Energy storage system fault diagnosis method and device and energy storage system

Abstract

The embodiment of the invention provides an energy storage system fault diagnosis method and device and an energy storage system, and the method comprises the steps: obtaining the battery data of each battery pack controlled by a current energy storage system, and storing the battery data in each block chain node of a current block chain network in the form of block chain data; processing the battery data based on an intelligent contract, and determining the operation state of each battery pack; when it is detected that any battery pack is in a fault state, fault information of the current battery pack is determined based on a plurality of preset diagnosis dimensions; and verifying the fault information based on other block chain nodes in the current block chain network, if the verification is passed, executing early warning processing related to the fault information, and if the verification fails, re-electing the main node in the current block chain network. The battery data is acquired through the plurality of block chain nodes, and the fault information is determined based on the smart contract, so that the efficiency of fault diagnosis can be improved, the security and credibility of the data are improved, and data support is provided for subsequent fault analysis and optimization.

Description

Technical Field

[0001] The present application relates to the field of energy storage control, and particularly to a method and device for fault diagnosis of an energy storage system and an energy storage system. Background Art

[0002] With the continuous development of the power system, the electrochemical energy storage system, as an important part of energy storage technology, is widely used in the energy storage field. The existing fault diagnosis methods for the Battery Management System (BMS) mainly include traditional diagnosis methods and intelligent diagnosis methods based on machine learning.

[0003] The traditional diagnosis method mainly presets a fault threshold. After the machine judges the fault threshold, a fault warning is generated. The setting of the fault threshold mainly depends on the experience of the operation and maintenance personnel. In different power grid environments, the fault thresholds are not exactly the same, and the operation and maintenance workload is large. This method has problems such as low accuracy, high cost, and low efficiency. The intelligent diagnosis method based on machine learning improves the efficiency and accuracy of fault diagnosis to a certain extent, but at the system level, it causes information islands, especially when there is an unknown fault in a single node, the diagnosis is likely to fail. Summary of the Invention

[0004] In view of this, the present application provides a method and device for fault diagnosis of an energy storage system and an energy storage system, which are conducive to solving the problems of low fault diagnosis efficiency and low accuracy in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a method for fault diagnosis of an energy storage system, including: Obtaining battery data of each battery pack controlled by the current energy storage system, and storing the battery data in the form of blockchain data in each blockchain node of the current blockchain network based on the hash algorithm and the encryption algorithm; Processing the battery data based on a smart contract to determine the operating status of each battery pack; When it is detected that any battery pack is in a fault state, determining the fault information of the current battery pack based on a preset plurality of diagnosis dimensions; Verifying the fault information based on other blockchain nodes in the current blockchain network. If the verification passes, performing warning processing related to the fault information. If the verification fails, re-electing a main node in the current blockchain network.

[0006] In an optional embodiment, the obtaining battery data of each battery pack controlled by the current energy storage system includes: Obtaining first battery data collected by a sensor connected to the current blockchain node; Obtain second battery data, where the second battery data is collected by sensors connected to other blockchain nodes in the current blockchain network and sent by other blockchain nodes to the current blockchain node.

[0007] In an alternative embodiment, storing the battery data in the form of blockchain data in each blockchain node of the current blockchain network based on a hash algorithm and an encryption algorithm includes: Preprocess the battery data; Process the battery data based on a hash algorithm and an encryption algorithm to generate corresponding blockchain data; Store the blockchain data in the current blockchain node and broadcast the blockchain data to other blockchain nodes in the current blockchain network.

[0008] In an alternative embodiment, the smart contract presets a threshold range for each battery data, and the battery data includes temperature data; Processing the battery data based on the smart contract to determine the operating status of each battery pack includes: Dynamically adjust the threshold range of each battery data based on the temperature data and a preset temperature threshold association rule; When it is detected that any battery data exceeds the corresponding threshold range, determine that the corresponding battery pack is in a faulty state.

[0009] In an alternative embodiment, dynamically adjusting the threshold range of each battery data based on the temperature data and a preset temperature threshold association rule includes: When the ambient temperature of the battery pack is greater than a first temperature value, as the ambient temperature continues to rise, subtract a first value from the voltage threshold; When the ambient temperature of the battery pack is less than a second temperature value, as the ambient temperature continues to drop, add a second value to the voltage threshold.

[0010] In an alternative embodiment, when it is detected that any battery data exceeds the corresponding threshold range, determining that the corresponding battery pack is in a faulty state includes: When it is detected that any voltage data exceeds a first voltage threshold and the duration exceeds a first time period, determine that the corresponding battery pack is in an overvoltage state; When it is detected that any voltage data is lower than a second voltage threshold and the duration exceeds a second time period, determine that the corresponding battery pack is in an undervoltage state.

[0011] In an alternative embodiment, processing the battery data based on the smart contract to determine the operating status of each battery pack includes: Input the battery data into the state of charge (SOC) estimation model for the remaining battery capacity to obtain the SOC predicted values of each battery pack; When it is detected that the difference between the actual SOC value of any battery pack and the corresponding SOC predicted value is greater than the first threshold, it is determined that the current battery pack is in a faulty state.

[0012] In an optional embodiment, the processing of the battery data based on the smart contract to determine the operating state of each battery pack includes: Input the battery data into the Kalman filter model to obtain the first SOC predicted values of each battery pack; Input the battery data into the open circuit voltage prediction model to obtain the second SOC predicted values of each battery pack; For any battery pack, if the difference between the first SOC predicted value and the second SOC predicted value of the current battery pack is less than the second threshold, perform a fault diagnosis on the current battery pack based on the first SOC predicted value or / and the second SOC predicted value; If the difference between the first SOC predicted value and the second SOC predicted value of the current battery pack is greater than or equal to the second threshold, execute the fault diagnosis process.

[0013] In an optional embodiment, the executing the fault diagnosis process if the difference between the first SOC predicted value and the second SOC predicted value of the current battery pack is greater than or equal to the second threshold includes: Query whether there is a fault code in the fault code library that matches the current SOC fault event; If there is a corresponding fault code, determine the cause of the fault based on the fault code; If there is no corresponding fault code, record the current SOC fault event and update the fault code library.

[0014] In an optional embodiment, the determining the fault information of the current battery pack based on a plurality of preset diagnosis dimensions includes: For any diagnosis dimension, extract the corresponding diagnosis parameter from the battery data; Process the diagnosis parameters of each diagnosis dimension based on a machine learning model to determine the fault information, where the fault information includes: fault type, fault location, or fault level.

[0015] In an optional embodiment, the processing the diagnosis parameters of each diagnosis dimension based on a machine learning model to determine the fault information includes: Determine a comprehensive diagnosis parameter based on the diagnosis parameters of each diagnosis dimension and the corresponding weights; Match in the fault information database based on the comprehensive diagnostic parameters. If there is fault information that matches the comprehensive diagnostic parameters, output the corresponding fault information. If there is no fault information that matches the comprehensive diagnostic parameters, add the corresponding fault information to the fault information database.

[0016] In an alternative embodiment, the verifying the fault information based on other blockchain nodes in the current blockchain network includes: Broadcast the fault information in the current blockchain network and receive the confirmation messages broadcast by other blockchain nodes; If the number of blockchain nodes sending the confirmation messages in the current blockchain network is greater than a third threshold, confirm that the verification of the fault information passes; If the number of blockchain nodes sending the confirmation messages in the current blockchain network is less than or equal to the third threshold, confirm that the verification of the fault information fails.

[0017] In an alternative embodiment, after performing the warning processing related to the fault information, the method further includes: Generate a hash fingerprint corresponding to the fault information; Store the hash fingerprint in the form of blockchain data in the current blockchain node, and broadcast the hash fingerprint in the current blockchain network.

[0018] In an alternative embodiment, the method further includes: Diagnose the hardware layer, protocol layer, and application layer of the energy storage system based on the smart contract to determine fault information; Among them, diagnosing the hardware layer includes: Judge whether there is a harness short circuit / short circuit or a bus transceiver failure based on the differential voltage of the bus transceiver, or locate the node with abnormal contact impedance based on the bus voltage waveform, or judge whether the power supply fails based on the output voltage of the DC-DC converter; Diagnosing the protocol layer includes: judging whether the protocol stack configuration is incorrect based on the receive error counter and the transmit error counter.

[0019] In a second aspect, an embodiment of the present application provides an energy storage system fault diagnosis device, including: An acquisition module, configured to acquire battery data of each battery pack controlled by the current energy storage system; A storage module, configured to store the battery data in the form of blockchain data in each blockchain node of the current blockchain network based on a hash algorithm and an encryption algorithm; A processing module, configured to process the battery data based on a smart contract to determine the operating status of each battery pack; The processing module is further configured to verify the fault information based on other blockchain nodes in the current blockchain network. If the verification is passed, the warning processing related to the fault information is executed. If the verification fails, a primary node is re-elected in the current blockchain network.

[0020] In a third aspect, an embodiment of the present application provides an energy storage system, including a memory for storing computer program instructions and a processor for executing the program instructions. When the computer program instructions are executed by the processor, the electronic device is triggered to execute the method according to any one of the first aspects above.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which includes a stored program. When the program runs, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of the first aspects.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes executable instructions. When the executable instructions are executed on a computer, the computer is caused to execute the method according to any one of the first aspects.

[0023] By adopting the solution provided by the embodiment of the present application, battery data of each battery pack controlled by the current energy storage system is obtained, and the battery data is stored in each blockchain node of the current blockchain network in the form of blockchain data based on the hash algorithm and the encryption algorithm; the battery data is processed based on the smart contract to determine the operating status of each battery pack; when it is detected that any battery pack is in a fault state, the fault information of the current battery pack is determined based on a preset plurality of diagnosis dimensions; the fault information is verified based on other blockchain nodes in the current blockchain network. If the verification is passed, the warning processing related to the fault information is executed. If the verification fails, a primary node is re-elected in the current blockchain network. By obtaining battery data through multiple blockchain nodes and determining fault information based on the smart contract, the efficiency of fault diagnosis can be improved. Storing the battery data and the fault information in each blockchain node of the blockchain network can improve the security and credibility of the data, and provide data support for subsequent fault analysis and optimization. Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1Schematic flowchart of a fault diagnosis method for an energy storage system provided by an embodiment of the present application; Figure 2 Schematic example diagram of another fault diagnosis method for an energy storage system provided by an embodiment of the present application; Figure 3 Schematic example diagram of another fault diagnosis method for an energy storage system provided by an embodiment of the present application; Figure 4 Schematic example diagram of another fault diagnosis method for an energy storage system provided by an embodiment of the present application; Figure 5 Schematic example diagram of another fault diagnosis method for an energy storage system provided by an embodiment of the present application; Figure 6 Schematic example diagram of another fault diagnosis method for an energy storage system provided by an embodiment of the present application; Figure 7 Schematic example diagram of another fault diagnosis method for an energy storage system provided by an embodiment of the present application; Figure 8 Schematic structural diagram of a fault diagnosis device for an energy storage system provided by an embodiment of the present application; Figure 9 Schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0026] For a better understanding of the technical solutions of the present application, the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0027] It should be clear that the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0028] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms of "a", "the" and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0029] It should be understood that the term " / and" used herein is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, a and / or b can represent: a exists alone, a and b exist simultaneously, and b exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0030] With the continuous development of the power system, as an important part of energy storage technology, the electrochemical energy storage system is widely used in the field of energy storage. The existing fault diagnosis technologies for energy storage systems have at least the following problems: (1) Data islands and lack of trust: The data of each control node in the energy storage system is stored independently, and cross-node diagnosis depends on a centralized server, making the data vulnerable to tampering or loss. (2) Low diagnosis efficiency: Traditional centralized algorithms are difficult to handle the real-time analysis of massive battery data (such as voltage, temperature, impedance spectrum, etc.). (3) Difficult to trace the source: Tracing the cause of faults depends on manual logs, lacking reliable timestamps and the full-life cycle data chain.

[0031] To address the above problems, the embodiments of the present application provide a fault diagnosis method for an energy storage system. By obtaining battery data through multiple blockchain nodes and determining fault information based on smart contracts, the efficiency of fault diagnosis can be improved. Storing the battery data and fault information in each blockchain node of the blockchain network can enhance the security and credibility of the data, providing data support for subsequent fault analysis and optimization.

[0032] The blockchain network includes multiple blockchain nodes. Any blockchain node can send messages to other blockchain nodes through broadcasting. Each blockchain node is independent and equal in status, without permanent special nodes and hierarchical structures, having the same functions and storing the same information, achieving mutual supervision. Based on the improved Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, the blockchain network can dynamically elect a primary node to coordinate other nodes to cross-verify abnormal data.

[0033] Figure 1 It is a schematic flowchart of a fault diagnosis method for an energy storage system provided by the embodiments of the present application. This method can be applied to the primary node of the blockchain network. Each processor in the energy storage system can be regarded as a blockchain node. As Figure 1 shown, this method may include: Step 101, obtain the battery data of each battery pack controlled by the current energy storage system, and store the battery data in the form of blockchain data in each blockchain node of the current blockchain network based on the hash algorithm and the encryption algorithm.

[0034] Each blockchain node can be connected to corresponding sensors to obtain the battery data of each battery pack in real time. Optionally, the battery data may include: current data, voltage data, temperature data, active / reactive power data, liquid cooling flow rate data, etc. Optionally, the ways for the master node to obtain the battery data may include: (1) obtaining first battery data collected by the sensors connected to the master node. (2) obtaining second battery data collected by the sensors connected to other blockchain nodes in the current blockchain network and sent by other blockchain nodes to the master node. The battery data collected by each blockchain node will be broadcast to other blockchain nodes. Therefore, each blockchain node in the current blockchain network can obtain the battery data of all battery packs.

[0035] For the obtained battery data, the master node first preprocesses the battery data, including steps such as format transformation, normalization, and feature extraction. After that, the master node can process the battery data based on the hash algorithm and encryption algorithm to generate corresponding blockchain data. The master node stores the blockchain data in its own node and broadcasts the blockchain data to other blockchain nodes in the current blockchain network. The distributed ledger feature of the blockchain data ensures the integrity and immutability of the battery data.

[0036] Step 102: Process the battery data based on the smart contract to determine the operating status of each battery pack.

[0037] Deploy a smart contract in the blockchain network, which contains the logic and algorithms for fault diagnosis. The smart contract can automatically perform fault diagnosis based on the obtained battery data to preliminarily determine whether there is a fault in the battery pack. Optionally, the smart contract presets the threshold range of each battery data. When it is detected that any battery data exceeds the corresponding threshold range, it can be determined that the corresponding battery pack is in a fault state.

[0038] In an optional embodiment, the fault state may include: overvoltage state and undervoltage state. When the master node detects that any battery data exceeds the first voltage threshold and the duration exceeds the first time period, it can be determined that the corresponding battery pack is in the overvoltage state. When the master node detects that any voltage data is lower than the second voltage threshold and the duration exceeds the second time period, it can be determined that the corresponding battery pack is in the undervoltage state.

[0039] For example, the first voltage threshold can be set to 3650 mV, and the first time period can be set to 10 s. When the voltage data of a certain battery pack exceeds 3650 mV and the duration exceeds 10 s, it can be determined that the battery pack is in the overvoltage state. The second voltage threshold can be set to 2500 mV, and the second time period can be set to 3 s. When the voltage data of a certain battery pack is lower than 2500 mV and the duration exceeds 3 s, it can be determined that the battery pack is in the undervoltage state.

[0040] In an alternative embodiment, the master node may also determine the fault status based on the state of charge (SOC) value of the battery. Specifically, the master node inputs the battery data into the SOC estimation model to obtain the SOC prediction values of each battery pack. When it is detected that the difference between the actual SOC value of any battery pack and the corresponding SOC prediction value is greater than the first threshold, it is determined that the battery pack is in a fault state. For example, the master node acquires the battery data once every 1 second and inputs it into the SOC estimation model. After obtaining the SOC prediction value, the deviation rate between the SOC prediction value and the actual SOC value is calculated. When the deviation rate of any battery pack is greater than 8% and the duration exceeds 30 s, it can be determined that the battery pack is in an abnormal SOC state.

[0041] Step 103: When it is detected that any battery pack is in a fault state, determine the fault information of the current battery pack based on a plurality of preset diagnostic dimensions.

[0042] For different types of battery data such as voltage data, current data, and SOC data, different diagnostic dimensions can be preset for fault determination. For any diagnostic dimension, the master node extracts the corresponding diagnostic parameters from the battery data. Then, based on the machine learning model, the diagnostic parameters of each diagnostic dimension are processed to determine the fault information. The fault information may include: fault type, fault location, or fault level.

[0043] In an alternative embodiment, the blockchain network may be preconfigured with a fault information library. The master node determines the comprehensive diagnostic parameters based on the diagnostic parameters of each diagnostic dimension and the corresponding weights, and then matches the comprehensive diagnostic parameters in the fault information library. If there is fault information that matches the comprehensive diagnostic parameters, the corresponding fault information is output. If there is no fault information that matches the comprehensive diagnostic parameters, the corresponding fault information is added to the fault information library.

[0044] Step 104: Verify the fault information based on other blockchain nodes in the current blockchain network. If the verification is passed, perform the warning processing related to the fault information. If the verification fails, re-elect the master node in the current blockchain network.

[0045] The master node broadcasts the fault information in the current blockchain network. Each blockchain node stores the same battery data and can verify the fault information. If it is determined that the fault information is correct, a confirmation message is sent to the master node. If the number of blockchain nodes sending confirmation messages in the current blockchain network is greater than the third threshold, it can be confirmed that the fault information verification is passed. If the number of blockchain nodes sending confirmation messages in the current blockchain network is less than or equal to the third threshold, it is confirmed that the fault information verification fails.

[0046] Through consensus voting, the accuracy of fault information can be guaranteed. After verification passes, the primary node can execute the warning process related to the fault information. The primary node will also store the fault information in the blockchain network. Specifically, the primary node generates a hash fingerprint corresponding to the fault information, stores the hash fingerprint in its own node in the form of blockchain data, and broadcasts the hash fingerprint in the current blockchain network. If the verification fails, the primary node needs to be re-elected in the current blockchain network.

[0047] In the embodiments of the present application, each blockchain node can obtain the same battery distance, avoiding the problem of information islands. Through multi-node collaborative diagnosis, the real-time performance, accuracy, and efficiency of system fault diagnosis are improved. Storing battery data, fault information, and other relevant data in the blockchain network in the form of blockchain data greatly reduces the possibility of data being tampered with and improves data security.

[0048] In an alternative embodiment, the smart contract also presets a temperature threshold association rule. In response to the change in the ambient temperature of the battery pack, the primary node can dynamically adjust the threshold range of the battery data, further improving the accuracy of fault determination. Optionally, the design of the voltage sudden drop threshold needs to be dynamically corrected in combination with the temperature gradient. When the ambient temperature of the battery pack is greater than the first temperature value, as the ambient temperature continues to rise, the voltage threshold is subtracted by the first value. When the ambient temperature of the battery pack is less than the second temperature value, as the ambient temperature continues to drop, the voltage threshold is subtracted by the second value. For example, the reference voltage threshold is 2500 mV. In a high-temperature environment (such as ambient temperature > 45°C), for every degree increase in the ambient temperature, the voltage threshold automatically decreases by 5 mV, avoiding false triggering due to temperature drift. In a low-temperature environment (such as ambient temperature < 0°C), boost compensation is enabled, and the threshold is increased to 2600 mV, or for every degree decrease in the ambient temperature, the voltage threshold automatically increases by 3 mV, preventing the battery from triggering protection due to increased internal resistance at low temperatures.

[0049] The above process can be referred to Figure 2 , specifically including: Step 201, determining the ambient temperature through the collected temperature data.

[0050] Step 202, ambient judgment.

[0051] The environment where the battery pack is located can include: normal temperature environment, high temperature environment, or low temperature environment. Different threshold ranges are adopted based on different environments.

[0052] Step 203, adopting the reference threshold range.

[0053] When the battery pack is in a normal temperature environment, the reference threshold range can be directly adopted without adjustment.

[0054] Step 204, dynamically adjusting the threshold range.

[0055] When the battery pack is in a high - temperature or low - temperature environment, the threshold range needs to be dynamically adjusted based on the corresponding rules.

[0056] Step 205, perform fault determination based on the threshold range.

[0057] Perform fault determination on the battery pack based on the reference threshold range or the dynamically adjusted threshold range.

[0058] In the embodiments of the present application, by dynamically adjusting the threshold range, the threshold range can be made more reasonable, reducing the probability of missed detection.

[0059] In an alternative embodiment, for the same fault phenomenon (such as SOC jump), the master node can perform parallel calculations using different algorithms (such as Kalman filtering, open - circuit voltage method, etc.), and improve the judgment credibility through result consistency verification. Specifically, the master node inputs the battery data into the Kalman filter model to obtain the first SOC prediction value of each battery pack, and at the same time inputs the battery data into the open - circuit voltage prediction model to obtain the second SOC prediction value of each battery pack. For any battery pack, if the difference between the first SOC prediction value and the second SOC prediction value of the current battery pack is less than the second threshold, then perform fault diagnosis on the current battery pack based on the first SOC prediction value or / and the second SOC prediction value. If the difference between the first SOC prediction value and the second SCO prediction value of the current battery pack is greater than or equal to the second threshold, then execute the fault code matching process.

[0060] The above process can refer to Figure 3 , and specifically may include: Step 301, collect battery data in real - time.

[0061] Step 302, the Kalman filter model determines the first SOC prediction value.

[0062] Step 303, the open - circuit voltage prediction model determines the second SOC prediction value.

[0063] There is an association relationship between the battery SOC and other battery data. Inputting the collected battery data into the Kalman filter model and the open - circuit voltage prediction model respectively can obtain the first SOC prediction value and the second SOC prediction value.

[0064] Step 304, determine the difference between the first SOC prediction value and the second SOC prediction value.

[0065] Step 305, if the difference is greater than the second threshold, then enter Step 306, otherwise enter Step 307.

[0066] Step 306, execute the fault diagnosis process.

[0067] If the SOC prediction values output by different models vary significantly, it can be considered that there are abnormal data in the battery data, and the master node executes a fault diagnosis process to determine the cause of the fault.

[0068] Step 307, battery SOC fault determination.

[0069] If the SOC prediction values output by different models vary slightly, it can be considered that there is no abnormality in the battery data, and the master node can determine the actual SOC value based on the first SOC prediction value or / and the second SOC prediction value. If there is a significant difference between the actual SOC value and the prediction value, the battery SOC fault can be determined.

[0070] In an optional embodiment, the fault diagnosis process in step 306 may include: querying whether there is a fault code in the fault code library that matches the current SOC fault event; if there is a corresponding fault code, determining the cause of the fault based on the fault code; if there is no corresponding fault code, recording the current SOC fault event and updating the fault code library. Refer to Figure 4 Specifically, it may include: Step 401, fault code matching.

[0071] Match the abnormal phenomenon with the fault codes stored in the fault code library.

[0072] Step 402, check if there is a matching code. If there is, go to step 403; otherwise, go to step 407.

[0073] Step 403, extract fault features and construct a fault tree.

[0074] If there is a matching fault code in the fault code library, extract the fault features from the fault code and construct a fault tree.

[0075] Step 404, determine the cause of the fault.

[0076] The master node analyzes the cause of the fault based on the fault tree.

[0077] Step 405, execute the control strategy and monitor the feedback.

[0078] The master node determines and executes the control strategy based on the cause of the fault, and simultaneously monitors the feedback result in real time.

[0079] Step 406, record unknown fault events.

[0080] If there is no matching fault code in the fault code library, record the unknown fault event.

[0081] Step 407, update the fault code library.

[0082] Add a fault code that matches the current fault event to the fault code library.

[0083] In the embodiments of the present application, in combination with the fault code library, rapid positioning of the fault cause can be achieved.

[0084] In an alternative embodiment, the process by which the master node determines the fault information based on multiple diagnostic dimensions can refer to Figure 5 , and specifically may include: Step 501, determine the diagnostic parameters of each diagnostic dimension.

[0085] The master node extracts the corresponding diagnostic parameters from the corresponding battery data based on different diagnostic dimensions.

[0086] Step 502, determine the comprehensive diagnostic parameters based on the diagnostic parameters and weights.

[0087] During the model training process of the weights of the diagnostic parameters, they have been determined and stored in each blockchain node.

[0088] Step 503, match the fault information in the fault information library.

[0089] Step 504, check whether the matching is successful. If the matching is successful, proceed to step 505; otherwise, proceed to step 506.

[0090] Step 505, output the fault information.

[0091] Step 506, update the fault information library.

[0092] If the fault information can be successfully matched in the fault information library, directly output the fault information. If the matching in the fault information library fails, after determining the fault information, add the correspondence between the current comprehensive diagnostic parameter fault information and the fault information to the fault information library.

[0093] By constructing the fault information library and performing similarity matching between the real-time battery data and historical fault information (such as sudden increase in battery internal resistance, equalization failure), rapid positioning of repetitive fault information can be achieved.

[0094] In an alternative embodiment, the process by which each blockchain node verifies the fault information may include: broadcasting the fault information in the current blockchain network and receiving the confirmation messages broadcast by other blockchain nodes; if the number of blockchain nodes sending confirmation messages in the current blockchain network is greater than the third threshold, confirm that the fault information verification is passed; if the number of blockchain nodes sending confirmation messages in the current blockchain network is less than or equal to the third threshold, confirm that the fault information verification fails.

[0095] The above process can refer to Figure 6 , and specifically may include: Step 601, the master node broadcasts the fault information.

[0096] Step 602. Other blockchain nodes verify the fault information.

[0097] Each blockchain node in the blockchain network stores the same data and is configured with the same algorithm. Other blockchain nodes except the primary node can verify the fault information based on the battery data obtained by themselves.

[0098] Step 603. The primary node receives the confirmation information.

[0099] For any blockchain node, if the verification is passed, the confirmation information is broadcast and sent.

[0100] Step 604. The primary node determines whether the number of blockchain nodes sending the confirmation information exceeds the third threshold. If so, go to Step 605; otherwise, go to Step 606.

[0101] Step 605. Execute the fault handling strategy.

[0102] When most blockchain nodes confirm that the fault information is correct, the primary node can execute the fault handling strategy. For example, when a certain battery pack is in an overvoltage state, the primary node turns off the charging MOS tube and starts forced discharge to prevent battery damage.

[0103] Step 606. Re-elect the primary node.

[0104] When most blockchain nodes verify and pass, it indicates that there are abnormal problems with the current primary node, and a new primary node needs to be selected in the blockchain network.

[0105] In the embodiments of the present application, by verifying the fault information through blockchain nodes, the accuracy of the fault information can be improved, and the probability of misjudging faults can be reduced.

[0106] In an alternative embodiment, after the primary node executes the fault handling strategy, it will store the fault information, the handling strategy, etc. in the blockchain network. Specifically, the primary node generates a hash fingerprint corresponding to the fault information; stores the hash fingerprint in the current blockchain node in the form of blockchain data, and broadcasts the hash fingerprint in the current blockchain network.

[0107] The above process can refer to Figure 7 , including: Step 701. Data encapsulation.

[0108] The primary node encapsulates the fault data of the entire process in a standardized format to improve cross-platform compatibility. The fault data includes fields such as timestamp, device ID, fault information, and operation log.

[0109] Step 702. Generate a hash fingerprint.

[0110] The master node generates a unique hash fingerprint for each piece of fault data, binds it to the blockchain transaction ID, and supports quick verification of data authenticity.

[0111] Step 703, the master node stores the hash fingerprint.

[0112] The master node stores the hash fingerprint in its own node.

[0113] Step 704, broadcast the hash fingerprint.

[0114] The master node sends the hash fingerprint to other blockchain nodes in the blockchain network.

[0115] Step 705, other nodes verify the hash fingerprint. If the verification passes, go to step 706; otherwise, go to step 707.

[0116] Step 706, other nodes store the hash fingerprint.

[0117] Step 707, mark it as suspicious data.

[0118] For other blockchain nodes, if the verification passes, store the hash fingerprint; otherwise, mark it as suspicious data.

[0119] By encrypting and storing the fault data in each blockchain node, the security of the data can be guaranteed. In an optional embodiment, the master node can also diagnose the hardware layer, protocol layer, and application layer of the energy storage system based on a smart contract to determine the fault information. Among them, diagnosing the hardware layer includes: judging whether there is a harness short circuit / short circuit or a bus transceiver fault based on the differential voltage of the bus transceiver, or locating the node with abnormal contact impedance based on the bus voltage waveform, or judging whether the power supply is faulty based on the output voltage of the DC-DC converter. For example, when a communication interruption is detected, combine the hardware status (such as the CAN transceiver voltage and the module power supply status) to distinguish between harness faults or protocol stack anomalies. Specifically, measure the differential voltage between CAN_H and CAN_L (the normal range is 1.5 - 3.5V). If the voltage is abnormal (<0.9V or >4.5V), then determine that there is a harness short circuit / open circuit or a transceiver fault. Another example is to use an oscilloscope to capture the bus waveform and detect the distortion of the dominant / recessive level (such as the rising edge time >200ns) to locate the node with abnormal contact impedance. Another example is to check the output voltage of the DC-DC module. The system allows a fluctuation range of ±15% (10.8 - 13.2V). If the voltage <9V, trigger a power supply fault alarm.

[0120] Diagnosing the protocol layer includes: judging whether the protocol stack configuration is incorrect based on the receive error counter and the transmit error counter. For example, when the receive error counter (REC) > 96 and the transmit error counter (TEC) < 128, it is determined that the protocol stack configuration is incorrect (such as baud rate mismatch). When the TEC accumulates to 255, it enters the bus-off state (BUS-OFF), and the CAN controller initialization parameters need to be checked.

[0121] Diagnosing the application layer includes: the communication service records the addresses of offline devices. If a single node is offline, check its power supply and transceiver; if multiple nodes are offline, check for faults in the backbone cable or gateway.

[0122] In the embodiments of the present application, by judging and verifying the fault information through blockchain nodes, the accuracy of the fault information can be improved. The whole process of fault diagnosis, early warning and processing is recorded on the blockchain. The distributed ledger feature of the blockchain ensures the integrity of the data, forming an immutable fault record. The blockchain can realize data sharing among nodes, avoiding data islands and trust deficiencies. Through the traceability function of the blockchain, the historical records of fault handling can be conveniently queried and verified, providing data support for subsequent fault analysis and optimization.

[0123] Figure 8 It is a schematic structural diagram of a fault diagnosis device for an energy storage system provided by an embodiment of the present application. As Figure 8 shown, the device may include: An acquisition module, configured to acquire battery data of each battery pack controlled by the current energy storage system.

[0124] A storage module, configured to store the battery data in the form of blockchain data in each blockchain node of the current blockchain network based on a hash algorithm and an encryption algorithm.

[0125] A processing module, configured to process the battery data based on a smart contract to determine the operating status of each battery pack.

[0126] The processing module is further configured to verify the fault information based on other blockchain nodes in the current blockchain network. If the verification passes, perform early warning processing related to the fault information. If the verification fails, re-elect the main node in the current blockchain network.

[0127] The embodiments of the present application further provide an energy storage system. In this energy storage system blockchain network, each processor can be regarded as a blockchain node, and the operation of each blockchain node can implement the above-mentioned fault diagnosis method.

[0128] Corresponding to the above embodiments, the present application further provides an electronic device. Figure 9A schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device 900 may include: a processor 901, a memory 902, and a communication unit 903. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the electronic device shown in the figure does not constitute a limitation to the embodiments of the present application. It can be a bus structure, a star structure, and may also include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0129] Among them, the communication unit 903 is used to establish a communication channel so that the electronic device can communicate with other devices. Receive user data sent by other devices or send user data to other devices.

[0130] The processor 901 is the control center of the electronic device. It uses various interfaces and lines to connect all parts of the entire electronic device. By running or executing software programs, instructions, and / or modules stored in the memory 902, and by calling the data stored in the memory, it executes various functions of the electronic device and / or processes data. The processor may be composed of an integrated circuit (IC). For example, it may be composed of a single packaged IC, or may be composed of multiple packaged ICs with the same or different functions connected together. For example, the processor 901 may only include a central processing unit (CPU). In the embodiment of the present application, the CPU may be a single arithmetic core or may include multiple arithmetic cores.

[0131] The memory 902 is used to store the execution instructions of the processor 901. The memory 902 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0132] When the execution instructions in the memory 902 are executed by the processor 901, the electronic device 900 can execute some or all of the steps in the above embodiments.

[0133] In specific implementation, the present application further provides a computer storage medium. The computer storage medium can store a program, and when the program is executed, it may include some or all of the steps in the embodiments of the energy storage system fault diagnosis method provided by the present application. The storage medium may be a magnetic disk, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like.

[0134] In specific implementation, the present application further provides a computer program product. The computer program product includes executable instructions, and when the executable instructions are executed on a computer, the computer is caused to execute some or all of the steps in the embodiments of the energy storage system fault diagnosis method provided by the present application.

[0135] The embodiments of the present application further provide a non-temporary computer-readable storage medium. The non-temporary computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the energy storage system fault diagnosis method provided by the embodiments of the present application.

[0136] The above non-temporary computer-readable storage medium may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (hereinafter referred to as: ROM), an erasable programmable read-only memory (hereinafter referred to as: EPROM), or a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.

[0137] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take many forms, including - but not limited to - electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0138] The program code contained on a computer-readable medium may be transmitted using any appropriate medium, including - but not limited to - wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0139] Those skilled in the art can clearly understand that the technologies in the embodiments of the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments of the present application.

[0140] For the same or similar parts among the various embodiments in this specification, reference may be made to each other. In particular, for the device embodiments and the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference may be made to the descriptions in the method embodiments for the relevant parts.

Claims

1. A method for diagnosing faults in an energy storage system, characterized in that: include: Obtain battery data of each battery pack controlled by the current energy storage system, and store the battery data in the form of blockchain data in each blockchain node of the current blockchain network based on a hash algorithm and an encryption algorithm; Processing the battery data based on the smart contract to determine the operating status of each battery pack; When any battery pack is detected to be in a fault state, the fault information of the current battery pack is determined based on a plurality of preset diagnostic dimensions; The fault information is verified based on other blockchain nodes in the current blockchain network. If the verification passes, the early warning processing related to the fault information is executed. If the verification fails, the master node is re-elected in the current blockchain network.

2. The energy storage system fault diagnosis method according to claim 1, characterized in that: The obtaining of battery data of each battery pack controlled by the current energy storage system includes: Acquire first battery data, where the first battery data is collected by a sensor connected to the current blockchain node; Acquire second battery data, where the second battery data is collected by sensors connected to other blockchain nodes in the current blockchain network and sent to the current blockchain node by the other blockchain nodes.

3. The energy storage system fault diagnosis method according to claim 1, characterized in that: The method of storing the battery data in the form of blockchain data in each blockchain node of the current blockchain network based on a hash algorithm and an encryption algorithm includes: Preprocessing the battery data; Processing the battery data based on a hash algorithm and an encryption algorithm to generate corresponding blockchain data; The blockchain data is stored in the current blockchain node, and the blockchain data is broadcasted to other blockchain nodes in the current blockchain network.

4. The energy storage system fault diagnosis method according to claim 1, characterized in that: The smart contract is preset with a threshold range of each battery data, and the battery data includes temperature data; The processing of the battery data based on the smart contract to determine the operating status of each battery pack includes: Dynamically adjust the threshold range of each battery data based on the temperature data and a preset temperature threshold association rule; When it is detected that any battery data exceeds the corresponding threshold range, it is determined that the corresponding battery pack is in a fault state.

5. The energy storage system fault diagnosis method according to claim 4, characterized in that: The dynamically adjusting the threshold range of each battery data based on the temperature data and the preset temperature threshold association rule includes: When the ambient temperature of the battery pack is greater than a first temperature value, as the ambient temperature continues to rise, the voltage threshold is cumulatively reduced by the first value; When the ambient temperature of the battery pack is lower than a second temperature value, the voltage threshold is accumulated by a second value as the ambient temperature continues to decrease.

6. The energy storage system fault diagnosis method according to claim 4, characterized in that: When any battery data is detected to be beyond a corresponding threshold range, determining that the corresponding battery pack is in a fault state includes: Detecting that any voltage data exceeds a first voltage threshold and lasts longer than a first time period, determining that the corresponding battery pack is in an overvoltage state; When it is detected that any voltage data is lower than a second voltage threshold and lasts for more than a second time period, it is determined that the corresponding battery pack is in an undervoltage state.

7. The energy storage system fault diagnosis method according to claim 1, characterized in that: The processing of the battery data based on the smart contract to determine the operating status of each battery pack includes: Inputting the battery data into a battery remaining capacity SOC estimation model to obtain an SOC prediction value of each battery pack; When it is detected that the difference between the actual SOC value of any battery pack and the corresponding predicted SOC value is greater than a first threshold, it is determined that the current battery pack is in a fault state.

8. The energy storage system fault diagnosis method according to claim 7, characterized in that: The processing of the battery data based on the smart contract to determine the operating status of each battery pack includes: Inputting the battery data into a Kalman filter model to obtain a first SOC prediction value of each battery pack; Inputting the battery data into an open circuit voltage prediction model to obtain a second SOC prediction value of each battery pack; For any battery pack, if the difference between the first SOC prediction value and the second SOC prediction value of the current battery pack is less than a second threshold, a fault diagnosis is performed on the current battery pack based on the first SOC prediction value and / or the second SOC prediction value; If the difference between the first SOC prediction value and the second SOC prediction value of the current battery pack is greater than or equal to the second threshold, the fault diagnosis process is executed.

9. The energy storage system fault diagnosis method according to claim 8, characterized in that: If the difference between the first SOC prediction value and the second SOC prediction value of the current battery pack is greater than or equal to the second threshold, the fault diagnosis process is executed, including: Check whether there is a fault code matching the current SOC fault event in the fault code library; If there is a corresponding fault code, determining the cause of the fault based on the fault code; If there is no corresponding fault code, the current SOC fault event is recorded and the fault code library is updated.

10. The energy storage system fault diagnosis method according to claim 1, characterized in that: The determining of the fault information of the current battery pack based on the preset multiple diagnostic dimensions includes: For any diagnostic dimension, extract the corresponding diagnostic parameter from the battery data; The diagnostic parameters of each diagnostic dimension are processed based on a machine learning model to determine the fault information, where the fault information includes: fault type, fault location or fault level.

11. The energy storage system fault diagnosis method according to claim 10, characterized in that: The processing of the diagnostic parameters of each diagnostic dimension based on the machine learning model to determine the fault information includes: Determine comprehensive diagnostic parameters based on diagnostic parameters of each diagnostic dimension and corresponding weights; A match is performed in a fault information library based on the comprehensive diagnostic parameters. If there is fault information matching the comprehensive diagnostic parameters, the corresponding fault information is output; if there is no fault information matching the comprehensive diagnostic parameters, the corresponding fault information is added to the fault information library.

12. The energy storage system fault diagnosis method according to claim 1, characterized in that: The verifying the fault information based on other blockchain nodes in the current blockchain network includes: Broadcast the fault information in the current blockchain network and receive confirmation messages broadcasted by other blockchain nodes; If the number of blockchain nodes that send the confirmation message in the current blockchain network is greater than the third threshold, it is confirmed that the fault information verification has passed; If the number of blockchain nodes that send the confirmation message in the current blockchain network is less than or equal to the third threshold, it is confirmed that the fault information verification has failed.

13. The energy storage system fault diagnosis method according to claim 1, characterized in that: After executing the early warning processing related to the fault information, the method further includes: Generate a hash fingerprint corresponding to the fault information; The hash fingerprint is stored in the current blockchain node in the form of blockchain data, and the hash fingerprint is broadcasted in the current blockchain network.

14. The energy storage system fault diagnosis method according to claim 1, characterized in that: The method further comprises: Diagnose the hardware layer, protocol layer and application layer of the energy storage system based on the smart contract to determine fault information; Wherein, diagnosing the hardware layer includes: Determine whether the wiring harness is short-circuited / short-circuited or the bus transceiver is faulty based on the differential voltage of the bus transceiver, or locate the node with abnormal contact impedance based on the bus voltage waveform, or determine whether the power supply is faulty based on the output voltage of the DC-DC converter; The diagnosing of the protocol layer includes: judging whether the protocol stack configuration is wrong based on a receiving error counter and a sending error counter.

15. A fault diagnosis device for an energy storage system, characterized in that: include: An acquisition module is used to acquire battery data of each battery pack controlled by the current energy storage system; A storage module, used to store the battery data in the form of blockchain data in each blockchain node of the current blockchain network based on a hash algorithm and an encryption algorithm; A processing module, used to process the battery data based on the smart contract, determine the operating status of each battery pack, and when any battery pack is detected to be in a fault state, determine the fault information of the current battery pack based on a plurality of preset diagnostic dimensions; The processing module is also used to verify the fault information based on other blockchain nodes in the current blockchain network. If the verification is successful, early warning processing related to the fault information is executed. If the verification fails, the master node is re-elected in the current blockchain network.

16. An energy storage system, characterized in that: It comprises a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the energy storage system executes the method according to any one of claims 1 to 14.

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