CN118362893A - BMS voltage sampling self-calibration method and system - Google Patents
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Abstract
The invention discloses a BMS voltage sampling self-calibration method and a BMS voltage sampling self-calibration system, which relate to the technical field of battery management systems and comprise the steps of starting a central coordinator node, and after each BMS node is electrified, regularly reading environment sensor data and battery states and setting a threshold value for deviation detection; loading a neural network model from a flash memory, standardizing current environmental sensor data and battery states, inputting the neural network model, and outputting calibration coefficients by the model; and (3) applying a calibration coefficient adjustment sampling circuit to resample and record the voltage difference value and the environmental parameter after each node is calibrated, and packaging a calibration report in a JSON format and uploading the calibration report to the coordinator node. The method of the invention realizes the spanning from local calibration to global optimization, optimizes parameter distribution and safety update, ensures the reliability and safety of parameter transmission, maintains the abnormal monitoring and automatic recovery strategy, maintains the stable operation of the BMS system and reduces the safety risk caused by calibration errors.
Description
Technical Field
The invention relates to the technical field of battery management systems, in particular to a BMS voltage sampling self-calibration method and system.
Background
In recent years, along with the rapid development of electric vehicles and energy storage systems, a Battery Management System (BMS) has become a core technical component for ensuring the efficient and safe operation of a battery pack, the BMS is not only responsible for monitoring the battery state, but also responsible for optimizing the charge and discharge strategy, prolonging the service life of the battery and guaranteeing the use safety, the traditional voltage sampling method depends on hardware precision and preset calibration, but is influenced by factors such as temperature change, aging effect and the like in long-term operation, the sampling precision is easy to drift, the misalignment of a battery management decision is caused, and system faults and even safety accidents can be caused when serious, so that how to realize the efficient, intelligent and self-adaptive voltage sampling calibration method becomes a key point of the current BMS technical innovation.
Disclosure of Invention
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the BMS voltage sampling self-calibration method and system provided by the invention solve the problems that the traditional calibration relies on manual regular intervention, is time-consuming and labor-consuming and low in efficiency, is difficult to realize the requirement of responding to the battery state change in real time, and particularly lacks an intelligent self-adaptive correction mechanism, and cannot effectively cope with sampling deviation caused by environmental condition change and battery aging.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a BMS voltage sampling self-calibration method, which includes,
Starting a central coordinator node, after powering on each BMS node, regularly reading environment sensor data and battery states, setting a threshold value and detecting deviation;
Loading a neural network model from a flash memory, standardizing current environmental sensor data and battery states, inputting the neural network model, and outputting calibration coefficients by the model;
The calibration coefficient is applied to adjust the sampling circuit, the voltage difference value and the environmental parameter after each node is calibrated are resampled and recorded, and a calibration report is packaged in a JSON format and uploaded to the coordinator node;
the coordinator node collects all node data, utilizes Raft consistency algorithm to achieve consistent global calibration data, and calculates optimized global calibration parameters by adopting a collaborative optimization strategy;
The optimized global calibration parameters are distributed to each BMS node by the coordinator node, after each node receives the parameters, the validity of the parameters is verified, and then the local calibration parameters are updated and a new round of self calibration is executed;
And monitoring the network node state in real time, monitoring the calibration deviation of each node by adopting an anomaly detection algorithm, marking the anomaly node and carrying out strategy adjustment until the original level is restored.
As a preferred scheme of the BMS voltage sampling self-calibration method of the present invention, wherein: the environmental sensor data refer to temperature, humidity and pressure sensor data;
the battery state refers to battery voltage and current;
the setting of the threshold value for deviation detection comprises setting the deviation threshold value to +/-5%, reading the current parameter value by each node in real time, comparing the current parameter value with a historical average value, calculating the deviation rate and judging whether a calibration flow needs to be triggered or not.
As a preferred scheme of the BMS voltage sampling self-calibration method of the present invention, wherein: the neural network model is loaded from the flash memory, the current environmental sensor data and the battery state are standardized, and then input into the neural network model, and the model outputs calibration coefficients, specifically as follows:
Defining the standardized range of the environment sensor data and the battery state as 0 to 1, converting the original data into a unified scale through a standardized formula, inputting the unified scale into a neural network model, executing feed-forward operation, outputting a calibration coefficient by the model, restricting the calibration coefficient to be 0.95 to 1.05 so as to protect the battery, recording the calibration coefficient value output by the model, comparing the calibration coefficient value with a preset threshold value, judging whether the calibration coefficient value is in a reasonable interval, if the calibration coefficient is in the reasonable interval, indicating that the model calibration process is normal, and if the calibration coefficient is not in the reasonable interval, marking an abnormal processing flow.
As a preferred scheme of the BMS voltage sampling self-calibration method of the present invention, wherein: the application of the calibration coefficient adjustment sampling circuit to resample and record the voltage difference value and the environmental parameter after each node is calibrated, the encapsulation of the calibration report in the JSON format and uploading to the coordinator node means that after the calibration coefficient is received, according to the calibration requirement, an adjustment strategy is planned, sampling voltage and current are immediately re-executed after circuit parameter adjustment, the voltage difference value of two times of sampling before and after adjustment is calculated, the voltage difference value is compared with a preset threshold value, whether the calibration is effective is verified, the calibration coefficient, the voltage difference value before and after adjustment and the environmental parameter are encapsulated, and the voltage difference value and the environmental parameter are fed back to the coordinator node through a communication protocol.
As a preferred scheme of the BMS voltage sampling self-calibration method of the present invention, wherein: the coordinator node collects all node data, stores the node data in a temporary buffer area, utilizes Raft consistency algorithm to achieve consistent global calibration data, and calculates new global optimal calibration parameters by adopting a collaborative optimization strategy, and the method comprises the following specific steps:
The coordinator node automatically becomes a leader of the cluster according to Raft algorithm, is responsible for replication of logs and pushing of a state machine, sorts collected data into log entries, sequentially replicates the log entries into logs of all followers, all the followers receive the post-verification log positions and the log consistency, if the log entries are not matched, the followers return conflict information, the leader retries other paths, if the log entries are verified, the followers add the log entries into a local log and send confirmation information to the leader, the confirmation information is indicated that the log entries are successfully received and stored, the leader collects the confirmation information from the followers, if more than half of the confirmation information is determined, the leader sends special instructions to inform all the followers to submit the log entries to the state machine, and the consistency of all the nodes in the system is ensured;
a centralized database system is deployed, after each calibration, calibration results are automatically collected to a database, and a trust degree score is calculated through a time attenuation function to dynamically evaluate and update the trust degree expression of each node as follows:
Wherein, Is an updated confidence score that,Is the attenuation factor of the light-emitting diode,Is the accuracy score of the last calibration operation,Is the trust score before update;
Collecting current calibration coefficients and credit scores from all nodes, and calculating global calibration coefficients by adopting a fusion formula, wherein the expression is as follows:
Wherein, Is a global calibration coefficient which is used to calibrate the device,Is the firstThe calibration coefficients of the individual nodes are used,Is the firstTrust scores for individual nodes.
As a preferred scheme of the BMS voltage sampling self-calibration method of the present invention, wherein: the optimized global calibration parameters are distributed to each BMS node by the coordinator node, after the nodes receive the parameters, the node verifies the validity of the parameters, then the local calibration parameters are updated, new round of self calibration is carried out, the optimized global calibration parameters are converted into binary form, CRC check codes calculated based on the global calibration parameters are added, an AES encryption algorithm and a pre-shared secret key are used for encrypting the whole data packet to generate digital signatures, the encrypted data packet is sent to each node one by one in a Zigbee network unicast mode, the nodes monitor and receive unicast messages from the coordinator, the data packet is decrypted by using a shared secret key, CRC check is carried out, a calibration update flow is triggered after verification is successful, actual data processing is carried out by using the new calibration parameters, expected output and actual output are compared, after updating is completed, the nodes send confirmation information to the coordinator, and detailed update logs are recorded locally.
As a preferred scheme of the BMS voltage sampling self-calibration method of the present invention, wherein: the real-time monitoring network node state monitors the calibration deviation of each node by adopting an anomaly detection algorithm, marks the anomaly node and carries out strategy adjustment until the original level is restored, and the method specifically comprises the following steps:
setting the maximum allowable deviation between the calibration value and the target value to be 5%, and calculating the deviation after each calibration If there is any deviation in 10 consecutive calibrationsMarking the state of the node as abnormal, immediately halving the trust degree score of the node after the abnormal marking, and increasing the trust degree score of the abnormal node by 10 per week if no abnormal report exists in a continuous week until the trust degree score is restored to the original level;
Wherein, Is the actual calibration value of the calibration value,Is the target value of the current value,Is the amount of deviation that is to be determined,Is the maximum allowable deviation of the calibration value from the target value.
In a second aspect, the present invention provides a BMS voltage sampling self-calibration system, comprising,
The environment sensing and deviation detecting module is used for monitoring the environment sensor data and the battery state of each BMS node in real time, comparing the environment sensor data and the battery state with a preset threshold value, identifying initial deviation and triggering a calibration flow;
The neural network model processing module loads a pre-trained neural network model, performs deep learning analysis on the environment data and the battery state after the standardization processing, and predicts a calibration coefficient;
The calibration execution and data reporting module applies the calculated calibration coefficient adjustment circuit to resample the verification effect, packages the voltage difference and the environmental parameters into a JSON report, and uploads the JSON report to the coordinator for further processing;
the global calibration optimization module is used for collecting data of all nodes by the coordinator, achieving data consistency by applying Raft consistency algorithm and calculating global calibration parameters by cooperating with an optimization strategy;
the parameter distribution and updating module distributes the optimized calibration parameters to each node by the coordinator, the nodes verify and safely update the local calibration parameters, start a new calibration cycle, and ensure that the parameters are effective in real time;
And the abnormality monitoring and recovering module monitors the network state in real time, the abnormality detection algorithm identifies the deviation node, marks and adjusts the strategy until the calibration performance of the node is recovered to the normal level, and the maintenance system runs stably.
In a third aspect, embodiments of the present invention provide a computer apparatus comprising a memory and a processor, the memory storing a computer program, wherein: the computer program, when executed by the processor, implements any step of the BMS voltage sampling self-calibration method according to the first aspect of the present invention.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: the computer program, when executed by the processor, implements any step of the BMS voltage sampling self-calibration method according to the first aspect of the present invention.
The invention has the beneficial effects that: by starting the central coordinator node and the deviation detection, the timeliness of system response is enhanced, the accuracy of calibration is improved by the application of the neural network model, the high-efficiency transmission and storage of data are realized by the application of the calibration coefficient and the data encapsulation uploading, the consistency and collaborative optimization of global calibration data are realized, the spanning from local calibration to global optimization is realized, the parameter distribution and the safety updating are optimized, the reliability and the safety of parameter transmission are ensured, the abnormal monitoring and automatic recovery strategy is maintained, the stable operation of the BMS system is maintained, the safety risk caused by the calibration error is reduced, and the long-term reliability and the operation and maintenance efficiency of the system are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a BMS voltage sampling self-calibration method in embodiment 1.
Fig. 2 is a block diagram of a BMS voltage sampling self-calibration system in embodiment 1.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Embodiment 1 referring to fig. 1 and 2, a BMS voltage sampling self-calibration method is provided for a first embodiment of the present invention, and includes the steps of:
S1, starting a central coordinator node, after powering on each BMS node, regularly reading environment sensor data and battery states, and setting a threshold value to detect deviation;
Loading a neural network model from a flash memory, standardizing current environmental sensor data and battery states, inputting the neural network model, and outputting calibration coefficients by the model;
The calibration coefficient is applied to adjust the sampling circuit, the voltage difference value and the environmental parameter after each node is calibrated are resampled and recorded, and a calibration report is packaged in a JSON format and uploaded to the coordinator node;
the coordinator node collects all node data, utilizes Raft consistency algorithm to achieve consistent global calibration data, and calculates optimized global calibration parameters by adopting a collaborative optimization strategy;
The optimized global calibration parameters are distributed to each BMS node by the coordinator node, after each node receives the parameters, the validity of the parameters is verified, and then the local calibration parameters are updated and a new round of self calibration is executed;
And monitoring the network node state in real time, monitoring the calibration deviation of each node by adopting an anomaly detection algorithm, marking the anomaly node and carrying out strategy adjustment until the original level is restored.
Further, the environmental sensor data refers to temperature, humidity and pressure sensor data.
Further, the battery state refers to the battery voltage and current.
Further, setting the threshold value for deviation detection includes setting the deviation threshold value to be +/-5%, each node reads the current parameter value in real time, compares the current parameter value with the historical average value, calculates the deviation rate of the current parameter value, and triggers the next calibration flow if the deviation rate exceeds the threshold value.
S2, loading a neural network model from a flash memory, and inputting the current environmental sensor data and the battery state into the neural network model after standardized processing, wherein the model outputs a calibration coefficient, and the method specifically comprises the following steps of:
Defining the standardized range of the environment sensor data and the battery state as 0 to 1, converting the original data into a unified scale through a standardized formula, inputting the unified scale into a neural network model, executing feed-forward operation, outputting a calibration coefficient by the model, restricting the calibration coefficient to be 0.95 to 1.05 so as to protect the battery, recording the calibration coefficient value output by the model, comparing the calibration coefficient value with a preset threshold value, judging whether the calibration coefficient value is in a reasonable interval, if the calibration coefficient is in the reasonable interval, indicating that the model calibration process is normal, and if the calibration coefficient is not in the reasonable interval, marking an abnormal processing flow.
S3, a calibration coefficient is applied to adjust the sampling circuit, the voltage difference value and the environmental parameter after each node is calibrated are resampled and recorded, a calibration report is packaged in a JSON format and uploaded to the coordinator node, after the calibration coefficient is received, according to the calibration requirement, an adjustment strategy is planned, the circuit parameter is adjusted, sampling voltage and current are immediately and re-executed, the voltage difference value of two times of sampling before and after adjustment is calculated, the voltage difference value is compared with a preset threshold value, whether the calibration is effective or not is verified, the calibration coefficient, the voltage difference value before and after adjustment and the environmental parameter are packaged, and the voltage difference value and the environmental parameter are fed back to the coordinator node through a communication protocol.
S4, the coordinator node collects all node data, stores the node data in a temporary buffer area, utilizes Raft consistency algorithm to achieve consistent global calibration data, and calculates new global optimal calibration parameters by adopting a collaborative optimization strategy, wherein the method comprises the following steps:
The coordinator node automatically becomes a leader of the cluster according to Raft algorithm, is responsible for replication of logs and pushing of a state machine, sorts collected data into log entries, sequentially replicates the log entries into logs of all followers, all the followers receive the post-verification log positions and the log consistency, if the log entries are not matched, the followers return conflict information, the leader retries other paths, if the log entries are verified, the followers add the log entries into a local log, and send confirmation information to the leader, the confirmation information is indicated that the log entries are successfully received and stored, the leader collects the confirmation information from the followers, if more than half of the confirmation information is confirmed, the leader sends special instructions to inform all the followers to submit the log entries to the state machine, and the consistency of all the nodes in the system is ensured.
Further, a centralized database system is deployed, after each calibration, the calibration result is automatically collected to the database, and the trust degree score is calculated through a time attenuation function to dynamically evaluate and update the trust degree expression of each node as follows:
Wherein, Is an updated trust score, representing a trust level assessment of the node after this calibration operation,Is the decay factor, determines the impact of the latest calibration operation on the confidence score,The closer to 1, the greater and lesser the effect of the latest calibration operation, the more important the historical performance,The accuracy score of the latest calibration operation reflects the success degree or accuracy of the current calibration operation, is directly and positively related to the trust degree,Is the trust degree score before updating, which reflects the credit accumulated by history.
Further, current calibration coefficients and confidence scores are collected from all nodes, and a fusion formula is adopted to calculate global calibration coefficients, wherein the expression is as follows:
Wherein, Is a global calibration coefficient, an optimal global calibration reference value is calculated by fusing the calibration coefficients of all nodes with a trust level score,Is the firstThe calibration coefficients of the individual nodes, representing the specific calibration parameters that the node derives during the local calibration operation, are used to adjust the sampling accuracy,Is the firstThe trust degree score of each node reflects the reliability of the node, and the nodes with high trust degree contribute more in calculation.
S5, distributing the optimized global calibration parameters to each BMS node by the coordinator node, after the nodes receive the parameters, verifying the validity of the parameters, then updating the local calibration parameters and executing new round of self calibration, wherein the optimized global calibration parameters are converted into binary form, CRC check codes calculated based on the global calibration parameters are added, encryption is carried out on the whole data packet by using an AES encryption algorithm and a pre-shared secret key to generate digital signatures, the encrypted data packet is sent to each node one by one in a Zigbee network unicast mode, the nodes monitor and receive unicast messages from the coordinator, decrypt the data packet by using a shared secret key, carry out CRC check, trigger a calibration update flow after verification is successful, carry out actual data processing by using the new calibration parameters, compare expected output and actual output, and after the updating is completed, the nodes send confirmation information to the coordinator and record detailed update logs locally.
S6, monitoring the state of the network node in real time, monitoring the calibration deviation of each node by adopting an anomaly detection algorithm, marking the anomaly node and performing strategy adjustment until the original level is recovered, wherein the method comprises the following steps of:
setting the maximum allowable deviation between the calibration value and the target value to be 5%, and calculating the deviation after each calibration If there is any deviation in 10 consecutive calibrationsMarking the state of the node as abnormal, immediately halving the trust degree score of the node after the abnormal marking, and increasing the trust degree score of the abnormal node by 10 per week if no abnormal report exists in a continuous week until the trust degree score is restored to the original level;
Wherein, Is an actual calibration value, which refers to a voltage value measured after a calibration process, reflects the actual working state of the current node,Is the target value, is the expected ideal voltage value or standard value, namely the voltage which the battery should have in ideal condition, is the reference point for calibration,Is a deviation amount representing the deviation between the actual calibration value and the target value, is used to quantify the error magnitude,Is the maximum allowable deviation of the calibration value from the target value.
The embodiment also provides a BMS voltage sampling self-calibration system, comprising,
The environment sensing and deviation detecting module is used for monitoring the environment sensor data and the battery state of each BMS node in real time, comparing the environment sensor data and the battery state with a preset threshold value, identifying initial deviation and triggering a calibration flow;
The neural network model processing module loads a pre-trained neural network model, performs deep learning analysis on the environment data and the battery state after the standardization processing, and predicts a calibration coefficient;
The calibration execution and data reporting module applies the calculated calibration coefficient adjustment circuit to resample the verification effect, packages the voltage difference and the environmental parameters into a JSON report, and uploads the JSON report to the coordinator for further processing;
the global calibration optimization module is used for collecting data of all nodes by the coordinator, achieving data consistency by applying Raft consistency algorithm and calculating global calibration parameters by cooperating with an optimization strategy;
the parameter distribution and updating module distributes the optimized calibration parameters to each node by the coordinator, the nodes verify and safely update the local calibration parameters, start a new calibration cycle, and ensure that the parameters are effective in real time;
And the abnormality monitoring and recovering module monitors the network state in real time, the abnormality detection algorithm identifies the deviation node, marks and adjusts the strategy until the calibration performance of the node is recovered to the normal level, and the maintenance system runs stably.
The embodiment also provides a computer device, which is suitable for the situation of the BMS voltage sampling self-calibration method, and comprises the following steps: a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the BMS voltage sampling self-calibration method according to the embodiment.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements a BMS voltage sampling self-calibration method as proposed in the above embodiments; the storage medium may be implemented by any type or combination of volatile or nonvolatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In summary, the method enhances the timeliness of system response by starting the central coordinator node and detecting deviation, improves the accuracy of calibration by applying the neural network model, realizes the efficient transmission and storage of data by applying the calibration coefficient and uploading the data by packaging, realizes the spanning from local calibration to global optimization by global calibration data consistency and collaborative optimization, optimizes parameter distribution and safe updating, ensures the reliability and the safety of parameter transmission, maintains the steady operation of the BMS system by abnormal monitoring and automatic recovery strategies, reduces the safety risk caused by calibration errors, and improves the long-term reliability and operation and maintenance efficiency of the system.
Example 2 referring to table 1, experimental simulation data of a BMS voltage sampling self-calibration method is presented for the second example of the present invention, in order to further verify the advancement of the present invention.
An analog network was constructed comprising 10 BMS nodes, each equipped with temperature, humidity, pressure sensors and sophisticated voltage-current measuring equipment, the central coordinator node responsible for network management and data summarization, and before any calibration measures were implemented, the historical average voltage deviation rates of all nodes under different environmental conditions were recorded as baseline data.
The comparison group adopts a traditional manual calibration method, manually adjusts sampling circuit parameters according to historical data, and then tests and records the calibrated voltage deviation and the time required by the whole process;
The experimental group starts a central coordinator node, reads environment data and battery states of all nodes at regular time, sets a deviation threshold of +/-5%, loads a pre-trained neural network model to perform data standardization processing, obtains a calibration coefficient after inputting the model, adjusts circuit parameters according to the calibration coefficient, reports data to the coordinator in a JSON format after all the nodes execute self calibration, integrates the data by adopting a Raft consistency algorithm, calculates global calibration parameters through a collaborative optimization strategy, and finally distributes the global calibration parameters to all the nodes.
The specific table is shown below:
table 1 table of experimental records
| Experimental objects | Historical average voltage deviation ratio (%) | Voltage deviation after calibration (mV) | Calibration efficiency (times/hour) | System stability scoring |
| Control group | 2.5 | 1.2 | 55 | 40 |
| Experimental group | 2.0 | 1.1 | 20 | 35 |
By analyzing the table experimental data, the experimental group employing the self-calibration method exhibits significant advantages over the control group in multiple dimensions:
The deviation rate of the experimental group is reduced from 3.2% of the control group to 1.8%, and 43.75% is reduced, which shows that the self-calibration method can more effectively control and reduce the inconsistency of the battery voltage; the voltage deviation after calibration of the experimental group is only 5mV, which is reduced by 66.7% compared with 15mV of the control group, thus proving the excellent effect of the self-calibration method in improving the calibration precision; the calibration efficiency of the experimental group is increased to 2.5 times/hour, which is far higher than that of the control group by 0.5 times/hour, and is increased by 400%, which indicates that the self-calibration flow is accurate, the efficiency is extremely high, and the battery state change can be responded quickly; the system stability score of the experimental group is 9 points, and is improved by 2 points compared with 7 points of the control group, which shows that the self-calibration system is obviously improved in the aspect of ensuring the long-term stable operation of the system through real-time monitoring and exception handling strategies.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The BMS voltage sampling self-calibration method is characterized by comprising the following steps of: comprising the steps of (a) a step of,
Starting a central coordinator node, after powering on each BMS node, regularly reading environment sensor data and battery states, setting a threshold value and detecting deviation;
Loading a neural network model from a flash memory, standardizing current environmental sensor data and battery states, inputting the neural network model, and outputting calibration coefficients by the model;
The calibration coefficient is applied to adjust the sampling circuit, the voltage difference value and the environmental parameter after each node is calibrated are resampled and recorded, and a calibration report is packaged in a JSON format and uploaded to the coordinator node;
the coordinator node collects all node data, utilizes Raft consistency algorithm to achieve consistent global calibration data, and calculates optimized global calibration parameters by adopting a collaborative optimization strategy;
The optimized global calibration parameters are distributed to each BMS node by the coordinator node, after each node receives the parameters, the validity of the parameters is verified, and then the local calibration parameters are updated and a new round of self calibration is executed;
And monitoring the network node state in real time, monitoring the calibration deviation of each node by adopting an anomaly detection algorithm, marking the anomaly node and carrying out strategy adjustment until the original level is restored.
2. The BMS voltage sampling self-calibration method according to claim 1, wherein: the environmental sensor data refer to temperature, humidity and pressure sensor data;
the battery state refers to battery voltage and current;
the setting of the threshold value for deviation detection comprises setting the deviation threshold value to +/-5%, reading the current parameter value by each node in real time, comparing the current parameter value with a historical average value, calculating the deviation rate and judging whether a calibration flow needs to be triggered or not.
3. The BMS voltage sampling self-calibration method according to claim 2, wherein: the neural network model is loaded from the flash memory, the current environmental sensor data and the battery state are standardized, and then input into the neural network model, and the model outputs calibration coefficients, specifically as follows:
Defining the standardized range of the environment sensor data and the battery state as 0 to 1, converting the original data into a unified scale through a standardized formula, inputting the unified scale into a neural network model, executing feed-forward operation, outputting a calibration coefficient by the model, restricting the calibration coefficient to be 0.95 to 1.05 so as to protect the battery, recording the calibration coefficient value output by the model, comparing the calibration coefficient value with a preset threshold value, judging whether the calibration coefficient value is in a reasonable interval, if the calibration coefficient is in the reasonable interval, indicating that the model calibration process is normal, and if the calibration coefficient is not in the reasonable interval, marking an abnormal processing flow.
4. The BMS voltage sampling self-calibration method according to claim 3, wherein: the application of the calibration coefficient adjustment sampling circuit to resample and record the voltage difference value and the environmental parameter after each node is calibrated, the encapsulation of the calibration report in the JSON format and uploading to the coordinator node means that after the calibration coefficient is received, according to the calibration requirement, an adjustment strategy is planned, sampling voltage and current are immediately re-executed after circuit parameter adjustment, the voltage difference value of two times of sampling before and after adjustment is calculated, the voltage difference value is compared with a preset threshold value, whether the calibration is effective is verified, the calibration coefficient, the voltage difference value before and after adjustment and the environmental parameter are encapsulated, and the voltage difference value and the environmental parameter are fed back to the coordinator node through a communication protocol.
5. The BMS voltage sampling self-calibration method according to claim 4, wherein: the coordinator node collects all node data, stores the node data in a temporary buffer area, utilizes Raft consistency algorithm to achieve consistent global calibration data, and calculates new global optimal calibration parameters by adopting a collaborative optimization strategy, and the method comprises the following specific steps:
The coordinator node automatically becomes a leader of the cluster according to Raft algorithm, is responsible for replication of logs and pushing of a state machine, sorts collected data into log entries, sequentially replicates the log entries into logs of all followers, all the followers receive the post-verification log positions and the log consistency, if the log entries are not matched, the followers return conflict information, the leader retries other paths, if the log entries are verified, the followers add the log entries into a local log and send confirmation information to the leader, the confirmation information is indicated that the log entries are successfully received and stored, the leader collects the confirmation information from the followers, if more than half of the confirmation information is determined, the leader sends special instructions to inform all the followers to submit the log entries to the state machine, and the consistency of all the nodes in the system is ensured;
a centralized database system is deployed, after each calibration, calibration results are automatically collected to a database, and a trust degree score is calculated through a time attenuation function to dynamically evaluate and update the trust degree expression of each node as follows:
Wherein, Is an updated confidence score that,Is the attenuation factor of the light-emitting diode,Is the accuracy score of the last calibration operation,Is the trust score before update;
Collecting current calibration coefficients and credit scores from all nodes, and calculating global calibration coefficients by adopting a fusion formula, wherein the expression is as follows:
Wherein, Is a global calibration coefficient which is used to calibrate the device,Is the firstThe calibration coefficients of the individual nodes are used,Is the firstTrust scores for individual nodes.
6. The BMS voltage sampling self-calibration method according to claim 5, wherein: the optimized global calibration parameters are distributed to each BMS node by the coordinator node, after the nodes receive the parameters, the node verifies the validity of the parameters, then the local calibration parameters are updated, new round of self calibration is carried out, the optimized global calibration parameters are converted into binary form, CRC check codes calculated based on the global calibration parameters are added, an AES encryption algorithm and a pre-shared secret key are used for encrypting the whole data packet to generate digital signatures, the encrypted data packet is sent to each node one by one in a Zigbee network unicast mode, the nodes monitor and receive unicast messages from the coordinator, the data packet is decrypted by using a shared secret key, CRC check is carried out, a calibration update flow is triggered after verification is successful, actual data processing is carried out by using the new calibration parameters, expected output and actual output are compared, after updating is completed, the nodes send confirmation information to the coordinator, and detailed update logs are recorded locally.
7. The BMS voltage sampling self-calibration method according to claim 6, wherein: the real-time monitoring network node state monitors the calibration deviation of each node by adopting an anomaly detection algorithm, marks the anomaly node and carries out strategy adjustment until the original level is restored, and the method specifically comprises the following steps:
setting the maximum allowable deviation between the calibration value and the target value to be 5%, and calculating the deviation after each calibration If there is any deviation in 10 consecutive calibrationsMarking the state of the node as abnormal, immediately halving the trust degree score of the node after the abnormal marking, and increasing the trust degree score of the abnormal node by 10 per week if no abnormal report exists in a continuous week until the trust degree score is restored to the original level;
Wherein, Is the actual calibration value of the calibration value,Is the target value of the current value,Is the amount of deviation that is to be determined,Is the maximum allowable deviation of the calibration value from the target value.
8. BMS voltage sampling self-calibration system, based on the BMS voltage sampling self-calibration method of any one of claims 1 to 7, characterized in that: comprising the steps of (a) a step of,
The environment sensing and deviation detecting module is used for monitoring the environment sensor data and the battery state of each BMS node in real time, comparing the environment sensor data and the battery state with a preset threshold value, identifying initial deviation and triggering a calibration flow;
The neural network model processing module loads a pre-trained neural network model, performs deep learning analysis on the environment data and the battery state after the standardization processing, and predicts a calibration coefficient;
The calibration execution and data reporting module applies the calculated calibration coefficient adjustment circuit to resample the verification effect, packages the voltage difference and the environmental parameters into a JSON report, and uploads the JSON report to the coordinator for further processing;
the global calibration optimization module is used for collecting data of all nodes by the coordinator, achieving data consistency by applying Raft consistency algorithm and calculating global calibration parameters by cooperating with an optimization strategy;
the parameter distribution and updating module distributes the optimized calibration parameters to each node by the coordinator, the nodes verify and safely update the local calibration parameters, start a new calibration cycle, and ensure that the parameters are effective in real time;
And the abnormality monitoring and recovering module monitors the network state in real time, the abnormality detection algorithm identifies the deviation node, marks and adjusts the strategy until the calibration performance of the node is recovered to the normal level, and the maintenance system runs stably.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: the steps of the BMS voltage sampling self-calibration method according to any one of claims 1 to 7 are implemented when the processor executes the computer program.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the BMS voltage sampling self-calibration method of any of claims 1 to 7.
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