CN114683852A - Early warning method and system for automobile battery discharge process based on multi-sensor fusion - Google Patents

Early warning method and system for automobile battery discharge process based on multi-sensor fusion Download PDF

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CN114683852A
CN114683852A CN202210190060.9A CN202210190060A CN114683852A CN 114683852 A CN114683852 A CN 114683852A CN 202210190060 A CN202210190060 A CN 202210190060A CN 114683852 A CN114683852 A CN 114683852A
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武骥
吕帮
刘奕阳
吴慕遥
刘兴涛
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Hefei University of Technology
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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
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • G08B17/10—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
    • 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
    • B60L2240/10—Vehicle control parameters
    • B60L2240/12—Speed
    • 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
    • B60L2240/10—Vehicle control parameters
    • B60L2240/14—Acceleration
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    • 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
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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Abstract

The embodiment of the invention provides an early warning method and system for a discharging process of an automobile battery based on multi-sensor fusion, and belongs to the technical field of discharging control of automobile batteries. The early warning method comprises the following steps: acquiring a plurality of point values of the current running speed of the automobile, the acceleration of the automobile, the transverse distance between the automobile and other vehicles, the longitudinal distance between the automobile and other vehicles and the bumping degree of the automobile in a preset period to construct a battery discharge state data matrix; determining the current battery discharge danger index according to the battery discharge state data matrix; calculating the sampling frequency of each sensor of the battery box according to the discharge danger index; adjusting each sensor according to the sampling frequency; acquiring an average value of each sensor in a current sampling period; determining the credibility of each sensor according to the average value; calculating a current fire early warning value according to a formula (1); and determining the probability of the current fire according to the fire early warning value.

Description

Early warning method and system for automobile battery discharge process based on multi-sensor fusion
Technical Field
The invention relates to the technical field of discharge control of automobile batteries, in particular to an early warning method and system for an automobile battery discharge process based on multi-sensor fusion.
Background
The prevention of the fire of the automobile battery is an important subject related to the safety of personnel and vehicles and is a great problem restricting the development of electric vehicles. The lithium ion battery which is widely applied to the current power battery has the characteristics of active chemical property, small volume and high energy density, but because of the active property, lithium metal in the lithium ion battery is easy to generate violent oxidation reaction with oxygen in the air, releases a large amount of heat, generates violent combustion, and simultaneously is easy to ignite electrolyte, so that the lithium ion battery has a large risk of spontaneous combustion. In recent years, the three main causes of electric vehicle ignition are battery spontaneous combustion, charging and vehicle collision. The battery fire prevention system on the electric automobile does not completely consider the characteristics of relevant parameters of the automobile battery fire at present, the adopted sensor is single, the general technology is mainly to monitor the temperature in a battery box through a BMS (battery management system), and once the temperature exceeds a threshold value, the output is cut off, so that accidents are prevented. However, the reliability and accuracy of the result obtained by the method of judging only through single information are poor, and erroneous judgment and missed judgment are easily caused. In addition, in the prior art, only static sampling operation is performed on the sensor, environmental factors such as the road condition of the automobile and the like are not considered, and reasonable calculation distribution cannot be realized by automatically adjusting the sampling frequency according to the environment of the automobile. Such as: when the collision risk of the automobile is higher, the risk value of the battery in fire is also higher, and the monitoring force should be increased; when the automobile normally runs, the monitoring force can be properly reduced, but the prior art cannot realize the monitoring force, so that the waste of resources such as computing power and the like is caused.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a system for early warning of a discharging process of an automobile battery based on multi-sensor fusion, and the method and the system can accurately judge the fire risk in the current discharging process of the automobile.
In order to achieve the above object, an embodiment of the present invention provides an early warning method for an automobile discharge process based on multi-sensor fusion, including:
acquiring a plurality of point values of the current running speed of the automobile, the acceleration of the automobile, the transverse distance between the automobile and other vehicles, the longitudinal distance between the automobile and other vehicles and the bumping degree of the automobile in a preset period so as to construct a battery discharge state data matrix;
determining the current battery discharge danger index according to the battery discharge state data matrix;
calculating the sampling frequency of each sensor of the battery box according to the discharge danger index;
adjusting each sensor according to the sampling frequency;
acquiring an average value of each sensor in a current sampling period;
determining the credibility of each sensor according to the average value;
calculating the current fire early warning value according to the formula (1),
Figure BDA0003524907250000021
wherein m is1→nIs the fire alarm value, m1、m2、…、mnRespectively, the credibility of each sensor, n is the number of the types of the sensors,
Figure BDA0003524907250000022
is an orthogonal sum operation;
and determining the probability of the current fire according to the fire early warning value.
Optionally, the obtaining a plurality of point values of the current driving speed of the automobile, the acceleration of the automobile, the lateral distance between the automobile and the other vehicles, the longitudinal distance between the automobile and the other vehicles, and the degree of jounce of the automobile within a predetermined period to construct the battery discharge state data matrix further comprises:
constructing the battery discharge state matrix according to equation (2),
Figure BDA0003524907250000023
wherein, a1、a2、…、aiIs the 1 st to i th point values of the current running speed of the automobile in a sampling period, b1、b2、…、biIs the 1 st to i th point values of the vehicle acceleration in a sampling period, c1、c2、…、ci1 to i-th point values of the lateral distance in a sampling period, d1、d2、…、di1 st to ith point values of the longitudinal distance in a sampling period, e1、e2、…、eiThe 1 st to ith point values of the degree of jounce in the sampling period, and Y is the battery discharge state matrix.
Optionally, determining the current battery discharge risk index according to the battery discharge state data matrix includes:
determining a battery discharge parameter matrix according to equation (3),
M＝[A B C D E]T， (3)
the battery charging method comprises the following steps of calculating a battery charging parameter matrix, calculating a transverse distance point value, calculating a battery charging parameter matrix, calculating a battery charging parameter matrix, and a battery charging parameter matrix, calculating a battery charging parameter matrix, and a battery charging parameter calculating method, and a battery charging parameter calculating a battery charging parameter matrix, and a battery charging parameter calculating method, and a battery charging parameter calculating method, and a battery charging parameter.
Optionally, determining the current battery discharge risk index according to the battery discharge state data matrix includes:
calculating a risk coefficient matrix according to the formula (4) and the formula (5),
Figure BDA0003524907250000031
T＝[Amax Bmax Cmax Dmax Emax]， (5)
wherein R is the risk coefficient matrix, T is a preset threshold matrix, Amax、Bmax、Cmax、Dmax、EmaxAnd the driving speed, the automobile acceleration, the transverse distance, the longitudinal distance and the threshold corresponding to the bumping degree are respectively.
Optionally, determining the current battery discharge risk index according to the battery discharge state data matrix includes:
calculating the battery discharge risk index according to equation (6) and equation (7),
Figure BDA0003524907250000041
α1+α2+α3+α4+α5＝1， (7)
wherein I is the discharge hazard index of the battery, R1、R2、R3、R4And R5Column vectors, α, of the first to fifth columns of the risk coefficient matrix, respectively1、α2、α3、α4To alpha5Respectively corresponding weight parameters.
Optionally, calculating a sampling frequency of each sensor of the battery box according to the discharge risk index includes:
judging the numerical value interval of the sampling frequency;
in the case where it is judged that the sampling frequency is greater than or equal to 0 and less than 35%, the sampling frequency of each sensor is adjusted according to the formula (8),
f＝5I×fmin， (8)
wherein f is the adjusted sampling frequency, and I is the discharge risk index of the battery，fminIs the lowest sampling frequency of the sensor;
in the case where it is judged that the sampling frequency is greater than or equal to 35% and less than 70%, the sampling frequency of each sensor is adjusted according to equation (9),
f＝10I×fmin， (9)
in the case where it is judged that the sampling frequency is greater than or equal to 70% and less than or equal to 100%, the sampling frequency of each sensor is adjusted according to the formula (10),
f＝20I×fmin， (10)。
optionally, the determining the probability of the current fire occurrence according to the fire early warning value includes:
judging a numerical range of the fire early warning value;
determining the probability of fire occurrence to be 0 under the condition that the fire early warning value is judged to be greater than or equal to 0 and less than 0.3;
determining that the sensor is abnormal under the condition that the fire early warning value is judged to be greater than or equal to 0.3 and less than 0.65;
and determining that the fire disaster occurs under the condition that the fire early warning value is judged to be greater than or equal to 0.65 and less than or equal to 1.
Optionally, the obtaining a plurality of point values of the current driving speed of the automobile, the acceleration of the automobile, the lateral distance of the automobile from other vehicles, the longitudinal distance of the automobile from other vehicles and the degree of jounce of the automobile in a predetermined period comprises:
the number of point values of each sensor is corrected according to equation (11),
Figure BDA0003524907250000051
wherein y is the corrected point value, y1、y2And y3The sampling time before correction is t1、t2And t3T is the time corresponding to the modified point value y.
In another aspect, the present invention further provides a pre-warning system for a discharging process of an automobile battery based on multi-sensor fusion, the pre-warning system comprising a processor configured to execute any one of the pre-warning methods described above.
In yet another aspect, the present disclosure also provides a computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform any one of the warning methods described above.
According to the technical scheme, the early warning method and the early warning system for the discharging process of the automobile battery based on the multi-sensor fusion are used for acquiring the running speed, the acceleration, the transverse distance, the longitudinal distance and the bumping degree of the automobile, determining the sampling frequency of the sensor of the current automobile battery based on matrix calculation, and finally determining the fire probability of the current battery discharging based on the calculation of the fire early warning value. Compared with the prior art, the technical scheme provided by the application can adjust the sampling frequency of the sensor through the driving speed, the acceleration, the transverse distance, the longitudinal distance and the bumping degree of the automobile, so that the sampling data of the sensor is more accurate and reliable, and the accuracy of fire risk judgment is improved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
fig. 1 is a flowchart of an early warning method for a multi-sensor fusion-based vehicle battery discharge process according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart illustrating an early warning method for a multi-sensor fusion-based vehicle battery discharge process according to an embodiment of the present invention. In fig. 1, the warning method may include:
in step S10, obtaining a plurality of point values of the current running speed of the automobile, the acceleration of the automobile, the lateral distance between the automobile and other vehicles, the longitudinal distance between the automobile and other vehicles, and the degree of jounce of the automobile within a predetermined period, so as to construct a battery discharge state data matrix;
in step S11, determining a current battery discharge risk index according to the battery discharge state data matrix;
in step S12, the sampling frequency of each sensor of the battery box is calculated from the discharge risk index;
in step S13, each sensor is adjusted according to the sampling frequency;
in step S14, an average value in the current sampling period of each sensor is acquired;
in step S15, the reliability of each sensor is determined from the average value;
in step S16, a current fire warning value is calculated according to formula (1),
Figure BDA0003524907250000071
wherein m is1→nIs a fire alarm value, m1、m2、…、mnRespectively, the credibility of each sensor, n is the number of the types of the sensors,
Figure BDA0003524907250000072
is an orthogonal sum operation;
in step S17, the probability of the current fire occurrence is determined according to the fire alarm value.
In the method shown in fig. 1, step S10 may be used to collect current form status data of the vehicle, that is: the current running speed of the automobile, the acceleration of the automobile, the transverse distance between the automobile and other automobiles, the longitudinal distance between the automobile and other automobiles and the bumping degree of the automobile. And after collecting the above-mentioned multiple point values, it needs to perform a preliminary process on the point values, for example, construct a battery discharge state matrix according to formula (2),
Figure BDA0003524907250000073
wherein, a1、a2、…、aiIs the 1 st to i th point values of the current running speed of the automobile in the sampling period, b1、b2、…、bi1 st to i th point values of the vehicle acceleration in the sampling period, c1、c2、…、ci1 st to ith point values of the lateral distance in the sampling period, d1、d2、…、di1 st to ith point values of longitudinal distance in sampling period, e1、e2、…、eiThe 1 st to ith point values of the degree of jounce in the sampling period, and Y is the battery discharge state matrix.
Although the plurality of point values shown in the formula (2) can accurately represent the driving state of the automobile at the time, the amount of calculation of the system is greatly increased in the actual processing process because the number of point values is large. Therefore, before the calculation is performed, step S11 is required to convert the point value, i.e. the battery discharge parameter matrix is determined by using equation (3),
M＝[A B C D E]T， (3)
the battery charging method comprises the following steps of obtaining a battery charging parameter matrix, obtaining a driving speed point value, obtaining a maximum value and a minimum value of the driving speed point value, obtaining a driving speed value, obtaining a battery charging parameter matrix, obtaining a driving speed value, obtaining a battery charging parameter matrix, obtaining a driving speed value, obtaining a battery charging parameter matrix, and a battery charging parameter matrix, obtaining battery charging parameter matrix, obtaining battery charging parameter and battery charging parameter matrix, obtaining battery charging, and battery charging.
After the battery discharge parameter matrix is determined, a risk coefficient matrix may be further calculated, namely: calculating a risk coefficient matrix according to the formula (4) and the formula (5),
Figure BDA0003524907250000081
T＝[Amax Bmax Cmax Dmax Emax]， (5)
wherein R is a risk coefficient matrix, T is a preset threshold matrix, Amax、Bmax、Cmax、Dmax、EmaxThe driving speed, the automobile acceleration, the transverse distance, the longitudinal distance and the threshold value corresponding to the bumping degree are respectively.
And further calculating a battery discharge risk index by combining the risk coefficient matrix, namely: calculating a battery discharge risk index according to the formula (6) and the formula (7),
Figure BDA0003524907250000082
α1+α2+α3+α4+α5＝1， (7)
wherein I is a battery discharge risk index, R1、R2、R3、R4And R5Column vectors, α, of the first to fifth columns, respectively, of the risk coefficient matrix1、α2、α3、α4To alpha5Respectively corresponding weight parameters.
In a conventional car battery box, in order to detect a fire state of a car lithium battery in time, a variety of sensors are required to be arranged in the battery box for sensing. For example, lithium batteries for automobiles can escape a large amount of combustible gas in thermal runaway conditions, which poses the risk of flame and explosion, and the combustible gas contains a large amount of hydrocarbons. Thus, in this embodiment, the detected carbon monoxide concentration and smoke concentration may be used as fire parameters, namely: the carbon monoxide sensor can be used as a sensor for collecting the concentration of the carbon monoxide, and the smoke concentration sensor can be used as a sensor for collecting the concentration of the smoke.
Secondly, consider that flame is one of the parameters that has a distinct fire signature. The flame sensor utilizes the characteristic that infrared rays are very sensitive to flames, uses a special infrared receiving tube to detect the flames, and then converts the brightness of the flames into level signals with variable heights. Therefore, in this embodiment, a flame sensor may be selected to detect the flame index.
Moreover, the ignition of the automobile battery often occurs in the overcharged state of the battery. When the battery is in an overcharged state, the temperature of the battery is increased, so that the temperature of the battery is another large parameter. Therefore, in this embodiment, a temperature sensor may be used as the sensor for collecting temperature data.
Finally, a large amount of combustible gas can escape from the lithium battery of the automobile under the condition of thermal runaway. Therefore, the air pressure is also an important indicator. Therefore, in this embodiment, an air pressure sensor may be used as the sensor for collecting air pressure data.
After the sensors are adopted, considering that each sensor has a variable sampling frequency, the battery box is often monitored by the higher sampling frequency at the stage with higher probability of fire occurrence, and the battery box is monitored by the lower sampling frequency at the stage with lower probability of fire occurrence. Therefore, in this embodiment, the sampling frequency of each sensor may be adjusted through steps S12 and S13, i.e., the sampling frequency of each sensor of the battery box is first calculated according to the discharge risk index, and then each sensor is adjusted according to the sampling frequency. Specifically, the sampling frequency may be determined in a numerical interval. In the case where it is judged that the sampling frequency is greater than or equal to 0 and less than 35%, the sampling frequency of each sensor is adjusted according to the formula (8),
f＝5I×fmin， (8)
wherein f is the adjusted sampling frequency, I is the battery discharge risk index, and fminFor minimum sampling of the sensorSample frequency;
in the case where it is judged that the sampling frequency is greater than or equal to 35% and less than 70%, the sampling frequency of each sensor is adjusted according to the formula (9),
f＝10I×fmin， (9)
in the case where it is judged that the sampling frequency is greater than or equal to 70% and less than or equal to 100%, the sampling frequency of each sensor is adjusted according to the formula (10),
f＝20I×fmin， (10)。
after the sampling frequency of each sensor at different stages is adjusted in a targeted manner, the sampled values of each sensor need to be assigned with confidence level. In one example of the present invention, the confidence level may be set manually and directly, for example, manually assigned empirically for each sensor, thereby enabling the calculation of the final fire warning value. However, it is considered that if the adjustment is performed only by relying on the experience, the fire alarm value may be inaccurate due to lack of practical operation experience support. Therefore, in another example of the present invention, the fire alarm value may also be calculated using a quadrature sum calculation method, that is, using formula (1) to calculate the fire alarm value,
Figure BDA0003524907250000101
wherein m is1→nIs a fire alarm value, m1、m2、…、mnRespectively, the credibility of each sensor, n is the number of the types of the sensors,
Figure BDA0003524907250000102
is a quadrature sum operation.
For the specific calculation process of the above quadrature sum operation, it may be, for example:
first, Θ is preset as the discrimination frame. The recognition framework consists of a complete and mutually incompatible set of propositions into a power set, namely: Θ is { fire, no fire, uncertain }. Wherein "uncertain" means fireDisaster or no fire. Defining thereon a basic trust assignment function:
Figure BDA0003524907250000103
a represents any subset of the recognition framework, m (A) represents the degree to which evidence supports proposition A, and m (A) satisfies the following condition:
Figure BDA0003524907250000104
then, presetting conditions: if it is
Figure BDA0003524907250000105
Eyes of a user
Figure BDA00035249072500001010
Called A is one focal element of theta.
Two evidential bodies m1, m2 based on the discrimination frame Θ are provided and contain the focal elements a1, a2, A3 and B1, B2, B3, respectively. Their combined operation is quadrature sum
Figure BDA0003524907250000107
Where m12 is the new evidence body generated after combination. Wherein:
Figure BDA0003524907250000108
Figure BDA0003524907250000109
Figure BDA0003524907250000118
k represents the degree of contradiction of 2 evidence bodies, and the result based on Dempster combination rule can be normalized according to the value of K in calculation. If K is 1, then the evidence bodies are completely contradictory, and the Dempster combination rule cannot be applied; if K is 0, normalization may not be used.
Based on the calculated K value, a fused value of the two sensors can be obtained,
Figure BDA0003524907250000111
Figure BDA0003524907250000112
Figure BDA0003524907250000113
since the combination of multiple evidences is order independent, the computation of multiple evidence combinations can be recursively derived using the computation of two evidence combinations, namely:
Figure BDA0003524907250000114
Figure BDA0003524907250000115
Figure BDA0003524907250000116
for the particular confidence assignments involved in the calculation of the respective probability values involved in the above formula, it may be for example as shown in table 1,
TABLE 1
Figure BDA0003524907250000117
Figure BDA0003524907250000121
For the calculated fire early warning value, the probability of the current fire occurrence can be further determined in a range numerical judgment mode. In an example of the present invention, the method for determining may be to determine a numerical range in which the fire alarm value is located; determining the probability of fire occurrence to be 0 under the condition that the fire early warning value is judged to be greater than or equal to 0 and less than 0.3; determining that the sensor is abnormal under the condition that the fire early warning value is judged to be greater than or equal to 0.3 and less than 0.65; and determining that the fire occurs in the case that the fire early warning value is judged to be greater than or equal to 0.65 and less than or equal to 1.
In addition, in the process of data acquisition of each sensor, due to different sampling frequencies of respective bases, when a battery discharge state matrix is established, the time point difference between each point value is large, and therefore the calculation of the subsequent fire early warning value is influenced. In a preferred example of the invention, therefore, it may be that the number of point values per sensor is modified according to equation (11),
Figure BDA0003524907250000122
wherein y is the corrected point value, y1、y2And y3The sampling time before correction is t1、t2And t3T is the time corresponding to the modified point value y.
In another aspect, the present invention further provides a pre-warning system for a discharging process of an automobile battery based on multi-sensor fusion, the pre-warning system comprising a processor configured to execute any one of the pre-warning methods described above.
In yet another aspect, the present disclosure also provides a computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform any one of the above-mentioned warning methods.
According to the technical scheme, the early warning method and the early warning system for the discharging process of the automobile battery based on the multi-sensor fusion are used for acquiring the running speed, the acceleration, the transverse distance, the longitudinal distance and the bumping degree of the automobile, determining the sampling frequency of the sensor of the current automobile battery based on matrix calculation, and finally determining the fire probability of the current battery discharging based on the calculation of the fire early warning value. Compared with the prior art, the technical scheme provided by the application can adjust the sampling frequency of the sensor through the driving speed, the acceleration, the transverse distance, the longitudinal distance and the bumping degree of the automobile, so that the sampling data of the sensor is more accurate and reliable, and the accuracy of fire risk judgment is improved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A pre-warning method for a discharging process of an automobile battery based on multi-sensor fusion is characterized by comprising the following steps:
acquiring a plurality of point values of the current running speed of the automobile, the acceleration of the automobile, the transverse distance between the automobile and other vehicles, the longitudinal distance between the automobile and other vehicles and the bumping degree of the automobile in a preset period to construct a battery discharge state data matrix;
determining the current battery discharge danger index according to the battery discharge state data matrix;
calculating the sampling frequency of each sensor of the battery box according to the discharge danger index;
adjusting each sensor according to the sampling frequency;
acquiring an average value of each sensor in a current sampling period;
determining the credibility of each sensor according to the average value;
calculating the current fire early warning value according to the formula (1),
Figure FDA0003524907240000011
wherein，m1→nIs the fire alarm value, m1、m2、…、mnRespectively, the credibility of each sensor, n is the number of the types of the sensors,
Figure FDA0003524907240000012
is an orthogonal sum operation;
and determining the probability of the current fire according to the fire early warning value.
2. The warning method according to claim 1, wherein obtaining a plurality of point values of the current driving speed of the automobile, the acceleration of the automobile, the lateral distance between the automobile and other vehicles, the longitudinal distance between the automobile and other vehicles, and the degree of jounce of the automobile within a predetermined period to construct the battery discharge state data matrix further comprises:
constructing the battery discharge state matrix according to equation (2),
Figure FDA0003524907240000013
wherein, a1、a2、…、aiIs the 1 st to i th point values of the current running speed of the automobile in a sampling period, b1、b2、…、biIs the 1 st to i th point values of the vehicle acceleration in a sampling period, c1、c2、…、ci1 to ith point values of the lateral distance in a sampling period, d1、d2、…、di1 st to ith point values of the longitudinal distance in a sampling period, e1、e2、…、eiThe 1 st to ith point values of the degree of jounce in the sampling period, and Y is the battery discharge state matrix.
3. The warning method of claim 1, wherein determining the current battery discharge risk index from the battery discharge state data matrix comprises:
determining a battery discharge parameter matrix according to equation (3),
M＝[A B C D E]T， (3)
the battery charging method comprises the following steps of calculating a battery charging parameter matrix, calculating a transverse distance point value, calculating a battery charging parameter matrix, calculating a battery charging parameter matrix, and a battery charging parameter matrix, calculating a battery charging parameter matrix, and a battery charging parameter calculating method, and a battery charging parameter calculating a battery charging parameter matrix, and a battery charging parameter calculating method, and a battery charging parameter calculating method, and a battery charging parameter.
4. The warning method of claim 3, wherein determining the current battery discharge risk index from the battery discharge state data matrix comprises:
calculating a risk coefficient matrix according to the formula (4) and the formula (5),
Figure FDA0003524907240000021
T＝[Amax Bmax Cmax Dmax Emax]， (5)
wherein R is the risk coefficient matrix, T is a preset threshold matrix, Amax、Bmax、Cmax、Dmax、EmaxAnd the driving speed, the automobile acceleration, the transverse distance, the longitudinal distance and the threshold corresponding to the bumping degree are respectively.
5. The warning method of claim 4, wherein determining the current battery discharge risk index from the battery discharge state data matrix comprises:
calculating the battery discharge risk index according to equation (6) and equation (7),
Figure FDA0003524907240000031
α1+α2+α3+α4+α5＝1， (7)
wherein I is the discharge hazard index of the battery, R1、R2、R3、R4And R5Column vectors, α, of the first to fifth columns of the risk coefficient matrix, respectively1、α2、α3、α4To alpha5Respectively corresponding weight parameters.
6. The warning method of claim 1, wherein calculating a sampling frequency for each sensor of a battery box from the discharge hazard index comprises:
judging the numerical value interval of the sampling frequency;
in the case where it is judged that the sampling frequency is greater than or equal to 0 and less than 35%, the sampling frequency of each sensor is adjusted according to the formula (8),
f＝5I×fmin， (8)
wherein f is the adjusted sampling frequency, I is the battery discharge risk index, fminIs the lowest sampling frequency of the sensor;
in the case where it is judged that the sampling frequency is greater than or equal to 35% and less than 70%, the sampling frequency of each sensor is adjusted according to equation (9),
f＝10I×fmin， (9)
in the case where it is judged that the sampling frequency is greater than or equal to 70% and less than or equal to 100%, the sampling frequency of each sensor is adjusted according to the formula (10),
f＝20I×fmin， (10)。
7. the warning method of claim 1, wherein determining the probability of the current fire occurrence according to the fire warning value comprises:
judging a numerical range of the fire early warning value;
determining the probability of fire occurrence to be 0 under the condition that the fire early warning value is judged to be greater than or equal to 0 and less than 0.3;
determining that the sensor is abnormal under the condition that the fire early warning value is judged to be greater than or equal to 0.3 and less than 0.65;
and determining that the fire occurs in the case that the fire early warning value is judged to be greater than or equal to 0.65 and less than or equal to 1.
8. The warning method according to claim 1, wherein the obtaining a plurality of point values of the current driving speed of the automobile, the acceleration of the automobile, the lateral distance of the automobile from other vehicles, the longitudinal distance of the automobile from other vehicles, and the degree of jounce of the automobile within a predetermined period comprises:
the number of point values per sensor is corrected according to equation (11),
Figure FDA0003524907240000041
wherein y is the corrected point value, y1、y2And y3The sampling time before correction is t1、t2And t3T is the time corresponding to the modified point value y.
9. An early warning system for a discharge process of a vehicle battery based on multi-sensor fusion, characterized in that the early warning system comprises a processor configured to execute the early warning method according to any one of claims 1 to 8.
10. A computer-readable storage medium having stored thereon instructions for reading by a machine to cause the machine to perform the warning method of any one of claims 1 to 8.
CN202210190060.9A 2022-02-28 2022-02-28 Early warning method and system for automobile battery discharge process based on multi-sensor fusion Pending CN114683852A (en)

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CN101791932A (en) * 2009-02-01 2010-08-04 傅黎明 Flat tire pressure monitoring module
CN102343131A (en) * 2011-07-06 2012-02-08 周小钧 Vehicle intelligent fire-proofing and early warning decision method and vehicle-loaded fire fighting system
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