CN115447439A - Charging safety early warning method, system, equipment and medium based on battery temperature - Google Patents
Charging safety early warning method, system, equipment and medium based on battery temperature Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/545—Temperature
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Abstract
The embodiment of the specification provides a charging safety early warning method, a system, equipment and a medium based on battery temperature, wherein the method comprises the steps of obtaining historical battery charging data; preprocessing the data; obtaining temperature difference characteristics of the preprocessed data through a characteristic derivation method, and determining a temperature difference value; respectively determining an absolute threshold and a relative threshold of the temperature according to the highest monomer temperature value and the temperature difference value of the battery through an abnormality detection algorithm; the method comprises the steps of obtaining real-time charging data of a battery, determining a highest monomer temperature value and a lowest monomer temperature value, comparing the highest monomer temperature value with an absolute threshold value, or comparing the difference value of the highest monomer temperature value and the lowest monomer temperature value with a relative threshold value to realize battery temperature monitoring in the charging process, and stopping battery charging action if any threshold value is exceeded. According to the invention, the temperature threshold of the vehicle type is determined through big data analysis, the phenomenon of missing report of the BMS system is effectively avoided, time is provided for preventing real occurrence of safety accidents, and the accident occurrence probability is greatly reduced.
Description
Technical Field
The present document relates to the field of data analysis technologies, and in particular, to a charging safety warning method, system, device, and medium based on battery temperature.
Background
The power battery is a power source for providing power source for the tool, and is a storage battery for providing power for electric automobiles, electric trains, electric bicycles and golf carts.
The BMS (battery management system) is an essential device of the electric vehicle, and its most basic functions are to monitor the operating state of the battery, predict the battery capacity (SOC) of the power battery and the corresponding remaining driving distance, and perform battery management to prevent discharge, charge, overheat, and serious voltage imbalance among the unit batteries.
When the temperature of the battery is abnormal, the charging pile can give an alarm or stop the battery according to a message instruction of the battery management system BMS for stopping the battery, and an early warning strategy in the battery management system is judged by depending on a temperature threshold value. However, the self-ignition accident of the existing electric car can be known, the possibility that the safety accident can not be identified exists in the strategy inside the power battery management system, the strategy of the existing BMS for temperature early warning is relatively lagged, and when the BMS sends an instruction, the situation is very serious, and timely and effective measures cannot be taken.
In view of the above, it is desirable to provide a charging safety pre-warning method for improving the prediction accuracy of a battery temperature by assisting a safety strategy in an existing power battery management system.
Disclosure of Invention
One or more embodiments of the present specification provide a charging safety pre-warning method based on a battery temperature, including the steps of:
acquiring historical battery charging data; preprocessing the data; obtaining temperature difference characteristics of the preprocessed data through a characteristic derivation method, and determining a temperature difference value; respectively determining an absolute threshold and a relative threshold of the charging temperature according to the highest monomer temperature value and the temperature difference value of the battery through an anomaly detection algorithm; acquiring real-time charging data of a battery, and determining a highest monomer temperature value and a lowest monomer temperature value; and if the highest monomer temperature value exceeds the absolute threshold of the charging temperature, or the difference value of the highest monomer temperature value and the lowest monomer temperature value exceeds the relative threshold of the charging temperature, stopping the battery charging action.
One or more embodiments of the present specification provide a charging safety warning system based on a battery temperature, the data acquisition unit: acquiring historical battery charging data or battery real-time charging data;
a data preprocessing unit: the data acquisition unit is used for acquiring data;
a temperature difference value determination unit: according to the preprocessing data determined by the data preprocessing unit, acquiring temperature difference characteristics by a characteristic derivation method, and determining a temperature difference value;
a threshold calculation unit: respectively determining an absolute threshold and a relative threshold of the charging temperature according to the highest monomer temperature value and the temperature difference value of the battery in the preprocessed data through an anomaly detection algorithm;
a battery monitoring unit: the real-time charging data of the battery are acquired by the data acquisition unit, and the absolute threshold and the relative threshold are determined by the threshold calculation unit; and monitoring whether the highest monomer temperature value exceeds an absolute threshold value of the charging temperature or the difference value of the highest monomer temperature value and the lowest monomer temperature value exceeds a relative threshold value of the charging temperature, and stopping the charging action of the battery.
One or more embodiments of the present specification provide a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the charging safety precaution method based on battery temperature as described above.
One or more embodiments of the present specification provide a storage medium, a computer-readable storage medium, which stores a computer program, which, when executed by a processor, implements the battery temperature-based charging safety warning method as described above.
According to the invention, the highest monomer temperature value and the lowest monomer temperature value of the battery are collected, the vehicle type temperature threshold value is determined through big data analysis and is used as an index for evaluating the health state of the battery, the abnormal state of the charging temperature of the vehicle is identified and monitored through the temperature threshold value, the phenomenon of failure report of a BMS system is effectively avoided, time is provided for preventing real safety accidents, and the accident occurrence probability is greatly reduced.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and that other drawings can be obtained by those skilled in the art without inventive exercise.
Fig. 1 is a flowchart of a charging safety warning method based on battery temperature according to one or more embodiments of the present disclosure;
fig. 2 is a diagram of a data acquisition and storage framework in a charging safety warning method based on battery temperature according to one or more embodiments of the present disclosure;
fig. 3 is a block diagram of a data preprocessing flow in a battery temperature-based charging safety warning method according to one or more embodiments of the present disclosure;
fig. 4 is a flow chart illustrating a battery temperature monitoring process in a charging safety warning method based on battery temperature according to one or more embodiments of the present disclosure;
fig. 5 is a schematic block diagram of a charging safety warning system based on battery temperature according to one or more embodiments of the present disclosure;
fig. 6 is a schematic structural diagram of a computer device according to one or more embodiments of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be clearly and completely described below with reference to the drawings in one or more embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.
The invention is described in detail below with reference to specific embodiments and the accompanying drawings.
Method embodiment
According to an embodiment of the present invention, a charging safety early warning method based on battery temperature is provided, as shown in fig. 1, which is a flowchart of the charging safety early warning method based on battery temperature provided in this embodiment, and the charging safety early warning method based on battery temperature according to the embodiment of the present invention includes the steps of:
s1, acquiring historical battery charging data; preferably, as shown in fig. 2, the historical charging data is from an HDFS storage system, the external gun data of the charging pile is sent to an externally open port of the big data cluster through a tcp protocol, the cluster uses lvs (Linux Virtual Server) to perform load balancing and then is received by the flux, and the flux sinks to the HDFS storage after processing the data by the interceptor.
S2, preprocessing the data (characteristic engineering) through the acquired battery charging data;
s3, obtaining temperature difference characteristics of the preprocessed data through a characteristic derivation method, and determining a temperature difference value;
s4, respectively determining an absolute threshold and a relative threshold of the charging temperature according to the highest monomer temperature value and the temperature difference value of the battery through an abnormality detection algorithm;
and S5, acquiring real-time battery charging data, determining the highest monomer temperature value and the lowest monomer temperature value, and monitoring to stop battery charging action if the highest monomer temperature value exceeds the absolute threshold of the charging temperature in the step S4 or the difference value of the highest monomer temperature value and the lowest monomer temperature value exceeds the relative threshold of the charging temperature.
This embodiment, gather the highest monomer temperature value of battery, minimum monomer temperature value through the charging station to establish motorcycle type temperature threshold value through big data analysis, regard it as an index of aassessment battery health status, discern the monitoring vehicle charging temperature abnormal state through the temperature threshold value, realize effectually avoiding because the phenomenon emergence that the BMS system missed the report, provide time, greatly reduced accident probability for preventing the real emergence of incident.
Preferably, as shown in fig. 3, the preprocessing of the data includes three parts, namely data selection, data cleaning and data exploration, wherein,
data selection: acquiring the last year calendar history data acquired by the HDFS system, and extracting a certain amount of sample data in the last year data by adopting a hierarchical sampling method (so that the sample size of each month and each region is kept balanced).
Data cleaning: carrying out processing such as abnormal value deletion, duplication removal and mismatching value deletion on the extracted sample data;
data exploration: and analyzing the trend and the overall summarization of each field of the cleaned data, wherein the trend and the overall summarization comprise descriptive statistical indexes such as maximum values, minimum values, median, quartiles, mode and the like.
In this embodiment, the data is cleaned to remove the interference data, so that the analysis and calculation processes are more accurate and faster, and the purpose of data exploration is to know the value range, distribution, relationship between fields and the business meaning represented by the fields.
In this embodiment, the temperature difference value is determined by the difference between the highest cell temperature and the lowest cell temperature using the feature derivation method, and since the directly collected data features are basic features, up to 70 more items. Most of the basic variables have no great significance to the current problem and are not suitable for temperature anomaly analysis. After the basic information is transformed and combined, the method has higher information value and improves the analysis efficiency.
Preferably, in this embodiment, the anomaly detection algorithm determines the absolute threshold and the relative threshold of the battery charging temperature by using an outlier algorithm of density, and the specific principle of the algorithm is as follows:
the algorithm principle is as follows: and searching a data interval [ a, b ] with the highest density and capable of covering 95.4% of sample data by using an iterative method, wherein a is the minimum value under the SOC, and b is the maximum value under the SOC. For data conforming to normal distribution, the correction result is [ a- δ σ, b + δ σ ], and b + δ σ is the final output threshold, where σ is the variance and δ is the coefficient, where 1 is taken, and the data of the sample is the data in steps S2 and S3.
Absolute threshold of charging temperature: obtaining the highest monomer temperature data through the density outlier algorithm;
relative threshold of charging temperature: the temperature difference data was obtained by the above density outlier algorithm.
Preferably, as shown in fig. 4, the charging process battery temperature monitoring process includes the following steps:
a1, collecting real-time charging data of a battery, and determining a highest monomer temperature value and a lowest monomer temperature value;
a2, judging whether the highest monomer temperature value exceeds an absolute threshold of the charging temperature, stopping the charging action of the battery, and if not, executing the next step;
a3, calculating and determining a difference value according to the highest monomer temperature value and the lowest monomer temperature value, and stopping the battery charging action if the difference value exceeds a relative threshold value of the charging temperature; if not, returning to the step A1.
In order to detect the effectiveness of the method, specific cases are applied and tested by the method provided by the embodiment, for example, the 2021 northern steam EU5 charging process is monitored and early-warned, the highest allowable temperature of the northern steam EU5 is 55 ℃, early warning is performed when the charging temperature reaches 55 ℃, abnormal charging behaviors are identified by the temperature early-warning method for 18 times in all northern steam EU5 data charged all the year round, and it is monitored by the embodiment that the output absolute threshold value is smaller than the highest allowable temperature of 55 ℃.
For example, the data of the Gelidihao EV253 vehicle with historical safety accidents are identified and analyzed by the temperature early warning method to realize abnormal warning, and early warning is carried out 79 minutes before the real accidents occur.
According to the case, the method can be used for early warning the fault vehicle which cannot be identified by the BMS or the vehicle with potential safety hazard in advance through the charging temperature data, effectively avoiding the phenomenon of missed report of the BMS system, and providing time for preventing real safety accidents.
System embodiment
According to an embodiment of the present invention, a charging safety pre-warning system based on battery temperature is provided, as shown in fig. 5, the charging safety pre-warning system based on battery temperature provided in this embodiment is a block diagram, and the charging safety pre-warning system based on battery temperature according to the embodiment of the present invention includes:
a data acquisition unit: acquiring historical charging data or real-time charging data of the battery;
a data preprocessing unit: the data acquisition unit is used for acquiring data;
a temperature difference value determination unit: according to the preprocessing data determined by the data preprocessing unit, acquiring temperature difference characteristics by a characteristic derivation method, and determining a temperature difference value;
a threshold calculation unit: respectively determining an absolute threshold and a relative threshold of the charging temperature according to the highest monomer temperature value and the temperature difference value of the battery in the preprocessed data through an anomaly detection algorithm;
a battery monitoring unit: the real-time charging data of the battery are acquired by the data acquisition unit, and the absolute threshold and the relative threshold are determined by the threshold calculation unit; and monitoring whether the highest monomer temperature value exceeds an absolute threshold value of the charging temperature or the difference value of the highest monomer temperature value and the lowest monomer temperature value exceeds a relative threshold value of the charging temperature, and stopping the charging action of the battery.
In this embodiment, data acquired by the data acquisition unit is from the storage unit, external gun data of the charging pile is sent to an externally open port of the big data cluster through a tcp protocol, the cluster uses lvs (Linux Virtual Server ) for load balancing and then is received by the flux, and the flux sinks to the HDFS for storage (for example, stores to N DataNote units) after processing the data by the interceptor.
Preferably, the data preprocessing unit preprocesses the data, including three parts of data selection, data cleaning and data exploration, wherein,
data selection: acquiring the recent calendar history data acquired by the HDFS, and extracting a certain amount of sample data in the data of the recent year by adopting a hierarchical sampling method (so that the sample size per month and per region is kept balanced).
Data cleaning: carrying out processing such as abnormal value deletion, duplication removal and mismatching value deletion on the extracted sample data;
data exploration: and analyzing the trend and the overall summarization of each field of the cleaned data, wherein the trend and the overall summarization comprise descriptive statistical indexes such as maximum values, minimum values, median, quartiles, mode and the like.
In this embodiment, the temperature difference value determining unit determines the temperature difference value by using a characteristic derivation method based on the difference between the highest cell temperature and the lowest cell temperature;
the threshold calculation unit specifically determines the absolute threshold and the relative threshold of the battery charging temperature through an outlier algorithm of density, and the specific principle of the algorithm is as follows:
the algorithm principle is as follows: searching a data interval [ a, b ] with the highest density and capable of covering 95.4% of sample data by using an iterative method, wherein a is the minimum value under the SOC, and b is the maximum value under the SOC; for data conforming to normal distribution, the correction result needs to be carried out on the original region, namely [ a-delta sigma, b + delta sigma ], and b + delta sigma is the threshold value of the final output, wherein sigma is the variance, delta is the coefficient, and 1 is taken here.
Absolute threshold of charging temperature: obtaining the highest monomer temperature data through the density outlier algorithm;
relative threshold of charging temperature: the temperature difference data was obtained by the above density outlier algorithm.
The detection process realized by the battery monitoring unit comprises the following steps:
b1, determining the highest monomer temperature value and the lowest monomer temperature value through the real-time charging data of the battery acquired by the data acquisition unit;
b2, judging whether the highest monomer temperature value exceeds an absolute threshold of the charging temperature, and stopping the battery charging action; if not, executing the next step;
and B3, calculating and determining a difference value according to the highest monomer temperature value and the lowest monomer temperature value, stopping the battery charging action if the difference value exceeds a relative threshold value of the charging temperature, and returning to the step B1 if the difference value does not exceed the relative threshold value of the charging temperature.
As shown in fig. 6, the present invention further provides a computer-readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing the charging safety warning method based on the battery temperature in the above embodiment, or the computer program, when being executed by the processor, implementing the charging safety warning method based on the battery temperature in the above embodiment, the computer program, when being executed by the processor, implementing the following method steps:
s1, acquiring historical charging data of a battery; preferably, in this embodiment, as shown in fig. 2, the historical charging data is from an HDFS storage unit, the external gun data of the charging pile is sent to an externally open port of the big data cluster through a tcp protocol, the cluster uses a lvs server to perform load balancing and then is received by the flux, and the flux sinks to the HDFS storage after processing the data by the interceptor.
S2, preprocessing the data through the acquired battery charging data;
s3, determining a temperature difference value of the preprocessed data through a characteristic derivation method;
s4, respectively determining an absolute threshold and a relative threshold of the charging temperature according to the highest monomer temperature value and the temperature difference value of the battery through an abnormality detection algorithm;
and S5, acquiring real-time charging data of the battery, determining the highest monomer temperature value and the lowest monomer temperature value, and monitoring to stop the battery charging action if the highest monomer temperature value exceeds the absolute threshold of the charging temperature in the step S4 or the difference value of the highest monomer temperature value and the lowest monomer temperature value exceeds the relative threshold of the charging temperature.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A charging safety early warning method based on battery temperature is characterized by comprising the following steps:
acquiring historical battery charging data; preprocessing the data; obtaining temperature difference characteristics of the preprocessed data through a characteristic derivation method, and determining a temperature difference value; respectively determining an absolute threshold and a relative threshold of the charging temperature according to the highest monomer temperature value and the temperature difference value of the battery through an anomaly detection algorithm; acquiring real-time charging data of a battery, and determining a highest monomer temperature value and a lowest monomer temperature value; and if the highest monomer temperature value exceeds the absolute threshold of the charging temperature, or the difference value of the highest monomer temperature value and the lowest monomer temperature value exceeds the relative threshold of the charging temperature, stopping the battery charging action.
2. The battery temperature-based charging safety warning method according to claim 1, wherein the data preprocessing comprises data selection, data cleaning and data exploration.
3. The battery temperature-based charging safety pre-warning method according to claim 1, wherein the anomaly detection algorithm is a density outlier algorithm, and the calculating includes:
searching a data interval [ a, b ] with the highest density and capable of covering 95.4% of sample data by using an iterative method, wherein a is the minimum value, and b is the maximum value;
for data conforming to normal distribution, the correction result is [ a-delta sigma, b + delta sigma ] on the original region, and b + delta sigma is the final output threshold; wherein σ is a variance and δ is a coefficient;
absolute threshold of charging temperature: obtaining by an outlier algorithm of said density using the highest monomer temperature data;
relative threshold of charging temperature: the temperature difference data is obtained by an outlier algorithm of the density.
4. The battery temperature-based charging safety pre-warning method according to claim 1, wherein the monitoring of the battery temperature during charging specifically comprises the steps of:
a1, collecting real-time charging data of a battery, and determining a highest monomer temperature value and a lowest monomer temperature value;
a2, judging whether the highest monomer temperature value exceeds an absolute threshold of the charging temperature, stopping the charging action of the battery, and if not, executing the next step;
a3, calculating and determining a difference value according to the highest monomer temperature value and the lowest monomer temperature value, and stopping the battery charging action if the difference value exceeds a relative threshold value of the charging temperature; if not, returning to the step A1.
5. A charging safety early warning system based on battery temperature is characterized by comprising
A data acquisition unit: acquiring historical battery charging data or battery real-time charging data;
a data preprocessing unit: the data acquisition unit is used for acquiring data;
a temperature difference value determination unit: according to the preprocessing data determined by the data preprocessing unit, acquiring temperature difference characteristics by a characteristic derivation method, and determining a temperature difference value;
a threshold calculation unit: respectively determining an absolute threshold and a relative threshold of the charging temperature according to the highest monomer temperature value and the temperature difference value of the battery in the preprocessed data through an anomaly detection algorithm;
a battery monitoring unit: the real-time charging data of the battery are acquired by the data acquisition unit, and the absolute threshold and the relative threshold are determined by the threshold calculation unit; and stopping the battery charging action if the highest monomer temperature value exceeds the absolute threshold of the charging temperature or the difference value of the highest monomer temperature value and the lowest monomer temperature value exceeds the relative threshold of the charging temperature.
6. The battery temperature-based charging safety warning system of claim 5, wherein the data preprocessing unit preprocesses data including data selection, data cleaning and data exploration.
7. The battery temperature-based charging safety warning system according to claim 5, wherein the threshold calculation unit determines the absolute threshold and the relative threshold of the charging temperature by an outlier algorithm of density, as follows:
searching a data interval [ a, b ] with the highest density and capable of covering 95.4% of samples by using an iteration method, wherein a is the minimum value, and b is the maximum value; for data conforming to normal distribution, the correction result on the original region is [ a-delta sigma, b + delta sigma ], and b + delta sigma is the threshold of the final output, wherein sigma is the variance, and delta is the coefficient;
absolute threshold of charging temperature: obtaining by an outlier algorithm of said density using the highest monomer temperature data;
relative threshold of charging temperature: the temperature difference data is obtained by an outlier algorithm of the density.
8. The battery temperature-based charging safety warning system according to claim 5, wherein the battery monitoring unit implements a detection process comprising the steps of:
b1, determining the highest monomer temperature value and the lowest monomer temperature value through the real-time charging data of the battery acquired by the data acquisition unit;
b2, judging whether the highest monomer temperature value exceeds an absolute threshold of the charging temperature, and stopping the battery charging action; if not, executing the next step;
and B3, calculating and determining a difference value according to the highest monomer temperature value and the lowest monomer temperature value, stopping the battery charging action if the difference value exceeds a relative threshold value of the charging temperature, and returning to the step B1 if the difference value does not exceed the relative threshold value of the charging temperature.
9. Computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the battery temperature based charging safety warning method according to any of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the battery temperature-based charging safety warning method according to any one of claims 1 to 4.
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| CN115986874A (en) * | 2022-12-26 | 2023-04-18 | 昆山金鑫新能源科技股份有限公司 | Battery charging safety monitoring and early warning method and system |
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