New energy automobile safety assessment system based on big data

Abstract

The invention relates to the technical field of new energy automobile driving safety, in particular to a new energy automobile safety evaluation system based on big data, and the system comprises a data collection module which is used for collecting the operation data, performance data, operation environment data and loss data of a new energy automobile battery; the risk analysis module is used for analyzing the operation risk of the battery; the abnormity monitoring module is used for optimizing the analysis process of the battery operation risk; the loss analysis module is used for analyzing the loss state of the battery and analyzing the abnormal index of the battery; the aging analysis module is used for analyzing the aging state of the battery according to the battery internal resistance and the battery capacity collected in the monitoring period; and the safety evaluation module is used for evaluating the safety of the battery and adjusting the evaluation process of the safety of the battery. According to the invention, the accuracy of new energy automobile battery safety assessment is improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicle driving safety technology, and in particular to a new energy vehicle safety assessment system based on big data. Background Art

[0002] With the increasing awareness of environmental protection and the transformation of energy structure, new energy vehicles have developed rapidly. Among them, new energy vehicles have attracted widespread attention due to their zero emissions and low noise characteristics. However, the safety of new energy vehicle power batteries has always been one of the main factors restricting their large-scale promotion. The safety of batteries is not only related to the reliability and service life of the vehicle itself, but also directly related to the life safety of passengers.

[0003] China Patent Publication No.: CN112836967A discloses a new energy vehicle battery safety risk assessment system, including the following modules: a vehicle driving information acquisition module, which is used to obtain driving data of new energy vehicles and perform data cleaning; an indicator extraction module, which performs binary Logistic regression analysis on the driving data after data cleaning and three dynamic indicators, determines the characteristic indicators related to the alarm of new energy vehicles and the influence weight of the alarm information corresponding to each characteristic indicator; a battery safety risk assessment module, which builds a Bayesian network model according to the relationship between the determined characteristic indicators and the corresponding alarm information, and verifies the information fusion degree of the characteristic indicators and alarm information identified by binary Logistic regression, and verifies the safety risk assessment results for each type of alarm situation, and finally outputs the quantitative results of the new energy vehicle battery safety assessment. The present invention can quantitatively evaluate the battery safety risk status of new energy vehicles and accurately identify the safety status of the battery; it can be seen that when evaluating the safety of new energy vehicle batteries, the scheme only analyzes parameters such as battery temperature and voltage, and there is a problem of low accuracy in the safety assessment of new energy vehicle batteries. Summary of the invention

[0004] To this end, the present invention provides a new energy vehicle safety assessment system based on big data to overcome the problem of low accuracy of new energy vehicle battery safety assessment in the prior art.

[0005] To achieve the above purpose, the present invention provides a new energy vehicle safety assessment system based on big data, comprising: Data collection module, used to collect operation data, performance data, operation environment data and loss data of new energy vehicle batteries; The risk analysis module is used to analyze the operation risk of the battery during the monitoring period according to the battery temperature collected during the temperature monitoring period and the battery voltage collected during the voltage monitoring period; The abnormality monitoring module is used to analyze the abnormality of the battery vibration frequency and environmental humidity collected during the monitoring period, and optimize the analysis process of battery operation risks based on the analysis results; The loss analysis module is used to analyze the battery loss state according to the battery charge state collected during the monitoring period, and analyze the battery abnormality index according to the analysis results and the analysis results of the battery operation risk during the monitoring period; Aging analysis module, used to analyze the aging status of the battery based on the battery internal resistance and battery capacity collected during the monitoring period; The safety assessment module is used to assess the safety of the battery based on the analysis results of the battery abnormality index and the battery aging status during the monitoring period, and to adjust the battery safety assessment process based on the battery insulation resistance collected during the monitoring period.

[0006] Furthermore, the risk analysis module is provided with a temperature analysis unit, a voltage analysis unit and a risk analysis unit. The temperature analysis unit is used to calculate the battery temperature change rate A1 based on the battery temperature collected during the temperature monitoring period, and set: , The temperature analysis unit compares the battery temperature change rate A1 with the preset temperature change rate a1, and analyzes the abnormality of the temperature change rate according to the comparison result, wherein: When A1≤a1, the temperature analysis unit determines that the rate of change of the battery temperature in the current temperature monitoring period is normal; When A1<a1, the temperature analysis unit determines that the change rate of the battery temperature in the current temperature monitoring period is abnormal; Among them, t1 is the battery temperature data collected at the beginning of the temperature monitoring cycle, t2 is the battery temperature data collected at the end of the temperature monitoring cycle, T 温 The duration of the temperature monitoring cycle.

[0007] Furthermore, the voltage analysis unit is used to calculate the battery voltage change rate A2 of the battery voltage collected during the voltage monitoring period, and set: , The voltage analysis unit compares the battery voltage change rate A1 with the preset voltage change rate a2, and analyzes the abnormality of the voltage change rate according to the comparison result, wherein: When A2≤a2, the voltage analysis unit determines that the rate of change of the battery voltage in the current voltage monitoring period is normal; When A2>a2, the voltage analysis unit determines that the rate of change of the battery voltage in the current voltage monitoring period is abnormal; Among them, V1 is the battery voltage data collected at the beginning of the voltage monitoring cycle, V2 is the battery voltage data collected at the end of the voltage monitoring cycle, T 电压 is the duration of the voltage monitoring cycle.

[0008] Furthermore, the risk analysis unit is used to analyze the operating risk of the battery according to the analysis results of the abnormality of the battery temperature change rate and the analysis results of the abnormality of the battery voltage change rate within the monitoring period. The risk analysis unit sets the risk coefficient of the battery to F, and sets F=x1×i1 / i0+x2×i3 / i2, wherein i0 is the number of temperature monitoring cycles within the monitoring period, i1 is the number of temperature monitoring cycles with abnormal battery temperature change rate within the monitoring period, i2 is the number of voltage monitoring cycles within the monitoring period, i3 is the number of voltage monitoring cycles with abnormal battery voltage change rate within the monitoring period, x1 is the temperature weight, and x2 is the voltage weight.

[0009] Furthermore, the abnormality monitoring module includes a vibration analysis unit and a humidity analysis unit, and the vibration analysis unit is used to compare the vibration frequency z0 of the battery collected during the monitoring period with the preset vibration frequency z1, and analyze the vibration abnormality of the battery according to the comparison result, wherein: When z0≤z1, the vibration analysis unit determines that the vibration of the battery is normal; When z0>z1, the vibration analysis unit determines that the vibration of the battery is abnormal; The vibration analysis unit optimizes the analysis process of the battery operation risk according to the analysis result of the battery vibration abnormality within the monitoring period, wherein: When L1 / T≤u1, the vibration analysis unit determines that the vibration of the battery in the current monitoring period is normal and does not perform optimization; When L1 / T>u1, the vibration analysis unit determines that the vibration of the battery in the current monitoring period is abnormal, and optimizes the analysis process of the battery operation risk, and sets the optimized battery risk coefficient to F', setting F'=F+{exp[sin(L1 / T-u1)]-1} / 6; Among them, L1 is the duration of abnormal battery vibration during the monitoring period, T is the duration of the monitoring period, and u1 is the preset vibration abnormality ratio.

[0010] Furthermore, the humidity analysis unit is used to analyze the abnormality of the battery environment humidity, and perform secondary optimization on the analysis process of the battery operation risk according to the analysis result of the abnormality of the battery environment humidity during the monitoring period, wherein: When L2 / T≤u2, the humidity analysis unit determines that the ambient humidity of the battery in the current monitoring period is normal and does not perform optimization; When L2 / T>u2, the humidity analysis unit determines that the ambient humidity of the battery in the current monitoring period is abnormal, and performs secondary optimization on the analysis process of the battery operation risk, and sets the optimized preset vibration abnormality ratio as u1', and sets u1'=u1×[1-α×(L2 / T-u2)]; Among them, L2 is the duration of abnormal humidity in the battery environment during the monitoring period, u2 is the preset humidity abnormality ratio, and α is the preset optimization coefficient.

[0011] Furthermore, the loss analysis module is provided with a loss analysis unit and an abnormality index calculation unit, and the loss analysis unit is used to compare the battery state of charge p0 collected during the monitoring period with each preset state of charge, and analyze the abnormality of the battery state of charge according to the comparison result, wherein: When p0≤p1 or p0>p2, the loss analysis unit determines that the state of charge of the battery is abnormal; When p1<p0<p2, the loss analysis unit determines that the state of charge of the battery is normal; The loss analysis unit calculates the battery charge abnormality index according to the analysis result of the battery charge state abnormality within the monitoring period, wherein: When K1 / T≤q1, the loss analysis unit determines that the state of charge of the battery in the current monitoring period is normal, and sets the battery loss index to R1; When K1 / T>q1, the loss analysis unit determines that the state of charge of the battery in the current monitoring cycle is normal, and sets the battery loss index to R2; Wherein, K1 is the duration of abnormal state of charge within the monitoring period, q1 is the preset abnormal charge index, p1 is the first preset state of charge, and p2 is the second preset state of charge; The abnormal index calculation unit calculates the abnormal index An of the battery according to the analysis result of the battery risk coefficient and the analysis result Rr of the battery loss index within the monitoring period, and sets An=w1×F+w2×Rr, wherein w1 is the risk weight and w2 is the loss weight.

[0012] Furthermore, the aging analysis module analyzes the aging state of the battery according to the battery internal resistance n0 and the battery capacity r0 collected during the monitoring period, wherein: When N1×(n0-n1) / n1+N2×sin[(r1-r0) / r1]≤M, the aging analysis module determines that the aging state of the battery is normal; When N1×(n0-n1) / n1+N2×sin[(r1-r0) / r1]>M, the aging analysis module determines that the aging state of the battery is abnormal; Among them, N1 is the internal resistance weight, N2 is the capacity weight, N1+N2=1, n1 is the initial resistance of the battery, r1 is the initial capacity of the battery, and M is the preset aging coefficient.

[0013] Furthermore, the safety assessment module is provided with a safety assessment unit and an adjustment unit, and the safety assessment unit is used to assess the safety of the battery according to the analysis results of the battery abnormality index and the battery aging state within the monitoring period, wherein: When the aging state of the battery is normal, if An≥H2, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level one, and issues a battery maintenance reminder to the user; if H1<An<H2, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level two, and issues a battery maintenance reminder to the user; if An≤H1, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level three, and issues a battery safety reminder to the user; When the aging state of the battery is abnormal, if An≥H3, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level one, and issues a battery maintenance reminder to the user; if H3<An<H3, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level two, and issues a battery maintenance reminder to the user; if An≤H3, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level three, and issues a battery safety reminder to the user; Among them, H1 is the first preset abnormal coefficient, H2 is the second preset abnormal coefficient, H3 is the third preset abnormal coefficient, and H4 is the fourth preset abnormal coefficient.

[0014] Furthermore, the adjustment unit compares the insulation resistance d0 of the battery collected during the monitoring period with the preset resistance d1, and adjusts the battery safety assessment process according to the comparison result, wherein: When d0≤d1, the adjustment unit determines that the insulation resistance of the battery in the current monitoring period is abnormal, and adjusts the evaluation process of the battery safety, and sets the adjusted preset abnormal coefficient as Hh', setting Hh'=Hh×exp[-(d1-d0) / d1]; When d0>d1, the adjustment unit determines that the insulation resistance of the battery in the current monitoring cycle is normal and does not make any adjustment.

[0015] Compared with the prior art, the beneficial effects of the present invention are that the risk analysis module improves the accuracy of the temperature change rate abnormality analysis by setting a preset temperature change rate, improves the accuracy of the voltage change rate abnormality analysis by setting a preset voltage change rate, and improves the accuracy of the battery operation risk analysis by setting various preset weights. The abnormality monitoring module reduces the influence of the vibration frequency abnormality on the battery operation risk analysis by setting a preset vibration frequency and a preset vibration abnormality ratio, thereby improving the accuracy of the battery operation risk analysis. The influence of the environmental humidity abnormality on the battery operation risk analysis is reduced by setting a preset humidity, a preset humidity abnormality ratio and a preset optimization coefficient, thereby improving the accuracy of the battery operation risk analysis. The loss analysis module improves the accuracy of the battery loss degree analysis by setting a preset state of charge and a preset charge abnormality index, and improves the accuracy of the battery abnormality index analysis by setting various preset weights. The aging analysis module improves the accuracy of the battery aging state analysis by setting various preset weights and preset aging coefficients, thereby improving the accuracy of the battery safety assessment. The safety assessment module improves the accuracy of the battery safety assessment by setting various preset abnormality coefficients, and improves the accuracy of the battery safety assessment by setting a preset resistance, thereby reducing the incidence of battery failure risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the structure of the new energy vehicle safety assessment system based on big data in this embodiment.

[0017] Figure 2 Schematic diagram of the structure of the risk analysis module of this embodiment.

[0018] Figure 3 Schematic diagram of the structure of the abnormality monitoring module in this embodiment.

[0019] Figure 4 Schematic diagram of the structure of the loss analysis module of this embodiment.

[0020] Figure 5 Schematic diagram of the structure of the security assessment module of this embodiment. DETAILED DESCRIPTION

[0021] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0023] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] See also Figure 1 As shown, it is a schematic diagram of the structure of the new energy vehicle safety assessment system based on big data in this embodiment, and the system includes: A data acquisition module is used to collect operation data, performance data, operation environment data and loss data of a new energy vehicle battery, wherein the operation data includes battery temperature and battery voltage, the operation environment data includes battery vibration frequency and ambient humidity, the battery vibration frequency is the vibration frequency of the battery shell, the ambient humidity is the local humidity near the battery shell, the performance data includes battery internal resistance, battery initial internal resistance, battery capacity, battery initial capacity and battery insulation resistance, the loss data is battery state of charge, and the battery state of charge is the ratio between the current amount of electricity stored in the battery and its maximum amount of electricity stored; in this embodiment, no specific limitation is made on the collection method of the operation data, performance data, operation environment data and loss data of the new energy vehicle battery, and those skilled in the art can freely set it, as long as the collection requirements of the operation data, performance data, operation environment data and loss data of the new energy vehicle battery are met, wherein the operation data, performance data and loss data of the new energy vehicle battery can be collected through the BMS, the battery vibration frequency can be collected through the vibration sensor, and the ambient humidity can be collected through the humidity sensor; A risk analysis module is used to analyze the operating risk of the battery in the monitoring period according to the battery temperature collected in the temperature monitoring period and the battery voltage collected in the voltage monitoring period. The risk analysis module is connected to the data acquisition module. In this embodiment, the setting of the temperature monitoring period, the voltage monitoring period and the monitoring period is not specifically limited. Those skilled in the art can freely set them as long as the setting requirements of the temperature monitoring period, the voltage monitoring period and the monitoring period are met. Among them, the setting of the temperature monitoring period should be greater than the setting of the voltage monitoring period, and the setting of the monitoring period should be much greater than the setting of the temperature monitoring period. The temperature monitoring period can be set to 1min, 2min, etc., the voltage monitoring period can be set to 1s, 2s, etc., and the monitoring period can be set to 20 days, 30 days, etc.; An abnormality monitoring module, used to analyze the abnormality of the battery vibration frequency and environmental humidity collected during the monitoring period, and optimize the analysis process of the battery operation risk according to the analysis results, and the abnormality monitoring module is connected to the risk analysis module; A loss analysis module, used to analyze the battery loss state according to the battery charge state collected during the monitoring period, and to analyze the battery abnormality index according to the analysis result and the analysis result of the battery operation risk during the monitoring period, the loss analysis module is connected to the data acquisition module; An aging analysis module, used to analyze the aging state of the battery according to the battery internal resistance and battery capacity collected during the monitoring period, and the aging analysis module is connected to the abnormal monitoring module and the loss analysis module; The safety assessment module is used to assess the safety of the battery according to the analysis results of the battery abnormality index and the battery aging status during the monitoring period, and to adjust the assessment process of the battery safety according to the insulation resistance of the battery collected during the monitoring period. The safety assessment module is connected to the aging analysis module.

[0025] Specifically, the new energy vehicle safety assessment system based on big data described in this embodiment is applied to the assessment of the safety of new energy vehicle batteries. The operating risks of the batteries are analyzed and the analysis process of the operating risks is adjusted according to the operating environment. The safety of the batteries is assessed in combination with the analysis results of the battery loss degree and aging status, thereby improving the accuracy of the safety assessment of new energy vehicle batteries.

[0026] See also Figure 2 As shown, it is a schematic diagram of the structure of the risk analysis module of this embodiment, including: A temperature analysis unit, used to analyze the abnormality of the temperature change rate according to the battery temperature collected during the temperature monitoring period; A voltage analysis unit, used to analyze the abnormality of the voltage change rate according to the battery voltage collected during the voltage monitoring period; The risk analysis unit is used to analyze the operation risk of the battery according to the analysis results of the abnormality of the battery temperature change rate and the abnormality of the battery voltage change rate within the monitoring period. The risk analysis unit is connected to the temperature analysis unit and the voltage analysis unit.

[0027] See also Figure 3 As shown, it is a schematic diagram of the structure of the abnormality monitoring module of this embodiment, including: A vibration analysis unit, used to optimize the analysis process of battery operation risk according to the vibration frequency of the battery collected during the monitoring period; The humidity analysis unit is used to perform secondary optimization on the analysis process of battery operation risk according to the ambient humidity of the battery collected during the monitoring period. The humidity analysis unit is connected to the vibration analysis unit.

[0028] See also Figure 4 As shown, it is a schematic diagram of the structure of the loss analysis module of this embodiment, including: A loss analysis unit is used to analyze the battery charge abnormality index according to the battery charge state collected during the monitoring period; the battery charge abnormality index described in this embodiment is a reflection of the battery loss state, and as the battery charge index increases, the battery loss state becomes more serious; The abnormality index calculation unit is used to calculate the abnormality index of the battery according to the analysis results of the battery risk coefficient and the battery loss index within the monitoring period. The abnormality index calculation unit is connected to the loss analysis unit.

[0029] See also Figure 5 As shown, it is a schematic diagram of the structure of the security assessment module of this embodiment, including: A safety assessment unit, used to assess the safety of the battery based on the analysis results of the battery abnormality index and the battery aging status within the monitoring period; The adjustment unit is used to adjust the battery safety assessment process according to the battery insulation resistance collected during the monitoring period, and the adjustment unit is connected to the safety assessment unit.

[0030] Specifically, the temperature analysis unit calculates the battery temperature change rate A1 based on the battery temperature collected during the temperature monitoring period, and sets ; The temperature analysis unit compares the battery temperature change rate A1 with the preset temperature change rate a1, and analyzes the abnormality of the temperature change rate according to the comparison result, wherein: When A1≤a1, the temperature analysis unit determines that the rate of change of the battery temperature in the current temperature monitoring period is normal; When A1<a1, the temperature analysis unit determines that the change rate of the battery temperature in the current temperature monitoring period is abnormal; Among them, t1 is the battery temperature data collected at the beginning of the temperature monitoring cycle, t2 is the battery temperature data collected at the end of the temperature monitoring cycle, T 温 The duration of the temperature monitoring cycle.

[0031] Specifically, the temperature analysis unit improves the accuracy of temperature change rate abnormality analysis by setting a preset temperature change rate, thereby improving the accuracy of battery operation risk analysis, thereby improving the accuracy of battery safety assessment, and ultimately reducing the incidence of battery failure risk; in this embodiment, there is no specific limitation on the setting of the preset temperature change rate, and technical personnel in this field can set it freely, as long as the setting requirements of the preset temperature change rate are met, where the optimal value of a1 is 2°C / min.

[0032] Specifically, the voltage analysis unit calculates the battery voltage change rate A2 of the battery voltage collected during the voltage monitoring period and sets ; The voltage analysis unit compares the battery voltage change rate A1 with the preset voltage change rate a2, and analyzes the abnormality of the voltage change rate according to the comparison result, wherein: When A2≤a2, the voltage analysis unit determines that the rate of change of the battery voltage in the current voltage monitoring period is normal; When A2>a2, the voltage analysis unit determines that the rate of change of the battery voltage in the current voltage monitoring period is abnormal; Among them, V1 is the battery voltage data collected at the beginning of the voltage monitoring cycle, V2 is the battery voltage data collected at the end of the voltage monitoring cycle, T 电压 is the duration of the voltage monitoring cycle.

[0033] Specifically, the voltage analysis unit improves the accuracy of the voltage change rate abnormality analysis by setting a preset voltage change rate, thereby improving the accuracy of the battery operation risk analysis, thereby improving the accuracy of the battery safety assessment, and ultimately reducing the incidence of battery failure risk; in this embodiment, there is no specific limitation on the setting of the preset voltage change rate, and technical personnel in this field can set it freely, as long as the setting requirements of the preset voltage change rate are met, where the optimal value of a2 is 0.1V / s.

[0034] Specifically, the risk analysis unit analyzes the operation risk of the battery according to the analysis results of the abnormality of the battery temperature change rate and the analysis results of the abnormality of the battery voltage change rate within the monitoring period. The risk analysis unit sets the risk coefficient of the battery to F, and sets F=x1×i1 / i0+x2×i3 / i2, wherein i0 is the number of temperature monitoring cycles within the monitoring period, i1 is the number of temperature monitoring cycles with abnormal battery temperature change rate within the monitoring period, i2 is the number of voltage monitoring cycles within the monitoring period, i3 is the number of voltage monitoring cycles with abnormal battery voltage change rate within the monitoring period, x1 is the temperature weight, x2 is the voltage weight, x1+x2=1, x1≥x2.

[0035] Specifically, the risk analysis unit improves the accuracy of battery operation risk analysis by setting preset weights, thereby improving the accuracy of battery safety assessment and ultimately reducing the incidence of battery failure risk. In this embodiment, there is no specific limitation on the setting of each preset weight, and technicians in this field can set it freely as long as the setting requirements of each preset weight are met. Among them, the optimal value of x1 is 0.6, and the optimal value of x2 is 0.4.

[0036] Specifically, the vibration analysis unit compares the vibration frequency z0 of the battery collected during the monitoring period with the preset vibration frequency z1, and analyzes the vibration abnormality of the battery according to the comparison result, wherein: When z0≤z1, the vibration analysis unit determines that the vibration of the battery is normal; When z0>z1, the vibration analysis unit determines that the vibration of the battery is abnormal; The vibration analysis unit optimizes the analysis process of the battery operation risk according to the analysis result of the battery vibration abnormality within the monitoring period, wherein: When L1 / T≤u1, the vibration analysis unit determines that the vibration of the battery in the current monitoring period is normal and does not perform optimization; When L1 / T>u1, the vibration analysis unit determines that the vibration of the battery in the current monitoring period is abnormal, and optimizes the analysis process of the battery operation risk, and sets the optimized battery risk coefficient to F', setting F'=F+{exp[sin(L1 / T-u1)]-1} / 6; Among them, L1 is the duration of abnormal battery vibration during the monitoring period, T is the duration of the monitoring period, u1 is the preset vibration abnormality ratio, 0<u1<0.1.

[0037] Specifically, the vibration analysis unit reduces the impact of abnormal vibration frequency on battery operation risk analysis by setting a preset vibration frequency and a preset vibration abnormality ratio, thereby improving the accuracy of battery operation risk analysis, thereby improving the accuracy of battery safety assessment, and ultimately reducing the incidence of battery failure risk; in this embodiment, there is no specific limitation on the setting of the preset vibration frequency and the preset vibration abnormality ratio, and technical personnel in this field can set them freely as long as the setting requirements of the preset vibration frequency and the preset vibration abnormality ratio are met, among which the optimal value of z1 is 50 Hz, and the optimal value of u1 is 0.05.

[0038] Specifically, the humidity analysis unit compares the ambient humidity s0 of the battery collected during the monitoring period with the preset humidity s1, and analyzes the abnormality of the ambient humidity of the battery according to the comparison result, wherein: When s0≤s1, the vibration analysis unit determines that the ambient humidity of the battery is normal; When s0>s1, the vibration analysis unit determines that the ambient humidity of the battery is abnormal; The humidity analysis unit performs secondary optimization on the analysis process of battery operation risk according to the analysis result of battery environment humidity abnormality within the monitoring period, wherein: When L2 / T≤u2, the humidity analysis unit determines that the ambient humidity of the battery in the current monitoring period is normal and does not perform optimization; When L2 / T>u2, the humidity analysis unit determines that the ambient humidity of the battery in the current monitoring period is abnormal, and performs secondary optimization on the analysis process of the battery operation risk, and sets the optimized preset vibration abnormality ratio as u1', and sets u1'=u1×[1-α×(L2 / T-u2)]; Among them, L2 is the duration of abnormal humidity in the battery environment during the monitoring period, u2 is the preset humidity abnormality ratio, α is the preset optimization coefficient, 0<u2<0.13.

[0039] Specifically, the humidity analysis unit reduces the impact of abnormal environmental humidity on battery operation risk analysis by setting a preset humidity, a preset humidity abnormality ratio and a preset optimization coefficient, thereby improving the accuracy of battery operation risk analysis, thereby improving the accuracy of battery safety assessment, and ultimately reducing the incidence of battery failure risk; in this embodiment, there is no specific limitation on the setting of preset humidity, preset humidity abnormality ratio and preset optimization coefficient, and technical personnel in this field can set them freely, as long as the setting requirements of preset humidity, preset humidity abnormality ratio and preset optimization coefficient are met, among which the optimal value of s1 is 30%RH, the optimal value of u2 is 0.08, and the optimal value of α is 0.45.

[0040] Specifically, the loss analysis unit compares the battery state of charge p0 collected during the monitoring period with each preset state of charge, and analyzes the abnormality of the battery state of charge according to the comparison result, wherein: When p0≤p1 or p0>p2, the loss analysis unit determines that the state of charge of the battery is abnormal; When p1<p0<p2, the loss analysis unit determines that the state of charge of the battery is normal; The loss analysis unit calculates the battery charge abnormality index according to the analysis result of the battery charge state abnormality within the monitoring period, wherein: When K1 / T≤q1, the loss analysis unit determines that the state of charge of the battery in the current monitoring period is normal, and sets the battery loss index to R1, setting: R1=0; When K1 / T>q1, the loss analysis unit determines that the state of charge of the battery in the current monitoring period is normal, and sets the battery loss index to R2, setting: R2=ln(K1 / T-q1+1); Among them, K1 is the duration of the abnormal state of charge within the monitoring period, q1 is a preset abnormal charge index, p1 is a first preset state of charge, p2 is a second preset state of charge, and p1<p2.

[0041] Specifically, the loss analysis unit improves the accuracy of battery loss analysis by setting a preset state of charge and a preset charge abnormality index, thereby improving the accuracy of battery safety assessment and ultimately reducing the incidence of battery failure risk; in this embodiment, there is no specific limitation on the setting of the preset state of charge and the preset charge abnormality index, and technical personnel in this field can set them freely as long as the setting requirements of the preset state of charge and the preset charge abnormality index are met, among which the optimal value of p1 is 15%, the optimal value of p2 is 90%, and the optimal value of q1 is 0.15.

[0042] Specifically, the abnormal index calculation unit calculates the battery abnormal index An according to the analysis results of the battery risk coefficient and the analysis results Rr of the battery loss index during the monitoring period, and sets An=w1×F+w2×Rr, wherein w1 is the risk weight, w2 is the loss weight, w1+w2=1, w1>w2, r=1,2.

[0043] Specifically, the abnormality index calculation unit improves the accuracy of battery abnormality index analysis by setting preset weights, thereby improving the accuracy of battery safety assessment and ultimately reducing the incidence of battery failure risk. In this embodiment, no specific limitation is made to the setting of each preset weight, and technicians in this field can set it freely as long as the setting requirements of each preset weight are met. Among them, the optimal value of w1 is 0.78, and the optimal value of w2 is 0.22.

[0044] Specifically, the aging analysis module analyzes the aging state of the battery according to the battery internal resistance n0 and the battery capacity r0 collected during the monitoring period, wherein: When N1×(n0-n1) / n1+N2×sin[(r1-r0) / r1]≤M, the aging analysis module determines that the aging state of the battery is normal; When N1×(n0-n1) / n1+N2×sin[(r1-r0) / r1]>M, the aging analysis module determines that the aging state of the battery is abnormal; Among them, N1 is the internal resistance weight, N2 is the capacity weight, N1+N2=1, n1 is the initial resistance of the battery, r1 is the initial capacity of the battery, and M is the preset aging coefficient.

[0045] Specifically, the aging analysis module improves the accuracy of battery aging status analysis by setting preset weights and preset aging coefficients, thereby improving the accuracy of battery safety assessment and ultimately reducing the incidence of battery failure risk. In this embodiment, no specific limitation is made to the setting of each preset weight and preset aging coefficient, and those skilled in the art can set them freely as long as the setting requirements of each preset weight and preset aging coefficient are met. Among them, the optimal value of N1 is 0.55, the optimal value of N2 is 0.45, and the optimal value of M is 0.36.

[0046] Specifically, the safety assessment unit assesses the safety of the battery according to the analysis results of the battery abnormality index and the battery aging status within the monitoring period, wherein: When the aging state of the battery is normal, if An≥H2, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level one, and issues a battery maintenance reminder to the user; if H1<An<H2, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level two, and issues a battery maintenance reminder to the user; if An≤H1, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level three, and issues a battery safety reminder to the user; When the aging state of the battery is abnormal, if An≥H3, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level one, and issues a battery maintenance reminder to the user; if H3<An<H3, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level two, and issues a battery maintenance reminder to the user; if An≤H3, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level three, and issues a battery safety reminder to the user; Among them, H1 is the first preset abnormal coefficient, H2 is the second preset abnormal coefficient, H3 is the third preset abnormal coefficient, H4 is the fourth preset abnormal coefficient, and H2

[0047] Specifically, the safety assessment unit improves the accuracy of battery safety assessment by setting various preset abnormal coefficients, thereby reducing the incidence rate of battery failure risk. In this embodiment, there is no specific limitation on the setting of each preset abnormal coefficient, and technical personnel in this field can set it freely as long as the setting requirements of each preset abnormal coefficient are met. Among them, the optimal value of H1 is 0.2, the optimal value of H2 is 0.18, the optimal value of H3 is 0.35, and the optimal value of H4 is 0.32.

[0048] Specifically, the adjustment unit compares the insulation resistance d0 of the battery collected during the monitoring period with the preset resistance d1, and adjusts the battery safety assessment process according to the comparison result, wherein: ​When d0≤d1, the adjustment unit determines that the insulation resistance of the battery in the current monitoring cycle is abnormal, and adjusts the battery safety assessment process, and sets the adjusted preset abnormal coefficient to Hh', setting Hh'=Hh×exp[-(d1-d0) / d1], where Hh is the preset abnormal coefficient, and h=1,2,3,4; When d0>d1, the adjustment unit determines that the insulation resistance of the battery in the current monitoring cycle is normal and does not make any adjustment.

[0049] Specifically, the adjustment unit improves the accuracy of battery safety assessment by setting a preset resistance, thereby reducing the incidence of battery failure risk. In this embodiment, there is no specific limitation on the setting of the preset resistance, and those skilled in the art can set it freely as long as the setting requirements of the preset resistance are met. Among them, the optimal value of d1 is 1MΩ.

[0050] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A new energy vehicle safety assessment system based on big data, characterized in that: include, Data collection module, used to collect operation data, performance data, operation environment data and loss data of new energy vehicle batteries; The risk analysis module is used to analyze the operation risk of the battery during the monitoring period according to the battery temperature collected during the temperature monitoring period and the battery voltage collected during the voltage monitoring period; The abnormality monitoring module is used to analyze the abnormality of the battery vibration frequency and environmental humidity collected during the monitoring period, and optimize the analysis process of battery operation risks based on the analysis results; The loss analysis module is used to analyze the battery loss state according to the battery charge state collected during the monitoring period, and analyze the battery abnormality index according to the analysis results and the analysis results of the battery operation risk during the monitoring period; Aging analysis module, used to analyze the aging status of the battery based on the battery internal resistance and battery capacity collected during the monitoring period; The safety assessment module is used to assess the safety of the battery based on the analysis results of the battery abnormality index and the battery aging status during the monitoring period, and to adjust the battery safety assessment process based on the battery insulation resistance collected during the monitoring period.

2. The new energy vehicle safety assessment system based on big data according to claim 1 is characterized in that: The risk analysis module is provided with a temperature analysis unit, a voltage analysis unit and a risk analysis unit. The temperature analysis unit is used to calculate the battery temperature change rate A1 based on the battery temperature collected during the temperature monitoring period, and set: ， The temperature analysis unit compares the battery temperature change rate A1 with the preset temperature change rate a1, and analyzes the abnormality of the temperature change rate according to the comparison result, wherein: When A1≤a1, the temperature analysis unit determines that the rate of change of the battery temperature in the current temperature monitoring period is normal; When A1<a1, the temperature analysis unit determines that the change rate of the battery temperature in the current temperature monitoring period is abnormal; Among them, t1 is the battery temperature data collected at the beginning of the temperature monitoring cycle, t2 is the battery temperature data collected at the end of the temperature monitoring cycle, T 温 The duration of the temperature monitoring cycle.

3. The new energy vehicle safety assessment system based on big data according to claim 2 is characterized in that: The voltage analysis unit is used to calculate the battery voltage change rate A2 of the battery voltage collected during the voltage monitoring period, and set: ， The voltage analysis unit compares the battery voltage change rate A1 with the preset voltage change rate a2, and analyzes the abnormality of the voltage change rate according to the comparison result, wherein: When A2≤a2, the voltage analysis unit determines that the rate of change of the battery voltage in the current voltage monitoring period is normal; When A2>a2, the voltage analysis unit determines that the rate of change of the battery voltage in the current voltage monitoring period is abnormal; Among them, V1 is the battery voltage data collected at the beginning of the voltage monitoring cycle, V2 is the battery voltage data collected at the end of the voltage monitoring cycle, T 电压 is the duration of the voltage monitoring cycle.

4. The new energy vehicle safety assessment system based on big data according to claim 3 is characterized in that: The risk analysis unit is used to analyze the operation risk of the battery according to the analysis results of the abnormality of the battery temperature change rate and the analysis results of the abnormality of the battery voltage change rate within the monitoring period. The risk analysis unit sets the risk coefficient of the battery to F, and sets F=x1×i1 / i0+x2×i3 / i2, wherein i0 is the number of temperature monitoring periods within the monitoring period, i1 is the number of temperature monitoring periods with abnormal battery temperature change rate within the monitoring period, i2 is the number of voltage monitoring periods within the monitoring period, i3 is the number of voltage monitoring periods with abnormal battery voltage change rate within the monitoring period, x1 is the temperature weight, and x2 is the voltage weight.

5. The new energy vehicle safety assessment system based on big data according to claim 4 is characterized in that: The abnormality monitoring module includes a vibration analysis unit and a humidity analysis unit. The vibration analysis unit is used to compare the vibration frequency z0 of the battery collected during the monitoring period with the preset vibration frequency z1, and analyze the vibration abnormality of the battery according to the comparison result, wherein: When z0≤z1, the vibration analysis unit determines that the vibration of the battery is normal; When z0>z1, the vibration analysis unit determines that the vibration of the battery is abnormal; The vibration analysis unit optimizes the analysis process of the battery operation risk according to the analysis result of the battery vibration abnormality within the monitoring period, wherein: When L1 / T≤u1, the vibration analysis unit determines that the vibration of the battery in the current monitoring period is normal and does not perform optimization; When L1 / T>u1, the vibration analysis unit determines that the vibration of the battery in the current monitoring period is abnormal, and optimizes the analysis process of the battery operation risk, and sets the optimized battery risk coefficient to F', setting F'=F+{exp[sin(L1 / T-u1)]-1} / 6; Among them, L1 is the duration of abnormal battery vibration during the monitoring period, T is the duration of the monitoring period, and u1 is the preset vibration abnormality ratio.

6. The new energy vehicle safety assessment system based on big data according to claim 5 is characterized in that: The humidity analysis unit is used to analyze the abnormality of the battery environment humidity, and perform secondary optimization on the analysis process of the battery operation risk according to the analysis result of the abnormality of the battery environment humidity during the monitoring period, wherein: When L2 / T≤u2, the humidity analysis unit determines that the ambient humidity of the battery in the current monitoring period is normal and does not perform optimization; When L2 / T>u2, the humidity analysis unit determines that the ambient humidity of the battery in the current monitoring period is abnormal, and performs secondary optimization on the analysis process of the battery operation risk, and sets the optimized preset vibration abnormality ratio as u1', and sets u1'=u1×[1-α×(L2 / T-u2)]; Among them, L2 is the duration of abnormal humidity in the battery environment during the monitoring period, u2 is the preset humidity abnormality ratio, and α is the preset optimization coefficient.

7. The new energy vehicle safety assessment system based on big data according to claim 6 is characterized in that: The loss analysis module is provided with a loss analysis unit and an abnormality index calculation unit. The loss analysis unit is used to compare the battery state of charge p0 collected during the monitoring period with each preset state of charge, and analyze the abnormality of the battery state of charge according to the comparison result, wherein: When p0≤p1 or p0>p2, the loss analysis unit determines that the state of charge of the battery is abnormal; When p1<p0<p2, the loss analysis unit determines that the state of charge of the battery is normal; The loss analysis unit calculates the battery charge abnormality index according to the analysis result of the battery charge state abnormality within the monitoring period, wherein: When K1 / T≤q1, the loss analysis unit determines that the state of charge of the battery in the current monitoring period is normal, and sets the battery loss index to R1; When K1 / T>q1, the loss analysis unit determines that the state of charge of the battery in the current monitoring cycle is normal, and sets the battery loss index to R2; Wherein, K1 is the duration of abnormal state of charge within the monitoring period, q1 is the preset abnormal charge index, p1 is the first preset state of charge, and p2 is the second preset state of charge; The abnormal index calculation unit calculates the abnormal index An of the battery according to the analysis result of the battery risk coefficient and the analysis result Rr of the battery loss index within the monitoring period, and sets An=w1×F+w2×Rr, wherein w1 is the risk weight and w2 is the loss weight.

8. The new energy vehicle safety assessment system based on big data according to claim 7 is characterized in that: The aging analysis module analyzes the aging state of the battery according to the battery internal resistance n0 and the battery capacity r0 collected during the monitoring period, wherein: When N1×(n0-n1) / n1+N2×sin[(r1-r0) / r1]≤M, the aging analysis module determines that the aging state of the battery is normal; When N1×(n0-n1) / n1+N2×sin[(r1-r0) / r1]>M, the aging analysis module determines that the aging state of the battery is abnormal; Among them, N1 is the internal resistance weight, N2 is the capacity weight, N1+N2=1, n1 is the initial resistance of the battery, r1 is the initial capacity of the battery, and M is the preset aging coefficient.

9. The new energy vehicle safety assessment system based on big data according to claim 8 is characterized in that: The safety assessment module is provided with a safety assessment unit and an adjustment unit, and the safety assessment unit is used to assess the safety of the battery according to the analysis results of the battery abnormality index and the battery aging state within the monitoring period, wherein: When the aging state of the battery is normal, if An≥H2, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level one, and issues a battery maintenance reminder to the user; if H1<An<H2, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level two, and issues a battery maintenance reminder to the user; if An≤H1, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level three, and issues a battery safety reminder to the user; When the aging state of the battery is abnormal, if An≥H3, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level one, and issues a battery maintenance reminder to the user; if H3<An<H3, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level two, and issues a battery maintenance reminder to the user; if An≤H3, the safety assessment unit determines that the safety level of the battery in the current monitoring period is level three, and issues a battery safety reminder to the user; Among them, H1 is the first preset abnormal coefficient, H2 is the second preset abnormal coefficient, H3 is the third preset abnormal coefficient, and H4 is the fourth preset abnormal coefficient.

10. The new energy vehicle safety assessment system based on big data according to claim 9 is characterized in that: The adjustment unit compares the insulation resistance d0 of the battery collected during the monitoring period with the preset resistance d1, and adjusts the evaluation process of the battery safety according to the comparison result, wherein: When d0≤d1, the adjustment unit determines that the insulation resistance of the battery in the current monitoring period is abnormal, and adjusts the evaluation process of the battery safety, and sets the adjusted preset abnormal coefficient as Hh', setting Hh'=Hh×exp[-(d1-d0) / d1]; When d0>d1, the adjustment unit determines that the insulation resistance of the battery in the current monitoring cycle is normal and does not make any adjustment.

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