A car battery operation data monitoring system based on big data

Abstract

The present invention relates to the field of automobile battery technology, and in particular to an automobile battery operation data supervision system based on big data, including a data acquisition module for collecting automobile battery monitoring data and automobile driving data; an operation monitoring module for analyzing the operation status of the automobile battery and providing a user with an abnormal battery operation status warning; a driving environment monitoring module for classifying the abnormal levels of the automobile driving environment; an environmental analysis module for analyzing the ice condensation state of the battery shell; an amplitude analysis module for analyzing the battery vibration state; a management module for providing a user with an abnormal state warning based on the result of the abnormal battery operation status warning in the second monitoring cycle, the result of the classification of the abnormal driving environment level, and the analysis result of the battery vibration state, and also for providing a user with a battery maintenance prompt based on the abnormal state warning result in the management cycle. The present invention improves the efficiency of automobile battery operation supervision.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile batteries, and in particular to an automobile battery operation data monitoring system based on big data. Background Art

[0002] At present, some existing automotive battery monitoring systems on the market are mostly limited to monitoring basic parameters such as battery temperature and voltage, and do not take into account the battery environment and its impact on battery health. In addition, the existing systems often have a lag in early warning mechanisms and cannot effectively and timely remind users of potential risks, resulting in battery failures or safety accidents that may not be avoided in some cases. Especially in extreme climatic conditions or complex road conditions, batteries are more susceptible to external factors, and the monitoring methods and technologies of existing systems are difficult to meet the regulatory needs in such situations.

[0003] Chinese patent publication number CN115991117A discloses a car battery operating environment monitoring system based on data processing, which belongs to the automotive field and is used to solve the problem that the current operating environment monitoring of car batteries is limited to shallow data such as temperature and dust. It includes a driving environment monitoring module, a monitoring level setting module, an environmental monitoring module, an intelligent early warning module and an appearance analysis module. The driving environment monitoring module is used to monitor the driving environment of the car where the car battery is located, the monitoring level setting module is used to set the monitoring level of the car battery to obtain the monitoring level of the car battery, the environmental monitoring module is used to monitor the environmental conditions of the car battery, the appearance analysis module is used to analyze the appearance of the car battery, and the intelligent early warning module is used to provide intelligent early warning for the operating environment of the car battery. It can be seen that the analysis dimension of this solution is relatively single, the early warning mechanism is lagging, and the internal health status of the battery is ignored, and there is a problem of low efficiency in the operation supervision of the car battery. Summary of the invention

[0004] The object of the present invention is to provide a vehicle battery operation data supervision system based on big data to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] Data acquisition module, used to collect vehicle battery monitoring data and vehicle driving data;

[0007] An operation monitoring module is used to analyze the operation status of the vehicle battery according to the battery voltage and battery temperature collected in the first monitoring cycle, and to warn the user of abnormal battery operation status;

[0008] A driving environment monitoring module is used to classify the abnormal level of the vehicle driving environment according to the vehicle tilt angle, vehicle tire angular velocity, tire radius and vehicle actual speed collected in the second monitoring cycle;

[0009] An environmental analysis module, used to analyze the ice condensation state of the battery casing according to the ice layer thickness and environmental humidity of the battery casing collected during the second monitoring period;

[0010] An amplitude analysis module, used to analyze the vibration state of the battery according to the battery casing amplitude collected during the second monitoring period;

[0011] The management module is used to warn the user of abnormal battery status based on the results of the abnormal battery operating status warning in the second monitoring cycle, the classification results of the abnormal level of the vehicle driving environment and the analysis results of the battery vibration status, and is also used to provide battery maintenance prompts to the user based on the abnormal battery status alarm results in the management cycle.

[0012] Further, the operation monitoring module compares the battery voltage a0 collected in the first monitoring cycle with the preset voltage a1 to analyze the abnormality of the battery voltage, the abnormality of the battery voltage includes normal battery voltage and abnormal battery voltage, and analyzes the battery voltage state of the first monitoring cycle according to the analysis result of the battery voltage abnormality, the battery voltage state includes normal battery voltage state and abnormal battery voltage state;

[0013] The operation monitoring module compares the battery temperature s0 collected in the first monitoring period with the preset temperature s1 to analyze the abnormality of the battery temperature, which includes normal battery temperature and abnormal battery temperature, and analyzes the battery temperature state in the first monitoring period according to the analysis result of the battery temperature abnormality, which includes normal battery temperature state and abnormal battery temperature state;

[0014] If the battery voltage state and the battery temperature state are normal in the first monitoring cycle, the operation monitoring module determines that the vehicle battery operation state is normal in the current first monitoring cycle, and does not issue an abnormal battery operation state warning to the user;

[0015] If the battery voltage state or the battery temperature state is abnormal in the first monitoring cycle, the operation monitoring module determines that the vehicle battery operation state is abnormal in the current first monitoring cycle, and issues an abnormal battery operation state warning to the user.

[0016] Further, the driving environment monitoring module includes a tilt analysis unit, a slip analysis unit and a grade classification unit, wherein the tilt analysis unit is used to compare the vehicle tilt angle θ1 collected during the second monitoring period with a preset tilt angle threshold θ to analyze the abnormality of the vehicle tilt angle, wherein the abnormality of the vehicle tilt angle includes a normal vehicle tilt angle and an abnormal vehicle tilt angle;

[0017] The tilt analysis unit constructs a vehicle tilt index QX according to the analysis result of the abnormality of the vehicle tilt angle in the second monitoring period.

[0018] Furthermore, the slip analysis unit is used to analyze the vehicle slip state according to the vehicle tire angular velocity, the vehicle actual speed and the tire radius collected in each time window within the second monitoring period, the vehicle slip state includes a normal vehicle slip state and an abnormal vehicle slip state, and a slip index HY is constructed according to the analysis results.

[0019] Further, the level classification unit is used to classify the abnormal level of the vehicle driving environment according to the construction result of the vehicle tilt index and the construction result of the slip index in the second monitoring period, wherein:

[0020] If u1×QX+u2×HY≤T1, the level classification unit determines that the abnormal level of the vehicle driving environment in the second monitoring period is level one;

[0021] If T1<u1×QX+u2×HY≤T2, the level classification unit determines that the abnormal level of the vehicle driving environment in the second monitoring period is level 2;

[0022] If u1×QX+u2×HY>T2, the level classification unit determines that the abnormal level of the vehicle driving environment in the second monitoring period is level three, u1 is the tilt weight, u2 is the slip weight, T1 is the first preset environmental coefficient, and T2 is the second preset environmental coefficient.

[0023] Furthermore, the environmental analysis module includes an ice layer analysis unit and a humidity analysis unit. The ice layer analysis unit is used to compare the ice layer thickness d0 collected during the second monitoring period with the preset thickness d1 to analyze the ice condensation state of the battery casing. The ice condensation state of the battery casing includes normal ice condensation state of the battery casing and abnormal ice condensation state of the battery casing. When the ice condensation state of the battery casing in the second monitoring period is abnormal, the process of dividing the abnormal level of the vehicle driving environment is adjusted, and the first preset environmental coefficient is adjusted to T1', and the second preset environmental coefficient is adjusted to T2'.

[0024] Furthermore, the humidity analysis unit is used to compare the ambient humidity hs0 collected during the second monitoring period with the preset humidity hs1 to analyze the abnormality of the ambient humidity, wherein the abnormality of the ambient humidity includes normal ambient humidity and abnormal ambient humidity. When the ambient humidity in the second monitoring period is abnormal, the preset thickness is adjusted to d1'.

[0025] Further, the amplitude analysis module is used to compare the battery shell amplitude z0 collected in the second monitoring period with the preset amplitude z1, and analyze the battery amplitude abnormality according to the comparison result, and the battery amplitude abnormality includes the battery shell amplitude being normal and the battery shell amplitude being abnormal;

[0026] The amplitude analysis module analyzes the battery vibration state according to the analysis result of the battery amplitude abnormality in the second monitoring period, and the battery vibration state includes a normal battery vibration state and an abnormal battery vibration state; when the battery vibration state in the second monitoring period is normal, the amplitude analysis module sets the battery vibration index to D1, and when the battery vibration state in the second monitoring period is abnormal, the amplitude analysis module sets the battery vibration index to D2.

[0027] Furthermore, the management module includes an alarm unit and a management unit, and the alarm unit is used to warn the user of abnormal battery status according to the result of the abnormal battery running status warning in the second monitoring cycle, the classification result of the abnormal level of the vehicle driving environment and the analysis result of the battery vibration status, wherein:

[0028] When the driving environment abnormality level is level one, if m1×r1 / r0+m2×battery vibration index ≤ E1, the alarm unit determines that the battery state in the second monitoring period is normal and does not issue an alarm; if m1×r1 / r0+m2×battery vibration index>E1, the alarm unit determines that the battery state in the second monitoring period is abnormal and issues an abnormal battery state alarm to the user;

[0029] When the driving environment abnormality level is level 2, if m1×r1 / r0+m2×battery vibration index ≤ E2, the alarm unit determines that the battery state in the second monitoring period is normal and does not issue an alarm; if m1×r1 / r0+m2×battery vibration index>E2, the alarm unit determines that the battery state in the second monitoring period is abnormal and issues an abnormal battery state alarm to the user;

[0030] When the driving environment abnormality level is level three, if m1×r1 / r0+m2×battery vibration index≤E3, the alarm unit determines that the battery status in the second monitoring period is normal and does not issue an alarm; if m1×r1 / r0+m2×battery vibration index>E3, the alarm unit determines that the battery status in the second monitoring period is abnormal and issues an abnormal battery status alarm to the user; m1 is the operating status weight, m2 is the vibration status weight, m1+m2=1, m1>m2, r1 is the number of first monitoring cycles in the second monitoring period for issuing an abnormal battery operating status warning to the user, r0 is the number of first monitoring cycles in the second monitoring period, E1 is the first abnormal threshold, E2 is the second abnormal threshold, and E3 is the third abnormal threshold.

[0031] Furthermore, the management unit is used to provide battery maintenance prompts to the user based on the abnormal battery status alarm results within the management cycle. If G1 / G0≤F, the management unit does not provide battery maintenance prompts to the user. If G1 / G0>F, the management unit provides battery maintenance prompts to the user. G1 is the number of second monitoring cycles within the management cycle during which the abnormal battery status alarm is provided to the user. G0 is the number of second monitoring cycles within the management cycle. F is a preset abnormality ratio.

[0032] The beneficial effects of the present invention are as follows: Through the efficient collaboration of multiple modules and units, the system can achieve comprehensive supervision of various data such as vehicle battery status, driving environment, vibration, etc., and provide timely early warning and alarm based on dynamic monitoring data, thereby improving the safety of battery use and vehicle driving safety, and improving the supervision efficiency of vehicle battery operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 This is a schematic diagram of the structure of the automobile battery operation data monitoring system based on big data in this embodiment.

[0035] Figure 2 Schematic diagram of the structure of the driving environment monitoring module of this embodiment.

[0036] Figure 3 Schematic diagram of the structure of the environmental analysis module of this embodiment.

[0037] Figure 4 It is a structural diagram of the management module of this embodiment. DETAILED DESCRIPTION

[0038] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0039] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0040] See also Figure 1 As shown, it is a structural diagram of the automobile battery operation data supervision system based on big data in this embodiment, and the system includes:

[0041] The data acquisition module is used to collect the vehicle battery monitoring data and the vehicle driving data, wherein the vehicle battery monitoring data includes the battery shell ice thickness, battery shell amplitude, environmental humidity, battery voltage and battery temperature, and the vehicle driving data includes the vehicle tilt angle, vehicle tire angular velocity, tire radius and vehicle actual speed; the battery shell ice thickness is the thickness of the ice layer formed on the battery shell surface, the environmental humidity is the humidity of the battery environment, the vehicle tilt angle is the front and rear tilt angle of the vehicle relative to the horizontal plane during driving, and the vehicle actual speed is the speed of the vehicle during actual driving; in this embodiment, the vehicle battery monitoring data and the vehicle driving data are not collected. The collection method is specifically limited and can be freely set by those skilled in the art as long as the collection requirements of vehicle battery monitoring data and vehicle driving data are met. The thickness of the ice layer on the shell can be collected by a laser ranging device installed on the outside of the battery, the amplitude of the battery shell can be collected by a vibration sensor installed on the battery shell, the ambient humidity can be collected by a humidity sensor, the battery voltage and battery temperature can be obtained through BMS, the vehicle tilt angle can be collected by a gyroscope or inclination sensor installed in the vehicle, the vehicle tire angular velocity can be collected by an angular velocity sensor, the tire radius can be obtained through interaction, and the actual speed of the vehicle can be collected through GPS.

[0042] Please continue reading Figure 1 As shown, the system includes an operation monitoring module, which is connected to the data acquisition module and is used to analyze the operating status of the vehicle battery according to the battery voltage and battery temperature collected in the first monitoring cycle, and to warn the user of abnormal battery operating status, wherein:

[0043] The operation monitoring module compares the battery voltage a0 collected in the first monitoring cycle with the preset voltage a1 to analyze the abnormality of the battery voltage. When a0≤a1, the operation monitoring module determines that the battery voltage is normal; when a0>a1, the operation monitoring module determines that the battery voltage is abnormal. If i1 / i0≤α1, the operation monitoring module determines that the battery voltage state in the current first monitoring cycle is normal. If i1 / i0>α1, the operation monitoring module determines that the battery voltage state in the current first monitoring cycle is abnormal. i1 is the duration of the battery voltage abnormality in the first monitoring cycle, i0 is the duration of the first monitoring cycle, and α1 is the voltage abnormality ratio threshold.

[0044] The operation monitoring module compares the battery temperature s0 collected in the first monitoring cycle with the preset temperature s1 to analyze the abnormality of the battery temperature. When s0≤s1, the operation monitoring module determines that the battery temperature is normal; when s0>s1, the operation monitoring module determines that the battery temperature is abnormal. If i2 / i0≤α2, the operation monitoring module determines that the battery temperature state in the current first monitoring cycle is normal. If i2 / i0>α2, the operation monitoring module determines that the battery temperature state in the current first monitoring cycle is abnormal. i2 is the duration of the abnormal battery temperature in the first monitoring cycle, and α2 is the temperature abnormality ratio threshold.

[0045] The operation monitoring module analyzes the operation status of the vehicle battery according to the analysis results of the battery voltage status and the battery temperature status in the first monitoring cycle, and warns the user of abnormal battery operation status according to the analysis results, wherein:

[0046] If the battery voltage state and the battery temperature state are normal in the first monitoring cycle, the operation monitoring module determines that the vehicle battery operation state is normal in the current first monitoring cycle, and does not issue an abnormal battery operation state warning to the user;

[0047] If the battery voltage state is abnormal or the battery temperature state is abnormal during the first monitoring cycle, the operation monitoring module determines that the vehicle battery operation state is abnormal during the current first monitoring cycle, and issues an abnormal battery operation state warning to the user; by analyzing the abnormal battery voltage and battery temperature, an early warning function is provided to help users discover potential battery problems in a timely manner. At the same time, through statistical analysis of the data during the first monitoring cycle, it ensures that the alarm is not triggered by only occasional abnormalities, thereby reducing the false alarm rate and improving the reliability of the system.

[0048] Specifically, in this embodiment, there is no specific limitation on the setting of the preset voltage, preset temperature, voltage abnormality ratio threshold, and temperature abnormality ratio threshold. Those skilled in the art can set them freely as long as the setting requirements of the preset voltage, preset temperature, voltage abnormality ratio threshold, and temperature abnormality ratio threshold are met. Among them, the best value of a1 is 90% of the rated voltage of the battery, the best value of s1 is 45°C, the best value of α1 is 0.12, and the best value of α2 is 0.1.

[0049] Specifically, in this embodiment, the setting of the first monitoring period is not specifically limited, and those skilled in the art can set it freely as long as the setting requirements of the first monitoring period are met. The first monitoring period can be set to 30s, 60s, etc.

[0050] Please continue reading Figure 1 As shown, the system includes a driving environment monitoring module, which is connected to the data acquisition module and is used to classify the abnormal level of the vehicle driving environment according to the vehicle tilt angle, vehicle tire angular velocity, tire radius and vehicle actual speed collected in the second monitoring cycle.

[0051] See also Figure 2 As shown, the driving environment monitoring module includes a tilt analysis unit, which is used to compare the vehicle tilt angle θ1 collected in the second monitoring period with a preset tilt angle threshold θ to analyze the abnormality of the vehicle tilt angle, thereby improving the accuracy of the vehicle tilt index construction. If θ1≤θ, the tilt analysis unit determines that the vehicle tilt angle is normal. If θ1>θ, the tilt analysis unit determines that the vehicle tilt angle is abnormal. The tilt analysis unit constructs the vehicle tilt index QX according to the analysis result of the abnormality of the vehicle tilt angle in the second monitoring period, and sets: QX=(q1 / q0) 2 / [1+(q1 / q0) 2 ], q1 is the duration of the abnormal vehicle tilt angle in the second monitoring cycle, and q0 is the duration of the second monitoring cycle; by accurately evaluating the vehicle tilt state and constructing the vehicle tilt index, the system can be helped to determine whether the vehicle is driving in a high-risk environment, and then based on this driving environment information, the auxiliary system can make a more detailed assessment of the battery status and overall driving safety.

[0052] Specifically, in this embodiment, the setting of the preset tilt angle threshold is not specifically limited, and those skilled in the art can freely set it as long as the setting requirements of the preset tilt angle threshold are met, wherein the optimal value of θ is 8°.

[0053] Specifically, in this embodiment, the setting of the second monitoring period is not specifically limited, and those skilled in the art can set it freely as long as the setting requirements of the second monitoring period are met. The second monitoring period can be set to 3h, 5h, 12h, etc.

[0054] Please continue reading Figure 2 As shown, the driving environment monitoring module includes a slip analysis unit, which is connected to the tilt analysis unit and is used to analyze the vehicle slip state according to the vehicle tire angular velocity, the vehicle actual speed and the tire radius collected in each time window in the second monitoring period to improve the accuracy of slip index construction, and construct the slip index HY according to the analysis result, wherein:

[0055] If (wj×r-vj) / (wj×r)≤η, the slip analysis unit determines that the slip state of the vehicle in the jth time window in the second monitoring period is normal; if (wj×r-vj) / (wj×r)>η, the slip analysis unit determines that the slip state of the vehicle in the jth time window in the second monitoring period is abnormal, wj is the angular velocity of the vehicle tire collected in the jth time window in the second monitoring period, vj is the actual speed of the vehicle collected in the jth time window in the second monitoring period, r is the tire radius, and η is the abnormal ratio threshold;

[0056] The slip analysis unit constructs the slip index HY according to the analysis result of the slip state of the vehicle in the second monitoring period, and sets: HY=3×(yj / J) 2 -2×(yj / J) 3 ; yj is the number of abnormal time windows of the vehicle slip state in the second monitoring period, and J is the number of time windows in the second monitoring period; the tire slip condition can be monitored in real time by comparing and analyzing the angular velocity, speed and radius of the tire, and the accuracy of the abnormal level classification of the vehicle driving environment is improved by calculating the slip index HY.

[0057] Specifically, in this embodiment, the setting of the abnormal ratio threshold is not specifically limited, and those skilled in the art can freely set it as long as the setting requirements of the abnormal ratio threshold are met, wherein the optimal value of η is 0.05.

[0058] Specifically, in this embodiment, no specific limitation is imposed on the setting of the time window duration, and those skilled in the art can freely set it as long as the setting requirements of the time window duration are met, wherein the time window can be set to 1s.

[0059] Please continue reading Figure 2As shown, the driving environment monitoring module further includes a level classification unit, which is connected to the slip analysis unit and is used to classify the abnormal level of the vehicle driving environment according to the construction result of the vehicle tilt index and the construction result of the slip index in the second monitoring period, wherein:

[0060] If u1×QX+u2×HY≤T1, the level classification unit determines that the abnormal level of the vehicle driving environment in the second monitoring period is level one;

[0061] If T1<u1×QX+u2×HY≤T2, the level classification unit determines that the abnormal level of the vehicle driving environment in the second monitoring period is level 2;

[0062] If u1×QX+u2×HY>T2, the level division unit determines that the abnormal level of the vehicle driving environment in the second monitoring period is level three, u1 is the tilt weight, u2 is the slip weight, u1+u2=1, u1>u2, T1 is the first preset environmental coefficient, T2 is the second preset environmental coefficient, T1<T2; through the combined analysis of the tilt index and the slip index, the abnormal level of the current driving environment is flexibly judged, and a reasonable weight allocation mechanism is introduced to ensure the accuracy of the abnormal environment level division, thereby improving the supervision efficiency of the vehicle battery.

[0063] Specifically, in this embodiment, there is no specific limitation on the setting of the tilt weight, the slip weight and the preset environmental coefficients. Technical personnel in this field can set them freely as long as the setting requirements of the tilt weight, the slip weight and the preset environmental coefficients are met. Among them, the optimal value of u1 is 0.7, the optimal value of u2 is 0.3, the optimal value of T1 is 0.12, and the optimal value of T2 is 0.3.

[0064] Please continue reading Figure 1 As shown, the system also includes an environmental analysis module, which is connected to the driving environment monitoring module and is used to analyze the ice condensation state of the battery casing according to the ice layer thickness and ambient humidity of the battery casing collected during the second monitoring cycle.

[0065] See also Figure 3 As shown, the environmental analysis module includes an ice layer analysis unit, which is used to compare the ice layer thickness d0 collected in the second monitoring period with the preset thickness d1 to analyze the ice condensation state of the battery shell. If d0≤d1, the ice layer analysis unit determines that the ice condensation state of the battery shell in the second monitoring period is normal. If d0>d1, the ice layer analysis unit determines that the ice condensation state of the battery shell in the second monitoring period is abnormal, and adjusts the classification process of the abnormal level of the vehicle driving environment, and sets the adjusted first preset environmental coefficient to T1', set to T1'=T1×{1-{exp[-3×(d0-d1) / (d1+d0)]-1} / (e-3 -1)}, set the adjusted second preset environmental coefficient to T2', and set T2'=T2×{1-{exp[-3×(d0-d1) / (d1+d0)]-1} / (e -3 -1)}, e is the natural logarithm; by analyzing the ambient humidity and ice thickness, the system can specifically evaluate the icing state of the battery casing, dynamically adjust the classification process of the abnormal level of the vehicle driving environment, analyze the icing state of the battery casing, especially when the vehicle passes through cold, ice and snow covered areas, timely monitor the formation of ice, and give effective warnings for possible problems during driving (such as obstructed battery heat dissipation), thereby improving the supervision efficiency of vehicle batteries.

[0066] Specifically, the setting of the preset thickness is not specifically limited in this embodiment, and those skilled in the art can set it freely as long as the setting requirements of the preset thickness are met, wherein the best value of d1 is 1 mm.

[0067] Please continue reading Figure 3 As shown, the environmental analysis module includes a humidity analysis unit, which is connected to the ice layer analysis unit and is used to compare the environmental humidity hs0 collected in the second monitoring period with the preset humidity hs1 to analyze the abnormality of the environmental humidity, and adjust the analysis process of the ice condensation state of the battery shell according to the analysis result to improve the accuracy of the ice condensation state analysis, wherein:

[0068] If hs0≤hs1, the humidity analysis unit determines that the ambient humidity in the second monitoring period is normal and does not make any adjustments; if hs0>hs1, the humidity analysis unit determines that the ambient humidity in the second monitoring period is abnormal and adjusts the preset thickness to d1', setting d1'=d1×{1-[(hs0-hs1) / hs1] 3 / {[(hs0-hs1) / hs1] 3 +[1-(hs0-hs1) / hs1] 3}}; By analyzing the abnormality of ambient humidity, adjustments are made to the possible impact on battery icing problems, effectively improving the accuracy of battery icing status prediction.

[0069] Specifically, the setting of the preset humidity is not specifically limited in this embodiment, and those skilled in the art can set it freely as long as the setting requirements of the preset humidity are met. Among them, the best value of hs1 is 80%.

[0070] Please continue reading Figure 1As shown, the system further includes an amplitude analysis module, which is connected to the data acquisition module and is used to compare the battery housing amplitude z0 collected in the second monitoring period with the preset amplitude z1, and analyze the battery vibration state according to the comparison result to improve the accuracy of the battery vibration state analysis, wherein:

[0071] If z0≤z1, the amplitude analysis module determines that the amplitude of the battery casing is normal; if z0>z1, the amplitude analysis module determines that the amplitude of the battery casing is abnormal. When q2 / q0≤β, the amplitude analysis module determines that the battery vibration state in the second monitoring period is normal, and sets the battery vibration index to D1, setting D1=0. When q2 / q0>β, the amplitude analysis module determines that the battery vibration state in the second monitoring period is abnormal, and sets the battery vibration index to D2, setting D2=exp[lg(q2 / q0-β)], q2 is the duration of the abnormal amplitude of the battery casing in the second monitoring period, and β is the preset state threshold. By analyzing the amplitude of the battery casing, the system helps detect changes in the battery state caused by vibration or impact, especially detects battery failure problems that may be caused by uneven roads, and provides real-time feedback, thereby ensuring the safe operation of the battery and improving the overall reliability of the system.

[0072] Specifically, in this embodiment, there is no specific limitation on the setting of the preset amplitude and the preset state threshold, and those skilled in the art can set them freely as long as the setting requirements of the preset amplitude and the preset state threshold are met. Among them, the optimal value of z1 is 0.5mm, and the optimal value of β is 0.1.

[0073] Please continue reading Figure 1 As shown, the system also includes a management module, which is connected to the operation monitoring module, the environment analysis module, and the amplitude analysis module. The management module is used to provide a battery status abnormality alarm to the user based on the result of the battery operation status abnormality warning in the second monitoring cycle, the classification result of the abnormal level of the vehicle driving environment, and the analysis result of the battery vibration state, and is also used to provide a battery maintenance prompt to the user based on the battery status abnormality alarm result in the management cycle.

[0074] See also Figure 4 As shown, the management module includes an alarm unit, which is used to warn the user of abnormal battery status according to the result of the abnormal battery running status warning in the second monitoring cycle, the classification result of the abnormal level of the vehicle driving environment and the analysis result of the battery vibration status, wherein:

[0075] When the driving environment abnormality level is level one, if m1×r1 / r0+m2×battery vibration index ≤ E1, the alarm unit determines that the battery state in the second monitoring period is normal and does not issue an alarm; if m1×r1 / r0+m2×battery vibration index>E1, the alarm unit determines that the battery state in the second monitoring period is abnormal and issues an abnormal battery state alarm to the user;

[0076] When the driving environment abnormality level is level 2, if m1×r1 / r0+m2×battery vibration index ≤ E2, the alarm unit determines that the battery state in the second monitoring period is normal and does not issue an alarm; if m1×r1 / r0+m2×battery vibration index>E2, the alarm unit determines that the battery state in the second monitoring period is abnormal and issues an abnormal battery state alarm to the user;

[0077] When the driving environment abnormality level is level three, if m1×r1 / r0+m2×battery vibration index ≤ E3, the alarm unit determines that the battery state in the second monitoring period is normal and does not issue an alarm; if m1×r1 / r0+m2×battery vibration index>E3, the alarm unit determines that the battery state in the second monitoring period is abnormal and issues an abnormal battery state alarm to the user;

[0078] Among them, m1 is the operating status weight, m2 is the vibration status weight, m1+m2=1, m1>m2, r1 is the number of first monitoring cycles in the second monitoring cycle for warning users of abnormal battery operating status, r0 is the number of first monitoring cycles in the second monitoring cycle, E1 is the first abnormal threshold, E2 is the second abnormal threshold, E3 is the third abnormal threshold, E1<E2<E3; through comprehensive analysis of battery operation abnormalities, driving environment, and vibration status, the alarm unit has extremely high sensitivity and can prompt users of abnormal battery status in a hierarchical manner, reduce false alarms, and ensure low-latency alarms under dangerous conditions, which helps to improve the stability and practicality of the battery monitoring system.

[0079] Specifically, in this embodiment, the setting of each weight and each abnormal threshold is not specifically limited, and technical personnel in this field can set it freely as long as the setting requirements of each weight and each abnormal threshold are met. Among them, the best value of m1 is 0.8, the best value of m2 is 0.2, the best value of E1 is 0.12, the best value of E2 is 0.15, and the best value of E3 is 0.18.

[0080] Please continue reading Figure 4As shown, the management module includes a management unit, which is connected to the alarm unit, and is used to provide a battery maintenance prompt to the user according to the abnormal battery status alarm result within the management cycle. If G1 / G0≤F, the management unit does not provide a battery maintenance prompt to the user. If G1 / G0>F, the management unit provides a battery maintenance prompt to the user, G1 is the number of second monitoring cycles within the management cycle for providing an abnormal battery status alarm to the user, G0 is the number of second monitoring cycles within the management cycle, and F is a preset abnormality ratio; by counting the abnormal alarm conditions, the user is prompted with a battery maintenance requirement when it exceeds a reasonable range, to ensure that the user will not cause sudden conditions in the battery or vehicle due to long-term neglect.

[0081] Specifically, in this embodiment, there is no specific limitation on the setting of the management period, and technical personnel in this field can set it freely as long as the setting requirements of the management period are met. Among them, the setting of the management period should be greater than the setting requirements of the second monitoring period, and the management period can be set to 30 days, 60 days, etc.

[0082] Specifically, in this embodiment, the setting of the preset abnormality ratio is not specifically limited, and those skilled in the art can set it freely, as long as the setting requirements of the preset abnormality ratio are met. Among them, the best value of F is 0.07.

[0083] Specifically, the system described in this embodiment is applied to monitoring of automobile battery and driving status, and is particularly suitable for monitoring battery status and driving environment under extreme climatic conditions or complex road conditions.

[0084] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A big data-based automobile battery operation data monitoring system, characterized in that: include, Data acquisition module, used to collect vehicle battery monitoring data and vehicle driving data; An operation monitoring module is used to analyze the operation status of the vehicle battery according to the battery voltage and battery temperature collected in the first monitoring cycle, and to warn the user of abnormal battery operation status; A driving environment monitoring module is used to classify the abnormal level of the vehicle driving environment according to the vehicle tilt angle, vehicle tire angular velocity, tire radius and vehicle actual speed collected in the second monitoring cycle; An environmental analysis module, used to analyze the ice condensation state of the battery casing according to the ice layer thickness and environmental humidity of the battery casing collected during the second monitoring period; An amplitude analysis module, used to analyze the vibration state of the battery according to the battery casing amplitude collected during the second monitoring period; The management module includes an alarm unit and a management unit, wherein the alarm unit is used to warn the user of abnormal battery status according to the result of the abnormal battery running status warning in the second monitoring cycle, the classification result of the abnormal level of the vehicle driving environment and the analysis result of the battery vibration status, wherein: When the driving environment abnormality level is level one, if m1×r1 / r0+m2×battery vibration index ≤ E1, the alarm unit determines that the battery state in the second monitoring period is normal and does not issue an alarm; if m1×r1 / r0+m2×battery vibration index>E1, the alarm unit determines that the battery state in the second monitoring period is abnormal and issues an abnormal battery state alarm to the user; When the driving environment abnormality level is level 2, if m1×r1 / r0+m2×battery vibration index ≤ E2, the alarm unit determines that the battery state in the second monitoring period is normal and does not issue an alarm; if m1×r1 / r0+m2×battery vibration index>E2, the alarm unit determines that the battery state in the second monitoring period is abnormal and issues an abnormal battery state alarm to the user; When the driving environment abnormality level is level three, if m1×r1 / r0+m2×battery vibration index≤E3, the alarm unit determines that the battery state in the second monitoring period is normal and does not issue an alarm; if m1×r1 / r0+m2×battery vibration index>E3, the alarm unit determines that the battery state in the second monitoring period is abnormal and issues an abnormal battery state alarm to the user; m1 is the operating state weight, m2 is the vibration state weight, r1 is the number of first monitoring periods in the second monitoring period in which the user is warned of abnormal battery operating state, r0 is the number of first monitoring periods in the second monitoring period, E1 is the first abnormal threshold, E2 is the second abnormal threshold, and E3 is the third abnormal threshold; The management unit is used to provide battery maintenance prompts to the user according to the abnormal battery status alarm results within the management cycle.

2. The automobile battery operation data monitoring system based on big data according to claim 1 is characterized in that: The operation monitoring module compares the battery voltage a0 collected in the first monitoring cycle with the preset voltage a1 to analyze the abnormality of the battery voltage, the abnormality of the battery voltage includes normal battery voltage and abnormal battery voltage, and analyzes the battery voltage state of the first monitoring cycle according to the analysis result of the battery voltage abnormality, the battery voltage state includes normal battery voltage state and abnormal battery voltage state; The operation monitoring module compares the battery temperature s0 collected in the first monitoring period with the preset temperature s1 to analyze the abnormality of the battery temperature, which includes normal battery temperature and abnormal battery temperature, and analyzes the battery temperature state in the first monitoring period according to the analysis result of the battery temperature abnormality, which includes normal battery temperature state and abnormal battery temperature state; If the battery voltage state and the battery temperature state are normal in the first monitoring cycle, the operation monitoring module determines that the vehicle battery operation state is normal in the current first monitoring cycle, and does not issue an abnormal battery operation state warning to the user; If the battery voltage state or the battery temperature state is abnormal in the first monitoring cycle, the operation monitoring module determines that the vehicle battery operation state is abnormal in the current first monitoring cycle, and issues an abnormal battery operation state warning to the user.

3. The automobile battery operation data monitoring system based on big data according to claim 2 is characterized in that: The driving environment monitoring module includes a tilt analysis unit, a slip analysis unit and a grade classification unit, wherein the tilt analysis unit is used to compare the vehicle tilt angle θ1 collected in the second monitoring period with a preset tilt angle threshold θ to analyze the abnormality of the vehicle tilt angle, and the abnormality of the vehicle tilt angle includes a normal vehicle tilt angle and an abnormal vehicle tilt angle; The tilt analysis unit constructs a vehicle tilt index QX according to the analysis result of the abnormality of the vehicle tilt angle in the second monitoring period.

4. The automobile battery operation data monitoring system based on big data according to claim 3 is characterized in that: The slip analysis unit is used to analyze the vehicle slip state according to the vehicle tire angular velocity, the vehicle actual speed and the tire radius collected in each time window within the second monitoring period. The vehicle slip state includes a normal vehicle slip state and an abnormal vehicle slip state, and a slip index HY is constructed according to the analysis results.

5. The automobile battery operation data monitoring system based on big data according to claim 4 is characterized in that: The level classification unit is used to classify the abnormal level of the vehicle driving environment according to the construction results of the vehicle tilt index and the slip index in the second monitoring period, wherein: If u1×QX+u2×HY≤T1, the level classification unit determines that the abnormal level of the vehicle driving environment in the second monitoring period is level one; If T1<u1×QX+u2×HY≤T2, the level classification unit determines that the abnormal level of the vehicle driving environment in the second monitoring period is level 2; If u1×QX+u2×HY>T2, the level classification unit determines that the abnormal level of the vehicle driving environment in the second monitoring period is level three, u1 is the tilt weight, u2 is the slip weight, T1 is the first preset environmental coefficient, and T2 is the second preset environmental coefficient.

6. The automobile battery operation data monitoring system based on big data according to claim 5 is characterized in that: The environmental analysis module includes an ice layer analysis unit and a humidity analysis unit. The ice layer analysis unit is used to compare the ice layer thickness d0 collected during the second monitoring period with the preset thickness d1 to analyze the ice condensation state of the battery casing. The ice condensation state of the battery casing includes a normal ice condensation state of the battery casing and an abnormal ice condensation state of the battery casing. When the ice condensation state of the battery casing is abnormal during the second monitoring period, the process of dividing the abnormal level of the vehicle driving environment is adjusted, and the first preset environmental coefficient is adjusted to T1', and the second preset environmental coefficient is adjusted to T2'.

7. The automobile battery operation data monitoring system based on big data according to claim 6 is characterized in that: The humidity analysis unit is used to compare the ambient humidity hs0 collected in the second monitoring period with the preset humidity hs1 to analyze the abnormality of the ambient humidity. The abnormality of the ambient humidity includes normal ambient humidity and abnormal ambient humidity. When the ambient humidity in the second monitoring period is abnormal, the preset thickness is adjusted to d1'.

8. The automobile battery operation data monitoring system based on big data according to claim 7 is characterized in that: The amplitude analysis module is used to compare the battery shell amplitude z0 collected in the second monitoring period with the preset amplitude z1, and analyze the battery amplitude abnormality according to the comparison result, and the battery amplitude abnormality includes the battery shell amplitude being normal and the battery shell amplitude being abnormal; The amplitude analysis module analyzes the battery vibration state according to the analysis result of the battery amplitude abnormality in the second monitoring period, and the battery vibration state includes a normal battery vibration state and an abnormal battery vibration state; when the battery vibration state in the second monitoring period is normal, the amplitude analysis module sets the battery vibration index to D1, and when the battery vibration state in the second monitoring period is abnormal, the amplitude analysis module sets the battery vibration index to D2.

9. The automobile battery operation data monitoring system based on big data according to claim 8 is characterized in that: The management unit is used to provide battery maintenance prompts to the user based on the abnormal battery status alarm results within the management cycle. If G1 / G0≤F, the management unit does not provide battery maintenance prompts to the user. If G1 / G0>F, the management unit provides battery maintenance prompts to the user. G1 is the number of second monitoring cycles within the management cycle during which the abnormal battery status alarm is provided to the user. G0 is the number of second monitoring cycles within the management cycle. F is the preset abnormality ratio.

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