodium-lithium-ion batteries, lithium metal batteries, sodium metal batteries, lithium-sulfur batteries, magnesium-ion batteries, nickel-metal hydride batteries, nickel-cadmium batteries, lead-acid batteries, and others. A battery can also be a single physical module comprising one or more cells to provide higher voltage and capacity. When there are multiple cells, they are connected in series, parallel, or in parallel via a busbar. The electrode plate is the main component of a single battery and directly determines the electrochemical performance and safety of the battery.

[0063] Battery SOH has become an important factor affecting the endurance and operating performance of equipment (e.g., vehicles, aircraft, ships, etc.). Real-time understanding of battery SOH is crucial for improving reliability, safety, and other aspects.

[0064] In related technologies, when determining a battery's SOH, the equipment is usually shut down before conducting a complete low-rate charge and discharge test, which results in low efficiency and complex operation. Although the equivalent circuit model is widely used due to its ease of calculation, it is difficult to accurately depict the physical and chemical changes within the battery and has a weak reflection on the capacity degradation mechanism. At the same time, although the neural network model has powerful sequence learning capabilities, the model results are difficult to explain the electrochemical processes behind them, and there are limitations in tracing parameter states and analyzing degradation mechanisms. In addition, due to the lack of full utilization of real operating data and the presence of invalid incentives, the predicted SOH has large deviations.

[0065] The embodiment of the present disclosure provides a method for determining the health state of a battery. First, the operating data of the battery under real working conditions is screened by referring to the sensitivity vector to obtain a second operating data set that has a high influence on the electrochemical parameters of the physical model, thereby providing high-quality data support for the subsequent parameter identification of the physical model, thereby improving the identification effect, identification efficiency and identification accuracy; secondly, by using the operating data of the battery under real working conditions to identify the electrochemical parameters of the physical model in real time, the influence of the real working conditions on the health state of the battery is fully considered, thereby improving the accuracy of the health state predicted by the subsequent physical model; finally, through the identified target physical Compared with using a trained neural network model to determine the health status, the model can more accurately simulate the migration behavior of lithium ions in electrodes and electrolytes, and can dynamically trace and analyze various electrochemical parameters, thereby improving the interpretability and accuracy of the health status. At the same time, since the health status of the battery is determined online directly through the target physical model, there is no need to power off the equipment, disassemble the battery, or perform a complete low-rate charge and discharge test on the equipment. Therefore, it not only improves efficiency and real-time performance while reducing operational complexity, but also improves deployability.

[0066] The methods provided in the embodiments of the present disclosure can be performed by an electronic device. The electronic device can be various types of terminals, such as a laptop computer, a tablet computer, a desktop computer, a vehicle terminal, a set-top box, a mobile device (e.g., a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, a portable gaming device), or a server. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, a content delivery network (CDN), and big data and artificial intelligence platforms.

[0067] Below, the technical solutions in the embodiments of the present disclosure will be clearly and completely described in conjunction with the drawings in the embodiments of the present disclosure.

[0068] Figure 1 A schematic diagram of a method for determining the health status of a battery according to an embodiment of the present disclosure is provided. Figure 1 As shown, the determination method includes steps S11 to S14, wherein:

[0069] Step S11: In response to receiving a detection instruction, obtaining a first operating data set of the battery under actual operating conditions.

[0070] Here, the detection instruction is mainly used to detect the SOH of the battery of the device. The detection instruction can contain any appropriate content. SOH can also be understood as the percentage of the current capacity of the battery to the factory capacity. The device can refer to a device equipped with a new energy battery, such as a vehicle, aircraft, ship, etc. The detection instruction can be triggered in any appropriate way. For example, the user clicks on "Battery Detection" on the central control screen or in the application. For another example, the user inputs the battery detection through voice. For another example, a battery detection instruction sent by other devices is received.

[0071] The actual operating condition refers to the operating condition of the equipment when it is in operation. For example, for a vehicle, its actual operating condition may refer to the operating condition of the vehicle in motion. For another example, for an aircraft, its actual operating condition may refer to the operating condition of the aircraft in flight.

[0072] In some embodiments, the first operating data set may be operating data within a preset duration. The preset duration may be any suitable duration, such as 1 hour, 2 hours, or 10 minutes. The preset duration may refer to a period starting from the current moment (i.e., the moment the detection instruction is received), a period before the current moment, or a period spanning both before and after the current moment. For example, if the preset duration is 10 minutes and the current moment is 10:00, then the preset duration may be 10:10, 9:30 to 9:40, or 9:55 to 10:05. In some embodiments, the detection instruction may include the preset duration. In some embodiments, the preset duration may be a pre-set duration. In some embodiments, the preset duration may be dynamically adjusted through a configuration interface, file, or other means. It is understood that the device must be in operation during the preset duration.

[0073] In some embodiments, the first operating data set may also be operating data within a preset mileage, which may be any appropriate mileage, such as 100 kilometers, 300 kilometers, etc.

[0074] In some embodiments, the first operating data set may include operating data at multiple sampling moments. It is understandable that the first operating data set may be a continuous period of operating data. The operating data mainly refers to the operating data of the battery, and the operating data of the battery may include but is not limited to voltage, current, temperature, etc. In some embodiments, the first operating data set may include a voltage-time curve, a current-time curve, a temperature-time curve, etc. within a preset time period. Figure 2 As shown, the first operating data set may include a current-time curve 21 of the battery.

[0075] The first operating data set may be obtained in any suitable manner. For example, the first operating data set within a preset time period may be read from the vehicle's BMS (Battery Management System). Another example is receiving the first operating data set sent by another device.

[0076] Step S12: Determine a second operating data set based on the reference sensitivity vector corresponding to the previous detection instruction and the first operating data set.

[0077] Here, the previous detection instruction refers to a detection instruction before the detection instruction. It is understandable that if the detection instruction is the first detection instruction, then the previous detection instruction is empty. At this time, the reference sensitivity vector corresponding to the previous detection instruction can be a default reference sensitivity vector; if the detection instruction is not the first detection instruction, then the previous detection instruction is not empty. At this time, the standard reference sensitivity vector corresponding to the SOH determined by the previous detection instruction can be used as the reference sensitivity vector. It is understandable that the default reference sensitivity vector can be a standard reference sensitivity vector corresponding to an SOH of 100%, and the standard reference sensitivity vector can be an average vector. In some embodiments, a correspondence between each SOH and each standard reference sensitivity vector can be established in advance. According to the correspondence, a standard reference sensitivity vector matching the SOH can be obtained.

[0078] The second operating data set includes at least one operating data. Each operating data in the second operating data set can be continuous or discrete. Each operating data in the second operating data set is an operating data in the first operating data set, and each operating data corresponds to a sampling time.

[0079] The second operating data set may be determined in any appropriate manner.

[0080] In some implementations, the reference sensitivity vector may be used to screen the various operating data in the first operating data set, and the high-information, high-quality, and critical operating data in the first operating data set may be added to the second operating data set.

[0081] In some embodiments, a correspondence between each reference sensitivity vector, each first operating data set, and each second operating data set can be pre-established. Based on the correspondence, a second operating data set that matches both the reference sensitivity vector and the first operating data set can be obtained.

[0082] In some embodiments, the second operational data set may be determined based on a reference sensitivity vector and sensitivity vectors of each operational data item in the first operational data set. During implementation, whether to include an operational data item in the second operational data set may be determined based on, for example, a distance or similarity between the reference sensitivity vector and the operational data item.

[0083] Step S13: Based on the second operating data set, identify the electrochemical parameters of the physical model to obtain a target physical model.

[0084] Here, the physical model can be any suitable model based on physical mechanisms, such as a P2D model (Pseudo Two-Dimensional Model) or a single-particle model. It is understood that different physical models may include different electrochemical parameters. The number of electrochemical parameters may be multiple.

[0085] In some embodiments, the physical model includes a pseudo-two-dimensional model, and the electrochemical parameters include at least one of the following: solid-phase positive electrode reaction rate constant, solid-phase negative electrode reaction rate constant, solid-phase positive electrode diffusion coefficient, solid-phase negative electrode diffusion coefficient, liquid phase diffusion coefficient, ohmic internal resistance, first positive electrode stoichiometric number, second positive electrode stoichiometric number, first negative electrode stoichiometric number, and second negative electrode stoichiometric number.

[0086] Here, the P2D model coupling mechanism includes the electrolyte phase (liquid phase) diffusion equation, the electrode particle (solid phase) diffusion equation, the charge conservation equation, the Butler-Volmer equation, etc. Figure 3 In the P2D model shown, the battery structure is simplified into five parts: a porous positive electrode 31, a porous negative electrode 32, a separator 33 and current collectors 34 on both sides.

[0087] The reaction rate constant is used to control the electrode interface reaction kinetics rate and satisfy the Butler-Volmer equation. The reaction rate constant includes the solid phase positive electrode reaction rate constant and solid-phase negative electrode reaction rate constant .

[0088] The solid phase diffusion coefficient describes the diffusion ability of lithium ions inside the active particles. The solid phase diffusion coefficient can include the solid phase positive electrode diffusion coefficient and the solid-phase negative electrode diffusion coefficient .

[0089] Liquid phase diffusion coefficient Describes the diffusion capacity of lithium ions in the electrolyte, which is affected by porosity and temperature.

[0090] Ohmic internal resistance This may include current collector resistance, electrode coating resistance, etc.

[0091] Stoichiometric number It refers to the ratio of the initial available lithium ion amount to the maximum available lithium ion amount. In some embodiments, the stoichiometric number can be defined by the following formula (1-1): ,Right now:

[0092] (1-1);

[0093] in, Indicates the number of lithium ions on the surface of electrode particles, Indicates the maximum number of lithium ions that the electrode particles can accommodate.

[0094] In the P2D model, the stoichiometric number may include the first positive electrode stoichiometric number , the second positive electrode stoichiometric number , the first negative electrode stoichiometric number and the second negative electrode stoichiometric number The subscripts 0 and 1 represent the lithium intercalation state of the electrode. For example, p0 means that the positive electrode is fully intercalated with lithium ions, and the lithium intercalation state reaches 100%. For another example, n1 means that the negative electrode is not intercalated with lithium ions, and the lithium intercalation state is 0%.

[0095] In this way, by identifying the positive and negative electrode reaction rates, positive and negative electrode diffusion coefficients, liquid phase diffusion coefficients, ohmic internal resistance and stoichiometric coefficients of P2D, the physical model is simplified, and the amount of calculation and complexity are significantly reduced while ensuring accuracy.

[0096] The target physical model refers to a model obtained by identifying the various electrochemical parameters of the physical model. In some embodiments, the electrochemical parameters can be identified using a suitable identification method. The identification method may include, but is not limited to, a Bayesian estimation algorithm, a wolf pack algorithm, a genetic algorithm, a particle swarm algorithm, and the like.

[0097] In some embodiments, a target round of iterations can be performed. In each round of iteration, the parameter values ​​of the electrochemical parameters in the current round of iteration are first determined. Then, based on the first voltage sequence generated by the physical model in the current round of iteration, the second operating data set, and the reference voltage sequence corresponding to the previous detection instruction, the output value of the objective function is determined. Finally, based on the output value of the objective function in each round of iteration, the optimal parameter value of the electrochemical parameter is determined. The target round of iterations can be any suitable number of rounds, for example, 40 rounds, 50 rounds, etc. The reference voltage sequence corresponding to the previous detection instruction can be a standard reference voltage sequence corresponding to a 100% SOH, or a standard reference voltage sequence corresponding to the SOH determined by the previous detection instruction. In some embodiments, a correspondence between each SOH and each reference voltage sequence can be pre-established. Based on this correspondence, a standard reference voltage sequence matching the SOH can be obtained. The objective function is used to measure the error between the output voltage of the physical model and the reference voltage. The objective function can be any suitable function, for example, root mean square error, residual sum of squares, mean square error, mean absolute error, etc.

[0098] Step S14: Determine the current health status of the battery based on the target physical model.

[0099] Here, the current SOH may be any appropriate SOH, for example, 90%, 80%, etc. The current SOH may be determined in any appropriate manner.

[0100] In some embodiments, step S14 includes step S141 and step S142, wherein:

[0101] Step S141: Using the target physical model, simulating battery discharge at a target low rate to generate a third voltage sequence;

[0102] Step S142: Determine the current health state of the battery based on the third voltage sequence.

[0103] Here, the target low rate may be any appropriate low rate, for example, C / 3, 0.05 C, etc. The third voltage sequence includes voltage values ​​at multiple moments, and each voltage in the third voltage sequence is continuous.

[0104] The current SOH may be determined by, but is not limited to, a ratio between the current total power and the maximum total power, a weighting of the ratio, etc. The current total power is generated based on the third voltage sequence. In some embodiments, the third voltage sequence may be integrated over time, and the product of the integrated value and the current value corresponding to the target low rate may be used as the current total power.

[0105] In the disclosed embodiment, a target physical model is used to simulate the low-rate discharge process of a battery to obtain the current health status of the battery. This not only enables a rapid and accurate prediction of the health status without disconnecting the power supply to the device, but also eliminates the need to charge and discharge the device, thereby reducing interference with the operation of the device and improving the deployability of dynamic prediction of the battery's health status.

[0106] In some implementations, a correspondence between each target physical model and each SOH may be established, and based on the correspondence, an SOH adapted to the target physical model may be obtained.

[0107] In the embodiment of the present disclosure, first, the operating data of the battery under real working conditions is screened by referring to the sensitivity vector to obtain a second operating data set that has a high influence on the electrochemical parameters of the physical model, providing high-quality data support for the subsequent parameter identification of the physical model, thereby improving the identification effect, identification efficiency and identification accuracy; secondly, by using the operating data of the battery under real working conditions to identify the electrochemical parameters of the physical model in real time, the influence of the real working conditions on the health state of the battery is fully considered, thereby improving the accuracy of the health state predicted by the subsequent physical model; finally, the battery's health is determined by the identified target physical model. Compared with using a trained neural network model to determine the health status, this physical model can more accurately simulate the migration behavior of lithium ions in electrodes and electrolytes, and can dynamically trace and analyze various electrochemical parameters, thereby improving the interpretability and accuracy of the health status. At the same time, since the health status of the battery is determined online directly through the target physical model, there is no need to power off the device, disassemble the battery, or perform a complete low-rate charge and discharge test on the device. Therefore, it not only improves efficiency and real-time performance while reducing operational complexity, but also improves deployability.

[0108] In some embodiments, the first operating data set includes operating data at multiple sampling moments, and step S12 includes step S121, wherein:

[0109] Step S121: For each operating data in the first operating data set, determine the detection result of the operating data based on the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction. When the detection result of the operating data is the first detection result, the operating data is used as one of the operating data in the second operating data set.

[0110] Here, the sampling time is determined according to the sampling frequency, and the sampling frequency may be any appropriate frequency, for example, 1 second (s), 0.5 s, etc.

[0111] The detection results may include, but are not limited to, a first detection result and a second detection result. The first detection result indicates that the operating data is a data point with high information, and the second detection result indicates that the operating data is a data point with low information. The detection results may be determined by, but are not limited to, the similarity between the sensitivity vector of the operating data and a reference sensitivity vector, the distance between the sensitivity vector of the operating data and the reference sensitivity vector, and the like.

[0112] For example, if the distance between the sensitivity vector of a certain operation data and the reference sensitivity vector is less than a distance threshold, the operation data is used as an operation data in the second operation data set. The distance threshold can be any suitable small value, such as 0.2, 0.4, 0.5, etc.

[0113] For another example, if the similarity between the sensitivity vector of a certain operation data and the reference sensitivity vector is greater than a similarity threshold, the operation data is included as one of the operation data in the second operation data set. The similarity threshold can be any suitable large value, such as 0.8, 0.85, 0.9, etc.

[0114] For example, if the distance between the sensitivity vector of a certain operating data and the reference sensitivity vector is less than the distance threshold, and the similarity between the sensitivity vector of the operating data and the reference sensitivity vector is greater than the similarity threshold, then the operating data is taken as one of the operating data in the second operating data set.

[0115] It is understandable that when the detection result of the operating data is the second detection result, the operating data is not added to the second operating data set. The second operating data set is empty by default.

[0116] In the disclosed embodiment, the operating data at each sampling moment is screened according to the reference sensitivity vector, which not only provides high-information and high-quality data support for the parameter identification of the subsequent physical model, but also reduces the possibility of data redundancy, invalid excitation, etc., and also achieves the purpose of improving the identification effect, identification efficiency and identification accuracy. Moreover, since battery aging is essentially a continuous attenuation process, by using the reference sensitivity vector corresponding to the previous detection instruction, the health status of the previous battery is fully utilized, and the current health status of the battery can be captured more accurately, thereby achieving the purpose of effective monitoring and precise control of battery aging.

[0117] In some embodiments, the step S121 of “determining a detection result of the operating data based on the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction” includes steps S1211 to S1214, wherein:

[0118] Step S1211: Perform sensitivity analysis on the operating data to obtain a sensitivity vector of the operating data;

[0119] Step S1212: determining the similarity between the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction;

[0120] Step S1213: determining the distance between the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction;

[0121] Step S1214: Determine the detection result of the running data based on the similarity and distance.

[0122] Here, sensitivity analysis may include, but is not limited to, single-parameter sensitivity analysis, multi-parameter sensitivity analysis, and global sensitivity analysis (GSA). GSA may include, but is not limited to, eFAST (Extended Fourier Amplitude Sensitivity Test), the Morris method, and the Sobol method. In some embodiments, eFAST may be performed on the operating data to obtain a sensitivity vector for the operating data.

[0123] Similarity is used to measure the similarity of two vectors in direction. The similarity may include but is not limited to cosine similarity, Pearson correlation coefficient, etc. In some embodiments, the cosine similarity between the sensitivity vector of each operation data in the first operation data set and the reference sensitivity vector may be measured. Figure 4 As shown, the curve 41 describes the cosine similarity between the sensitivity vector of the operating data sampled at each sampling moment and the reference sensitivity vector.

[0124] Distance is used to measure the modulus between two vectors. The distance may include but is not limited to Euclidean distance, Ming distance, Manhattan distance, etc. In some embodiments, the Euclidean distance between the sensitivity vector of each operation data in the first operation data set and the reference sensitivity vector may be measured. Figure 5 As shown, the curve 51 describes the Euclidean distance between the sensitivity vector of the operating data sampled at each sampling time and the reference sensitivity vector.

[0125] The detection result can be determined in any suitable manner.

[0126] In some embodiments, step S1214 includes step S12141 and / or step S12142, wherein:

[0127] Step S12141: When the similarity is greater than the set similarity threshold and the distance is less than the set distance threshold, the first detection result is used as the detection result of the operation data;

[0128] Step S12142: When the similarity is not greater than the similarity threshold and / or the distance is not less than the distance threshold, the second detection result is used as the detection result of the operation data.

[0129] Here, the distance threshold may be any suitable smaller value, such as 0.2, 0.4, 0.5, etc. The similarity threshold may be any suitable larger value, such as 0.8, 0.85, 0.9, etc. It is understood that the distance threshold and the similarity threshold may be dynamically adjusted.

[0130] In this way, the detection results of the running data are determined based on whether the similarity is greater than the similarity threshold and whether the distance is less than the distance threshold, which realizes comparison in multiple dimensions and improves the accuracy of the detection results, thereby achieving the purpose of efficient screening.

[0131] In some implementations, a correspondence between various similarities, various distances, and various detection results may be pre-established, and based on the correspondence, a detection result that is compatible with both the similarity and the distance may be obtained.

[0132] In the disclosed embodiments, on the one hand, a sensitivity analysis is performed on the operating data to systematically evaluate the degree of influence of different variables on the health status of the battery, and the key variables affecting the health status can be accurately identified; on the other hand, the detection results of the operating data are determined based on the similarity and distance between the sensitivity vector of the operating data and the reference sensitivity vector, thereby improving the accuracy of the detection results and realizing the rapid positioning of the operating data closest to the target in the operating data set, thereby achieving the purpose of efficient screening and reducing computational complexity.

[0133] In some embodiments, step S13 includes step S131 and step S132, wherein:

[0134] Step S131: For each round of iteration in the target round, determine the current parameter value of the electrochemical parameter in the current round, generate a first voltage sequence based on the current parameter value of the electrochemical parameter using a physical model, and determine the output value of the objective function based on the first voltage sequence, the second operating data set, and the reference voltage sequence corresponding to the previous detection instruction.

[0135] Here, the way of determining the current parameter value can be any appropriate way. In some embodiments, for non-first round iterations, the current parameter value of this round iteration can be dynamically determined based on the parameter value of the previous round iteration and the value range of the electrochemical parameter. In some embodiments, for the first round iteration, the optimal parameter value corresponding to the previous detection instruction, the default parameter value, or a random value from the value range of the electrochemical parameter can be used as the current parameter value. It can be understood that the value ranges of different electrochemical parameters can be the same or different. In some embodiments, the value range of the solid phase diffusion coefficient can be [1e-13, 1e-11], the value range of the liquid phase diffusion coefficient can be [1e-11, 1e-9], the value range of the reflection rate constant can be [1e-9, 1e-2], the ohmic internal resistance ... ohmic internal resistance can be [1e-13, 1e-11], the ohmic internal resistance can be [1e-11, 1e-9], the ohmic internal resistance can be [1e-11, 1e-9], the ohmic internal resistance can be [1e-11, 1e-9], the ohmic internal resistance can be [1e-11, 1e-9], the ohmic internal resistance can be [1e-11, 1e-9], the ohmic internal resistance can be [1e-11, 1e-9], the ohmic internal resistance can be [1e-11, 1e-2], the ohmic internal resistance can be [1e-11, 1e-9], the ohmic internal resistance can be [1e The value range can be [1e-8, 1e-4], and The value range can be [0.01, 0.30], and The value range of can be [0.65, 0.99].

[0136] The first voltage sequence refers to the simulated voltage sequence output by the physical model according to the current parameter values.

[0137] The reference voltage sequence can be a standard reference voltage sequence corresponding to a SOH of 100%, or a standard reference voltage sequence corresponding to a SOH determined by a previous detection instruction. It is understood that the length of the reference voltage sequence is the same as the length of the first voltage sequence. The reference voltage sequence includes measured voltages at multiple moments.

[0138] The objective function may be any suitable function. For example, root mean square error, residual sum of squares, mean square error, mean absolute error, etc. The output value of the objective function may be determined in any suitable manner. In some embodiments, a correspondence between each first voltage sequence, each second operating data set, each reference voltage sequence, and each output value may be pre-established. Based on this correspondence, an output value that is compatible with the first voltage sequence, the second operating data set, and the reference voltage sequence may be obtained.

[0139] In some embodiments, a second voltage sequence can be first determined based on the first voltage sequence and the second operating data set; then, an output value of the objective function can be determined based on the second voltage sequence and a reference voltage sequence. The second voltage sequence has the same length as the reference voltage sequence. The second voltage sequence can be generated based on a voltage set obtained by filtering the first voltage sequence with the second operating data set.

[0140] Step S132: Based on the output value of the objective function in each round of iteration, the electrochemical parameters of the physical model are identified to obtain the target physical model.

[0141] Here, the optimal parameter values ​​can be selected from the parameter values ​​obtained during multiple iterations based on the output values ​​of the objective function in each iteration. For example, the parameter value corresponding to the minimum output value can be selected as the optimal parameter value. In another example, the parameter value closest to the target output value can be selected as the optimal parameter value. During implementation, the optimal parameter values ​​can be used as the parameter values ​​of the electrochemical parameters to obtain the target physical model.

[0142] In the disclosed embodiments, first, the parameter values ​​of each electrochemical parameter in each round of iteration are determined in real time, which improves the rationality, flexibility and accuracy of the parameter values ​​compared to the set values; second, the output value of the objective function is determined based on the voltage sequence generated by the physical model, the screened operating data set and the reference voltage sequence, which improves the accuracy of the objective function value; finally, each electrochemical parameter is identified based on the output value of the objective function in each round of iteration, and the parameter sensitivity is reflected in real time through the objective function value, which improves the convergence speed and the parameter identification accuracy.

[0143] In some embodiments, the step S131 of “determining the current parameter value of the electrochemical parameter in the current iteration” includes steps S1311 and S1312, wherein:

[0144] Step S1311: When the current iteration is the first iteration, the default parameter value or the optimal parameter value corresponding to the previous detection instruction is used as the current parameter value;

[0145] Step S1312: When the current iteration is another iteration, the current parameter value is determined based on the parameter value of the electrochemical parameter in the previous iteration and the value range of the electrochemical parameter.

[0146] Here, the default parameter value may be pre-set. It is understood that the default parameter value is within the value range of the electrochemical parameter.

[0147] The optimal parameter value corresponding to the previous detection instruction refers to the optimal parameter value of the physical model determined when the SOH was determined last time. It is understandable that the optimal parameter value corresponding to the previous detection instruction is also within the value range of the electrochemical parameter.

[0148] For iterations other than the first, the current parameter value can be determined in any suitable manner. The current parameter value can be greater than, less than, or the same as the parameter value of the previous iteration. The magnitude of the increase or decrease can be random or set.

[0149] For example, the parameter values ​​of some electrochemical parameters may be fixed unchanged, and the parameter values ​​of other electrochemical parameters may be increased based on the parameter values ​​of the previous iteration, and the increased parameter values ​​are within the corresponding value range.

[0150] For another example, the parameter value of the previous iteration may be increased, and the increased parameter value is within the corresponding value range.

[0151] For another example, the parameter value of the previous iteration may be reduced, and the reduced parameter value is within the corresponding value range.

[0152] For another example, some parameters may be reduced based on the parameter values ​​of the previous iteration, and some other electrochemical parameters may be increased based on the parameter values ​​of the previous iteration.

[0153] In the disclosed embodiment, the current parameter value of the electrochemical parameter is dynamically determined according to the number of iteration rounds, which improves the rationality, accuracy and flexibility of the current parameter value. At the same time, since the output value of the objective function can reflect the parameter sensitivity in real time, the parameter values ​​of each electrochemical parameter in the previous iteration can be used to achieve priority fine-tuning of highly sensitive parameters and fixed boundaries of low-sensitivity parameters, thereby providing support for improving the efficiency and accuracy of parameter identification.

[0154] In some embodiments, the step S131 of “determining the output value of the objective function based on the first voltage sequence, the second operating data set, and the reference voltage sequence corresponding to the previous detection instruction” includes steps S141 to S143, wherein:

[0155] Step S141, determining a voltage set based on the first voltage sequence and the second operating data set;

[0156] Step S142: generating a second voltage sequence based on the voltage set;

[0157] Step S143: Determine an output value of the objective function based on the second voltage sequence and the reference voltage sequence.

[0158] Here, a voltage set includes multiple voltages. The voltage set can be determined in any suitable manner. In some embodiments, a correspondence between each first voltage sequence, each second operating data set, and each voltage set can be pre-established. Based on this correspondence, a voltage set that matches both the first voltage sequence and the second operating data set can be obtained. In some embodiments, the voltages in the first voltage sequence can be screened based on the sampling time corresponding to each operating data in the second operating data set, and the voltage at the same sampling time is selected as a voltage in the voltage set.

[0159] The second voltage sequence includes predicted voltages at each moment. The second voltage sequence can be generated in any suitable manner. In some embodiments, the individual voltages in the voltage set can be interpolated to obtain the second voltage sequence. In some embodiments, the individual voltages in the voltage set can also be convolved to obtain the second voltage sequence.

[0160] Methods for determining the output value of the objective function may include, but are not limited to, taking the square root of the mean, weighting the square root of the mean, and the like. The mean refers to the average of the deviation values, and the deviation value is determined based on the difference between the predicted voltage at a time point in the second voltage sequence and the measured voltage at a time point in the reference voltage sequence.

[0161] In the embodiment of the present disclosure, on the one hand, the first voltage sequence is screened according to the second operating data set to obtain a voltage set, thereby combining the actual operating conditions with the output of the physical model, thereby improving the reliability of the output of the identified physical model; on the other hand, the output value of the objective function is determined according to the voltage set and the reference voltage sequence, and the simulated output voltage and the measured voltage are compared to improve the accuracy of the output value of the objective function.

[0162] In some embodiments, the second voltage sequence includes predicted voltages at multiple moments, and the reference voltage sequence includes actual voltages at multiple moments; step S143 includes step S1431 and step S1432, wherein:

[0163] Step S1431: for each of the multiple moments, determine a deviation value corresponding to the moment based on the predicted voltage at the moment and the actual voltage at the moment;

[0164] Step S1432: Determine the output value of the objective function based on the mean value between the deviation values ​​corresponding to each moment.

[0165] Here, the predicted voltage at each moment in the second voltage sequence corresponds to an actual voltage in the reference voltage sequence. The length of the second voltage sequence is the same as that of the reference voltage sequence. The reference voltage sequence is obtained by performing a target low-rate charge and discharge reference performance test (RPT) on the battery before installation.

[0166] Methods for determining the deviation value may include, but are not limited to, the first product, a weighted version of the first product, and the like. The first product refers to the product of the weight at the time and the first value. The first value is determined based on the difference between the predicted voltage at the time and the actual voltage at the time. For example, the first value may be the square of the difference. In another example, the first value may be the weighted version of the square of the difference.

[0167] Methods for determining the output value of the objective function may include, but are not limited to, square root of the mean, weighted square root of the mean, and the like.

[0168] In some embodiments, the objective function can be expressed by the following formula (1-2): ,Right now:

[0169] (1-2);

[0170] in, Indicates the The predicted voltage at the moment, Indicates the The measured voltage at a moment, Indicates the The weight corresponding to each moment, Indicates the length of the voltage sequence.

[0171] In the disclosed embodiment, the objective function value is determined by the voltage deviation value at each moment, which improves the accuracy of the objective function value, provides support for the objective function to achieve a closed loop of error feedback-parameter correction-physical verification, and transforms abstract mathematical optimization into a quantitative expression of the electrochemical mechanism, thereby achieving the purpose of improving the accuracy and efficiency of parameter identification.

[0172] In some embodiments, the step S1431 of “determining the deviation value corresponding to the time instant based on the predicted voltage at the time instant and the actual voltage at the time instant” includes steps S151 to S153, wherein:

[0173] Step S151, determining the difference between the predicted voltage at the time instant and the actual voltage at the time instant;

[0174] Step S152: Determine the weight corresponding to the moment;

[0175] Step S153: Determine the deviation value corresponding to the moment based on the difference and the weight corresponding to the moment.

[0176] Here, different moments can correspond to the same or different weights. In some embodiments, the weight of the sampling moment corresponding to each operating data in the second operating data can be much greater than the weights of the sampling moments corresponding to other operating data. For example, if the moment coincides with the sampling moment corresponding to a certain operating data in the second operating data set, the weight of the moment can be 1, 0.99, etc.; if the moment is different from the sampling moment corresponding to any operating data in the second operating data set, the weight of the moment can be 0, 0.01, etc.

[0177] Methods for determining the deviation value may include, but are not limited to, the first product, a weighted version of the first product, and the like. The first product refers to the product of the weight at the moment and the first value. The first value is determined based on the difference. Methods for determining the first value may include, but are not limited to, the square of the difference, a weighted version of the square of the difference, and the like.

[0178] In the embodiment of the present disclosure, the deviation value corresponding to each moment is determined based on the voltage difference and the weight, thereby improving the accuracy of the deviation value, thereby facilitating further adjustment of subsequent electrochemical parameters.

[0179] In some embodiments, step S132 includes steps S1321 to S1323, wherein:

[0180] Step S1321: Determine a target round iteration from each round of iteration based on the output value of the objective function in each round of iteration;

[0181] Step S1322: taking the parameter value of the electrochemical parameter in the target round iteration as the optimal parameter value corresponding to the detection instruction;

[0182] Step S1323: Input the optimal parameter value corresponding to the detection instruction into the physical model to obtain the target physical model.

[0183] Here, the target iteration can be the iteration with the smallest output value, or the iteration with the output value closest to the target value. For example, the iteration with the smallest output value is used as the target iteration. During implementation, the optimal parameter values ​​are input into the physical model to obtain the target physical model. It is understood that the optimal parameter values ​​are the parameter values ​​obtained after the electrochemical parameters are identified.

[0184] In the disclosed embodiment, the optimal parameter value is determined according to the output value of the objective function in each round of iteration, thereby improving the accuracy of the optimal parameter value and thus improving the accuracy of the target physical model.

[0185] Based on the above embodiments, the present disclosure also provides a system for determining the health status of a battery. Figure 6 A schematic diagram of the structure of a battery health status determination system provided by an embodiment of the present disclosure is shown in FIG. Figure 6 As shown, the battery health status determination system 60 includes an edge terminal 61 and a service terminal 62, wherein:

[0186] The edge terminal 61 is configured to obtain a first operating data set of the battery under actual operating conditions in response to receiving a detection instruction; and determine a second operating data set based on a reference sensitivity vector corresponding to a previous detection instruction and the first operating data set;

[0187] The server 62 is configured to identify the electrochemical parameters of the physical model based on the second operating data set to obtain a target physical model; and determine the current health state of the battery based on the target physical model.

[0188] Here, the edge end can be any suitable device end, such as a vehicle, an airplane, a ship, etc. It is understandable that if the edge end includes a battery, the SOH of the battery of the edge end can be predicted, and the SOH of the batteries of other edge ends can also be predicted, which is not limited in the embodiments of the present disclosure.

[0189] The detection instruction is mainly used to detect the SOH of the battery. During implementation, the process of the edge terminal 61 acquiring the first operating data set can refer to the specific implementation of the aforementioned step S11.

[0190] The first operating data set includes operating data at multiple sampling moments.

[0191] The second operating data set includes at least one operating data. Each operating data in the second operating data set can be continuous or discrete. Each operating data in the second operating data set is a high-information, high-quality, and key data point.

[0192] In some embodiments, before the batteries leave the factory, a batch of batteries are subjected to a low-rate charge and discharge RPT at preset step intervals from high to low according to the SOH to obtain a response platform. Then, a sensitivity analysis is performed on the electrochemical parameters of the physical model for the response platform to obtain the standard reference sensitivity vector corresponding to each SOH. The response platform includes a data table under each SOH, which may include but is not limited to a data table between voltage and time, a correspondence table between current and time, a correspondence table between capacity and time, etc. The preset step size can be any suitable step size, for example, 1%, 2%, etc. Sensitivity analysis may include but is not limited to single-parameter sensitivity analysis, multi-parameter sensitivity analysis, GSA, etc. In some embodiments, the physical model can be eFASTed according to the capacity-time under each SOH to obtain a reference sensitivity vector corresponding to each SOH. .

[0193] In some embodiments, a sliding window or segmented GSA may be performed on all the operating data in the first operating data set to obtain a sensitivity vector of each operating data. In implementation, according to the Corresponding to the previous detection instruction The second running data set is determined based on the similarity, distance, etc. During implementation, the process of the edge terminal 61 determining the second running data set can refer to the specific implementation of the aforementioned step S12.

[0194] The server 62 may be any suitable device with strong computing power. For example, the server 62 may be a cloud.

[0195] The physical model may be any suitable model based on physical mechanisms, such as a P2D model, a single particle model, etc. In implementation, the process of identifying the electrochemical parameters of the physical model by the server 62 may refer to the specific implementation of the aforementioned step S13.

[0196] The current SOH may be any appropriate SOH, for example, 90%, 80%, etc. In implementation, the process of the server 62 determining the current SOH may refer to the specific implementation of the aforementioned step S14.

[0197] Figure 7 A schematic block diagram of an implementation process of a method for determining the health status of a battery provided in an embodiment of the present disclosure, such as Figure 7 Shown, including:

[0198] Step S71, perform RPT of C / 3 on the battery;

[0199] Here, the equipment performs RPT before the battery is loaded onto the vehicle to obtain the battery capacity value.

[0200] Step S72: eFAST analysis of capacity;

[0201] Here, eFAST analysis is performed on the capacity obtained from the PRT to obtain the reference sensitivity vectors corresponding to each SOH. During implementation, eFAST analysis can be performed on the equipment before the battery is installed on the vehicle, or on the vehicle after the battery is installed.

[0202] Step S73: collecting real operation data (corresponding to the first operation data set);

[0203] Here, the data may be collected after the vehicle receives the detection instruction, or may be collected in real time.

[0204] Step S74: eFAST analysis;

[0205] Here, the vehicle performs eFAST analysis on the real operating data to obtain the sensitivity vector of the real operating data;

[0206] Step S75: data screening;

[0207] Here, the vehicle calculates the similarity and distance between the sensitivity vector of the real operating data and the reference sensitivity vector, and filters out high-information data (corresponding to the second operating data set);

[0208] Step S76: parameter identification;

[0209] Here, the cloud uses the Bayesian estimation algorithm to identify the electrochemical parameters of the P2D model based on high-information data to obtain the target P2D. During implementation, the physical model can be established first, and then the objective function shown in formula (1-2) can be constructed. The Bayesian estimation algorithm is used to perform a global search in multiple parameter dimensions to minimize the deviation between the predicted voltage output by the P2D simulation and the actual voltage. The physical model includes 10 electrochemical parameters, namely: In the iterative optimization, the filtered operating data are given a higher weight, and the remaining operating data are given a low or zero weight, and finally a set of electrochemical parameters related to capacity identified by the real operating data is obtained. .

[0210] Step S77, SOH prediction;

[0211] Here, the cloud uses the target P2D to simulate the discharge process of C / 3 to obtain the predicted SOH. The model obtained after importing the P2D model.

[0212] Step S78: Compare.

[0213] Here, the predicted SOH output by the P2D model is compared with the experimental SOH obtained by RPT to evaluate the P2D model's prediction accuracy. During implementation, this comparison can be performed in the cloud: the cloud obtains the actual SOH from the device and then compares the actual SOH with the predicted SOH to evaluate the P2D model's prediction accuracy. If the predicted accuracy exceeds the set accuracy, the P2D model's electrochemical parameters, target number of iterations, and other factors can be adjusted to ensure that the P2D model's prediction accuracy does not exceed the preset accuracy. The preset accuracy can be any suitable sufficiently small value, such as 0.5 or 0.89.

[0214] Figure 8 A schematic diagram of a voltage sequence predicted by different methods provided in an embodiment of the present disclosure is shown in FIG. Figure 8 As shown, first curve 81 is the voltage-time curve obtained by performing RPT on the battery at the target rate; second curve 82 is the predicted voltage-time curve generated by the P2D model at the target low rate after directly identifying the P2D model using the first operating data set; third curve 83 is the predicted voltage-time curve generated by the P2D model at the target low rate after parameter identification using the second operating data set. As can be seen from the figure, third curve 83 fits the first curve 81 more closely, with a smaller error than the first curve 81.

[0215] In the embodiment of the present disclosure, first, the edge side screens the operating data of the battery under real working conditions by referring to the sensitivity vector, and obtains a second operating data set that has a high influence on the electrochemical parameters of the physical model, providing high-quality data support for the subsequent parameter identification of the physical model, thereby improving the identification effect, identification efficiency and identification accuracy; wherein, the server side uses the operating data of the battery under real working conditions to identify the electrochemical parameters of the physical model in real time, fully considering the impact of the real working conditions on the health status of the battery, thereby improving the accuracy of the health status predicted by the subsequent physical model; finally, the server side uses the identified target physical model Compared to using a trained neural network model to determine the battery's state of health, this physical model can more accurately simulate the migration behavior of lithium ions in the electrode and electrolyte, and can dynamically trace and analyze various electrochemical parameters, improving the interpretability and accuracy of the health state. Furthermore, since the battery's health state is determined online directly through the target physical model, there is no need to power off the device, disassemble the battery, or perform a complete low-rate charge and discharge test on the device. Therefore, it not only improves efficiency and real-time performance while reducing operational complexity and enhancing deployability. Furthermore, by building a cloud-edge collaborative architecture, operating data collection and data screening are performed at the edge, and the server with more abundant computing resources is used to perform physical model parameter identification and health state prediction. Compared to performing parameter identification and prediction at the edge, this improves the real-time nature of parameter identification and efficient perception of the battery's health state, and reduces the possibility of identification and prediction failures due to insufficient computing resources.

[0216] Based on the above embodiments, the present disclosure further provides a device for determining the health status of a battery. Figure 9 A schematic diagram of the structure of a device for determining the health status of a battery provided in an embodiment of the present disclosure is shown in FIG. Figure 9 As shown, the battery health status determination device 90 includes an acquisition module 91, a first determination module 92, an identification module 93 and a second determination module 94, wherein:

[0217] an acquisition module 91, configured to acquire a first operating data set of the battery under actual operating conditions in response to receiving a detection instruction;

[0218] A first determining module 92 is configured to determine a second operating data set based on a reference sensitivity vector corresponding to a previous detection instruction and the first operating data set;

[0219] an identification module 93 for identifying electrochemical parameters of the physical model based on the second operating data set to obtain a target physical model;

[0220] The second determination module 94 is configured to determine the current health state of the battery based on the target physical model.

[0221] In some embodiments, the first operating data set includes operating data at multiple sampling moments; the first determination module 92 is further used to: for each operating data in the first operating data set, determine the detection result of the operating data based on the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction; when the detection result of the operating data is the first detection result, the operating data is used as one of the operating data in the second operating data set.

[0222] In some embodiments, the first determination module 92 is further used to: perform sensitivity analysis on the operating data to obtain a sensitivity vector of the operating data; determine the similarity between the sensitivity vector of the operating data and a reference sensitivity vector corresponding to the previous detection instruction; determine the distance between the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction; and determine the detection result of the operating data based on the similarity and distance.

[0223] In some embodiments, the first determination module 92 is further used to: when the similarity is greater than a set similarity threshold and the distance is less than a set distance threshold, use the first detection result as the detection result of the operation data; when the similarity is not greater than the similarity threshold and / or the distance is not less than the distance threshold, use the second detection result as the detection result of the operation data.

[0224] In some embodiments, the identification module 93 is also used to: determine the current parameter value of the electrochemical parameter in the current round of iteration for each round of iteration in the target round, generate a first voltage sequence based on the current parameter value of the electrochemical parameter using a physical model, and determine the output value of the objective function based on the first voltage sequence, the second operating data set, and the reference voltage sequence corresponding to the previous detection instruction; identify the electrochemical parameters of the physical model based on the output value of the objective function in each round of iteration to obtain the target physical model.

[0225] In some embodiments, the identification module 93 is further used to: determine a voltage set based on the first voltage sequence and the second operating data set; generate a second voltage sequence based on the voltage set; and determine an output value of the objective function based on the second voltage sequence and the reference voltage sequence.

[0226] In some embodiments, the second voltage sequence includes predicted voltages at multiple moments, and the reference voltage sequence includes actual voltages at multiple moments; the identification module 93 is further used to: for each of the multiple moments, determine the deviation value corresponding to the moment based on the predicted voltage at the moment and the actual voltage at the moment; and determine the output value of the objective function based on the mean between the deviation values ​​corresponding to each moment.

[0227] In some embodiments, the identification module 93 is further configured to: determine a difference between a predicted voltage at a moment and an actual voltage at a moment; determine a weight corresponding to the moment; and determine a deviation value corresponding to the moment based on the difference and the weight corresponding to the moment.

[0228] In some embodiments, the identification module 93 is also used to: when the current round of iteration is the first round of iteration, use the default parameter value or the optimal parameter value corresponding to the previous detection instruction as the current parameter value; when the current round of iteration is other rounds of iteration, determine the current parameter value based on the parameter value of the electrochemical parameter in the previous round of iteration and the value range of the electrochemical parameter.

[0229] In some embodiments, the identification module 93 is also used to: determine a target round iteration from each round of iteration based on the output value of the objective function in each round of iteration; use the parameter value of the electrochemical parameter in the target round iteration as the optimal parameter value corresponding to the detection instruction; input the optimal parameter value corresponding to the detection instruction into the physical model to obtain the target physical model.

[0230] In some embodiments, the second determination module 94 is further configured to: utilize a target physical model to simulate battery discharge at a target low rate to generate a third voltage sequence; and determine the current health status of the battery based on the third voltage sequence.

[0231] In some embodiments, the physical model includes a pseudo-two-dimensional model, and the electrochemical parameters include at least one of the following: solid-phase positive electrode reaction rate constant, solid-phase negative electrode reaction rate constant, solid-phase positive electrode diffusion coefficient, solid-phase negative electrode diffusion coefficient, liquid phase diffusion coefficient, ohmic internal resistance, first positive electrode stoichiometric number, second positive electrode stoichiometric number, first negative electrode stoichiometric number, and second negative electrode stoichiometric number.

[0232] The description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiment of the present disclosure, please refer to the description of the method embodiment of the present disclosure for understanding.

[0233] It should be noted that in the embodiments of the present disclosure, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present disclosure, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The software product is stored in a storage medium and includes a number of instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present disclosure. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk. In this way, the embodiments of the present disclosure are not limited to any specific combination of hardware and software.

[0234] The present disclosure also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements any of the above methods when executing the computer program.

[0235] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the above method when executed by a processor. The computer-readable storage medium may be transient or non-transient.

[0236] The present disclosure also provides a computer program product, comprising a computer program or instructions that, when executed by a processor, implement some or all of the steps in any of the aforementioned methods. The computer program product may be implemented in hardware, software, or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK).

[0237] It should be noted that Figure 10 A hardware entity diagram of an electronic device provided in an embodiment of the present disclosure, such as Figure 10 As shown, the hardware entity of the electronic device 1000 includes: a processor 1001, a communication interface 1002 and a memory 1003, wherein:

[0238] The processor 1001 generally controls the overall operations of the electronic device 1000 .

[0239] The communication interface 1002 enables the electronic device to communicate with other terminals or servers through a network.

[0240] Memory 1003 is configured to store instructions and applications executable by processor 1001. It can also cache data to be processed or processed by processor 1001 and various modules in electronic device 1000 (e.g., image data, audio data, voice communication data, and video communication data). This can be implemented using flash memory (FLASH) or random access memory (RAM). Data can be transmitted between processor 1001, communication interface 1002, and memory 1003 via bus 1004.

[0241] It should be noted that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of the present disclosure, please refer to the description of the method embodiments of the present disclosure for understanding.

[0242] It should be understood that references to "one embodiment" or "an embodiment" throughout this specification mean that specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present disclosure. Therefore, the appearance of "in one embodiment" or "in an embodiment" throughout this specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of the present disclosure, the order of execution of the above-mentioned processes does not necessarily indicate a precedence in execution. The execution order of each process should be determined by its function and inherent logic and should not constitute any limitation on the implementation of the embodiments of the present disclosure. The above-mentioned numbers of the embodiments of the present disclosure are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. It should be noted that, in this document, the terms "comprise," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, the phrase "comprises an..." does not preclude the presence of other identical elements in the process, method, article, or apparatus comprising such elements.

[0243] In the several embodiments provided in this disclosure, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units described is merely a logical functional division. In actual implementation, other division methods may be used, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not implemented. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between the devices or units can be electrical, mechanical, or other forms. The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, the functional units in the embodiments of this disclosure may be all integrated into a single processing unit, each unit may be a separate unit, or two or more units may be integrated into a single unit; the integrated units may be implemented in the form of hardware or hardware plus software functional units.

[0244] The above is only an embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed in the present disclosure, and they should all be covered by the protection scope of the present disclosure.

Claims

1. A method for determining the health status of a battery, characterized in that: include: In response to receiving the detection instruction, obtaining a first operating data set of the battery under actual operating conditions; Determining a second operating data set based on a reference sensitivity vector corresponding to a previous detection instruction and the first operating data set; wherein the first operating data set includes operating data at multiple sampling moments; Identifying electrochemical parameters of the physical model based on the second operating data set to obtain a target physical model; determining a current state of health of the battery based on the target physical model; The determining of the second operating data set based on the reference sensitivity vector corresponding to the previous detection instruction and the first operating data set includes: For each piece of operating data in the first operating data set, determining a detection result of the operating data based on a sensitivity vector of the operating data and a reference sensitivity vector corresponding to the previous detection instruction, and if the detection result of the operating data is a first detection result, using the operating data as one piece of operating data in the second operating data set; wherein the first detection result indicates that the operating data is high-information data; The determining, based on the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction, a detection result of the operating data, includes: performing a sensitivity analysis on the operating data to obtain a sensitivity vector of the operating data; Determining a similarity between the sensitivity vector of the operating data and a reference sensitivity vector corresponding to the previous detection instruction; determining a distance between a sensitivity vector of the operating data and a reference sensitivity vector corresponding to the previous detection instruction; A detection result of the operation data is determined based on the similarity and the distance.

2. The determination method according to claim 1, characterized in that The determining the detection result of the operation data based on the similarity and the distance includes: When the similarity is greater than a set similarity threshold and the distance is less than a set distance threshold, taking the first detection result as the detection result of the operation data; When the similarity is not greater than the similarity threshold and / or the distance is not less than the distance threshold, the second detection result is used as the detection result of the operation data.

3. The determination method according to claim 1 or 2, characterized in that: The identifying electrochemical parameters of the physical model based on the second operating data set to obtain a target physical model includes: For each target round iteration, determining a current parameter value of the electrochemical parameter in the current round iteration, generating a first voltage sequence based on the current parameter value of the electrochemical parameter using the physical model, and determining an output value of the objective function based on the first voltage sequence, the second operating data set, and a reference voltage sequence corresponding to the previous detection instruction; Based on the output value of the objective function in each round of iteration, the electrochemical parameters of the physical model are identified to obtain the target physical model.

4. The determination method according to claim 3, characterized in that: The determining the output value of the objective function based on the first voltage sequence, the second operating data set, and the reference voltage sequence corresponding to the previous detection instruction includes: determining a voltage set based on the first voltage sequence and the second operational data set; generating a second voltage sequence based on the voltage set; An output value of the objective function is determined based on the second voltage sequence and the reference voltage sequence.

5. The determination method according to claim 4, characterized in that: The second voltage sequence includes predicted voltages at multiple moments, and the reference voltage sequence includes actual voltages at the multiple moments; and determining the output value of the objective function based on the second voltage sequence and the reference voltage sequence includes: For each of the multiple moments, determining a deviation value corresponding to the moment based on the predicted voltage at the moment and the actual voltage at the moment; An output value of the objective function is determined based on a mean value between the deviation values ​​corresponding to each of the moments.

6. The determination method according to claim 5, characterized in that: The determining the deviation value corresponding to the moment based on the predicted voltage at the moment and the actual voltage at the moment includes: determining a difference between a predicted voltage at the time and an actual voltage at the time; Determining a weight corresponding to the moment; Based on the difference and the weight corresponding to the moment, a deviation value corresponding to the moment is determined.

7. The determination method according to claim 3, characterized in that: Determining the current parameter value of the electrochemical parameter in the current iteration includes: In a case where the current iteration is the first iteration, a default parameter value or an optimal parameter value corresponding to a previous detection instruction is used as the current parameter value; In a case where the current iteration is another iteration, the current parameter value is determined based on the parameter value of the electrochemical parameter in the previous iteration and the value range of the electrochemical parameter.

8. The determination method according to claim 3, characterized in that: The step of identifying the electrochemical parameters of the physical model based on the output value of the objective function in each iteration to obtain the target physical model includes: Determining a target round iteration from each round iteration based on an output value of the objective function in each round iteration; Using the parameter value of the electrochemical parameter in the target round iteration as the optimal parameter value corresponding to the detection instruction; The optimal parameter value corresponding to the detection instruction is input into the physical model to obtain the target physical model.

9. The determination method according to claim 1 or 2, characterized in that: Determining the current health state of the battery based on the target physical model includes: Using the target physical model, simulating discharge of the battery at a target low rate to generate a third voltage sequence; Based on the third voltage sequence, a current state of health of the battery is determined.

10. The determination method according to claim 1 or 2, characterized in that: The physical model includes a pseudo-two-dimensional model, and the electrochemical parameters include at least one of the following: a solid-phase positive electrode reaction rate constant, a solid-phase negative electrode reaction rate constant, a solid-phase positive electrode diffusion coefficient, a solid-phase negative electrode diffusion coefficient, a liquid-phase diffusion coefficient, an ohmic internal resistance, a first positive electrode stoichiometric number, a second positive electrode stoichiometric number, a first negative electrode stoichiometric number, and a second negative electrode stoichiometric number.

11. A system for determining the health status of a battery, characterized in that: It includes edge side and service side, including: The edge terminal is configured to, in response to receiving a detection instruction, obtain a first operating data set of the battery under actual operating conditions; and determine a second operating data set based on a reference sensitivity vector corresponding to a previous detection instruction and the first operating data set; wherein the first operating data set includes operating data at multiple sampling moments; The server is configured to identify electrochemical parameters of the physical model based on the second operating data set to obtain a target physical model; and determine a current health state of the battery based on the target physical model; The edge end is further configured to determine, for each piece of operating data in the first operating data set, a detection result of the operating data based on a sensitivity vector of the operating data and a reference sensitivity vector corresponding to the previous detection instruction, and, if the detection result of the operating data is a first detection result, use the operating data as one piece of operating data in the second operating data set; wherein the first detection result indicates that the operating data is high-information data; The edge end is further used to perform sensitivity analysis on the operating data to obtain a sensitivity vector of the operating data; determine the similarity between the sensitivity vector of the operating data and a reference sensitivity vector corresponding to the previous detection instruction; determine the distance between the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction; and determine a detection result of the operating data based on the similarity and the distance.

12. A device for determining the health status of a battery, characterized in that: include: an acquisition module, configured to acquire, in response to receiving a detection instruction, a first operating data set of the battery under actual operating conditions; a first determining module, configured to determine a second operating data set based on a reference sensitivity vector corresponding to a previous detection instruction and the first operating data set; wherein the first operating data set includes operating data at multiple sampling moments; an identification module, configured to identify electrochemical parameters of the physical model based on the second operating data set to obtain a target physical model; a second determining module, configured to determine a current health state of the battery based on the target physical model; The first determining module is further configured to determine, for each piece of operating data in the first operating data set, a detection result of the operating data based on a sensitivity vector of the operating data and a reference sensitivity vector corresponding to the previous detection instruction, and, if the detection result of the operating data is a first detection result, use the operating data as one of the operating data in the second operating data set; wherein the first detection result indicates that the operating data is high-information data; The first determination module is further used to perform sensitivity analysis on the operating data to obtain a sensitivity vector of the operating data; determine the similarity between the sensitivity vector of the operating data and a reference sensitivity vector corresponding to the previous detection instruction; determine the distance between the sensitivity vector of the operating data and the reference sensitivity vector corresponding to the previous detection instruction; and determine a detection result of the operating data based on the similarity and the distance.

13. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the method according to any one of claims 1 to 10 is implemented.

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

15. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instructions are executed by a processor, the method according to any one of claims 1 to 10 is implemented.

Images